Health at a Glance 2015: OECD Indicators

Transcript

Health at a Glance 2015
OECD INDICATORS
OECD INDICATORS
This work is published under the responsibility of the Secretary-General of the OECD. The
opinions expressed and arguments employed herein do not necessarily reflect the official
views of OECD member countries.
This document and any map included herein are without prejudice to the status of or
sovereignty over any territory, to the delimitation of international frontiers and boundaries
and to the name of any territory, city or area.
Please cite this publication as:
OECD (2015), Health at a Glance 2015: OECD Indicators, OECD Publishing, Paris.
http://dx.doi.org/10.1787/health_glance-2015-en
ISBN 978-92-64-23257-0 (print)
ISBN 978-92-64-24351-4 (PDF)
Series: Health at a Glance
ISSN 1995-3992 (print)
ISSN 1999-1312 (online)
The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use
of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli
settlements in the West Bank under the terms of international law.
Photo credits: Cover © Oleksiy Mark/Shutterstock.com; Chapter 3: © Comstock/Jupiterimages; Chapter 4: © Comstock/
Jupiterimages; Chapter 5: © Randy Faris/Corbis; Chapter 6: © Vincent Hazat/Photo Alto; Chapter 7: © onoky – Fotolia.com;
Chapter 8: © CREATAS/Jupiterimages; Chapter 9: © Tetraimages/Inmagine; Chapter 10: © RGtimeline/Shutterstock.com;
Chapter 11: © Thinkstock/iStockphoto.com.
Corrigenda to OECD publications may be found on line at: www.oecd.org/about/publishing/corrigenda.htm.
© OECD 2015
You can copy, download or print OECD content for your own use, and you can include excerpts from OECD publications, databases and
multimedia products in your own documents, presentations, blogs, websites and teaching materials, provided that suitable
acknowledgement of OECD as source and copyright owner is given. All requests for public or commercial use and translation rights should
be submitted to [email protected]. Requests for permission to photocopy portions of this material for public or commercial use shall be
addressed directly to the Copyright Clearance Center (CCC) at [email protected] or the Centre français d’exploitation du droit de copie (CFC)
at [email protected].
FOREWORD
Foreword
T
his 2015 edition of Health at a Glance – OECD Indicators presents the most recent comparable
data on key indicators of health and health systems across the 34 OECD member countries. For a
subset of indicators, it also reports data for partner countries, including Brazil, China, Columbia,
Costa Rica, India, Indonesia, Latvia, Lithuania, the Russian Federation and South Africa. This edition
includes two new features: a set of dashboard indicators on health and health systems, presented in
Chapter 1, summarising the comparative performance of OECD countries, and a special chapter on
recent trends in pharmaceutical spending across OECD countries, presented in Chapter 2.
The production of Health at a Glance would not have been possible without the contribution
of OECD Health Data National Correspondents, Health Accounts Experts, and Health Care Quality
Indicators Experts from the 34 OECD countries. The OECD gratefully acknowledges their effort in
supplying most of the data contained in this publication. The OECD also acknowledges the
contribution of other international organisations, especially the World Health Organization and
Eurostat, for sharing some of the data presented here, and the European Commission for supporting
data development work.
This publication was prepared by a team from the OECD Health Division under the co-ordination
of Gaétan Lafortune. Chapter 1 was prepared by Gaétan Lafortune and Nelly Biondi; Chapter 2 by
Valérie Paris, Annalisa Belloni, David Morgan and Michael Mueller; Chapter 3 by Nelly Biondi and
Gaétan Lafortune; Chapter 4 by Marion Devaux, Nelly Biondi and Franco Sassi; Chapter 5 by Gaétan
Lafortune, Frédéric Daniel, Liliane Moreira and Michael Gmeinder; Chapter 6 by Gaétan Lafortune,
Frédéric Daniel and Nelly Biondi; Chapter 7 by Gaétan Lafortune, Marion Devaux, Michael Mueller,
Marie-Clémence Canaud, Frédéric Daniel and Nelly Biondi; Chapter 8 by Ian Brownwood, Ian Forde,
Rie Fujisawa, Nelly Biondi, Emily Hewlett, Carol Nader, Luke Slawomirski and Niek Klazinga; Chapter 9
by David Morgan, Michael Mueller, Yuki Murakami and Michael Gmeinder; Chapter 10 by Valérie Paris,
Annalisa Belloni, David Morgan, Michael Mueller, Luke Slawomirski and Marie-Clémence Canaud;
Chapter 11 by Tim Muir, Yuki Murakami, Gaétan Lafortune, Marie-Clémence Canaud and Nelly Biondi.
This publication benefited from useful comments from Francesca Colombo.
HEALTH AT A GLANCE 2015 © OECD 2015
3
TABLE OF CONTENTS
Table of contents
Executive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
Reader’s guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
Chapter 1. Dashboards of health indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Health status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Risk factors to health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Access to care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Quality of care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Health care resources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17
19
20
20
21
21
Chapter 2. Pharmaceutical spending trends and future challenges . . . . . . . . . . . . . . . .
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
One in every five health dollars is spent on pharmaceuticals . . . . . . . . . . . . . . . . . .
The share of private funding of pharmaceuticals increases . . . . . . . . . . . . . . . . . . .
Pharmaceutical expenditure growth is driven by changes in quantity, prices
and therapeutic mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Drivers of spending growth vary across therapeutic areas. . . . . . . . . . . . . . . . . . . . .
New challenges in the pharmaceutical market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
30
30
32
Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
42
Chapter 3. Health status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Life expectancy at birth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Life expectancy by sex and education level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mortality from cardiovascular diseases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mortality from cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mortality from transport accidents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Suicide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Infant mortality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Infant health: Low birth weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Perceived health status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cancer incidence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
45
46
48
50
52
54
56
58
60
62
64
Chapter 4. Non-medical determinants of health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tobacco consumption among adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alcohol consumption among adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fruit and vegetable consumption among adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Obesity among adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Overweight and obesity among children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
68
70
72
74
76
34
38
40
41
5
TABLE OF CONTENTS
Chapter 5. Health workforce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Doctors (overall number) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Doctors by age, sex and category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Medical graduates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
International migration of doctors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remuneration of doctors (general practitioners and specialists) . . . . . . . . . . . . . . .
Nurses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nursing graduates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
International migration of nurses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remuneration of nurses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
79
80
82
84
86
88
90
92
94
96
Chapter 6. Health care activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
Consultations with doctors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Medical technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hospital beds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hospital discharges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Average length of stay in hospitals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cardiac procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hip and knee replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Caesarean sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ambulatory surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
100
102
104
106
108
110
112
114
116
Chapter 7. Access to care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Coverage for health care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unmet needs for medical care and dental care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Out-of-pocket medical expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Geographic distribution of doctors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Waiting times for elective surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
119
120
122
124
126
128
Chapter 8. Quality of care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Avoidable hospital admissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Diabetes care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Prescribing in primary care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mortality following acute myocardial infarction (AMI) . . . . . . . . . . . . . . . . . . . . . . . .
Mortality following stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Waiting times for hip fracture surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Surgical complications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Obstetric trauma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Care for people with mental health disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Screening, survival and mortality for cervical cancer . . . . . . . . . . . . . . . . . . . . . . . .
Screening, survival and mortality for breast cancer . . . . . . . . . . . . . . . . . . . . . . . . . .
Survival and mortality for colorectal cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Childhood vaccination programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Influenza vaccination for older people . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Patient experience with ambulatory care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
131
132
134
136
138
140
142
144
146
148
150
152
154
156
158
160
Chapter 9. Health expenditure and financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Health expenditure per capita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Health expenditure in relation to GDP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Health expenditure by function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Financing of health care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
163
164
166
168
170
TABLE OF CONTENTS
Expenditure by disease and age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
Capital expenditure in the health sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Chapter 10. Pharmaceutical sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pharmaceutical expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Financing of pharmaceutical expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pharmacists and pharmacies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pharmaceutical consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Share of generic market. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Research and development in the pharmaceutical sector . . . . . . . . . . . . . . . . . . . . .
177
178
180
182
184
186
188
Chapter 11. Ageing and long-term care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Demographic trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Life expectancy and healthy life expectancy at age 65 . . . . . . . . . . . . . . . . . . . . . . .
Self-reported health and disability at age 65 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dementia prevalence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Recipients of long-term care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Informal carers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Long-term care workers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Long-term care beds in institutions and hospitals . . . . . . . . . . . . . . . . . . . . . . . . . . .
Long-term care expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
191
192
194
196
198
200
202
204
206
208
Annex A. Additional information on demographic and economic context,
and health expenditure and financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Follow OECD Publications on:
http://twitter.com/OECD_Pubs
http://www.facebook.com/OECDPublications
http://www.linkedin.com/groups/OECD-Publications-4645871
http://www.youtube.com/oecdilibrary
OECD
Alerts
http://www.oecd.org/oecddirect/
This book has...
StatLinks2
A service that delivers Excel® files from the printed page!
Look for the StatLinks2at the bottom of the tables or graphs in this book.
To download the matching Excel® spreadsheet, just type the link into your
Internet browser, starting with the http://dx.doi.org prefix, or click on the link from
the e-book edition.
7
EXECUTIVE SUMMARY
Executive Summary
H
ealth at a Glance 2015 presents cross-country comparisons of the health status of
populations and the performance of health systems in OECD countries, candidate
countries and key emerging economies. This edition offers two new features: a set of
dashboard indicators on health outcomes and health systems (presented in Chapter 1),
which summarise the comparative performance of OECD countries; and a special chapter
on recent trends in pharmaceutical spending across OECD countries. The key findings of
this publication are as follows.
New drugs will push up pharmaceutical spending unless policy adapts
●
Across OECD countries, pharmaceutical spending reached around USD 800 billion in
2013. This amounts to about 20% of total health spending on average when
pharmaceutical consumption in hospital is added to the purchase of pharmaceutical
drugs in the retail sector.
●
The growth of retail pharmaceutical spending has slowed down in recent years in most
OECD countries, while spending on pharmaceuticals in hospital has generally increased.
●
The emergence of new high-cost, specialty medicines targeting small populations and/or
complex conditions has prompted new debate on the long-term sustainability and
efficiency of pharmaceutical spending.
Life expectancy continues to rise, but widespread differences persist across
countries and socio-demographic groups
●
Life expectancy continues to increase steadily in OECD countries, rising on average by 34 months each year. In 2013, life expectancy at birth reached 80.5 years on average, an
increase of over ten years since 1970. Japan, Spain and Switzerland lead a group of eight
OECD countries in which life expectancy now exceeds 82 years.
●
Life expectancy in key emerging economies, such as India, Indonesia, Brazil and China,
has increased over the past few decades, converging rapidly towards the OECD average.
There has been much less progress in countries such as South Africa (due mainly to the
epidemic of HIV/AIDS) and the Russian Federation (due mainly to a rise in riskincreasing behaviours among men).
●
Across OECD countries, women can expect to live more than 5 years longer than men,
but this gap has narrowed by 1.5 years since 1990.
●
People with the highest level of education can expect to live six years longer on average
than those with the lowest level. This difference is particularly pronounced for men,
with an average gap of almost eight years.
9
EXECUTIVE SUMMARY
The number of doctors and nurses has never been higher in OECD countries
●
Since 2000, the number of doctors and nurses has grown in nearly all OECD countries,
both in absolute number and on a per capita basis. The growth was particularly rapid in
some countries that had fewer doctors in 2000 (e.g., Turkey, Korea, Mexico and the United
Kingdom), but there was also a strong rise in countries that already had a relatively large
number of doctors (e.g., Greece, Austria and Australia).
●
Growth was pushed by increased student intakes in domestic medical and nursing
education programmes, as well as by more foreign-trained doctors and nurses working
in OECD countries in response to short-term needs.
●
There are more than two specialist doctors for every generalist on average across the
OECD. In several countries, the slow growth in the number of generalists raises concerns
about access to primary care for all the population.
Out-of-pocket spending remains a barrier to accessing care
●
All OECD countries have universal health coverage for a core set of services, except
Greece, the United States and Poland. In Greece, the economic crisis led to a loss in
health insurance coverage among long-term unemployed and many self-employed
workers. However, since June 2014, measures have been taken to provide the uninsured
population with access to prescribed pharmaceuticals and emergency services. In the
United States, the percentage of the population uninsured has come down from 14.4% in
2013 to 11.5% in 2014 following the implementation of the Affordable Care Act and is
expected to diminish further in 2015.
●
Out-of-pocket spending by households can create barriers to health care access. On
average across OECD countries, about 20% of health spending is paid directly by patients,
ranging from less than 10% in France and the United Kingdom to over 30% in Mexico,
Korea, Chile and Greece. In Greece, the share of health spending paid directly by
households has increased by 4 percentage points since 2009, as public spending was
reduced.
●
Low-income households are four to six times more likely to report unmet needs for
medical care and dental care for financial or other reasons than those with high income.
In some countries, like Greece, the share of the population reporting some unmet
medical care needs has more than doubled during the economic crisis.
Too many lives are still lost because quality of care is not improving
fast enough
10
●
Better treatment of life-threatening conditions such as heart attack and stroke has led to
lower mortality rates in most OECD countries. On average, mortality rates following
hospital admissions for heart attack fell by about 30% between 2003 and 2013 and for
stroke by about 20%. Despite the progress achieved so far, there is still room in many
countries to improve the implementation of best practices in acute care to further
reduce mortality after heart attack and stroke.
●
Survival has also improved for many types of cancer in most countries, due to earlier
diagnosis and better treatment. For example, the relative five-year survival for breast
cancer and colorectal cancer has increased from around 55% on average for people
diagnosed and followed up in the period 1998-2003 to over 60% for those diagnosed and
followed up ten years later (2008-13). Still, several countries such as Chile, Poland and the
EXECUTIVE SUMMARY
United Kingdom are still lagging behind the best performers in survival following
diagnosis for different types of cancer.
●
The quality of primary care has improved in many countries, as illustrated by the
continuing reduction in avoidable hospital admissions for chronic diseases. Still, there is
room in all countries to improve primary care to further reduce costly hospital
admissions, in the context of population ageing and a growing number of people with
one or more chronic diseases.
●
Pharmaceutical prescribing practices can also be used as indicators of health care
quality. For example, antibiotics should be prescribed only where there is an evidencebased need, to reduce the risk of antimicrobial resistance. Total volumes of antibiotic
consumption vary more than four-fold across OECD countries, with Chile, the
Netherlands and Estonia reporting the lowest, and Turkey and Greece reporting the
highest. Reducing unnecessary antibiotic use is a pressing, yet complex problem,
requiring multiple co-ordinated initiatives including surveillance, regulation and
education of professionals and patients.
11
READER’S GUIDE
Reader’s guide
H
ealth at a Glance 2015 presents comparisons of key indicators of health and health
system performance across the 34 OECD countries, as well as for candidate and key
partner countries where possible (Brazil, China, Colombia, Costa Rica, India, Indonesia,
Latvia, Lithuania, the Russian Federation and South Africa). The data presented in this
publication come mainly from official national statistics, unless otherwise indicated.
Content of the publication
This new edition of Health at a Glance contains two main new features: 1) a series of
dashboards are presented in Chapter 1 to summarise, in a clear and user-friendly way, the
relative strengths and weaknesses of OECD countries on a selected set of key indicators on
health and health system performance which are presented in other chapters of this
publication; and 2) a special focus is put on the pharmaceutical sector, including an
analysis of recent trends and future challenges in the management of pharmaceutical
expenditure in Chapter 2, as well as a new chapter on the pharmaceutical sector
(Chapter 10), combining both indicators that were previously shown in other chapters and
some new indicators based on the two-page format used in most of this publication.
The general framework underlying the indicators presented in this publication
assesses the performance of health systems in the context of a broader view of public
health (Figure 0.1). It is based on a framework that was endorsed for the OECD Health Care
Quality Indicators project (Kelley and Hurst, 2006; Arah et al., 2006). This framework
recognises that the goal of health systems is to improve the health status of the population.
Many factors influence health status, including a number that fall outside health care
systems, such as the physical environment in which people live, and individual lifestyles
and behaviours. The performance of health care systems also contributes obviously to the
health status of the population. This performance includes several dimensions, including
the degree of access to care and the quality of care provided. Performance measurement
also needs to take into account the financial resources required to achieve these access and
quality goals. The performance of health systems depends also greatly on the health
workers providing the services, and the training and equipment at their disposal. Finally, a
number of contextual factors also affect the health status of the population and the
demand for and supply of health services also need to be taken into account, including the
demographic context, and economic and social development.
Health at a Glance 2015 compares OECD countries on each component of this general
framework.
13
Figure 0.1. Conceptual framework for health system performance assessment
Health status
(Chapter 3)
Non-medical determinants of health
(Chapter 4)
Health care system performance
How does the health system perform?
What is the level of quality of care and access to services?
What does this performance cost?
Quality of care
(Chapter 8)
Access to care
(Chapter 7)
Health expenditure and financing
(Chapter 9)
Health care resources and activities
Health workforce
(Chapter 5)
Health care activities
(Chapter 6)
Demographic and economic context, and health expenditure and financing
(Annex A)
Source: Adapted from Kelley, E. and J. Hurst (2006).
Following the first two new chapters presenting the set of dashboards of indicators
and the special focus on pharmaceutical expenditure, Chapter 3 on health status highlights
variations across countries in life expectancy, some of the main causes of mortality and
other measures of population health status. This chapter also includes measures of
inequality in health status by education and income level for key indicators such as life
expectancy and perceived health status.
Chapter 4 on non-medical determinants of health focuses on health-related lifestyles
and behaviours, including tobacco smoking, alcohol drinking, nutrition, and overweight
and obesity problems among children and adults. Most of these factors can be modified by
public health and prevention policies.
Chapter 5 looks at the health workforce, focusing on the supply and remuneration of
doctors and nurses in OECD countries. This chapter presents trends in the number of new
graduates from medical and nursing education programmes and also features new
indicators on the international migration of doctors and nurses, highlighting the fact that
the number and share of foreign-trained doctors and nurses has increased in many OECD
countries over the past decade.
Chapter 6 on health care activities describes some of the main characteristics of health
service delivery in different OECD countries, starting with the number of consultations
with doctors (which is often the “entry point” of patients to health care systems),
hospitalisation rates, the utilisation rates of different diagnostic and surgical procedures,
as well as the development of ambulatory surgery for interventions such as cataract
surgery and tonsillectomy.
Chapter 7 on access to care presents a set of indicators related to financial access to
care, geographic access, and timely access (waiting times), as well as indicators of selfreported unmet needs for medical care and dental care.
READER’S GUIDE
Chapter 8 examines quality of care or the degree to which care is delivered in
accordance with established standards and improves health outcomes. It provides
comparisons on quality of care for chronic conditions and pharmaceutical prescriptions,
acute care for life-threatening diseases such as heart attack and stroke, patient safety,
mental health care, cancer care, the prevention of communicable diseases, as well as some
important aspects of patient experiences.
Chapter 9 on health expenditure and financing compares how much OECD countries
spend on health, both on a per capita basis and in relation to GDP. The chapter also
provides an analysis of the different types of health services and goods consumed across
OECD countries. It also looks at how these health services and goods are paid for and the
mix between public funding, private health insurance and direct out-of-pocket payments
by households in different countries.
Chapter 10 is a new chapter on the pharmaceutical sector, which combines some
indicators that were previously shown in other chapters and some new indicators. The
chapter begins by comparing recent trends and levels of pharmaceutical expenditure
across countries and how these expenditure are paid for, and then goes on to compare the
consumption of certain high-volume pharmaceutical drugs and the share of the generic
market in different countries. It concludes by reviewing spending on research and
development (R&D) to develop new products in the pharmaceutical sector.
Chapter 11 focuses on ageing and long-term care, starting by a review of demographic
trends which highlights the steady growth in the share of the population aged over 65 and
80 in all OECD countries. The chapter presents the most recent data on life expectancy and
life expectancy free of disability at age 65, along with data on self-reported health and
disability status, as important factors affecting the current and future demand for longterm care. It then focuses on people currently receiving long-term care at home or in
institutions and people providing formal or informal care, and concludes with a review of
levels and trends in long-term care expenditure in different countries.
A statistical annex provides additional information on the demographic and economic
context within which health and long-term care systems operate.
Presentation of indicators
With the exception of the first two chapters, each of the indicators covered in the rest
of the publication is presented over two pages. The first provides a brief commentary
highlighting the key findings conveyed by the data, defines the indicator and signals any
significant national variation from the definition which might affect data comparability.
On the facing page is a set of figures. These typically show current levels of the indicator
and, where possible, trends over time. Where an OECD average is included in a figure, it is
the unweighted average of the OECD countries presented, unless otherwise specified.
Data limitations
Limitations in data comparability are indicated both in the text (in the box related to
“Definition and comparability”) as well as in footnotes to figures.
Data sources
Readers interested in using the data presented in this publication for further analysis
and research are encouraged to consult the full documentation of definitions, sources and
methods presented in OECD Health Statistics on OECD.Stat (http://stats.oecd.org/index.aspx,
15
READER’S GUIDE
then choose “Health”). More information on OECD Health Statistics is available at
www.oecd.org/health/health-data.htm.
Population figures
The population figures presented in the Annex and used to calculate rates per capita
throughout this publication come from the OECD Historical Population Data and
Projections (as of end of May 2015), and refer to mid-year estimates. Population estimates
are subject to revision, so they may differ from the latest population figures released by the
national statistical offices of OECD member countries.
Note that some countries such as France, the United Kingdom and the United States
have overseas colonies, protectorates or territories. These populations are generally
excluded. The calculation of GDP per capita and other economic measures may, however,
be based on a different population in these countries, depending on the data coverage.
OECD country ISO codes
Australia
AUS
Japan
JPN
Austria
AUT
Korea
KOR
Belgium
BEL
Luxembourg
LUX
Canada
CAN
Mexico
MEX
Chile
CHL
Netherlands
NLD
Czech Republic
CZE
New Zealand
NZL
Denmark
DNK
Norway
NOR
Estonia
EST
Poland
POL
Finland
FIN
Portugal
PRT
France
FRA
Slovak Republic
SVK
Germany
DEU
Slovenia
SVN
Greece
GRC
Spain
ESP
Hungary
HUN
Sweden
SWE
Iceland
ISL
Switzerland
CHE
Ireland
IRL
Turkey
TUR
Israel
ISR
United Kingdom
GBR
Italy
ITA
United States
USA
Brazil
BRA
Indonesia
IDN
China
CHN
Latvia
LVA
Colombia
COL
Lithuania
LTU
Costa Rica
CRI
Russian Federation
RUS
India
IND
South Africa
ZAF
Partner country ISO codes
References
Arah, O. et al. (2006), “A Conceptual Framework for the OECD Health Care Quality Indicators Project”,
International Journal for Quality in Health Care, Vol. 18, Supplement No. 1, pp. 5-13.
Kelley, E. and J. Hurst (2006), “Health Care Quality Indicators Project: Conceptual Framework”, OECD
Health Working Paper, No. 23, OECD Publishing, Paris, http://dx.doi.org/10.1787/440134737301.
16
© OECD 2015
Chapter 1
Dashboards of health indicators
This chapter presents, for the first time, a set of dashboards which are designed to
shed light on how well OECD countries do in promoting the health of their
population and improving their health system performance. These dashboards do
not have the ambition of identifying which countries have the best health system
overall. They summarise some of the relative strengths and weaknesses of countries
on a selected set of indicators on health and health system performance, to help
identify possible priority areas for actions. These dashboards, which take the form
of summary tables, highlight how well OECD countries are doing along five
dimensions: 1) health status; 2) risk factors to health; 3) access to care; 4) quality of
care; and 5) health care resources. For each of these five dimensions, a selected set
of key indicators are presented. The selection of these indicators is based on three
main criteria: 1) policy relevance; 2) data availability; and 3) data interpretability
(i.e., no ambiguity that a higher/lower value means a better/worse performance).
There is, however, one exception to the application of this third criterion: for the fifth
dashboard on health care resources, more health spending or more human or
physical resources does not necessarily mean better performance. This is why the
ranking of countries is displayed differently.
The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli
authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights,
East Jerusalem and Israeli settlements in the West Bank under the terms of international law.
17
1.
DASHBOARDS OF HEALTH INDICATORS
A
cross the OECD, policy makers have a keen interest to understand how good the health
of their people is, and how well their health systems are able to deliver good results. A look
at indicators contained in this publication shows that much progress has already been
achieved. People in OECD countries are living longer than ever before, with life expectancy
now exceeding 80 years on average, thanks to improvements in living conditions and
educational attainments, but also to progress in health care. In most countries, universal
health coverage provides financial protection against the cost of illness and promotes
access to care for the whole population. The quality of care has also generally improved, as
illustrated by the reduction in deaths after heart attacks and strokes, and the earlier
detection and improved treatments for serious diseases such as diabetes and cancer. But
these improvements have come at a cost. Health spending now accounts for about 9% of
GDP on average in OECD countries, and exceeds 10% in many countries. Higher health
spending is not a problem if the benefits exceed the costs, but there is ample evidence of
inequities and inefficiencies in health systems which need to be addressed. There is also a
need to achieve a proper balance between spending on disease prevention and treatment.
Despite these improvements, important questions about how successful countries are
in achieving good results on different dimensions of health system performance remain.
What are the main factors explaining differences in health status and life expectancy
across OECD countries? Is the increase in certain risk factors such as inactivity and obesity
offsetting some of the gains from the reduction in other risk factors like smoking? To what
extent do all citizens have adequate and timely access to care, and good financial
protection against the cost of health care? What do we know about the quality and safety
of care provided to people with different health conditions? What are the financial, human
and technical resources allocated to health systems in different countries? And how does
this translate into beneficial activities and better health outcomes?
Answering these questions is by no mean an easy task. But the dashboards presented
in this chapter can help shed light on how well countries do in promoting the health of
their population and on several dimensions of health system performance. These
dashboards do not have the ambition of identifying which countries have the best health
system overall. However, they summarise some of the relative strengths and weaknesses
of OECD countries on a selected set of indicators on health and health system
performance, and can be useful to identify possible priority areas for actions.
These dashboards, which take the form of summary tables, highlight how well OECD
countries are doing along five dimensions: 1) health status; 2) risk factors to health;
3) access to care; 4) quality of care; and 5) health care resources. For each of these five
dimensions, a selected set of key indicators (ranging from 4 to 7) are presented in a
summary table. The selection of these indicators is based on three main criteria: 1) policy
relevance; 2) data availability; and 3) data interpretability (i.e., no ambiguity that a higher/
lower value means a better/worse performance). There is, however, one notable exception
to the application of this third criterion: for the fifth dashboard on health care resources,
18
1.
more health spending or more human or physical resources does not necessarily mean
better performance. This is why the ranking of countries is displayed differently (through
different colours) in this last dashboard. Box 1.1 at the end of this chapter summarises
some of the main limitations in interpreting these dashboards.
In most of the dashboards, countries are classified in three groups: 1) top third
performer; 2) middle third performer; and 3) bottom third performer. In addition, the
specific ranking of countries is indicated in each cell to provide further information on how
close countries may be to the other group. The ranking is based on the number of countries
for which data are available for each indicator (with a maximum of 34, when all countries
are covered), with countries separated in three equal groups. For the first indicator related
to access to care (the percentage of the population with health coverage), the grouping of
countries is based on a different method because most countries are at or close to 100%
coverage: the top countries are defined as those with a population coverage rate between
95% and 100%, the middle countries with a coverage between 90% and 95%, and the bottom
countries with a coverage of less than 90%. The availability of comparable data is also more
limited for indicators of access to care, either because of a lack of harmonisation in survey
instruments (for indicators related to unmet care needs) or limitations in administrative
data (for indicators on waiting times).
Health status
The broad measures of population health status shown in Table 1.1, such as life
expectancy at various ages, are not only related to health spending and the performance of
health systems, but also to a wide range of non-medical determinants of health (with some
of the lifestyle and behavioural factors presented in Table 1.2). Countries that perform well
on life expectancy at birth for men and women usually also tend to do well on life
expectancy at older ages, and typically have lower rates of mortality from cardiovascular
diseases (the main causes of death in nearly all OECD countries).
Japan, Spain, Switzerland, Italy and France are among the countries that have the
highest life expectancy at birth and at older ages, although France does not perform so well
in terms of life expectancy at birth for men, reflecting higher mortality rates among
younger and middle-aged men.
Mexico, Hungary, the Slovak Republic and Turkey have the lowest life expectancy at
birth and older ages, although Turkey has achieved huge gains in longevity over the past
few decades and is quickly moving towards the OECD average (see the first indicator on life
expectancy in Chapter 3 for trends over time).
While higher health spending per capita is generally associated with higher life
expectancy, this relationship is less pronounced in countries with the highest health
spending per capita. Japan, Spain and Korea stand out as having relatively high life
expectancies, and the United States relatively low life expectancies, given their levels of
health spending (see Table 1.5). Life expectancy in the United States is lower than in most
other OECD countries because of higher mortality rates from various health-related
behaviors (including higher calorie consumption and obesity rates, higher consumption of
legal and illegal drugs, higher deaths from road traffic accidents and homicides), adverse
socio-economic conditions affecting a large segment of the US population, and poor access
and co-ordination of care for certain population groups.
19
1.
Risk factors to health
Most countries do not perform well for at least one or more indicators of risk factors to
health, whether that is the proportion of their population smoking tobacco, alcohol
consumption, or overweight and obesity among children and adults (Table 1.2). This
highlights the importance of countries putting a higher priority on health promotion and
disease prevention policies to reduce modifiable risk factors to health and mortality from
related diseases.
The United States, Canada, Australia and Mexico have achieved remarkable progress
over the past few decades in reducing tobacco smoking among adults and have very low
rates now, but they face the challenge of tackling relatively high rates of overweight and
obesity among children and adults. Some countries like Italy and Portugal currently have a
relatively low rate of obesity among adults, but the current high rate of overweight and
obesity among children is likely to translate into higher rates among adults in the future.
Other countries like Turkey and Greece have relatively low levels of alcohol consumption,
but still have a way to go to reduce tobacco smoking. Alcohol consumption remains high in
Austria, Estonia, the Czech Republic, Hungary, France and Germany, although the overall
level of consumption has come down in many of these countries over the past few decades
(see the indicator on alcohol consumption in Chapter 4).
Access to care
Most OECD countries have achieved universal (or near-universal) coverage of health
care costs for a core set of services, with the exception of Greece, the United States and
Poland, where a sizeable proportion of the population is still not covered (Table 1.3). In the
United States, the percentage of the population uninsured has started to decrease
significantly in 2014, following the implementation of the Affordable Care Act which is
designed to expand health insurance coverage. In Greece, the response to the economic
crisis has reduced health insurance coverage among people who have become long-term
unemployed, and many self-employed workers have also not renewed their health
insurance plans because of reduced disposable income. However, since June 2014,
uninsured people are covered for prescribed pharmaceuticals and for services in
emergency departments in public hospitals, as well as for non-emergency hospital care
under certain conditions.
The financial protection that people have against the cost of illness depends not only
on whether they have a health insurance, but also on the range of goods and services
covered and the extent to which these goods and services are covered. In countries like
France and the United Kingdom, the amount that households have to pay directly for
health services and goods as a share of their total consumption is relatively low, because
most such goods and services are provided free or are fully covered by public and private
insurance, with only small additional payments required. Some other countries, such as
Korea and Mexico, have achieved universal (or quasi-universal) health coverage, but a
relatively small share of the cost of different health services and goods are covered,
leaving a significant amount to be paid by households. Direct out-of-pocket payments
can create financial barriers to health care, dental care, prescribed pharmaceutical drugs
or other health goods or services, particularly for low-income households. The share of
household consumption spent on direct medical expenditure is highest in Korea,
Switzerland, Portugal, Greece and Mexico, although some of these countries have put in
place proper safeguards to protect access to care for people with lower income.
20
1.
Access to health care may be restricted not only because of financial reasons, but also
because of geographic barriers, waiting times and other reasons. In Europe, around 3% of
the population on average in countries that are OECD members reported unmet needs for
medical examination due to cost, travel distance or waiting lists in 2013, according to the
EU-SILC survey. The share of the population reporting such unmet medical care needs was
highest in Greece and Poland, and lowest in the Netherlands and Austria. In nearly all
countries, a higher proportion of the population reports some unmet needs for dental care,
reflecting that public coverage for dental care is generally lower. People in Portugal, Iceland,
Italy and Greece reported the highest rates of unmet needs for dental care among
European countries that are OECD members in 2013.
Waiting times for different health services indicate the extent to which people have
timely access to care for specific interventions such as elective surgery. Denmark, Canada
and Israel have relatively low waiting times for interventions such as cataract surgery and
knee replacement among the limited group of countries that provide these data, while
Poland, Estonia and Norway have relatively long waiting times.
Quality of care
Improving quality of care is a high priority in most OECD countries. Based on the
available data, no country consistently performs in the top group on all indicators of
quality of care (Table 1.4), even those that spend much more on health. This suggests that
there is room for improvement in all countries in the governance of health care quality and
prevention, early diagnosis and treatment of different health problems.
The United States is doing well in providing acute care for people having a heart attack
or a stroke and preventing them from dying, but is not performing very well in preventing
avoidable hospital admissions for people with chronic conditions such as asthma and
diabetes. The reverse is true in Portugal, Spain and Switzerland, which have relatively low
rates of hospital admissions for certain chronic conditions, but relatively high rates of
mortality for patients admitted to hospital for a heart attack or stroke.
Finland and Sweden do relatively well in having high survival of people following
diagnosis for cervical, breast or colorectal cancer, while the survival for these types of
cancer remains lower in Chile, Poland, the Czech Republic, the United Kingdom and
Ireland. An important pillar to achieve progress in the fight against cancer is to establish a
national cancer control plan to focus political and public attention on performance in
cancer prevention, early diagnosis and treatment.
Health care resources
Higher health spending is not always closely related to a higher supply of health
human resources or to a higher supply of physical and technical equipment in health
systems.
The United States continues to spend much more on health per capita than all other
OECD countries, but is not in the top group in terms of the number of doctors or nurses per
population. Following the United States, the next biggest spenders on health are
Switzerland, Norway, the Netherlands and Sweden, whereas the lowest per capita
spenders are Mexico and Turkey (Table 1.5). Health spending per capita is also relatively
low in Chile, Poland and Korea, although it has grown quite rapidly over the past decade.
Greece, Austria and Norway have the highest number of doctors per capita, while
Switzerland, Norway and Denmark have the highest number of nurses. The mix between
different categories of health workers varies widely, with some countries choosing to have
21
1.
relatively more doctors (such as Greece and Austria) and others opting to rely more on
nurses and other health care providers to deliver some services (such as Finland and the
United States).
Some Central and Eastern European countries such as Hungary, Poland and the Slovak
Republic continue to have a relatively high number of hospital beds, reflecting an excessive
focus of activities in hospital. The number of hospital beds per capita is lowest in Mexico,
Chile, Sweden, Turkey, Canada and the United Kingdom. Relatively low number of hospital
beds may not create any capacity problem if primary care systems are sufficiently
developed to reduce the need for hospitalisation.
The availability of expensive technological equipment such as MRI and CT scanners is
highest in Japan and the United States, and much lower in Mexico, Hungary, Israel and the
United Kingdom. There is no ideal number of MRI units or CT scanners per population, and
there is also evidence in many countries of inappropriate and excessive use of these
expensive diagnostic technologies.
Higher health spending and other human or technical resources are not always
correlated with greater access to care or higher quality of care, as shown by the lack of any
consistent correlation in countries’ relative position between health spending and various
indicators of access or quality of care. For example, Norway has high levels of health
spending and also relatively high numbers of doctors and nurses, and does generally well
on many indicators of quality of care, but still faces some persisting issues in terms of
access to care, for instance, on waiting times for elective surgery. On the other hand, the
Czech Republic spends much less on health and is achieving good results for several
indicators on access to care, but could improve public health and prevention programmes
and improve the quality of care for people who have chronic diseases such as diabetes. The
performance of health systems in achieving the key policy goals of universal access and
quality depends not only on allocating more money on health care, but also on making a
more rational use of resources and providing the right incentives to ensure the best value
for money spent.
22
1.
Table 1.1. Health status
Top third performers
Middle third performers
Bottom third performers
Note: Countries are listed in alphabetical order. The number in the cell indicates the position of each country among all countries
for which data is available. For the mortality indicator, the top performers are countries with the lowest rates.
Indicator
Life expectancy
at birth - Men
Australia
Life expectancy
at birth - Women
Life expectancy
at 65 - Men*
Life expectancy
at 65 - Women*
Mortality from
cardiovascular
diseases**
8
7
3
7
7
Austria
18
13
16
13
26
Belgium
22
19
23
14
15
Canada
13
17
10
10
5
Chile
27
27
27
28
16
Czech Rep.
28
28
29
30
31
Denmark
21
25
25
26
10
Estonia
32
26
31
27
32
Finland
23
8
20
9
24
France
15
3
2
2
2
Germany
18
19
16
22
25
Greece
17
9
13
11
27
Hungary
33
33
34
34
33
Iceland
2
16
10
20
23
Ireland
15
23
19
24
21
Israel
3
11
3
17
3
Italy
3
4
8
4
17
Japan
5
1
6
1
1
Korea
20
5
20
5
4
9
11
6
8
12
Mexico
34
34
28
32
22
Netherlands
11
19
16
20
8
New Zealand
11
19
8
17
18
Luxembourg
Norway
9
13
15
14
11
Poland
30
29
30
28
30
Portugal
24
9
23
11
14
Slovak Rep.
31
31
33
31
34
Slovenia
28
25
17
26
14
Spain
5
2
3
3
6
Sweden
5
13
10
17
19
Switzerland
1
6
1
5
13
Turkey
29
32
32
33
29
United Kingdom
United States
14
26
24
29
14
22
23
25
9
20
* Life expectancy at 65 is not presented in chapter 3 on health status, but rather in chapter 11 on ageing and longterm care.
** Mortality from cardiovascular diseases includes deaths from ischemic heart diseases and cerebrovascular diseases
shown in Chapter 3, as well as other cardiovascular diseases.
Source: Health at a Glance 2015.
1 2 http://dx.doi.org/10.1787/888933281467
23
1.
Table 1.2. Risk factors
Note: Countries are listed in alphabetical order. The number in the cell indicates the position of each country among all countries for which
data is available.
Indicator
Smoking in adults
Alcohol
consumption
Obesity in adults*
Overweight and
obesity in children**
4
22
30*
20
Austria
26
34
8
14
Belgium
15
20
9
5
6
11
29*
21
28
Australia
Canada
Chile
33
10
28*
Czech Rep.
25
32
20*
Denmark
12
17
10
Estonia
31
33
18
7
Finland
10
14
26
17
France
30
30
11
13
Germany
23
28
25*
Greece
34
7
Hungary
5
23
3
19
33
31*
24
32
30
Iceland
2
6
Ireland
16
26
Israel
11
2
13
18
Italy
24
4
4
31
21
24*
9
11
Japan
17
7
1*
15
Korea
19
12
2*
16
Luxembourg
9
29
23*
19
Mexico
3
3
33*
30
Netherlands
13
14
6
7
New Zealand
8
16
32*
27
1
7
5
3
Poland
27
27
14
2
Portugal
14
25
12
25
Slovak Rep.
18
22
16*
Slovenia
22
17
17
22
Spain
29
20
15
26
1
7
7
9
21
22
4
11
Norway
Sweden
Switzerland
3
Turkey
27
1
22*
n.a.
United Kingdom
United States
20
5
19
13
27*
34*
32
29
* Data on obesity in adults are based on measured height and weight for all the countries marked with an *. These
result in more accurate data and higher obesity rates compared with all other countries that are providing selfreported height and weight.
** Data on overweight or obesity in children are all based on measured data, but refer to different age groups across
countries.
1 2 http://dx.doi.org/10.1787/888933281473
24
1.
Table 1.3. Access to care
Top third performers (or between 95% and 100% for health care coverage)
Middle third performers (or between 90% and 95% for health care coverage)
Bottom third performers (or less than 90% for health care coverage)
Note: Countries are listed in alphabetical order. The number in the cell indicates the position of each country among all countries for
which data is available. For out-of-pocket medical expenditure, unmet care needs and the waiting times indicators, the top performers
in terms of access are countries with the lowest expenditure as a share of household consumption, the lowest unmet care needs
or lowest waiting times.
Share of out of
pocket medical
expenditure in
houselhold
consumption
Waiting times
for cataract
surgery
- median
Waiting times
for knee
replacement
- median
Indicator
Health care
coverage
Australia
1
Austria
1
Belgium
1
20
11
Canada
1
11
n.a.
n.a.
Chile
1
28
n.a.
n.a.
13
8
Czech Rep.
1
7
5
4
n.a.
n.a.
Denmark
1
14
7
10
4
1
Estonia
2
12
21
19
9
13
Finland
1
18
19
11
10
7
France
1
3
15
15
n.a.
n.a.
Germany
1
5
9
5
n.a.
n.a.
Greece
3
32
23
20
n.a.
n.a.
Hungary
1
30
14
9
1
6
Iceland
1
21
18
22
n.a.
n.a.
Ireland
1
22
17
17
n.a.
n.a.
Israel
1
16
n.a.
n.a.
3
3
Italy
1
22
20
21
n.a.
n.a.
Japan
1
9
n.a.
n.a.
n.a.
n.a.
Korea
1
34
n.a.
n.a.
n.a.
n.a.
Luxembourg
1
5
4
3
n.a.
n.a.
Mexico
1
30
n.a.
n.a.
n.a.
n.a.
Netherlands
1
2**
1
n.a.
n.a.
New Zealand
1
9
n.a.
n.a.
7
5
Norway
1
16
8
15
12
10
Poland
2
13
22
13
14
14
Portugal
1
29
16
23
6
11
Slovak Rep.
2
22
11
6
n.a.
n.a.
Slovenia
1
7
n.a.
n.a.
n.a.
n.a.
Spain
1
26
3
18
11
9
Sweden
1
26
11
14
n.a.
n.a.
Switzerland
1
33
6
12
n.a.
n.a.
Turkey
1
1
n.a.
n.a.
n.a.
n.a.
United Kingdom
United States
1
3
3
14
9
n.a.
7
n.a.
4
n.a.
2
n.a.
Unmet medical
care needs*
Unmet dental
care needs*
22
n.a.
n.a.
18
1
1
8
12
2
n.a.
n.a.
8
n.a.
n.a.
2
4
* Unmet medical or dental care needs may be for financial reasons, waiting times or long distance to travel to get
access to services. The data only cover European countries because they are based on the EU-SILC survey.
** The ranking for the Netherlands is overrated as it excludes compulsory co-payments to health insurers (if these
were included, this would move the Netherlands in the middle third category).
1 2 http://dx.doi.org/10.1787/888933281483
25
1.
Table 1.4. Quality of care
Note: Countries are listed in alphabetical order. The number in the cell indicates the position of each country among all countries for which
data is available. For the indicators of avoidable hospital admissions and case-fatality rates, the top performers are countries with the
lowest rates.
Diabetes
hospital
admission
Case-fatality
for AMI
(admissionbased)
Case-fatality
for ischemic
stroke
(admissionbased)
Breast
cancer
survival
Colorectal
cancer
survival
11
5
3
19
19
7
12
4
Cervical
cancer
survival
Indicator
Asthma and
COPD hospital
admission
Australia
29
17
1
20
Austria
28
29
27
8
Belgium
16
20
19
20
16
Canada
18
10
11
26
12
8
13
6
27
31
16
25
23
n.a.
Czech Rep.
12
23
11
22
13
22
21
Denmark
26
14
7
17
5
11
18
Estonia
27
n.a.
28
29
8
25
22
Finland
10
15
9
4
6
4
7
France
7
21
17
13
n.a.
n.a.
n.a.
Chile
Germany
21
25
25
8
15
15
10
Greece
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
Hungary
31
11
30
22
n.a.
n.a.
n.a.
Iceland
14
4
15
14
7
10
n.a.
Ireland
32
16
8
24
20
20
19
Israel
19
9
11
6
10
7
2
Italy
2
1
5
7
3
15
12
Japan
1
18
29
1
4
9
4
Korea
24
30
24
2
2
14
1
Luxembourg
9
19
16
17
n.a.
n.a.
n.a.
Mexico
5
31
32
31
n.a.
n.a.
n.a.
Netherlands
11
6
20
12
16
16
11
New Zealand
30
22
10
14
14
12
15
Norway
17
7
11
5
1
2
13
Poland
20
28
3
n.a.
24
24
23
Portugal
Slovak Rep.
Slovenia
3
8
26
27
18
6
16
23
26
17
28
n.a.
n.a.
n.a.
8
13
4
30
23
18
17
Spain
15
3
23
24
n.a.
n.a.
n.a.
Sweden
13
12
2
8
9
1
6
4
2
22
11
n.a.
n.a.
n.a.
Turkey
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
United Kingdom
United States
22
25
5
24
20
5
19
3
22
21
21
2
20
9
Switzerland
1 2 http://dx.doi.org/10.1787/888933281494
26
1.
Table 1.5. Health care resources
Top third in health spending or resources
Middle third in health spending or resources
Bottom third in health spending or resources
Note: Countries are listed in alphabetical order. The number in the cell indicates the position of each country among all countries for
which data is available. Although countries are ranked from highest health spending or availability of resources to lowest, this does not
necessarily mean better performance.
Nurses
per capita
(active)
Hospital beds
per capita
MRI units
per capita*
14
10
18
12*
2
21
4
9
Indicator
Health expenditure
per capita
Doctors
per capita
(active)
Australia
13
8
Austria
CT scanners
per capita*
2*
10
Belgium
11
21
15
9
19*
11*
Canada
10
28
16
29
22
23
Chile
30
33
27
32
26
26
Czech Rep.
27
10
20
7
24
22
Denmark
7
11
3
23
10
5
Estonia
31
18
23
12
17
15
Finland
17
20
5
13
6
13
France
12
16
17
8
21
24
16*
Germany
6
5
6
3
15*
Greece
25
1
32
14
5
8
Hungary
29
19
22
5
31*
31*
Iceland
15
11
4
21
7
4
Ireland
16
25
7
26
13
17
Israel
24
13
31
22
30
29
Italy
20
8
24
19
3
9
Japan
14
29
13
1
1
1
Korea
26
31
29
2
4
6
9
22
9
11
14
12
33
32
33
33
32
32
Luxembourg
Mexico
Netherlands
4
17
8
n.a.
16
28
New Zealand
18
22
14
26
18
20
n.a.
Norway
3
3
2
17
n.a.
Poland
32
30
28
6
28
19
Portugal
22
4
25
20
27*
14*
Slovak Rep.
28
14
26
10
25
21
Slovenia
23
26
18
16
23
27
Spain
21
9
30
24
11
18
5
7
11
31
n.a.
n.a.
Sweden
Switzerland
2
6
1
15
Turkey
34
34
34
30
20
8*
25
7
United Kingdom
United States
19
1
24
27
19
12
26
25
29
2
30
3
* Data for most countries marked with an * do not include MRI units and CT scanners installed outside hospitals,
leading to an under-estimation. In Australia and Hungary, the data only include MRI units and CT scanners eligible
for public reimbursement, also leading to an under-estimation.
1 2 http://dx.doi.org/10.1787/888933281500
27
1.
Box 1.1. Limitations in the interpretation and use of the dashboards
The previous dashboards should be interpreted and used with caution for several reasons:
●
Due to limitations in data availability, the indicators selected on each topic do not generally provide a
complete coverage of all important aspects related to this topic. For instance, the indicators of health
status relate solely to mortality because mortality data are more widely available and comparable across
countries than morbidity data. While life expectancy undoubtedly is a key indicator of health status, the
lack of indicators about the physical and mental health status of people while they are alive is an
important limitation. The same limitations also apply to the dashboards on risk factors (which only
include some risk factors to health), access to care and quality of care.
●
There are limitations in data comparability for some indicators which should be kept in mind in
interpreting the ranking of countries. One notable example is the indicator on obesity rates among
adults, which in several countries are based on self-reported height and weight, resulting in an underestimation compared to those countries that provide more reliable data based on measured obesity.
●
The grouping of countries in three groups (tertiles) is based on a simple method using only the point
estimates of each country and dividing them in three equal groups. It does not take into account the
distribution of the data around the OECD average, nor the confidence intervals for those indicators
where these have been calculated (notably for several indicators of quality of care).
●
These dashboards only present the current situation and in this respect may hide the progress that some
OECD countries might have achieved over time and the fact that they may be moving quickly towards the
OECD average. These key trends are discussed in the publication.
Because of these limitations in data availability, comparability and statistical significance, there is no
attempt to calculate any summary indicator of performance for each of the dimensions or across
dimensions. These dashboards should be used to get a first impression on the relative strengths and
weaknesses of different OECD countries on the set of indicators selected. It should be complemented by a
more in-depth review of the data and the factors influencing the cross-country variations presented in the
following chapters of this publication.
28
© OECD 2015
Chapter 2
Pharmaceutical spending trends
and future challenges
Across OECD countries, pharmaceutical spending reached around USD 800 billion
in 2013, accounting for about 20% of total health spending on average when
pharmaceutical consumption in hospital is added to the purchase of pharmaceutical
drugs in the retail sector. This chapter looks at recent trends in pharmaceutical
spending across OECD countries. It examines the drivers of recent spending trends,
highlighting differences across therapeutic classes. It shows that while the
consumption of medicines continues to increase and to push pharmaceutical
spending up, cost-containment policies and patent expiries of a number of topselling products have put downward pressure on pharmaceutical prices in recent
years. This resulted in a slower pace of growth over the past decade.
The chapter then looks at emerging challenges for policy makers in the management
of pharmaceutical spending. The proliferation of high-cost specialty medicines will
be a major driver of health spending growth in the coming years. While some of
these medicines bring great benefits to patients, others provide only marginal
improvements. This challenges the efficiency of pharmaceutical spending.
29
2.
PHARMACEUTICAL SPENDING TRENDS AND FUTURE CHALLENGES
Introduction
Pharmaceutical spending across OECD countries reached around USD 800 billion in
2013, accounting for about 20% of total health spending on average when pharmaceutical
consumption in hospitals is added to the purchase of pharmaceutical drugs in the retail
sector. Retail pharmaceutical spending growth has slowed down in most OECD countries in
the last decade, while spending on pharmaceuticals used in hospital has increased in most
countries where this information is available. Current market developments, such as the
multiplication of high-cost medicines targeting small populations and/or complex
conditions, have prompted new debates on the sustainability and efficiency of
pharmaceutical spending. Will OECD countries be able to afford access to these high-cost
medicines to all patients who need them and at what price? Will they get value for the
money they will spend?
This chapter looks first at recent trends in pharmaceutical spending and financing
across OECD countries. Then, it examines the drivers of recent spending trends,
highlighting differences across drug classes. Finally, it focuses on current and predicted
trends in pharmaceutical markets and associated challenges in the management of
pharmaceutical expenditure.
One in every five health dollars is spent on pharmaceuticals
In 2013, OECD countries spent an average of more than 500 USD per person on retail
pharmaceuticals1 (Figure 2.1). In the United States, the level of spending was twice the
OECD average, and more than 35% higher than in Japan, the next highest spender. At the
other end of the scale, Denmark spent less than half the OECD average.
The data on pharmaceutical spending shown in Figure 2.1 only include those
purchased in the retail sector, as many countries are not able to supply data on the cost of
pharmaceuticals consumed in hospitals and other health care facilities. In those countries
that are able to supply these data, the inclusion of pharmaceutical expenditure in hospital
and other facilities adds another 10% on top of the retail pharmaceutical spending in the
case of Germany, Canada and Australia, and more than 25% in countries such as Spain,
Czech Republic and Portugal (Figure 2.2). Such differences stem from the budgetary and
distributional channels within a country. On average, the use of pharmaceuticals in
hospitals and other health care facilities raises the pharmaceutical bill by around 20%,
meaning that a little more than one health dollar in five goes towards purchasing
pharmaceuticals.
Prior to 2005, spending on retail pharmaceuticals grew at a faster rate than other key
components of health care, such as inpatient and outpatient care, and was a major
contributor in driving up overall health expenditures (see Figure 2.3). Over the subsequent
decade, however, retail pharmaceutical spending growth was seriously affected by patent
expiries of several blockbuster drugs and cost-containment policies, particularly as a
consequence of the economic crisis. As a result, retail pharmaceutical spending decreased
dramatically in some countries, for example in Portugal, Denmark and Greece.
30
2. PHARMACEUTICAL SPENDING TRENDS AND FUTURE CHALLENGES
Figure 2.1. Expenditure on retail pharmaceuticals per capita and as a share of GDP, 2013
(or nearest year)
Pharmaceutical expenditure per capita
Pharmaceutical expenditure as a share of GDP
1026
752
721
713
678
666
652
603
596
590
572
536
533
526
515
503
481
459
459
436
397
396
392
381
367
364
326
287
273
240
1200
1000
USD PPP
800
600
400
1.9
United States
Japan
Greece 1
Canada
Germany
Switzerland
Ireland1
Belgium
France
Australia
Italy1
Austria
Slovak Republic1
Spain
OECD29
Hungary
Slovenia
Finland
Sweden
Korea
Netherlands1
Iceland
Portugal1
Czech Republic
Norway
Luxembourg 2
Poland
Israel1
Estonia
Denmark
200
0
2.1
2.8
1.7
1.5
1.2
1.4
1.4
1.6
1.3
1.6
1.2
2.0
1.6
1.4
2.2
1.7
1.2
1.0
1.3
0.9
0.9
1.4
1.3
0.6
0.6
1.4
1.0
1.1
0.5
0
1
2
3
4
% GDP
1. Includes medical non-durables.
2. Excludes over-the-counter drugs (OTC).
Source: OECD Health Statistics 2015
1 2 http://dx.doi.org/10.1787/888933280639
Figure 2.2. Total (retail and hospital) pharmaceutical spending,
per capita USD PPP, 2013 (or nearest year)
Retail pharmaceuticals
Hospital pharmaceuticals
USD PPP
1 000
800
+9%
+10%
+27%
600
+10%
+44%
+33%
+9%
400
+19%
200
0
Canada
Germany
Spain
Portugal
(2011)
Australia
(2010)
Czech Republic
Korea
Estonia
(2011)
Note: Data for Portugal are OECD estimates based on adjusted total and retail pharmaceutical spending figures.
Source: OECD Health Statistics 2015.
1 2 http://dx.doi.org/10.1787/888933280642
31
2.
Figure 2.3. Average annual growth in pharmaceutical and total health expenditure
per capita, in real terms, average across OECD countries, 1990 to 2013
(or nearest year)
Health expenditure (including pharmaceutical spending)
%
8
Pharmaceutical expenditure
6
4
2
0
-2
-4
1990
1995
2000
2005
2013
2010
1 2 http://dx.doi.org/10.1787/888933280658
Over the same period, spending on hospital medicines grew faster in several countries
(see Figure 2.4). The multiplication of specialty drugs2 offers a partial explanation, as these
are often delivered in a hospital setting (including in an outpatient department) rather
than dispensed via pharmacies (Hirsch et al., 2014) and are coming to the market with
increasingly high prices.
Figure 2.4. Annual average growth in retail and hospital pharmaceutical
expenditure, in real terms, 2005 and 2013 (or nearest year)
Retail pharmaceuticals
%
12
Hospital pharmaceuticals
10.1
6
4.7
3.0
2.2
1.5
2.2
1.5
2.1
0
-0.2
-0.8
-1.8
-5.1
-6
Czech Republic
Korea
Spain
Germany
Canada
Portugal
Note: OECD estimates for Portugal exclude expenditure on other medical products from reported total and retail
spending.
1 2 http://dx.doi.org/10.1787/888933280663
The share of private funding of pharmaceuticals increases
Private funding in the purchasing of pharmaceuticals is greater than for other
categories of health care. On average in OECD countries, 43% of retail pharmaceutical
32
spending is paid for from private sources (private health insurance or out-of-pocket),
compared with 21% for inpatient and outpatient care. Most of the private spending for
drugs (37%) comes directly from households’ pockets, reflecting both the high cost-sharing
requirements and the extent of self-consumption of over-the-counter (OTC) medicines (see
the indicator on pharmaceutical expenditure in Chapter 10). Countries such as France,
Germany and Japan report a relatively low private share of pharmaceutical spending of
around 25-30%, whereas the United States and Canada (both countries where private
health insurance plays a large role in financing pharmaceutical spending), as well as
Poland (where spending on OTC drugs is significant), all report more than 60% of the
pharmaceutical bill being covered by private sources.
In a majority of OECD countries, private spending on pharmaceuticals has grown
faster than public spending over the last decade (Figure 2.5). In particular, since 2009,
private spending on drugs did not decline to the same extent as public spending. This
is due in part to an observed shift of some of the cost-burden to households. For
example, in Hungary, the out-of-pocket share of spending on prescribed medicines rose
from 40% to 45% between 2010 and 2013 (Figure 2.6). The Czech Republic and Slovak
Republic also reported increases in the households' share of medicines to 38% and 33%
respectively.
Total expenditure on pharmaceuticals, annual average growth rate
Figure 2.5. Annual growth in public and total retail pharmaceutical spending, OECD countries,
2005-2013
%
15
10
5
2005-2009
%
15
2009-2013
GRC
Private spending
growth higher
than public
EST
SVK
JPN
IRL
CHE
NLD
NOR
0
USA
ESP
ISL
NOR
-5
5
JPN
KOR
USA
HUN
0
10
Private spending
growth higher
than public
KOR
EST
Private spending
growth lower
than public
-10
DNK
GRC
PRT
Private spending
growth lower
than public
LUX
-15
-15
-10
-5
0
5
10
15 %
-15
-10
-5
0
5
10
-5
-10
-15
15 %
Public expenditure on pharmaceuticals, annual average growth rate
1 2 http://dx.doi.org/10.1787/888933280679
The trends in public and private spending are partly explained by a range of policy
measures adopted by countries to contain public spending on pharmaceuticals, such as
increases in cost-sharing, as well as the increasing use of OTC drugs (usually not
reimbursed) compared with prescription drugs (usually reimbursed) in several countries. In
Slovenia, Poland and Spain, the OTC share of pharmaceutical spending has significantly
increased.
33
2.
Figure 2.6. Expenditure on retail pharmaceuticals by type of financing, 2013
(or nearest year)
Public
Private insurance
Private out-of-pocket
Other
% of retail pharmaceutical expenditure
100
90
13
19
25
28
1
80
17
18
5
7
32
32
33
33
33
33
38
14
42
34
37
44
46
48
46
1
70
50
58
1
75
71
69
68
68
30
20
67
67
66
65
62
58
57
68
27
1
50
80
51
2
5
82
30
45
1
60
40
47
55
54
53
2
52
52
49
48
5
6
43
43
38
30
36
36
34
32
10
Lu
xe
m
Ne bo
t h ur g
er
la
Ge nds
rm
an
y
Ja
pa
Fr n
an
ce
Sp
ain
A
Sl us t
ov r i
ak a
Re
G r p.
ee
Be ce
S w l gi
i t z um
e
Cz rla
ec nd
h
Re
N o p.
rw
OE ay
CD
26
Ko
re
Es a
to
n
Fi ia
nl
an
Sw d
ed
Po en
r tu
Au gal
st
ra
Sl li a
ov
e
Hu ni a
ng
De ar y
nm
a
Ic r k
el
an
Un C a d
i te nad
d
St a
at
e
Po s
la
nd
0
1 2 http://dx.doi.org/10.1787/888933280689
Pharmaceutical expenditure growth is driven by changes in quantity, prices
and therapeutic mix
The increasing demand for medicines and the introduction of new drugs into the
market are the main drivers of spending growth. At the same time, the availability of
generics and biosimilars combined with the introduction and strengthening of costcontainment policies have exerted a downward pressure on spending in recent years
(Belloni et al., forthcoming).
An increasing demand for pharmaceuticals and new treatment opportunities push
pharmaceutical spending up
The quantity of drugs consumed has increased over time in many therapeutic classes.
Between 2000 and 2013, among countries for which data are available, the use of
antihypertensive, antidiabetic and anti-depressant medications nearly doubled, while the
use of cholesterol-lowering drugs tripled (see indicator on “Pharmaceutical consumption”
in Chapter 10). These trends reflect an increasing demand for pharmaceuticals, resulting
from the rising prevalence of chronic diseases, population ageing, changes in clinical
practices and coverage extensions, as well as new treatment opportunities.
The prevalence of many chronic diseases, such as cancer, diabetes and mental illness has
increased, leading to an increased demand for medical treatments. Improvements in
diagnosis, leading to earlier recognition of conditions and earlier treatment with
medicines, as well as the development of more medicines (both prescribed and OTC) to
treat common conditions have also contributed to increase the consumption of medicines.
Population ageing also increases the demand for pharmaceutical treatments. With age,
the tendency to develop health conditions which require some kind of medication
increases. As shown in Figure 2.7 for Korea and the Netherlands, per capita spending on
pharmaceuticals increases rapidly with age.
34
Figure 2.7. Per capita spending on retail pharmaceuticals by age, Korea and the Netherlands,
2011
Males
Females
Korea (won)
Netherlands (euros)
2 000
1 200 000
1 500
800 000
1 000
400 000
500
th
Le
ss
th
ss
Le
an
5 5
10 to 9
t
15 o 14
t
20 o 19
to
25 24
t
30 o 29
to
35 34
t
4 0 o 39
to
45 44
t
5 0 o 49
to
55 54
t
6 0 o 59
to
65 64
to
70 6 9
t
75 o 74
t
8 0 o 79
to
Ov 84
er
85
0
an
5 5
10 to 9
t
15 o 14
t
20 o 19
to
25 24
t
30 o 29
to
35 34
t
4 0 o 39
to
45 44
t
5 0 o 49
to
55 54
t
6 0 o 59
to
65 64
to
70 6 9
t
75 o 74
t
8 0 o 79
to
Ov 84
er
85
0
Source: OECD Database on Expenditure by Disease, Age and Gender (unpublished).
1 2 http://dx.doi.org/10.1787/888933280694
New and innovative drugs expand treatment options and increase treatment costs. New
drugs can be new chemical entities or new formulations of existing drugs. Both categories
may increase treatment options, for instance, for previously unmet needs or for new
population targets (e.g. children), increasing the quantity of drugs consumed. While the
approval of new drugs in existing market segments can increase competition and lead to
potential savings, usually new drugs offering therapeutic advantages for patients are
priced higher than their competitors and contribute significantly to pharmaceutical
spending growth.
In recent years, the proliferation of specialty pharmaceuticals with high prices, in
particular oral cancer drugs and immune modulators,3 has played an increasing role in
pharmaceutical spending growth (Express Scripts, 2015; Trish et al., 2014). In the United
States, specialty drugs represented just 1% of total prescriptions but accounted for 25% of
total prescription drug spending in 2012 (Express Scripts, 2015).
Changes in clinical practice guidelines also influence the consumption of pharmaceuticals
upward. Updated guidelines have often recommended earlier treatments, higher dosages
or longer treatment durations for secondary prevention or management of chronic
diseases, leading to increases in volume consumed. This is the case for instance for
guidelines for cholesterol-lowering drugs (e.g. statins), one of the fastest-growing
therapeutic classes of prescription drugs all over the world. Prescription guidelines have
been updated several times since the end of the 1990s, recommending wider screening and
lower lipid level targets as an indication for prescription in Canada, the United Kingdom
and the United States (CIHI, 2012; ACC/AHA, 2014; NICE, 2014).
In a few countries, coverage expansion has contributed to pharmaceutical spending
growth. In the United States, Medicare Part D was introduced in 2006 and the Affordable
Care Act was implemented in 2014, contributing to a substantial reduction in the number
of people uninsured. In Korea, with the establishment of the National Health Insurance
35
2.
(NHI) in 1989 and successive steps in coverage expansion, pharmaceutical spending
increased rapidly – at a rate of more than 10% each year on average between 2000 and 2004
(Yang et al., 2008) and continued to grow since then, albeit at a slower rate.
Cost-containment policies and patent losses have put downward pressure on
spending growth
Pharmaceutical policies have the potential to influence spending trends and the
efficiency (cost-effectiveness) of pharmaceutical spending. In recent years, and in
particular after the economic crisis in 2008, OECD countries have implemented or
strengthened a number of cost-containment policies (Table 2.1).
Table 2.1. Pharmaceutical cost-containment policies introduced since 2008
in a selection of OECD countries
Policies
Examples
Extent of implementation
Pricing policies
One-off cut in ex-factory prices of on-patent
medicines
Austria, Belgium, Czech Republic, France, Germany,
Greece, Ireland, Italy, Portugal, Spain, Switzerland,
United Kingdom
Implementation of external price referencing or
change in the method or basket of countries
Greece, Portugal, Slovak Republic, Spain, Switzerland
Reduction in value-added tax (VAT) rates
Austria, Czech Republic, Greece
Reduction of mark-ups for distributors
Czech Republic, Estonia, Greece, Hungary, Ireland,
Portugal, Spain
Increase of rebates paid by manufacturers or
distributors
Germany
Extra-ordinary price reviews
Greece, Ireland, Portugal, Slovak Republic, Spain,
Switzerland
Pressure on prices of branded medicines (e.g. group
purchasing or negotiation)
Canada
Change in the reference price system (max.
reimbursement price by cluster)
Estonia, Greece, Ireland, Portugal, Slovak Republic,
Spain
Delisting of products
Czech Republic, Greece, Ireland, Portugal, Spain
Increase in cost-sharing
Austria, Czech Republic, Estonia, France, Greece,
Ireland, Italy, Portugal, Slovenia, Slovak Republic,
Spain, Sweden
Introduction of health-technology assessment (HTA)
to inform coverage/pricing decisions
Germany
Managed-entry agreements
Belgium, Italy, United Kingdom
Reimbursement policies
Policies to exploit the potential Implementation of voluntary or mandatory International Belgium, Estonia, France, Italy, Luxembourg,
of off-patent drugs
Non-proprietary Name (INN) prescribing
Portugal, Slovak Republic, Spain
Incentives for physicians to prescribe generics
Belgium, France, Greece, Hungary, Japan
Incentives for pharmacists to dispense generics
Belgium, France, Ireland, Japan
Incentives and information for patients to purchase
generics
Austria, Estonia, France, Iceland, Ireland,
Luxembourg, Portugal, Spain
Pressure on generic prices (e.g. tendering, price cuts) Canada, France, Greece, Portugal
Source: Belloni et al. (forthcoming), complemented by Thomson et al. (2014) on cost-sharing policies.
Since 2008, price cuts have been very common. At least one third of OECD countries
implemented measures to reduce regulated prices of pharmaceuticals. They most often
imposed cuts on ex-factory prices of on-patent and/or generic drugs (e.g. Greece, Ireland,
Portugal and Spain), but many of these countries also reduced distribution margins at least
for some categories of medicines. Germany increased temporarily the mandatory rebates
imposed on pharmaceutical companies from 6% to 16% between 2010 and 2013. In
April 2014, the mandatory rebate was set at 7% for all medicines except generics. In
Canada, several provinces and territories entered in joint price negotiations for brandname drugs covered by public plans. Finally, five countries changed VAT rates imposed on
36
medicine, either to reduce pharmaceutical spending (e.g. Austria, Czech Republic and
Greece) or to increase public revenues (e.g. Estonia, Portugal) resulting in increased spending.
Greece, Portugal, the Slovak Republic, Spain and Switzerland reformed their external
reference price system, expanding or reducing the basket of countries used for international
benchmarking or revising the method for setting prices. For example, the Slovak Republic
included Greece in the basket of benchmarked countries in 2010.
A range of policy measures have shifted some of the burden of pharmaceutical spending to
private payers (households or complementary private insurance). These rarely took the form
of delisting products (i.e. excluding them from reimbursement), with the notable
exceptions of Greece, where 49 medicines were delisted after a price review in 2011, Czech
Republic, Ireland, Portugal and Spain. At least a dozen of countries introduced or increased
user charges for retail prescription drugs (Austria, Czech Republic, Estonia, France, Greece,
Ireland, Italy, Portugal, Slovak Republic, Slovenia, Spain and Sweden) (see Thomson et al.,
2014; and Belloni et al., forthcoming).
Some countries decided to give a greater role to health technology assessment (HTA) in their
reimbursement and/or pricing process. In Germany, for instance, a new law, which took
effect in January 2011, introduced a systematic and formal assessment of the “added
therapeutic benefit” of new medicines after market entry to allow negotiation of a
reimbursement price where needed. Expected savings for health insurance funds are up to
several million Euros for some individual products (Henschke, 2013).
In parallel, many OECD countries have introduced or expanded the use of managed
entry agreements (MEAs), which are arrangements between the manufacturer and the
payer that allow coverage of drugs subject to defined conditions. Managed-entry
agreements cover a wide range of contractual arrangements, which can be just financial
or performance-based (i.e. reimbursement and pricing conditions are linked to observed
performance of a product in real life). They take the form of price-volume agreements,
coverage with evidence development, performance-based outcome guarantees, patient
access scheme, etc. Their implementation varies across countries. The United
Kingdom, Italy, Germany and Poland have taken the lead in using these arrangements
(Ferrario and Kanavos, 2013). In Italy, the amounts recouped by the government from
manufacturers through performance-based arrangements are modest and represent 5%
of total expenditure for the relevant indications. This is due, at least partly, to high
administrative and management costs of the scheme (Garattini et al., 2015, Navarria et
al., 2015, van de Vooren et al., 2014). Their impact in other jurisdictions has not yet been
evaluated.
Since the onset of the economic crisis, several countries have strengthened their generic
policies (see Table 2.1 and Figures 10.12 and 10.13 in Chapter 10). While no formal
evaluation is available, these policies – associated with the “patent cliff” – have certainly
contributed to the significant increase in the generic market share observed over the past
decade in most countries.
From the mid-2000s, a number of blockbuster drugs lost patent protection, contributing to
the decline of pharmaceutical spending growth. Several products worth more than
USD 30 billion a year in US sales lost their patents in 2011-12, among which Plavix®
(antiplatelet agent), Lipitor® (anti-cholesterol) and Actos® (diabetes), which accounted
together for nearly USD 15 billion in sales (Managed Care, 2011).
Patent expiries offer huge opportunities to make savings without affecting the quality
of care. In the United States, for instance, where the generic market is very dynamic, the
37
2.
price of a generic drug is on average 80 to 85 % lower than that of the brand name product.
In 2012, 84% of all prescriptions filled in the United States were for generic drugs (IMS
Institute for Healthcare Informatics, 2013, see also indicator on “Share of generic market”
in Chapter 10).
Biosimilars can also lead to significant savings, although the potential is perhaps not
as high as with generics of small molecules, due to longer and costlier development and
production costs. Entry barriers are higher: Europe established a pathway for the
approval of biosimilars in 2005, Japan approved biosimilars’ regulation in 2009 and Korea
in 2010. The United States approved the legislative framework for licensing follow-on
biologic products in 2010, but the FDA only recently approved the first biosimilar in March
2015. In addition, countries’ regulations often restrict market growth potential and price
competition. In many countries, prescribing by International Non-proprietary
Names (INN) is not allowed, patients cannot be switched to a biosimilar and substitution
by the pharmacist is not allowed (European Biopharmaceutical Enterprises, 2015).
Drivers of spending growth vary across therapeutic areas
All the drivers of spending growth listed before interact differently across therapeutic
classes, leading to contrasting trends.
In the case of antidiabetic medicines for instance, where use has been steadily
increasing in line with the increasing prevalence of type-2 diabetes, the existence of longstanding treatments with generic versions resulted in a 'cost of treatment' which remained
relatively stable over a number of years. However, the arrival of new and more expensive
treatments in recent years significantly increased the average daily treatment cost. The
shift from existing medications to new drugs has therefore been the main contributor to
pharmaceutical spending growth in this therapeutic class in the recent period, as shown
for Denmark between 2005 and 2013 in Figure 2.8.
Figure 2.8. Annual growth in sales, volumes and cost per defined daily dosage
(DDD) of antidiabetic drugs, Denmark, 2005-13
Volume (DDD per 1000/day)
%
Cost per DDD
Sales (per capita)
16
12
8
4
0
-4
2005
2006
2007
2008
2009
2010
2011
2012
2013
1 2 http://dx.doi.org/10.1787/888933280701
38
By contrast, in the class of cholesterol lowering medications, the expiry of the patent
for some of the top selling statins in the mid-2000s and the introduction of generics has led
to a pattern of decreasing treatment costs in many countries in recent years. For example,
costs per defined daily dose (DDD) typically fell by more than 10% per year, on average,
since 2005 in Germany (Figure 2.9).
Figure 2.9. Annual growth in sales, volumes and cost per defined daily dosage
(DDD) of lipid-lowering drugs, Germany, 2005-13
Volume (DDD per 1000/day)
%
20
Cost per DDD
Sales (per capita)
10
0
-10
-20
-30
2005
2006
2007
2008
2009
2010
2011
2012
2013
1 2 http://dx.doi.org/10.1787/888933280715
The high price of new drugs has been the main driver of spending growth in other
therapeutic areas.
In the area of cancer for instance, the price of specialty medicines has steadily
increased, especially since 2000. In the United States, the median monthly price of cancer
treatment for Medicare patients has increased from around USD 5 000 in 2000-05 to around
USD 10 000 in 2010-15.4 In 2012, 12 out of 13 cancer-approved drugs cost more than
USD 100 000 per year (Light and Kantarjian, 2013). These price increases are observed
everywhere. In Australia, the average reimbursement price per anticancer prescription
drug more than doubled in real terms between 1999-2000 and 2011-12, while the price of
all other prescription drugs only increased by about one-third during that period (Karikios
et al., 2014).
Treatment costs for multiple sclerosis and pulmonary hypertension are also very
high and increasing (Lotvin et al., 2014). The first generation of multiple sclerosis therapies,
originally costing USD 8 000 to USD 11 000 per year in 1993-96, now cost about USD 60 000
per year, reflecting an increase five to seven times higher than prescription drug inflation
over the period 1993-2013. Newer therapies entered the market with a cost 25%-60% higher
than existing ones (Hartung et al., 2015).
In 2013 and 2014, new treatments for hepatitis C became available, posing an
unprecedented challenge to many OECD countries. These medicines represent a great
medical advancement: they are much better tolerated than previous treatments and reach
cure rates of 95% or higher for sub-groups of patients with hepatitis C. For these target
groups, these treatments are even cost-effective. The immediate budget impact of treating
the entire population affected proved to be unaffordable for OECD countries, due to high
prices and high prevalence of the disease. In reaction, many countries sought to reach
39
2.
agreements with manufacturers to limit the budget impact and to recommend priority use
for the most severely affected patients, generating frustration for physicians, patients and
decision makers alike.
Orphan drugs5 also typically have high prices. The median cost per patient and per
year is 19 times higher for an orphan drug than for a non-orphan drug (EvaluatePharma,
2014). The premium for ultra-rare indications is very high. The number of newly approved
molecular entities classified as orphans has been increasing since the implementation of
policies designed to encourage their development and medicines with orphan designation
now account for one-third of new chemical entities approved by the FDA (IMS Institute for
Healthcare Informatics, 2014).
New challenges in the pharmaceutical market
Changes in the pharmaceutical market, with the increased availability of high-cost
drugs, suggest that future pharmaceutical spending growth may pick up again, instead of
continuing its recent path, at least in some countries. Countries will face a number of
challenges to make new high-cost medicines available to patients, contain spending
growth and ensure value for money.
The IMS Institute for Healthcare Informatics predicts worldwide pharmaceutical
sales6 to be 30% higher in 2018 than in 2013 (IMS Institute for Healthcare Informatics, 2014).
The average annual growth rate is slightly higher than in previous years due to a smaller
number of patent expiries and a higher number of new specialty drugs. Emerging markets,
in addition to the United States, are expected to contribute most of this growth, while
European markets will make more modest contributions.
The United States is the largest pharmaceutical market, accounting for one third of
global sales, and is expected to continue to grow. The IMS Institute for Healthcare
Informatics predicted peaks in US spending growth of 14% in 2014 and 8% in 2015, followed
by annual growth rates of 4-5% until 2018. According to CMS projections, prescription drug
spending is expected to grow at an average annual rate of over 6% per year between 2016
and 2024 (Keehan, 2015).
The largest European markets are predicted to experience lower levels of growth.
According to the IMS Institute for Healthcare Informatics, the top 5 European markets
(Germany, France, the United Kingdom, Italy and Spain) will see annual growth rates of
between 1 and 4% during the period 2014 to 2018. Pharmaceutical spending in the United
Kingdom and Germany should experience the highest growth, while France and Spain will
have zero to negative growth (IMS Institute for Healthcare Informatics, 2014). In an earlier
study, Urbinati et al. (2014) had predicted a decrease in pharmaceutical spending in all
European countries studied – except Poland – between 2012 and 2016.
Specialty drugs will continue to be a major contributor to pharmaceutical spending growth.
Since 2010, one out of every two FDA approvals is a specialty drug and, as the population
ages, the number of patients eligible for specialty drugs such as treatments for rheumatoid
arthritis and cancer is increasing (Lotvin et al., 2014). Increased spending on these drugs is
projected to account for 53% of total growth in North America between 2013 and 2018,
while in Europe it is expected to account for 94% of the (much slower) growth over the same
period (IMS Institute for Healthcare Informatics, 2014). The huge contribution of specialty
medicines to pharmaceutical spending growth is explained by the fact that there will be
more of them, priced at very high levels, with more patients needing them.
40
Cancer is the therapeutic area with the highest expected spending growth, driven by
new drug approvals and the increasing incidence of cancer worldwide (IMS Institute for
Healthcare Informatics, 2014). Many orphan drugs approvals are also expected in the years
to come. Their predicted budget impact by 2020 in several European countries ranges
from 4-5% to 9-11% of pharmaceutical spending, depending on the success rate of
products in development (Schey et al., 2011; Hutchings et al., 2014). Another study
estimated that the share of orphan drugs in the worldwide pharmaceutical market for
non-generic prescription drugs is expected to increase from 14% in 2014 to 19% in 2020
(EvaluatePharma, 2014).
High prices of drugs are an important barrier to access, and this does not concern
developing countries only. The results of a recent survey conducted among policy makers
(reported in WHO, 2015) show that policy makers in European countries consider the high
price of drugs as the main challenge to provide access to new medicines given the
budgetary constraints they have. Many drugs, including drugs providing important
benefits, are not available at all, or not accessible to all patients who need them. For
example, as already noted, a lot of countries restricted access to the new hepatitis C
treatments to the most severely affected patients and a few countries have not yet
reimbursed the new medicines at all (e.g. Poland).
A further challenge is that high prices of new medicines do not always appear to be
justified by high clinical benefits (Howard et al., 2015; Light and Kantarjian, 2013). For
example, many new cancer drugs provide small added benefits over existing ones. Among
the 12 new anticancer drugs approved by the FDA in 2012, only one provides survival gains
that exceed two months. Sometimes cancer drugs are used for several indications with
varying levels of efficacy, but the price is usually unique (Bach, 2014). Examining the launch
prices of cancer drugs approved between 1995 and 2013, Howard et al. (2015) observed that
patients and insurers paid USD 54 100 for a year of life gained in 1995, USD 139 100 a
decade later and USD 207 000 in 2013 for the same benefit (in constant 2013 dollars,
adjusting earlier costs for inflation).
Similarly, many orphan drugs do not pass the test of cost-effectiveness. In the
Netherlands, medicines used for the treatment of Pompe’s and Fabry’s disease have been
assessed to cost several million Euros per QALY gained, which triggered a discussion about
the opportunity to maintain health insurance coverage of these products. However, they
were not delisted, since these medicines are used for severe diseases for which no
alternative treatment is available (van den Brink, 2014).
Conclusions
Retail pharmaceutical spending has increased at a slower pace than before or even
decreased in recent years due to patent losses of several blockbusters and costcontainment policies, while pharmaceutical spending in hospital has increased in most
countries for which data are available.
New high-cost specialty drugs are coming to the market and are expected to account
for 50% or more of pharmaceutical spending growth in the near future. Their increasing
availability, combined with population ageing, suggests that pharmaceutical expenditure
may pick up again after the recent stagnation or decline.
Pharmaceutical spending growth is not necessarily a problem in itself. Medicines play
an important role in the management of a number of chronic diseases (e.g. diabetes,
asthma) and, in some circumstances, they prevent complications and the use of costly
health care services. However, the increasing availability and sky-rocketing prices of new
41
2.
medicines, especially in cancer, hepatitis C, pulmonary hypertension and multiple
sclerosis, or for rare diseases, have raised a number of questions about accessibility, budget
impact and the legitimacy of such high prices.
While some of these high-price medicines bring great benefits to patients, others
provide only marginal improvement of patients’ outcomes. In reality, prices seem more
determined by market conditions (high unmet medical need, small population target) than
by any conception of value in terms of clinical or wider benefits for patients. Many of these
medicines are not cost-effective, according to standard thresholds. This challenges both
the static and dynamic efficiency of pharmaceutical spending and raises questions about
the best ways to align societies’ interests with those of pharmaceutical companies and
investors.
Notes
1. Retail pharmaceuticals are delivered to patients via community pharmacies and other retail outlets.
Pharmaceuticals are also consumed in other care settings – primarily the hospital sector – where by
convention the pharmaceuticals used are considered as an input to the overall service treatment
and not separately accounted. That said, health accounts do allow for an additional reporting item
to report a total pharmaceutical spending estimate covering all modes of provision. Currently only
about one-third of OECD countries submit such figures.
2. Specialty medicines include most injectable and biologic agents used to treat complex conditions
such as rheumatoid arthritis, multiple sclerosis and cancer and often require special handling or
delivery mechanisms.
3. Biologics used in the treatment of certain types of immunologic and inflammatory diseases,
including rheumatoid arthritis, psoriasis, Crohn’s disease, and ulcerative colitis.
4. https://www.mskcc.org/research-areas/programs-centers/health-policy-outcomes/cost-drugs.
5. Orphan drugs refer to medicines developed for rare conditions. The United States and the
European Union have implemented policies to encourage private investments in R&D for rare
diseases (e.g. increased market exclusivity) and have consequently defined criteria to be met by a
medicine to be granted an “orphan drug status”. In the European Union, those criteria are: the
severity of the disease; the fact that it serves an unmet need; and either prevalence below one in
2 000 or a negative expected return on investment.
6. IMS data report market sales at ex-manufacturer prices and do not reflect off-invoice discounts
and rebates (IMS Institute for Healthcare Informatics, 2014). By contrast, pharmaceutical spending,
as reported in the System of Health Accounts, are estimated at retail prices (including VAT) and are
in principle net of off-invoice discounts and rebates. Both sets of data are not directly comparable
but are expected to show more or less consistent trends.
References
ACC/AHA – American College of Cardiology/American Heart Association (2014), “2013 ACC/AHA
Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in
Adults”, Journal of the American College of Cardiology, Vol. 63(25_PA), pp. 2889-2934.
Bach, P.B. (2014), “Indication-Specific Pricing for Cancer Drugs”, Journal of American Medical Association,
Vol. 312, No. 16, pp. 1629-1630.
Belloni, A., D. Morgan and V. Paris (forthcoming), “Pharmaceutical Expenditure and Policies: Past
Trends and Future Challenges”, OECD Health Working Papers, OECD Publishing.
CIHI – Canadian Institute for Health Information (2012), Drivers of Prescription Drug Spending in Canada,
Canadian Institute for Health Information, Ottawa, Ontario.
European Biopharmaceutical Enterprises (2015), “What Pricing and Reimbursement Policies to Use for
Off-patent Biologicals? Results from the EBE 2014 Biological Medicines Policy Survey”, Gabi Journal,
Vol. 4, No. 1, pp. 17-24.
EvaluatePharma (2014), “Budget-busters: The Shift to High-priced Innovator Drugs in the USA”.
42
EvaluatePharma (2014), Orphan Drug Report 2014,
http://info.evaluategroup.com/rs/evaluatepharmaltd/images/2014OD.pdf.
Express Scripts (2015), The 2014 Drug Trend Report Highlights, March.
Ferrario, A. and P. Kanavos (2013), Managed Entry Agreements for Pharmaceuticals: The European Experience,
EMINet.
Garattini, L., A. Curto and K. van de Vooren (2015), “Italian Risk-sharing Agreements on Drugs: Are
They Worthwhile?”, European Journal of Health Economics, Vol. 16, pp. 1-3.
Hartung, D. et al. (2015), “The Cost of Multiple Sclerosis Drugs in the US and the Pharmaceutical
Industry – Too Big to Fail?”, Neurology, Vol. 84, May 26, pp. 1-8.
Henschke, C., L. Sundmacher and R. Busse (2013), “Structural Changes in the German Pharmaceutical
Market: Price Setting Mechanisms Based on the Early Benefit Evaluation”, Health Policy, Vol. 109,
pp. 263-269.
Hirsch, B.R., S. Balu and K.A. Schulman (2014), “The Impact of Specialty Pharmaceuticals As Drivers of
Health Care Costs”, Health Affairs, Vol. 33, No. 10, pp. 1714-1720.
Howard, D. et al. (2015), “Pricing in the Market for Anticancer Drugs”, Journal of Economic Perspectives,
Vol. 29, No. 1, pp. 139-162.
Hutchings, A. et al. (2014), “Estimating the Budget Impact of Orphan Drugs in Sweden and France 20132020”, Orphanet Journal of Rare Diseases, Vol. 9, pp. 9-22.
IMS Institute for Healthcare Informatics (2013), Declining Medicine Use and Costs: For Better or For Worse?
– A Review of the Use of Medicines in the United States in 2012.
IMS Institute for Healthcare Informatics (2014), Medicine Use and Shifting Costs of Healthcare. A Review of
the Use of Medicines in the United States in 2013, April 2014.
Karikios, D.J. et al. (2014), “Rising Cost of Anticancer Drugs in Australia”, Internal Medical Journal, Vol. 44,
No. 5, pp. 458-463.
Keehan, S.K. et al. (2015), “National Health Expenditure Projections, 2014-24: Spending Growth Faster
Than Recent Trends”, Health Affairs, Vol. 34, No. 8, pp. 1407-1417.
Light, D.W. and H. Kantarjian (2013), “Market Spiral Pricing of Cancer Drugs”, Cancer, Vol. 15,
No. 119(22), pp. 3900-3902, November.
Lotvin, A.M. et al. (2014), “Specialty Medications: Traditional and Novel Tools Can Address Rising
Spending on These Costly Drugs”, Health Affairs, Vol. 33, No. 10, pp. 1736-1744.
Managed Care® (2011), “Patent Cliff: Billions To Be Saved – Starting Now”,
http://www.managedcaremag.com/content/patent-cliff-billions-be-saved-%E2%80%94-starting-now.
Navarria, A. et al. (2015), “Do Current Performance-based Schemes in Italy Really Work? ‘Success Fee’:
A Novel Measure for Cost-containment of Drug Expenditure”, Value in Health, Vol. 18, pp. 131-136.
NICE – National Institute for Health and Care Excellence (2014), “NICE Clinical Guideline 181, Lipid
Modification: Cardiovascular Risk Assessment and the Modification of Blood Lipids for the Primary
and Secondary Prevention of Cardiovascular Disease”, July 2014.
Schey, C., T. Milanova and A. Hutchings (2011), “Estimating the Budget Impact of Orphan Medicines in
Europe: 2010-2020”, Orphanet Journal of Rare Diseases, Vol. 6, No. 62, pp. 1-10.
Thomson, S. et al. (2014), “Economic Crisis, Health Systems and Health in Europe: Impact and
Implications for Policy”, WHO Regional Office for Europe and European Observatory on Health
Systems and Policies.
Trish, E., G. Joyce and D.P. Goldman (2014), “Specialty Drug Spending Trends Among Medicare and
Medicare Advantage Enrollees, 2007-11”, Health Affairs, Vol. 33, No. 11, November, pp. 2018-2024.
van den Brink, R. (2014), “Reimbursement of Orphan Drugs: The Pompe and Fabry Case in the
Netherlands”, Orphanet Journal of Rare Diseases, Vol. 9, Suppl. 1, O17.
van de Vooren, K. et al. (2014), “Market-access Agreements for Anti-cancer Drugs”, Journal of the Royal
Society of Medicine, Vol. 108, No. 5, pp. 166-170.
WHO – World Health Organization (2015), “Access to New Medicines in Europe: Technical Review of
Policy Initiatives and Opportunities for Collaboration and Research”, WHO Regional Office for
Europe, Copenhagen.
Yang B., E. Bae and J. Kim (2008), “Economic Evaluation and Pharmaceutical Reimbursement Reform In
South Korea’s National Health Insurance”, Health Affairs, Vol. 27, No. 1, pp. 179-187.
43
3. HEALTH STATUS
Life expectancy at birth
Life expectancy by sex and education level
Mortality from cardiovascular diseases
Mortality from cancer
Mortality from transport accidents
Suicide
Infant mortality
Infant health: Low birth weight
Perceived health status
Cancer incidence
authorities. The use of such data by the OECD is without prejudice to the status of the Golan
Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of
international law.
45
3. HEALTH STATUS
Life expectancy at birth continues to increase steadily in
OECD countries, going up on average by 3 to 4 months each
year, with no sign of slowing down. These gains in longevity can be attributed to a number of factors including
improved lifestyle and better education, and progress in
health care.
In 2013, life expectancy on average across OECD countries
reached 80.5 years, an increase of more than ten years
since 1970 (Figure 3.1). Japan, Spain and Switzerland lead a
large group of 25 OECD countries in which life expectancy
at birth now exceeds 80 years. A second group, including
the United States, Chile and a number of Central and
Eastern European countries, has a life expectancy between
75 and 80 years.
Among OECD countries, Mexico had the lowest life expectancy in 2013, still slightly below 75 years. Since 2000, life
expectancy in Mexico has increased more slowly than in
other OECD countries, with a gain of just over a year (from
73.3 to 74.6 years) compared with an average gain of more
than three years across OECD countries. The gap in longevity between Mexico and other OECD countries has therefore widened from about four years to six years between
2000 and 2013. The slow progress in life expectancy in
Mexico is due to a number of factors, including harmful
health-related behaviours such as poor nutrition and very
high obesity rates, a lack of progress in reducing mortality
from cardiovascular diseases, very high death rates from
road traffic accidents and homicides, as well as persistent
barriers of access to high-quality care.
In the United States, the gains in life expectancy over the
past few decades have also been more modest than in most
other OECD countries. While life expectancy in the United
States used to be one year above the OECD average in 1970,
it is now more than one year below the average. Many
factors can explain these lower gains in life expectancy,
including: 1) the highly fragmented nature of the US health
system, with relatively few resources devoted to public
health and primary care, and a large share of the population uninsured; 2) health-related behaviours, including
higher calorie consumption per capita and greater obesity
rates, higher consumption of prescription and illegal drugs,
more deaths from road traffic accidents and higher homicide rates; and 3) adverse socio-economic conditions
affecting large segments of the US population, with higher
rates of poverty and income inequality than in most other
OECD countries (National Research Council and Institute of
Medicine, 2013).
Although the life expectancy in partner countries such as
India, Indonesia, Brazil and China remains well below the
OECD average, these countries have achieved considerable
gains in longevity over the past decades, with the level
converging rapidly towards the OECD average. There has
been much less progress in countries such as South Africa
(due mainly to the epidemic of HIV/AIDS), and the Russian
46
Federation (due mainly to the impact of the economic
transition in the 1990s and a rise in risk increasing behaviours
among men, notably rising alcohol consumption).
Higher national income (as measured by GDP per capita) is
generally associated with higher life expectancy at birth,
although the relationship is less pronounced at the highest
levels of national income (Figure 3.2). There are also notable
differences in life expectancy between countries with
similar income per capita. For example, Japan, Spain and
Italy have higher, and the United States and the Russian
Federation have lower life expectancies than would be predicted by their GDP per capita alone.
Figure 3.3 shows the relationship between life expectancy
at birth and current health expenditure per capita (excluding
capital investments) across OECD, candidate and partner
countries. Higher health spending per capita is generally
associated with higher life expectancy at birth, although
this relationship tends to be less pronounced in countries
with the highest health spending per capita. Japan, Spain
and Korea stand out as having relatively high life expectancies, and the United States and the Russian Federation
relatively low life expectancies, given their levels of health
spending.
Variation in life expectancy across countries can be
explained by many factors beyond national income and
total health spending.
Definition and comparability
Life expectancy at birth measures how long, on average,
people would live based on a given set of age-specific
death rates. However, the actual age-specific death
rates of any particular birth cohort cannot be known in
advance. If age-specific death rates are falling (as has
been the case over the past decades), actual life spans
will be higher than life expectancy calculated with
current death rates.
The methodology used to calculate life expectancy
can vary slightly between countries. This can change
a country’s estimates by a fraction of a year.
Life expectancy at birth for the total population is calculated by the OECD Secretariat for all OECD countries,
using the unweighted average of life expectancy of
men and women.
References
National Research Council and Institute of Medicine,
S. Woolf and L. Aron (eds) (2013), U.S. Health in International Perspective: Shorter Lives, Poorer Health, National
Academies Press, Washington, DC.
3. HEALTH STATUS
3.1. Life expectancy at birth, 1970 and 2013 (or nearest years)
66.5
70
70.7
73.5
70.9
74.6
73.9
75.2
75.0
75.7
75.4
76.6
76.5
77.3
77.1
78.8
78.3
80
78.8
80.4
80.0
80.5
80.4
80.8
2013
80.7
81.1
81.1
81.1
81.4
81.2
81.4
81.4
81.8
81.5
81.9
81.8
82.1
82.0
82.1
82.3
82.2
82.9
82.8
83.4
83.2
80.9
1970
Years
90
56.8
60
50
Ja
p
Sw S an
i t z p ai
er n
la
nd
It a
Fr l y
Au anc
st e
r
Ic a li a
el
an
Is d
Lu S w r ael
xe e d
m en
bo
N o ur g
rw
Ko a y
Ca rea
na
N e Gr d a
th ee
N e er c e
w lan
Ze ds
Un
a
i te A land
d us
K i tr
ng i a
d
F i om
nl
a
Ir n d
Ge elan
rm d
Po an
rt y
B e uga
lg l
O E ium
C
Sl D 3
ov 4
De en
C o nm i a
s t ar k
aR
Un
ic
i te C a
d hil
S
Cz ta e
ec te
h s
R
E s e p.
to
Po ni a
la
Sl Tu nd
ov r k
ak ey
Hu Rep
ng .
ar
y
Co Chi
lo na
m
b
Br i a
a
M z il
ex
i
L co
Li at v
th ia
I ua
Ru n d o n i a
ss ne
i a si a
n
Fe
So
d
u t In d .
h ia
Af
ric
a
40
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en.
1 2 http://dx.doi.org/10.1787/888933280727
3.2. Life expectancy at birth and GDP per capita, 2013
(or latest year)
Life expectancy in years
85
Life expectancy in years
85
ESP ITA JPN ISL CAN
AUS
FRA
SWE
ISR
FIN
NLD
GRC
KOR
IRL AUT
NZL
PRT
DEU
SVN
CHL
DNK
CZE GBR
BEL
EST
TUR POL
COL BRA
SVK
HUN
CHN
MEX
LVA LTU
80
75
IDN
CHE NOR
USA
ESP JPN AUS
ISR ITA
CHE
FRA SWE
ISL
KOR
NZL LUX
NOR
CAN AUT
GRC
NLD
CRI PRT GBR
80
DEU
BEL
SVN FIN
DNK
CHL
IRL
EST
CZE
TUR POL
SVK
COL CHN
HUN
75
BRA
MEX
LVA
IDN
RUS
70
3.3. Life expectancy at birth and health spending
per capita, 2013 (or latest year)
LTU
RUS
70
IND
USA
R² = 0.58
R² = 0.51
IND
65
65
0
10 000
20 000
30 000
40 000
50 000 60 000 70 000
GDP per capita (USD PPP)
1 2 http://dx.doi.org/10.1787/888933280727
0
2 000
4 000
6 000
8 000
10 000
Health spending per capita (USD PPP)
1 2 http://dx.doi.org/10.1787/888933280727
Information on data for Israel: http://oe.cd/israel-disclaimer
47
3. HEALTH STATUS
There remain large gaps in life expectancy between women
and men in all OECD countries. On average across OECD
countries, life expectancy at birth for women reached 83.1
years in 2013, compared with 77.8 years for men, a gap of
5.3 years (Figure 3.4).
The gender gap in life expectancy increased substantially
in many OECD countries during the 1970s and early 1980s
to reach a peak of almost seven years in the mid-1980s, but
it has narrowed during the past 25 years, reflecting higher
gains in life expectancy among men than among women.
This can be attributed at least partly to the narrowing of
differences in risk-increasing behaviours, such as smoking,
accompanied by sharp reductions in mortality rates from
cardiovascular diseases among men.
In 2013, the life expectancy for women in OECD countries
ranged from less than 80 years in Turkey, Hungary and
Mexico to more than 85 years in Japan, Spain, France, Italy
and Switzerland. Life expectancy for men ranged from less
than 75 years in Mexico, Hungary, Estonia, the Slovak
Republic, Poland and Turkey to over 80 years in Switzerland,
Iceland, Italy, Israel, Japan, Spain, Sweden and Australia.
In the United States, the life expectancy for both women
and men is now slightly shorter than the OECD average,
and the gap with leading countries has been widening. The
life expectancy for US men in 2013 was 4.3 years shorter
than in Switzerland (up from less than three years in 1970);
for US women, it was 5.4 years shorter than in Japan in
2013 (there was no gap in 1970). Possible explanations for
this slower progress are provided under the indicator “Life
expectancy at birth”.
Among OECD countries, the gender gap in life expectancy is
relatively narrow in Iceland, Israel, Sweden, the Netherlands,
New Zealand and the United Kingdom (a gap of less than
four years), but much larger in Estonia (around nine years),
Poland (around eight years), the Slovak Republic and Hungary
(around seven years).
Life expectancy in OECD countries varies not only by gender,
but also by socio-economic status as measured, for
instance, by education level (Figure 3.5). Higher education
level not only provides the means to improve the socioeconomic conditions in which people live and work, but
may also promote the adoption of healthier lifestyles and
facilitate access to appropriate health care. On average
among 15 OECD countries for which recent data are available,
48
people with the highest level of education can expect to live
six years longer than people with the lowest level of education at age 30 (53 years versus 47 years). These differences
in life expectancy by education level are particularly
pronounced for men, with an average gap of almost
eight years. The differences are especially large in Central
and Eastern European countries (Czech Republic, Estonia,
Hungary and Poland), where the life expectancy gap
between higher and lower educated men is more than
ten years. This is largely explained by the greater prevalence of risk factors among men, such as tobacco and alcohol use. Differences in other countries such as Portugal,
Sweden, Switzerland and Italy are less pronounced.
Life expectancy at birth measures how long, on average, people would live based on a given set of agespecific death rates. However, the actual age-specific
death rates of any particular birth cohort cannot be
known in advance. If age-specific death rates are falling (as has been the case over the past decades),
actual life spans will be higher than life expectancy
calculated with current death rates.
The methodology used to calculate life expectancy
can vary slightly between countries. This can change
a country’s estimates by a fraction of a year.
To calculate life expectancies by education level,
detailed data on deaths by sex, age and education
level are needed. However, not all countries have
information on education as part of their deaths data.
Data linkage to another source (e.g. a census) which
does have information on education may be required
(Corsini, 2010).
References
Corsini, V. (2010), “Highly Educated Men and Women Likely
to Live Longer: Life Expectancy by Educational Attainment”, Eurostat Statistics in Focus 24/2010, European Commission, Luxembourg.
3. HEALTH STATUS
3.4. Life expectancy at birth by sex, 2013 (or latest year)
71.7
77.4
79.1
80.1
72.2
72.9
73.7
79.4
81.2
81.7
72.8
75.2
73.0
81.3
81.2
81.4
76.3
76.4
83.6
82.4
78.3
83.1
77.8
77.2
84.0
83.2
78.1
83.2
77.6
78.6
83.1
84.1
79.0
82.9
78.0
79.2
79.5
83.2
83.8
Women
78.6
84.0
83.2
79.5
78.7
83.6
85.1
79.3
78.5
83.8
83.9
79.8
79.8
83.8
83.9
80.2
80.5
80.3
83.7
84.3
80.1
85.2
80
79.0
80.3
80.7
85.0
86.1
86.6
80.2
80.2
85.6
Men
Years
90
70
60
50
Ja
pa
n
S w Sp
i t z a in
er
la
nd
It a
l
Fr y
an
Au c e
st
ra
l
Ic i a
el
an
d
Is
r
S w ael
Lu
xe e d e
m n
bo
u
No r g
rw
ay
Ko
re
Ca a
na
da
N e Gr e
th ece
N e er l a
w nd
Ze s
al
an
Un
i te Au d
d s tr
Ki
ng ia
do
Fi m
nl
an
Ir e d
la
Ge nd
rm
Po any
r tu
Be gal
lg
i
O E um
CD
Sl 3 4
ov
e
De ni a
nm
ar
k
Un
i t e Chi
l
d
S e
Cz t at
ec es
h
Re
E s p.
to
n
Po i a
la
nd
Sl Turk
ov
ak ey
R
H u e p.
ng
a
M ry
ex
ic
o
40
Note: Countries are ranked in descending order of life expectancy for the whole population.
1 2 http://dx.doi.org/10.1787/888933280737
3.5. Gap in life expectancy at age 30 by sex and educational level, 2012 (or latest year)
Men
Women
3.6
Italy
3.7
Sweden
3.0
Portugal
4.3
1.8
Netherlands (2011)
4.5
5.1
Norway
5.2
Mexico
5.4
Denmark
5.5
Finland
4.2
3.9
4.6
3.8
3.5
Israel
5.8
4.7
Slovak Rep.
6.9
3.5
OECD15
7.7
4.2
Slovenia
9.1
4.4
Poland
11.6
5.0
Hungary
12.1
5.5
Estonia
15.0
8.1
Czech Rep.
17.8
20
Gap in years
1.8
15
10
5
0
5.2
0
2
4
6
8
10
Gap in years
Note: The figures show the gap in the expected years of life remaining at age 30 between adults with the highest level (“tertiary education”) and the
lowest level (“below upper secondary education”) of education.
Source: Eurostat database complemented with national data for Israel, Mexico and Netherlands.
1 2 http://dx.doi.org/10.1787/888933280737
49
3. HEALTH STATUS
Despite substantial declines in recent decades, cardiovascular diseases remain the main cause of mortality in most
OECD countries, accounting for nearly one-third (32.3%) of
all deaths in 2013. Prospects for further reductions may be
hampered by a rise in certain risk factors such as obesity
and diabetes (OECD, 2015). Cardiovascular diseases cover a
range of illnesses related to the circulatory system, including ischemic heart disease (often referred to as heart
attack) and cerebrovascular diseases such as stroke.
Ischemic heart disease (IHD) is caused by the accumulation
of fatty deposits lining the inner wall of a coronary artery,
restricting blood flow to the heart. IHD alone was responsible for nearly 20% of all deaths in OECD countries in 2013.
However, mortality from IHD varies considerably across
countries (Figure 3.6). Central and Eastern European countries report the highest IHD mortality rates; Japan, France
and Korea report the lowest rates. Across OECD countries,
IHD mortality rates in 2013 were around 84% higher for
men than women.
IHD mortality rates have declined in nearly all OECD countries, with an average reduction of 45% since 1990, contributing greatly to gains in life expectancy, particularly among
men. The decline has been most remarkable in Denmark,
the Netherlands, and Norway, where rates fell by twothirds or more. Declining tobacco consumption contributed
significantly to reducing the incidence of IHD (see Indicator
“Tobacco consumption among adults” in Chapter 4), and consequently to reducing mortality rates. Improvements in medical care have also contributed to reduced mortality rates (see
the indicators on “Cardiac procedures” in Chapter 6 and
“Mortality following acute myocardial infarction” in
Chapter 8).
In Korea, IHD mortality rates have increased substantially
since 1990, although they remain low compared with
nearly all other OECD countries and have started to fall
after peaking in 2006. The initial rise in IHD mortality rates
in Korea has been attributed to changes in lifestyle and
dietary patterns as well as environmental factors at the
time of birth, with people born between 1940 and 1950 facing
higher relative risks. In 2006, Korea introduced a Comprehensive Plan to tackle cardiovascular diseases that encompassed prevention and primary care as well as better acute
care, contributing to the reduction in mortality in recent
years (OECD, 2012).
Cerebrovascular disease was the underlying cause for
about 7% of all deaths in OECD countries in 2013. Cerebrovascular disease refers to a group of diseases that relate to
problems with the blood vessels that supply the brain.
Common manifestations of cerebrovascular disease
include ischemic stroke, which develops when the brain's
blood supply is blocked or interrupted, and haemorrhagic
stroke which occurs when blood leaks from blood vessels
into the surface of the brain. In addition to being an important cause of mortality, the disability burden from stroke
and other cerebrovascular diseases is also substantial
(Murray et al., 2015).
50
There are large variations in cerebrovascular disease mortality rates across countries (Figure 3.7). The Slovak Republic and Hungary report a cerebrovascular mortality that is
more than three times higher than that of Switzerland,
Canada and France, and have the highest mortality rates
for both IHD and cerebrovascular disease. The high prevalence of risk factors common to both diseases (such as
smoking and high blood pressure) may explain this link.
Since 1990, cerebrovascular disease mortality has
decreased in all OECD countries, although to a lesser extent
in Poland and the Slovak Republic. On average, the mortality burden from cerebrovascular disease has halved across
OECD countries. In Estonia, Luxembourg and Portugal, the
rates have been cut by at least two-thirds, although in Estonia
this is partly due to a change in death recording practices
with a greater recording of other related causes of death
such as hypertension. As with IHD, the reduction in mortality from cerebrovascular disease can be attributed at least
partly to a reduction in risk factors as well as improvements in medical treatments (see indicator “Mortality
following stroke” in Chapter 8), but rising obesity and diabetes threatens progress in tackling cerebrovascular disease
(OECD, 2015).
Mortality rates are based on numbers of deaths registered in a country in a year divided by the size of the
corresponding population. The rates have been
directly age-standardised to the 2010 OECD population to remove variations arising from differences in
age structures across countries and over time. The
source is the WHO Mortality Database.
Deaths from ischemic heart disease are classified to
ICD-10 codes I20-I25, and cerebrovascular disease to
I60-I69.
References
Murray, C.J.L. et al. (2015), “Global, Regional, and National
Disability-adjusted Life Years (DALYs) for 306 Diseases
and Injuries and Healthy Life Expectancy (HALE) for 188
Countries, 1990-2013: Quantifying the Epidemiological
Transition”, The Lancet, published online: 26 August 2015.
OECD (2015), Cardiovascular Disease and Diabetes: Policies for
Better Health and Quality of Care, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264233010-en.
OECD (2012), OECD Reviews of Health Care Quality: Korea:
Raising Standards, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264173446-en.
3. HEALTH STATUS
3.6. Ischemic heart disease mortality, 2013 and change 1990-2013 (or nearest years)
2013
Change 1990-2013
Japan
France
Korea
Netherlands
Portugal
Spain
Belgium
Luxembourg
Chile
Israel
Denmark
Norway
Switzerland
Greece
Italy
Slovenia
Canada
United Kingdom
Australia
Sweden
Poland
Germany
OECD34
United States
Iceland
Ireland
New Zealand
Austria
Mexico
Turkey
Finland
Estonia
Czech Rep.
Hungary
Slovak Rep.
35
43
43
50
51
56
63
66
68
70
71
78
82
83
84
94
95
98
98
105
106
115
117
128
133
136
138
140
140
146
154
260
260
297
404
500
400
300
200
Age-standardised rates per 100 000 population
100
0
-38
-52
63
-73
-57
-47
-48
-56
-58
-68
-77
-70
-49
-36
-38
-47
-59
-67
-64
-62
-27
-48
-45
-50
-46
-59
-53
-38
-1
n.a.
-55
-60
-41
-10
9
-100
-50
0
50
100
Change in %
1 2 http://dx.doi.org/10.1787/888933280741
3.7. Cerebrovascular disease mortality, 2013 and change 1990-2013 (or nearest years)
2013
Change 1990-2013
Switzerland
Canada
France
Israel
United States
Spain
Luxembourg
Austria
Norway
Belgium
Netherlands
Australia
Germany
United Kingdom
Japan
Denmark
Sweden
Mexico
Iceland
Ireland
Finland
OECD34
New Zealand
Italy
Estonia
Korea
Chile
Poland
Portugal
Slovenia
Czech Rep.
Turkey
Greece
Hungary
Slovak Rep.
37
38
38
44
44
45
46
49
49
51
51
51
52
53
54
54
55
60
60
61
65
66
67
67
68
77
80
86
88
92
97
101
106
118
137
150
100
50
0
-60
-52
-56
-58
-42
-69
-77
-69
-64
-55
-52
-55
-63
-62
-61
-51
-50
-38
-41
-54
-57
-54
-44
-54
-79
-56
-42
-14
-73
-51
-69
n.a.
-51
-54
-2
-100
-50
0
50
100
Change in %
1 2 http://dx.doi.org/10.1787/888933280741
51
3. HEALTH STATUS
Cancer is the second leading cause of mortality in OECD
countries after cardiovascular diseases, accounting for 25%
of all deaths in 2013, up from 15% in 1960. In a number of
countries, cancer is now the most frequent cause of death.
The rising share of deaths due to cancer reflects the fact
that mortality from other causes, particularly cardiovascular diseases, has been declining more rapidly than mortality from cancer.
There are more than 100 different types of cancers, with
most named for the organ in which they start. For a large
number of cancer types, the risk of developing the disease
rises with age. While genetics is a risk factor, only about 5%
to 10% of all cancers are inherited. Modifiable risk factors
such as smoking, obesity, lack of exercise and excess sun
exposure, as well as environmental exposures, explain up
to 90-95% of all cancer cases (Anand et al., 2008). Prevention, early detection and treatment remain at the forefront
in the battle to reduce the burden of cancer (OECD, 2013).
In 2013, the average rate of mortality attributable to cancer
across OECD countries was just over 200 per 100 000 population (Figure 3.8). Mortality due to cancer was lowest in
Mexico, Turkey, Finland, Switzerland and Japan, with rates
less than 180 per 100 000 population. Hungary, Slovenia,
the Slovak Republic and Denmark bear the highest cancer
mortality burden, with rates in excess of 240 per
100 000 population.
Mortality due to cancer is consistently higher for men than
for women in all countries. The gender gap is particularly
wide in Korea, Turkey, Estonia, Spain and Portugal, with
rates among men more than twice those for women. This
gender gap can be explained partly by the greater prevalence of risk factors among men, notably smoking rates.
Among men, lung cancer imposes the highest mortality
burden, accounting for 26% of all cancer-related deaths
(Figure 3.9). In Turkey, Greece, Poland, Hungary and
Belgium, this percentage was over 30%. For women, lung
cancer accounted for 17% of all cancer-related deaths. In
many countries, lung cancer mortality rates for men have
decreased over the last 20 years. But lung cancer mortality
has risen for women in several countries such as France
and Spain where it has more than doubled since 1990.
These conflicting trends are, to a large degree, explained by
the high number of females who started smoking several
decades later than males (in the 1980s and 1990s).
Breast cancer is the second most common cause of cancer
mortality in women in many OECD countries. While there
has been an increase in the incidence of breast cancer over
the past decade, mortality has declined in most countries
due to earlier diagnosis and better treatment. Mortality
from breast cancer increased somewhat in Korea and
Japan, although the rates there remained the lowest in
2013. Mortality rates from breast cancer in 2013 were highest in Denmark, Hungary, Belgium, Ireland, Slovenia and
the Netherlands (see indicator “Screening, survival and
mortality for breast cancer” in Chapter 8).
52
Colorectal cancer is a major cause of cancer mortality
among both men and women (second-highest cause of
cancer mortality in men and third in women). In 2013,
colorectal cancer mortality was lowest in Mexico and
Turkey, and highest in Hungary and the Slovak Republic
(see indicator “Survival and mortality for colorectal cancer”
in Chapter 8).
Prostate cancer has become the most common cancer
among men in many OECD countries, particularly among
men aged 65 years and over. Mortality from prostate cancer
remains lower than for lung cancer in all countries except
in Chile and Mexico, where it is the leading cause of cancer
deaths in men, and in some Nordic countries (Iceland,
Norway and Sweden) where mortality from prostate and
lung cancer are almost equal. Mortality rates from prostate
cancer in 2013 were lowest in Japan and Korea, and highest
in Estonia and Iceland.
In most OECD countries, cancer-related mortality rates
have fallen since 1990. On average, rates fell by 17%
between 1990 and 2013. Substantial declines in mortality
from stomach cancer, colorectal cancer, lung cancer for
men, breast, cervical and ovarian cancer for women, as
well as prostate cancer for men contributed to this reduction. However, these gains were partially offset by increases
in the number of deaths due to cancer of the liver, skin and
pancreas for both sexes, as well as lung cancer for women.
source is the WHO Mortality Database. Deaths from all
cancers are classified to ICD-10 codes C00-C97. The
international comparability of cancer mortality data
can be affected by differences in medical training
and practices as well as in death certification across
countries.
References
Anand, P. et al. (2008), “Cancer is a Preventable Disease that
Requires Major Lifestyle Changes”, Pharmaceutical
Research, Vol. 25, No. 9, pp. 2097-2116.
OECD (2013), Cancer Care: Assuring Quality to Improve Survival, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264181052-en.
3. HEALTH STATUS
3.8. Cancer mortality, 2013 (or nearest year)
Men
Women
Total
400
350
300
250
200
150
100
50
M
ex
ic
Tu o
rk
e
F y
S w inl a
i t z nd
er
la
nd
Ja
pa
n
Ko
re
a
Is
ra
e
S
Lu we l
xe d e
m n
bo
ur
g
Sp
a
Po in
r tu
g
No al
rw
a
Au y
st
A ri
Un us t a
i te r al
i
d
St a
at
e
Gr s
ee
ce
Ch
il
Fr e
a
Ge nc e
rm
an
y
It a
OE ly
CD
Be 3 4
lg
iu
Ca m
na
da
N e Ic el
w an
Ze d
al
an
Un
i t e Ir e d
l
d
K i and
Ne ngd
t h om
er
C z lan
ec ds
h
Re
E s p.
to
n
Po i a
la
De nd
Sl nm
ov ar
ak k
R
S l e p.
ov
e
Hu ni a
ng
ar
y
0
1 2 http://dx.doi.org/10.1787/888933280758
3.9. Main causes of cancer deaths among men and women in OECD countries, 2013
Men
Women
Stomach
5%
Stomach
6%
Colorectal
11%
Others
36%
Others
40%
Liver
6%
Colorectal
11%
Pancreas
7%
Pancreas
6%
Lung
17%
Prostate
9%
Lung
26%
Ovary
5%
Breast
15%
1 2 http://dx.doi.org/10.1787/888933280758
53
3. HEALTH STATUS
Injuries from transport accidents – most of which are due
to road traffic – are a major public health problem in OECD
countries, causing the premature deaths of more than
100 000 people in 2013 (more than 1% of all deaths). Almost
three-quarters of these deaths occurred among men. In
addition, more than 5 million people were injured in road
accidents. The direct and indirect financial costs of transport accidents are substantial, with estimates ranging from
1 to 3% of GDP annually (OECD/ITF, 2015).
Most fatal traffic injuries occur in passenger vehicles,
although other road users also face substantial risks. In
Korea, Japan, Israel and Poland, pedestrians account for
over one third of all road user fatalities. Cyclists in the
Netherlands and motorcyclists in Greece, Italy and France
account for over one fourth of road traffic accident deaths
in these countries (OECD/ITF, 2015).
The average OECD mortality rate due to transport accidents was 7 per 100 000 population in 2013 (Figure 3.10).
There is considerable variation between countries with
transport accidents claiming more than five times as
many lives per 100 000 population in Mexico compared to
the United Kingdom and Sweden. Mortality rates from road
transport accidents were also relatively high in Korea, Chile
and the United States.
Much transport accident injury and mortality is preventable. Road safety for car occupants has increased greatly
over the past decades in many countries through improvements of road systems, education and prevention campaigns as well as vehicle design. In addition, the adoption
of new laws and regulations and the enforcement of these
laws to improve compliance with speed limits, seatbelt use
and drink-driving rules have had a major impact on reducing the burden of road transport accidents.
As a result, deaths due to transport accidents have
decreased in almost all countries over the last few decades.
Since 1990, the average OECD mortality rate due to transport
accidents has fallen by more than 70% (Figure 3.11). These
gains are even more impressive when considering the
increase in the number of vehicle kilometers travelled over
this period (OECD/ITF, 2015). Chile is the only country
where deaths due to transport accidents have increased. In
1990, Chile’s mortality rate was comparatively low, but then
54
rose during the 1990s as the economy and the number of
vehicles grew and has remained relatively high since then
(Nghiem et al., 2013).
Declines in mortality rates for vulnerable road users such
as pedestrians, cyclists and motorcyclists were substantially less than those for car occupants. Reductions in
deaths among pedestrians, cyclists and motorcyclists have
levelled-off and some increases have been recorded. As a
consequence, road safety priorities in many countries have
recently shifted to vulnerable road users in urban areas
(OECD/ITF, 2015).
The economic crisis has contributed to the reduction in
road traffic deaths in many countries, by reducing the distance travelled (especially by young men and by trucks).
However, this impact is likely to be short-lived and, over the
longer term, effective road safety policies will remain the
primary contributor to reduced mortality (OECD/ITF, 2015).
source is the WHO Mortality Database.
Deaths from transport accidents are classified to
ICD-10 codes V01-V89.
References
Nghiem, H., L. Connelly and S. Gargett (2013), “Are Road
Traffic Crash Fatality Rates Converging among OECD
Countries?”, Accident Analysis & Prevention, Vol. 52,
pp. 162-170.
OECD/ITF (2015), IRTAD Road Safety 2015 Annual Report,
OECD Publishing.
3. HEALTH STATUS
3.10. Transport accident mortality, 2013 (or nearest year)
Men
Women
Total
30
25
20
15
10
5
Un
i te
d
Ki
ng
do
Sw m
ed
De en
N e nm
th ar k
er
la
nd
s
Sw Jap
it z an
er
la
n
Ir e d
la
nd
Sp
Ge a in
rm
a
No ny
rw
ay
Is
ra
Au el
st
ri
I
Lu c el a
xe a n
m d1
bo
ur
Fi g 1
nl
an
Fr d
an
ce
It a
Es ly
to
B e ni a
lg
A u ium
st
r
Po a li a
r tu
g
C a al
na
OE da
Cz CD
e 34
Ne ch R
w
e
Z e p.
al
an
Hu d
ng
Sl ar y
o
Sl ve
ov ni
ak a
Re
p
Po .
la
nd
Tu
rk
e
Un Gr y
e
i te ec
d
St e
at
es
Ch
il e
Ko
re
M a
ex
ic
o
0
1. Three-year average.
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en and Ministry of Health for New Zealand.
1 2 http://dx.doi.org/10.1787/888933280766
3.11. Trends in transport accident mortality, selected OECD countries, 1990-2013
10
12
20
02
04
20
20
8
6
00
20
19
9
4
19
9
19
9
19
9
19
9
20
20
20
20
20
20
20
19
9
19
9
19
9
19
9
19
9
2
0
0
0
12
5
10
5
08
10
06
10
04
15
02
15
00
20
8
20
6
25
4
25
2
30
0
30
Spain
OECD
20
Greece
08
France
United Kingdom
20
Mexico
06
Chile
OECD
20
Canada
United States
1 2 http://dx.doi.org/10.1787/888933280766
55
3. HEALTH STATUS
Suicide
Suicide is a significant cause of death in many OECD countries, accounting for over 150 000 deaths in 2013. A complex
set of reasons may explain why some people choose to
attempt or commit suicide. A high proportion of people
who have attempted or committed suicide are suffering
from psychiatric disorders such as severe depression, bipolar
disorder and schizophrenia. The social context in which an
individual lives is also important. Low income, alcohol and
drug abuse, unemployment and social isolation are all
associated with higher rates of suicide.
Figure 3.12 shows that suicide rates in 2013 were lowest in
Turkey, Greece, Mexico, Italy and Israel, at seven or fewer
deaths per 100 000 population, although the number of suicides in certain countries may be under-reported because
of the stigma associated with the act or data unreliability
associated with reporting criteria (see “Definition and comparability”). Korea had the highest suicide rate with nearly
30 deaths per 100 000 population, followed by Japan,
Hungary and Slovenia with nearly 20 deaths per 100 000
population. Mortality rates from suicide are three-to-four
times greater for men than for women across OECD countries (Figure 3.12). In Poland and the Slovak Republic, men
are seven times more likely to commit suicide than
women. The gender gap is narrower for attempted suicides,
reflecting the fact that women tend to use less fatal methods
than men. Suicide is also related to age, with young people
aged under 25 and elderly people especially at risk. While
suicide rates among the latter have generally declined over
the past two decades, less progress has been observed
among younger people.
Since 1990, suicide rates have decreased by around 30%
across OECD countries, with the rates being halved in
countries such as Hungary and Finland (Figure 3.13). In
Estonia, after an initial rise in the early 1990s, the rates
have also fallen sharply. On the other hand, death rates
from suicides have increased in Korea and Japan. In Japan,
there was a sharp rise in the mid-to-late 1990s, coinciding
with the Asian financial crisis, but rates have started to
come down in recent years. In Korea, suicide rates rose
steadily over the past two decades peaking around 2010,
before starting to come down (Lim et al., 2014). Suicide is
the number one cause of death among teenagers in Korea.
Suicide is often linked with depression and the abuse of alcohol and other substances. Early detection of these psychosocial problems in high-risk groups by families and health
professionals is an important part of suicide prevention
campaigns, together with the provision of effective support
and treatment. Many countries are developing national
strategies for prevention, focusing on at-risk groups. Mental
health services in Korea lag behind those of other countries
with fragmented support, focused largely around institutions,
and insufficient or ineffective support services provided to
those who remain in the community. Further efforts are
56
also needed to remove the stigma associated with seeking
care (OECD, 2014).
Previous studies have shown a strong link between adverse
economic conditions and higher levels of suicide (Van Gool
and Pearson, 2014). Suicide rates rose slightly at the start of
the economic crisis in 2008-2009 in a number of countries,
but this trend did not persist in most. In Greece, suicide
rates were stable in 2009 and 2010, but have increased since
2011 (Figure 3.13). All countries need to continue monitoring
developments closely in order to be able to respond quickly,
including monitoring high-risk populations such as the
unemployed and those with psychiatric disorders (see
indicator “Mental health care” in Chapter 8).
The World Health Organization defines suicide as an
act deliberately initiated and performed by a person in
the full knowledge or expectation of its fatal outcome.
Comparability of data between countries is affected by
a number of reporting criteria, including how a person’s intention of killing themselves is ascertained,
who is responsible for completing the death certificate, whether a forensic investigation is carried out,
and the provisions for confidentiality of the cause of
death. Caution is required therefore in interpreting
variations across countries.
source is the WHO Mortality Database. Deaths from suicide are classified to ICD-10 codes X60-X84.
References
Lim, D. et al. (2014), “Trends in the Leading Causes of Death
in Korea, 1983-2012”, Journal of Korean Medical Science,
Vol. 29, No. 12, pp. 1597-1603.
OECD (2014), Making Mental Health Count: The Social and Economic costs of Neglecting Mental Health Care, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264208445-en.
Van Gool, K. and M. Pearson (2014), “Health, Austerity and
Economic Crisis: Assessing the Short-term Impact in
OECD Countries”, OECD Health Working Papers, No. 76,
OECD Publishing, Paris,
http://dx.doi.org/10.1787/5jxx71lt1zg6-en.
3. HEALTH STATUS
Suicide
3.12. Suicide, 2013 (or nearest year)
Men
Women
Total
45
40
35
30
25
20
15
10
5
ic
o
It a
ly
Is
ra
Un
el
i te
S
p
d
K i ain
ng
d
P om
Lu or t
xe u g
m al
bo
u
Au rg 1
st
ra
C li a
Ne an
th ada
er
la
Ge nds
rm
a
No ny
rw
ay
Ch
il e
N e Ir e
w lan
Ze d
al
De and
Sl nm
ov ar
ak k
Re
I c p.
el
a
OE nd 1
Sw CD
it z 3 4
er
la
n
Un S w d
i te ede
d
St n
at
e
Au s
C z s tr
ec ia
h
Re
p
Po .
la
nd
Fr
an
c
Fi e
nl
an
Es d
to
B e ni a
lg
i
Sl um
ov
en
ia
Ja
pa
Hu n
ng
ar
y
Ko
re
a
M
ex
y
ke
ee
Gr
Tu
r
ce
0
1. Three-year average.
1 2 http://dx.doi.org/10.1787/888933280778
3.13. Trends in suicide, selected OECD countries, 1990-2013
10
12
20
20
20
20
19
9
19
9
19
9
19
9
19
9
20
20
20
20
20
20
20
19
9
19
9
19
9
19
9
19
9
04
0
02
0
00
5
8
5
6
10
4
10
2
15
0
15
12
20
10
20
08
25
06
25
04
30
02
30
00
35
8
35
6
40
4
40
2
45
0
45
Mexico
OECD
20
Korea
08
Japan
United States
20
Greece
06
Finland
OECD
20
Estonia
Hungary
1 2 http://dx.doi.org/10.1787/888933280778
57
3. HEALTH STATUS
Infant mortality
Infant mortality, the rate at which babies and children of
less than one year of age die, reflects the effect of economic
and social conditions on the health of mothers and newborns, the social environment, individual lifestyles as well
as the characteristics and effectiveness of health systems.
In most OECD countries, infant mortality is low and there is
little difference in rates (Figure 3.14). In 2013, the average in
OECD countries was less than four deaths per 1 000 live
births, with rates being the lowest in Iceland, Slovenia,
Finland, Estonia and Japan. A small group of OECD countries still have comparatively high infant mortality (Mexico,
Turkey and Chile), although in these three countries infant
mortality has reduced considerably over the past few
decades (Figure 3.15).
In some large partner countries (India, South Africa and
Indonesia), infant mortality remains above 20 deaths per
1 000 live births. In India, one-in-twenty-five children die
before their first birthday, although the rates have fallen
sharply over the past few decades. Infant mortality rates
have also reduced greatly in Indonesia.
In OECD countries, around two-thirds of the deaths that
occur during the first year of life are neonatal deaths (i.e.,
during the first four weeks). Birth defects, prematurity and
other conditions arising during pregnancy are the main
factors contributing to neonatal mortality in developed
countries. With an increasing number of women deferring
childbearing and a rise in multiple births linked with fertility treatments, the number of pre-term births has tended
to increase (see indicator “Infant health: low birth weight”). In
a number of higher-income countries, this has contributed to
a levelling-off of the downward trend in infant mortality
over the past few years. For deaths beyond a month (postneonatal mortality), there tends to be a greater range of
causes – the most common being SIDS (sudden infant
death syndrome), birth defects, infections and accidents.
In the United States, the reduction in infant mortality has
been slower than in most other OECD countries. In 2000,
the US rate was below the OECD average, but it is now
higher (Figure 3.14). One of the explanations that have been
given for that the high rate of infant mortality in the United
States is that it is based on a more complete registration of
very premature and low birth weight babies than in many
other countries (Joseph et al., 2012). In order to remove the
impact of differences in registration practices of very small
babies, the figures shown in Figure 3.14 for a majority of
countries (including the United States) exclude deaths of
babies of less than 22 weeks of gestation period or
500 grams birth weight. The rate in the United States nonetheless remains higher than the OECD average, especially
for post-neonatal mortality (deaths after one month) which
is greater in the United States than in most other OECD
countries. There are large differences in infant mortality
among racial groups in the United States, with Black
women more likely to give birth to low birth weight infants,
58
and with infant mortality more than double that for White
women (10.9 vs. 5.1 in 2012) (NCHS, 2015).
Many studies use infant mortality as a health outcome to
examine the effect of a variety of medical and non-medical
determinants of health. Although most analyses show that
higher health spending tends to be associated with lower
infant mortality, the fact that some countries with a high
level of health expenditure do not exhibit low levels of
infant mortality suggests that more health spending is not
necessarily required to obtain better results (Retzlaff-Roberts
et al., 2004).
The infant mortality rate is the number of deaths of
children under one year of age, expressed per
1 000 live births. Some of the international variation
in infant mortality rates is related to variations in registering practices for very premature infants. While
some countries register all live births including very
small babies with low odds of survival, several countries apply a minimum threshold of a gestation period
of 22 weeks (or a birth weight threshold of 500 grams)
for babies to be registered as live births (Euro-Peristat,
2013). To remove this data comparability limitation,
the data presented in this section are now based on a
minimum threshold of 22 weeks of gestation period
(or 500 grams birth weight) for a majority of OECD
countries that have provided these data. However, the
data for some countries (e.g., Canada and Australia)
continue to be based on all registered live births,
resulting in some over-estimation.
References
Euro-Peristat (2013), European Perinatal Health Report: The
Health and Care of Pregnant Women and their Babies in
2010, Luxembourg.
Joseph, K.S. et al. (2012), “Influence of Definition Based
Versus Pragmatic Registration on International Comparisons of Perinatal and Infant Mortality: Population Based
Retrospective study”, British Medical Journal, Vol. 344,
e746.
NCHS (2015), Health, United States, 2014, with Special
Feature on Adults Aged 55-64, NCHS, Hyattsville, United
States.
Retzlaff-Roberts, D., C. Chang and R. Rubin (2004), “Technical Efficiency in the Use of Health Care Resources: A
Comparison of OECD Countries”, Health Policy, Vol. 69,
pp. 55-72.
3. HEALTH STATUS
Infant mortality
3.14. Infant mortality, 2013 (or nearest year)
Deaths per 1 000 live births
41.4
45
32.8
40
35
24.5
30
25
13.0
10.9
8.4
5.1
5.0
5.0
4.8
4.5
4.4
4.4
4.0
3.8
3.7
3.7
3.6
3.6
3.5
3.5
3.5
3.3
3.1
2.9
2.9
2.9
2.8
2.6
2.5
2.5
2.5
2.4
2.4
2.3
2.0
1.7
1.7
1.3
5
2.0
7.0
10
8.2
10.2
15
12.3
17.5
20
Ic
el
Sl and 1
ov
e
F i ni a
nl
E s and
to
n
Ja ia
No pan
rw
a
S y
S w p ain
Cz e
ec de
h n
De Re
n m p.
a
Is r k
ra
Au el
Ge s tr
rm ia
an
y
It a
Ko l y
Po r e
r a
Au tug
S
Un w s tr al
i te i t z a li a
d er l
K i an
ng d
B e dom
lg
L u I ium
xe r e l
m an
bo d
u
Fr r g 1
an
G ce
Li ree
th ce
ua
N e O E C ni a
t D
N e h er 3 4
w lan
Ze ds
al
a
L a nd
tv
Po i a
l
C a and
na
H
Un u d
i te ng a
a
Sl d S t r y
ov a t
ak es
Re
Ru
p
s s Ch .
i a il e
n
Co F
st ed
aR .
Tu ica
rk
e
Ch y
in
Br a
M a zi
C o ex i l
l co
In o m
S o do bi a
ut nes
h ia
Af
ric
In a
di
a
0
Note: The data for most countries are based on a minimum threshold of 22 weeks of gestation period (or 500 grams birthweight) to remove the impact
of different registration practices of extremely premature babies across countries.
1. Three-year average (2011-13).
1 2 http://dx.doi.org/10.1787/888933280782
3.15. Trends in infant mortality, selected OECD countries, 2000-13
Chile
Deaths per 1 000 live births
40
Mexico
Turkey
OECD
United States
35
30
25
20
15
10
5
13
20
12
20
11
20
10
20
09
20
08
20
07
20
06
20
05
20
04
20
03
20
02
20
01
20
20
00
0
1 2 http://dx.doi.org/10.1787/888933280782
59
3. HEALTH STATUS
Low birth weight – defined as newborns weighing less than
2 500 grams – is an important indicator of infant health
because of the close relationship between birth weight and
infant morbidity and mortality. There are two categories of
low birth weight babies: those occurring as a result of
restricted foetal growth and those resulting from pre-term
birth. Low birth weight infants have a greater risk of poor
health or death, require a longer period of hospitalisation
after birth, and are more likely to develop significant
disabilities. Risk factors for low birth weight include maternal smoking, excessive alcohol consumption, poor nutrition, low body mass index, lower socio-economic status, and
having had in-vitro fertilisation treatment and multiple
births.
One in 15 babies born in OECD countries in 2013 – or 6.6% of
all births – weighed less than 2 500 grams at birth
(Figure 3.16). The proportions of low-weight births were
lowest in Nordic countries (Iceland, Finland, Sweden,
Norway, with the exception of Denmark) and Estonia, with
less than 5% of live births defined as low birth weight.
Japan, had the highest proportion of low birth weight
infants among OECD countries, with rates close to 10%,
followed by Greece, Hungary and Portugal.
Despite the widespread use of a 2 500 grams limit for low
birth weight, physiological variations in size occur across
different countries and population groups, and these need
to be taken into account when interpreting differences
(Euro-Peristat, 2013). Some populations may have lower
than average birth weights than others because of genetic
differences.
In almost all OECD countries, the proportion of low birth
weight infants has increased over the past two decades,
mainly due to increases in pre-term births (Euro-Peristat,
2013). There are several reasons for this rise, including a
growing number of multiple pregnancies mainly as a result
of the rise in fertility treatment, and a rise in maternal age
(Delnord et al., 2015). Another factor which may explain the
rise in low birth weight infants is the increased use of delivery management techniques such as induction of labour
and caesarean delivery, which have increased the survival
rates of low birth weight babies.
Korea, Greece, Spain, Portugal and Japan have seen large
increases of low birth weight babies over the past two
decades, although the proportions remain below the OECD
average in Korea (Figure 3.17). In Japan, this increase can be
explained by changes in obstetric interventions, in particular the greater use of caesarean sections, along with
changes in maternal socio-demographic and behavioural
factors (Yorifuji et al., 2012). In Greece, the rise in the proportion of low birth weight babies started in the mid-1990s,
well before the economic crisis, and peaked in 2010. Some
researchers have suggested that the high rates of low birth
60
weight babies between 2009 and 2012 were linked to the
economic crisis and its impact on unemployment rates and
lowering family incomes in Greece (Kentikelenis, 2014). In
2013, the rate came down to levels observed before the crisis.
Comparisons of different population groups within countries indicate that the proportion of low birth weight
infants may also be influenced by differences in education
level, income and associated living conditions. In the
United States, there are marked differences in the proportion
of low birth weight infants among racial groups, with black
infants having a rate almost double that of white infants
(13% versus 7% in 2013) (NCHS, 2015). Similar differences
have also been observed among the indigenous and nonindigenous populations in Australia, Mexico and New
Zealand, often reflecting the disadvantaged living conditions of many of these mothers.
The proportion of low birth weight infants is also generally
higher among women who smoke than for non-smokers.
Low birth weight is defined by the World Health
Organization (WHO) as the weight of an infant at birth
of less than 2 500 grams (5.5 pounds) irrespective of
the gestational age of the infant. This threshold is
based on epidemiological observations regarding the
increased risk of death to the infant and serves for
international comparative health statistics. The number
of low weight births is expressed as a percentage of
total live births.
References
Delnord, M. et al. (2015), “What Contributes to Disparities
in the Preterm Birth Rate in European Countries?”, Current Opinion in Obstetrics and Gynecology, Vol. 27, No. 2,
pp. 133-142, April.
Euro-Peristat (2013), European Perinatal Health Report: The
Health and Care of Pregnant Women and their Babies in 2010,
Luxembourg.
Kentikelenis, A. (2014), “Greece’s Health Crisis: From
Austerity to Denialism”, The Lancet, Vol. 383, No. 9918,
pp. 748-753.
NCHS – National Center for Health Statistics (2015), Health,
United States, 2014, With Special Feature on Adults Aged
55-64, NCHS, Hyattsville, United States.
Yorifuji, T. et al. (2012), “Trends of Preterm Birth and Low
Birth Weight in Japan: A One Hospital-Based Study”,
BMC Pregnancy and Childbirth, Vol. 12:162.
3. HEALTH STATUS
3.16. Low birth weight infants, 2013 (or nearest year)
% of newborns weighing less than 2 500 g
7.0
7.0
6.8
6.8
6.7
6.6
6.6
8.9
8.8
8.7
8.4
8.1
8.0
7.9
7.8
7.6
7.5
6.2
6.1
6.0
6.0
6.0
6.0
5.8
5.8
4.6
4.3
4.3
3.7
4.1
5.5
6
5.5
6.6
8
7.3
9.6
10
4
2
to
n
Sw ia
ed
No en
rw
ay
Ko
re
M a
ex
ic
o
I
r
e
Ne
l
a
th nd
N e er l a
w nd
Ze s
al
an
d
Ch
il e
Po
la
Sl nd
ov
en
Ca ia
na
Au da
st
r
Ge a li a
Sw rma
i t z ny
er
la
OE nd
CD
3
Fr 4
an
c
Au e
st
r
Un
i
i te Belg a
d
K i ium
Lu ngd
xe om
m
bo
ur
g
It a
De l y
Sl nm
ov ar
ak k
Re
p.
Sp
ain
Un I
i t e sr a
e
d
S l
Cz t at
e
ec
h s
Re
p
Tu .
r
Po ke y
r tu
Hu g al
ng
ar
Gr y
ee
ce
Ja
pa
n
d
an
Es
nl
Fi
Ic
el
an
d
0
1 2 http://dx.doi.org/10.1787/888933280794
3.17. Trends in low birth weight infants, selected OECD countries, 1990-2013
Greece
% of newborns weighing less than 2 500 g
10
Japan
Korea
Portugal
OECD
Spain
9
8
7
6
5
4
3
2
1
0
1990
1995
2000
2005
2010
1 2 http://dx.doi.org/10.1787/888933280794
61
3. HEALTH STATUS
Most OECD countries conduct regular health surveys which
allow respondents to report on different aspects of their
health. A commonly asked question relates to selfperceived health status, of the type: “How is your health in
general?”. Despite the subjective nature of this question,
indicators of perceived general health have been found to
be a good predictor of people’s future health care use and
mortality (DeSalvo et al., 2005).
For the purpose of international comparisons, cross-country
variations in perceived health status are difficult to interpret
because responses may be affected by the formulation of
survey questions and responses, and by social and cultural
factors. In addition, since older people report poor health
more often than younger people, countries with a larger
proportion of aged persons will also have a lower proportion of people reporting to be in good health.
With these limitations in mind, in almost all OECD countries, a majority of adults reports being in good health
(Figure 3.18). New Zealand, Canada, the United States and
Australia are the four leading countries, with more than
85% of people reporting to be in good health. However, the
response categories offered to survey respondents in these
four countries are different from those used in European
countries and Asian OECD countries, which introduce an
upward bias (see box on “Definition and comparability”).
On the other hand, less than half of adults in Japan, Korea
and Portugal rate their health as being good. The proportion is also relatively low in Estonia, Hungary, Poland, Chile
and the Czech Republic, where less than 60% of adults consider themselves to be in good health.
In all OECD countries, men are more likely than women to
report being in good health, except in Australia, New Zealand,
Canada and United Kingdom where the proportion is
almost equal. As expected, people’s rating of their own
health tends to decline with age. In many countries, there
is a particularly marked decline in how people rate their
health after age 45 and a further decline after age 65
(OECD, 2015).
There are large disparities in self-reported health across
different socio-economic groups, as measured by income
or education level. Figure 3.19 shows that, in all countries,
people with a lower level of income tend to report poorer
health than people with higher income, although the gap
varies. On average across OECD countries, nearly 80% of
people in the highest income quintile report being in good
health, compared with just over 60% for people in the lowest income group. These disparities may be explained by
differences in living and working conditions, as well as
differences in lifestyles (e.g., smoking, harmful alcohol
drinking, physical inactivity, and obesity problems). In
addition, people in low-income households may have limited
access to certain health services for financial or other reasons
(see Chapter 7 on “Access to care”). A reverse causal link is
also possible, with poor health status leading to lower
employment and lower income.
62
Greater emphasis on public health and disease prevention
among disadvantaged groups, and improving access to
health services may contribute to further improvements in
population health status in general and reducing health
inequalities.
Perceived health status reflects people’s overall perception of their health. Survey respondents are typically
asked a question such as: “How is your health in
general? very good, good, fair, poor, very poor”.
Caution is required in making cross-country comparisons of perceived health status, for at least two reasons.
First, people’s assessment of their health is subjective
and can be affected by cultural factors. Second, there
are variations in the question and answer categories
used to measure perceived health across surveys and
countries. In particular, the response scale used in the
United States, Canada, New Zealand, Australia and
Chile is asymmetric (skewed on the positive side),
including the following response categories: “excellent,
very good, good, fair, poor”. The data in OECD Health
Statistics refer to respondents answering one of the
three positive responses (“excellent, very good or
good”). By contrast, in most other OECD countries, the
response scale is symmetric, with response categories
being: “very good, good, fair, poor, very poor”. The
data reported from these countries refer only to the
first two categories (“very good, good”). In Israel, the
scale is symmetric but there is no middle category
related to “fair” health. Such differences in response
categories biases upward the results from those
countries that are using an asymmetric scale or a
symmetric scale but without any middle category.
Self-reported health by income level is reported for
the first quintile (lowest 20% of income group) and
the fifth quintile (highest 20%). Depending on the
surveys, the income may relate either to the individual or the household (in which case the income is
equivalised to take into account the number of persons
in the household).
References
DeSalvo, K.B. et al. (2005), “Predicting Mortality and Healthcare Utilization with a Single Question”, Health Services
Research, Vol. 40, pp. 1234-1246.
OECD (2015), OECD Health Statistics 2015, online, OECD Publishing, Paris, www.oecd.org/health/health-data.
3. HEALTH STATUS
3.18. Perceived health status among adults, 2013 (or nearest year)
Fair
Good or very good
% of population aged 15 years and over
100
2
3
3
4
4
4
4
8
9
90
9
11
14
15
16
6
8
5
9
11
8
8
8
8
9
9
Bad or very bad
12
9
13
12
8
11
6
7
13
14
20
18
16
19
17
80
16
18
20
21
20
22
22
21
24
21
22
27
25
29
16
16
19
15
16
49
49
35
35
35
28
27
70
27
31
35
60
50
90
89
88
40
85
82
81
81
80
77
76
76
74
74
74
72
72
72
69
69
68
67
66
66
30
65
65
65
60
59
58
57
53
46
20
10
Ne
w
Ze
al
an
d
Un C a n 1
i te ad
d
a
St 1
a
Au tes 1
st
ra
li a
Ir e 1
la
S nd
S w wed
i t z en
er
la
nd
Is
ra
e
Ic l 1
el
an
N d
N e or w
th
a
er y
la
nd
Be s
lg
iu
Un
m
i t e Gr
d eec
Ki
e
Lu ngd
xe om
m
bo
D e ur g
nm
ar
k
Sp
O E a in
CD
3
Au 3
st
ri
Tu a
rk
e
Fr y
an
ce
Sl
ov It al y
ak
R
G e e p.
rm
a
Sl ny
ov
en
Fi ia
C z nl a n
ec
d
h
Re
p.
Ch
il e 1
Po
la
Hu nd
ng
a
Es r y
to
Po ni a
r tu
ga
Ja l
pa
n
Ko
re
a
0
1. Results for these countries are not directly comparable with those for other countries, due to methodological differences in the survey
questionnaire resulting in an upward bias. In Israel, there is no category related to fair health.
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en (EU-SILC for European countries).
1 2 http://dx.doi.org/10.1787/888933280801
3.19. Perceived health status by income level, 2013 (or nearest year)
Lowest income
Highest income
78
69
67
53
54
42
40
40
39
48
49
52
52
54
60
62
63
62
61
60
60
50
69
77
77
76
77
74
74
76
78
80
69
62
67
64
66
58
67
75
73
67
70
69
70
79
80
81
83
86
83
84
84
82
85
89
73
80
77
79
83
90
89
96
91
89
87
93
94
95
96
% of population aged 15 years and over reporting to be in good health
100
31
28
40
30
20
10
Ne
w
Ze
al
an
d
Un C a n 1
i te ad
d
a
St 1
a
Au tes 1
st
ra
li a
Ir e 1
la
S nd
S w wed
i t z en
er
la
nd
Is
ra
e
Ic l 1
el
an
N d
N e or w
th
a
er y
la
nd
Be s
lg
iu
Un
m
i t e Gr
d eec
Ki
e
Lu ngd
xe om
m
bo
D e ur g
nm
ar
k
Sp
O E a in
CD
3
Au 3
st
ri
Tu a
rk
e
Fr y
an
ce
Sl
ov It al y
ak
R
G e e p.
rm
a
Sl ny
ov
en
Fi ia
C z nl a n
ec
d
h
Re
p.
Ch
il e 1
Po
la
Hu nd
ng
a
Es r y
to
Po ni a
r tu
ga
Ja l
pa
n
Ko
re
a
0
Note: Countries are ranked in descending order of perceived health status for the whole population.
1. Results for these countries are not directly comparable with those for other countries, due to methodological differences in the survey
questionnaire resulting in an upward bias. In Israel, data by income group relate to the employed population.
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en (EU-SILC for European countries).
1 2 http://dx.doi.org/10.1787/888933280801
63
3. HEALTH STATUS
Cancer incidence
In 2012, an estimated 5.8 million new cases of cancer were
diagnosed in OECD countries, 54% (around 3.1 million)
occurring in men and 46% (around 2.7 million) in women.
The most common were breast cancer (12.9% of all new
cancer cases) and prostate cancer (12.8%), followed by lung
cancer (12.3%) and colorectal cancer (11.9%). These four
cancers represented half of the estimated overall burden of
cancer in OECD countries (Ferlay et al., 2014).
Large variations exist in cancer incidence across OECD
countries. Cancer incidence rates are highest in Denmark,
Australia, Belgium, Norway, United States, Ireland, Korea,
Netherlands and France registering more than 300 new
cancer cases per 100 000 population in 2012 (Figure 3.20).
The lowest rates were reported in some Latin American
and Mediterranean countries such as Mexico, Greece, Chile
and Turkey, with around 200 new cases or less per
100 000 population. These variations reflect not only variations in the prevalence of risk factors for cancer, but also
national policies regarding cancer screening and differences in quality of reporting.
Cancer incidence was higher for men in all OECD countries
in 2012 except in Mexico. However, the gender gap varies
widely across countries. In Turkey, Estonia and Spain, incidence among men were around 60% higher than among
women, whereas in the United Kingdom, Denmark and Iceland, the gap was less than 10%.
Breast was by far the most common primary sites in
women (28% on average), followed by colorectal (12%), lung
(10%), and cervical (3%). The causes of breast cancer are not
fully understood, but the risk factors include age, family
history, breast density, exposure to oestrogen, being overweight or obese, alcohol intake, radiation and hormone
replacement therapy. Incidence rates in 2012 were highest
in Belgium, Denmark and Netherlands, with rates 25% or
more than the OECD average (Figure 3.21). Chile and Mexico
had the lowest rate, followed by Turkey and Greece. The
variation in breast cancer incidence across OECD countries
may be at least partly attributed to variation in the extent
and type of screening activities. Although mortality rates
for breast cancer have declined in most OECD countries
since the 1990s due to earlier detection and improvements
in treatments, breast cancer continues to be the leading
cause of death from cancer among women (see Indicator
“Mortality from cancer” in Chapter 3 and “Screening, survival
and mortality from breast cancer” in Chapter 8).
Prostate cancer has become the most commonly diagnosed
cancer among men in almost all OECD countries, except in
Hungary, Poland, Turkey and Greece where lung cancer is
still predominant, and in Japan and Korea where colorectal
cancer is the main cancer among men. On average across
OECD countries, prostate cancer accounted for 24% of all
64
new cancer diagnoses in men in 2012, followed by lung
(14%) and colorectal (12%). Similar to breast cancer, the
causes of prostate cancer are not well-understood but age,
ethnic origin, family history, obesity, lack of exercise and
poor nutrition are the main risk factors. Incidence in 2012
was highest in Norway, Sweden, Australia and Ireland, with
rates more than 50% higher than the OECD average
(Figure 3.22). Greece had the lowest rates, followed by
Mexico, Korea and Japan. Prostate cancer incidence rates
have increased in most OECD countries since the late 1990s
with increased use of prostate specific antigen (PSA) tests
having led to greater detection (Ferlay et al., 2014). Differences between countries’ rates can be partly attributed to
differences in the use of PSA testing. Mortality rates from
prostate cancer have decreased in some OECD countries as
a consequence of early detection and improvements in
treatments (see indicator “Mortality from cancer”).
Cancer incidence rates are based on numbers of new
cases of cancer registered in a country in a year per
100 000 population. The rates have been directly agestandardised based on Segi’s world population to
remove variations arising from differences in age structures across countries and over time. The data come
from the International Agency for Research on Cancer
(IARC), GLOBOCAN 2012, available at globocan.iarc.fr.
GLOBOCAN estimates for 2012 may differ from
national estimates due to differences in methods.
Cancer registration is well established in most OECD
countries, although the quality and completeness of
cancer registry data may vary. In some countries, cancer
registries only cover subnational areas. The international comparability of cancer incidence data can also
be affected by differences in medical training and
practice.
The incidence of all cancers is classified to ICD-10
codes C00-C97 (excluding non-melanoma skin
cancer C44). Breast cancer corresponds to C50, and
prostate cancer to C61.
References
Ferlay, J. et al. (2014), “Cancer Incidence and Mortality
Worldwide: Sources, Methods and Major Patterns in
GLOBOCAN 2012”, International Journal of Cancer, Vol. 136,
No. 5, pp. E359-E386.
3. HEALTH STATUS
Cancer incidence
3.20. All cancers incidence, men and women, 2012
Men
Women
Total
400
350
300
250
200
150
100
50
M
ex
ic
Gr o
ee
ce
Ch
il e
Tu
rk
ey
Ja
pa
Po n
la
n
Es d
to
Po ni a
r tu
ga
Sp l
a
Au in
st
r
Fi ia
nl
an
Sw d
e
Un O d en
i te EC
D
d
Ki 3 4
n
Sl gd
ov om
ak
Re
p.
Lu
xe I t a l
m y
bo
ur
g
Is
r
Ge ael
rm
an
Ic y
el
an
Hu d
S w ng
i t z ar y
e
Cz rla
e nd
Ne ch R
w
e
Z e p.
al
an
Ca d
na
Sl d a
ov
en
ia
Ne Fr a
th nce
er
la
nd
s
Ko
re
U n Ir e a
i te lan
d
St d
at
No es
rw
B e ay
lg
A u ium
st
r
De a li a
nm
ar
k
0
Source: International Agency for Research on Cancer (IARC), GLOBOCAN 2012.
3.21. Breast cancer incidence, women, 2012
Chile
Mexico
Turkey
Greece
Japan
Estonia
Poland
Korea
Hungary
Slovak Rep.
Slovenia
Spain
Portugal
Austria
Czech Rep.
Norway
OECD34
Canada
Sweden
Israel
Switzerland
New Zealand
Australia
Luxembourg
Finland
France
Italy
Germany
Ireland
United States
United Kingdom
Iceland
Netherlands
Denmark
Belgium
35
35
39
44
52
52
52
52
55
58
67
67
68
68
70
73
74
80
80
81
83
85
86
89
89
90
91
92
92
93
95
96
99
105
112
0
25
50
75
100
125
150
Age-standardised rates per 100 000 women
Source: International Agency for Research on Cancer (IARC), GLOBOCAN
2012.
1 2 http://dx.doi.org/10.1787/888933280811
1 2 http://dx.doi.org/10.1787/888933280811
3.22. Prostate cancer incidence, men, 2012
Greece
Mexico
Korea
Japan
Poland
Hungary
Turkey
Slovak Rep.
Chile
Portugal
Spain
Italy
Czech Rep.
United Kingdom
Austria
OECD34
Germany
Luxembourg
Slovenia
Netherlands
Israel
Canada
Belgium
Denmark
New Zealand
Estonia
Finland
France
United States
Iceland
Switzerland
Ireland
Australia
Sweden
Norway
20
27
30
30
36
38
41
50
52
64
65
68
72
73
75
76
77
79
83
83
84
89
91
91
92
94
97
98
98
107
107
114
115
119
130
0
25
50
75
100
125
150
Age-standardised rates per 100 000 men
Source: International Agency for Research on Cancer (IARC), GLOBOCAN
2012.
1 2 http://dx.doi.org/10.1787/888933280811
65
4. NON-MEDICAL DETERMINANTS
OF HEALTH
Tobacco consumption among adults
Alcohol consumption among adults
Fruit and vegetable consumption among adults
Obesity among adults
Overweight and obesity among children
international law.
67
4. NON-MEDICAL DETERMINANTS OF HEALTH
Tobacco kills nearly 6 million people each year, of whom
more than 5 million are from direct tobacco use and more
than 600 000 are non-smokers exposed to second-hand
smoke (WHO, 2015). Tobacco is a major risk factor for at
least two of the leading causes of premature mortality –
cardiovascular diseases and cancer, increasing the risk of
heart attack, stroke, lung cancer, cancers of the larynx and
mouth, and pancreatic cancer, among others. In addition, it
is a dominant contributing factor for respiratory diseases
such as chronic obstructive pulmonary disease (US DHHS,
2014). Smoking in pregnancy can lead to low birth weight
and illness among infants. Smoking remains the largest
avoidable risk factor for health in OECD countries and
worldwide.
The proportion of daily smokers in the adult population
varies greatly, even between neighboring countries
(Figure 4.1). Nineteen of 34 OECD countries had less than
20% of the adult population smoking daily in 2013. Rates
were lowest in Sweden, Iceland, Mexico and Australia (less
than 13%). Rates were also less than 13% in Brazil, Colombia,
and India, although the proportion of smokers among men
is high, up to 23% in India. On the other hand, smoking
rates remain high in Greece in both men and women, and
in Latvia and Indonesia where more than one in two men
smoke daily. Smoking prevalence is higher among men
than among women in all OECD countries except in Sweden
and Iceland. The gender gap in smoking rates is particularly
large in Korea, Japan, and Turkey, as well as in the Russian
Federation, India, Indonesia, Latvia, Lithuania, South Africa
and China (Figure 4.1).
Smoking rates across most OECD countries have shown a
marked decline, although other forms of smokeless
tobacco use, such as snuff in Sweden, are not taken into
account. On average, smoking rates have decreased by
about one fourth since 2000, from 26% in 2000 to 20% in
2013. Large reductions occurred in Norway, Iceland, Sweden, Denmark and Ireland, as well as in India.
In the period that followed World War II, smoking rates
were very high among men (50% or more) in most OECD
countries through to the 1960s and 1970s, while the 1980s
and the 1990s were characterised by a marked downturn in
tobacco consumption. Non-OECD countries and emerging
economies stand at an earlier phase of the evolution of
smoking, with high rates and a wide gender gap. In OECD
countries, much of the decline in tobacco use can be attributed to policies aimed at reducing tobacco consumption
through public awareness campaigns, advertising bans,
increased taxation, and restriction of smoking in public
spaces and restaurants, in response to rising rates of
tobacco-related diseases. More stringent policies and
higher level of taxes have led to bigger reductions in smoking rates between 1996 and 2011 in OECD countries (OECD,
2015). As governments continue to reinforce their anti-
68
tobacco policies, new strategies such as plain packaging for
tobacco products aimed to restrict branding have been
implemented (e.g. in Australia) and are being adopted by an
increasing number of countries.
Several studies provide strong evidence of socio-economic
differences in smoking and mortality (Mackenbach et al.,
2008). People in less affluent social groups have a greater
prevalence and intensity of smoking, a higher all-cause
mortality rate and lower rates of cancer survival (Woods et
al., 2006). The influence of smoking as a determinant of
overall health inequalities is such that, if the entire population was non-smoking, mortality differences between
social groups would be halved (Jha et al., 2006).
The proportion of daily smokers is defined as the percentage of the population aged 15 years and over who
report smoking every day. International comparability
is limited due to the lack of standardisation in the
measurement of smoking habits in health interview
surveys across OECD countries. Variations remain in
the age groups surveyed, the wording of questions,
response categories and survey methodologies (e.g. in
a number of countries, respondents are asked if they
smoke regularly, rather than daily). Self-reports of
behaviours may also suffer from social desirability bias
that may potentially limit cross-country comparisons.
References
Jha, P. et al. (2006), “Social Inequalities in Male Mortality,
and in Male Mortality from Smoking: Indirect Estimation
from National Death Rates in England and Wales,
Poland, and North America”, The Lancet, Vol. 368,
No. 9533, pp. 367-370.
Mackenbach, J.P. et al. (2008), “Socio-economic Inequalities
in Health in 22 European Countries”, New England Journal
of Medicine, Vol. 358, pp. 2468-2481.
http://dx.doi.org/10.1787/9789264233010-en.
US DHHS – US Department of Health and Human Services
(2014), The Health Consequences of Smoking – 50 Years of
Progress: A Report of the Surgeon General, Atlanta.
WHO (2015), “Tobacco”, Fact Sheet No. 339, available at:
www.who.int/mediacentre/factsheets/fs339/en/index.html.
Woods, L.M., B. Rachet and M.P. Coleman (2006), “Origins of
Socio-economic Inequalities in Cancer Survival: A Review”,
Annals of Oncology, Vol. 17, No. 1, pp. 5-19.
ed
e
Br n
a
Ic z il
el
a
M nd
ex
ic
o
In
A
Un u s di a
i te tr a
d li a
St
a
C a tes
na
Ne No da
w rw
Lu Zea ay
xe l a
m nd
bo
u
Fi rg
nl
an
I d
D sr a
N e enm el
th a
er r k
la
Po nd
r tu s
Be ga
lg l
iu
Ir e m
la
n
Sl J a d
o p
So vak an
u t Re
h p.
Af
OE ric a
CD
Un
3
i te
d Ko 3
Ki re
S w ngd a
i t z om
er
Ge l an
rm d
an
Cz I y
ec t al
h y
L i Re
t h p.
ua
Au ni a
st
Po r ia
la
Tu nd
rk
e
Sp y
Ru F r a i n
s s an
ia ce
n
Fe
d
Ch .
E s in a
t
Hu oni
ng a
ar
y
Ch
i
L a le
In t v
do i a
ne
Gr s i a
ee
ce
Sw
14
13
13
13
15
12
11
11
11
17
16
24
24
24
24
24
26
27
26
26
27
28
27
26
27
26
30
30
30
31
32
33
33
32
32
33
36
39
38
40
35
34
35
Women
24
23
22
24
25
24
27
31
2000
22
21
21
20
20
20
22
24
29
32
ed
e
Br n
Ic a z il
el
M and
Co ex i
lo co
m
bi
In a
A
Un u di
i te s tr a
d al
Co S t a ia
s t tes
aR
Ca ic a
n
Ne No ada
w rw
Z
Lu e ay
xe a l a
m nd
bo
F i ur g
nl
an
I d
D e sr a
Ne n el
th ma
er r k
l
Po and
r tu s
Be ga
lg l
i
Ir e u m
la
Sl J a nd
ov p
ak an
R
S o O E C e p.
ut D
h 34
Af
Un
ric
i te
d Ko a
Ki re
n
Sw g a
i t z dom
er
Sl l an
ov d
Ge en
rm ia
an
Cz I y
ec t al
h y
L i Re
t h p.
ua
Au ni a
s
Po tr ia
la
Tu nd
rk
e
Sp y
Ru F a i n
r
s s an
ia ce
n
Fe
Ch d.
E s in a
Hu toni
ng a
ar
Ch y
il e
In L a t v
do i a
ne
Gr s i a
ee
ce
Men
20
20
20
19
21
26
25
35
19
19
22
23
22
25
30
19
19
19
20
22
25
16
16
16
15
15
18
20
19
Sw
4.1. Daily smoking in adults, 2013 (or nearest year)
Total
70
60
50
40
30
20
10
0
Note: Countries are ranked in ascending order of smoking rates for the whole population.
1 2 http://dx.doi.org/10.1787/888933280827
4.2. Change in daily smoking in adults, 2000 and 2013 (or nearest years)
2013
10
5
0
1 2 http://dx.doi.org/10.1787/888933280827
69
The health burden related to harmful alcohol consumption, both in terms of morbidity and mortality, is considerable in most parts of the world (Rehm et al., 2009; WHO,
2014; OECD, 2015). Alcohol use is associated with numerous
harmful health and social consequences, including an
increased risk of a range of cancers, stroke, and liver cirrhosis,
among others. Foetal exposure to alcohol increases the risk
of birth defects and intellectual impairment. Alcohol also
contributes to death and disability through accidents and
injuries, assault, violence, homicide and suicide. The use of
alcohol is estimated to cause more than 3.3 million deaths
worldwide per year, and accounts for 5.1% of the global
burden of disease (WHO, 2014). Health care costs associated
with excessive drinking in the United States are estimated
at USD 25.6 billion (Bouchery et al., 2011). In the Russian
Federation, alcohol misuse was a major contributing factor to
the sharp rise in premature mortality and decline in life
expectancy during the 1990s (OECD, 2012). The use of alcohol also has broader societal consequences, accounting for
large losses in work productivity through absenteeism and
premature mortality, as well as injuries and death among
non-drinkers (e.g. because of traffic accidents caused by
drivers under the influence of alcohol).
Alcohol consumption, as measured by recorded data on
annual sales, stands at 8.9 litres per adult, on average,
across OECD countries, based on the most recent data
available (Figure 4.3). Austria, Estonia and the Czech Republic,
as well as Lithuania, reported the highest consumption of
alcohol with 11.5 litres or more per adult per year in 2013.
Low alcohol consumption was recorded in Turkey and
Israel, as well as in Indonesia and India, where religious
and cultural traditions restrict the use of alcohol in some
population groups.
Although average alcohol consumption has gradually
fallen in many OECD countries since 2000, it has risen in
Poland, Sweden and Norway, as well as in Latvia, Lithuania
and the Russian Federation. However, national aggregate
data does not permit to identify individual drinking patterns
and the populations at risk. OECD analysis based on individual-level data show that hazardous drinking and heavy episodic drinking are on the rise in young people and women
especially. Men of low socioeconomic status are more likely
to drink heavily than those of a higher socioeconomic status,
while the opposite is observed in women (OECD, 2015).
Alcohol consumption is highly concentrated, as the large
majority of alcohol is drunk by the 20% of the population
who drink the most (Figure 4.4), with some variation across
countries. The 20% heaviest drinkers in Hungary consume
about 90% of all alcohol consumed, while in France the
share is about 50%.
In 2010, the World Health Organization endorsed a global
strategy to combat the harmful use of alcohol, through
direct measures such as medical services for alcohol-
70
related health problems, and indirect measures such as the
dissemination of information on alcohol-related harm
(WHO, 2010). The OECD used this as a starting point to
identify a set of policy options to be assessed in an economic
evaluation, and showed that several policies have the
potential to reduce heavy drinking, regular or episodic, as
well as alcohol dependence. Governments seeking to tackle
binge drinking and other types of alcohol abuse can use a
range of policies that have proven to be effective, including
counselling heavy drinkers, stepping up enforcement of
drinking-and-driving laws, as well as raising taxes, raising
prices, and increasing the regulation of the marketing of
alcoholic drinks (OECD, 2015).
Alcohol consumption is defined as annual sales of
pure alcohol in litres per person aged 15 years and
over. The methodology to convert alcoholic drinks to
pure alcohol may differ across countries. Official statistics do not include unrecorded alcohol consumption,
such as home production. WHO produces estimates
for unrecorded alcohol consumption.
Survey-based estimates of the amount of alcohol
drunk by the 20% heaviest drinkers rely on the data
analysis of the latest available national health surveys
for 13 OECD countries. The list of surveys is provided
in Table A.1 in Annex A in the publication Tackling
Harmful Alcohol Use – Economics and Public Health Policy
(OECD, 2015).
References
Bouchery, E.E. et al. (2011), “Economic Costs of Excessive
Alcohol Consumption in the U.S., 2006”, American Journal
of Preventive Medicine, Vol. 41, No. 5, pp. 516-524.
OECD (2015), Tackling Harmful Alcohol Use – Economics and
Public Health Policy, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264181069-en.
OECD (2012), OECD Reviews of Health Systems: Russian
Federation, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264168091-en.
Rehm, J. et al. (2009), “Global Burden of Disease and Injury
and Economic Cost Attributable to Alcohol Use and
Alcohol-use Disorder”, The Lancet, Vol. 373, pp. 2223-2233.
WHO (2014), Global Status Report on Alcohol and Health 2014,
Geneva.
WHO (2010), Global Strategy to Reduce the Harmful Use of
Alcohol, Geneva.
4.3. Alcohol consumption among adults, 2000 and 2013 (or nearest years)
2013
2000
Liters per capita (15 years +)
16
14
12
10
8
6
4
2
In
do
ne
Tu sia
rk
e
In y
d
C o Isr i a
s t ae
a
Co Ri l
lo c a
m
M bia
ex
ic
Ch o
in
a
I
No t al y
rw
Ic ay
el
an
Br d
Gr a z il
ee
Ja ce
Sw pan
ed
e
Ch n
S o C a il e
ut na
h da
Af
ric
Un
i t e Ko a
d re
St a
OE ate
CD s
N e F in 3 4
th lan
N e er d
w lan
Ze ds
De alan
nm d
Un
i te Slo ar k
d ve
Ki n
ng ia
B e dom
lg
iu
S m
Au pa
Sl s t in
ov r a
S w a k li a
i t z Rep
er .
la
L nd
Po a t v i
r tu a
Ir e g a l
la
Po nd
l
G
L u er a nd
xe m a
m ny
bo
u
Fr r g
a
Ru H u n c e
ss nga
i
C z an F r y
ec ed
h .
R
E s e p.
to
Au ni a
Li s tr
th ia
ua
ni
a
0
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en; WHO for non-OECD countries.
1 2 http://dx.doi.org/10.1787/888933280835
4.4. Share of total alcohol consumed by the 20% of the population who drink the most, 2012 (or nearest year)
(%)
100
91
73
75
66
63
63
61
59
58
69
68
66
54
50
50
25
ar
ng
Hu
at
St
d
Un
i te
y
es
n
pa
Ja
da
Ca
na
a
re
Ko
d
an
nl
Fi
nd
la
la
ng
UK
(E
Ir e
nd
)
y
an
rm
al
Ze
w
Ne
Ge
an
d
ain
Sp
la
er
it z
Sw
Fr
an
ce
nd
0
Source: OECD (2015), Tackling Harmful Alcohol Use – Economics and Public Health Policy.
1 2 http://dx.doi.org/10.1787/888933280835
71
Nutrition is an important determinant of health. Insufficient consumption of fruit and vegetables is one factor that
can play a role in increased risk of morbidity (Bazzano et
al., 2003; Riboli and Norat, 2003). Food insecurity, that is the
inability to afford enough food for a healthy and active life,
is also associated with adverse health effects (Seligman et
al., 2010). Proper nutrition assists in preventing a number of
chronic conditions, including cardiovascular disease,
hypertension, type-2 diabetes, stroke, certain cancers,
musculoskeletal disorders and a range of mental health
conditions.
In response to a health survey question asking “How often
do you eat fruit?”, the percentage of adults consuming fruit
daily varied from about 30% in Finland, to 94% in Australia
(Figure 4.5). Across the 29 countries providing data, on
average 55% of men and 66% of women reported to eat fruit
daily. Women reported eating fruit more often than men in
all countries except in Switzerland, with the largest gender
differences in Germany, Slovenia, and Iceland (20 percentage
points or more). In Australia, Greece, Mexico, and the United
Kingdom, gender differences were much smaller, under
5 percentage points.
Persons aged 65 and over were more likely to eat fruit than
those in younger age group; with the lowest consumption
in people aged 15-24 years. Fruit consumption also varies
by education level, generally being highest among persons
with higher educational levels.
Daily vegetable consumption ranged from around 33% in
men in Slovenia to nearly 100% in Korea, with Australia and
New Zealand at about the same levels, but counting potatoes as vegetables (Figure 4.6). The average across 29 OECD
countries was 61% for men and 70% for women. Again,
more women than men reported eating vegetables daily in
all countries, except in Korea, Australia and Mexico where
vegetable consumption is not significantly different
between men and women. In Sweden, Switzerland, Norway,
Germany and Slovenia, gender differences exceeded
16 percentage points.
Patterns of vegetable consumption across age groups and
by level of education are similar to those observed for fruit.
Older persons are more likely to eat vegetables daily. Highly
educated persons eat vegetables more often.
The availability of fruit and vegetables is the most important determinant of consumption. Despite large variations
between countries, vegetable, and especially fruit, availability is higher in Southern European countries, with cereals
and potatoes more available in Central and Eastern European countries. Fruit and vegetable availability also tends
72
to be higher in families where household heads have a
higher level of education (Elmadfa, 2009).
The promotion of fruit and vegetable consumption, especially
in schools and at the workplace, features in the EU platform
for action on diet, physical activity and health (European
Commission, 2014).
Estimates of daily fruit and vegetable consumption
are derived from national and European Health Interview Survey questions. Typically, respondents were
asked “How often do you eat fruit (excluding juice)?”
and “How often do you eat vegetables or salad
(excluding juice and potatoes)?”.
Data for Greece and Switzerland include juices as a
portion of fruit, and juices and soups as a portion of
vegetable. Data for Australia, Greece, New Zealand,
and the United Kingdom include potatoes as vegetables.
Data rely on self-reporting, and are subject to errors in
recall. The same surveys also ask for information on
age, sex and educational level. Data are not age standardised, with aggregate country estimates representing
crude rates among respondents aged 15 years and over
in all countries, except Germany and Australia which is
18 years and over.
References
Bazzano, L.A., M.K. Serdula and S. Liu (2003), “Dietary
Intake of Fruits and Vegetables and Risk of Cardiovascular Disease”, Current Atherosclerosis Reports, Vol. 5,
pp. 492-499.
Elmadfa, I. (ed.) (2009), European Nutrition and Health Report
2009, Basel, Switzerland.
European Commission (2014), EU Platform on Diet, Physical
Activity and Health, 2014 Annual Report, European Commission, Brussels.
Riboli, E. and T. Norat (2003), “Epidemiologic Evidence of the
Protective Effect of Fruit and Vegetables on Cancer Risk”,
American Journal of Clinical Nutrition, Vol. 78 (Suppl.),
pp. 559S–569S.
Seligman, H.K., B.A. Laraia and M.B. Kushel (2010), “Food
Insecurity Is Associated with Chronic Disease among
Low-income NHANES Participants”, Journal of Nutrition,
Vol. 140, pp. 304-310.
4.5. Daily fruit eating among adults, 2013 (or nearest year)
Men
Women
Total
100
80
60
40
20
De ay
n
Sw ma
it z rk
er
la
nd
Ko
re
a
Sp
ain
Po
la
nd
Gr
ee
c
Sw e
ed
e
OE n
CD
Ge 2 9
rm
an
Es y
to
n
Sl i a
ov
en
ia
Tu
rk
ey
Ir e
la
n
Be d
lg
iu
m
Un Fr a
i te nce
d
St
at
es
M
ex
ic
o
Ic
el
an
d
Ch
il e
Fi
nl
an
d
p.
rw
Re
No
ak
Re
h
ec
ov
Cz
Sl
Ne
Un
i te
p.
y
da
Hu
ng
ar
el
Ca
Is
na
ra
ly
d
It a
m
an
w
Ze
al
do
ng
d
Ki
Au
st
ra
li a
0
1 2 http://dx.doi.org/10.1787/888933280841
4.6. Daily vegetable eating among adults, 2013 (or nearest year)
Men
Women
Total
100
80
60
40
20
ia
en
ov
Sl
Fi
nl
an
d
y
k
an
ar
nm
rm
Ge
De
d
ain
Sp
y
an
ar
el
ng
Hu
Ic
a
p.
ni
Re
ak
Sl
ov
Es
to
ce
ly
an
Fr
p.
It a
il e
Re
h
ec
Cz
nd
Ch
la
Po
y
ce
ke
ee
Gr
ay
Tu
r
29
rw
No
OE
CD
nd
nd
la
Ir e
la
er
it z
Sw
Sw
ed
en
da
Ca
na
m
es
iu
lg
Be
o
el
i te
d
St
at
ra
Is
Un
M
ex
ic
m
d
do
Ki
ng
li a
an
Un
i te
d
w
Ze
al
ra
st
Ne
Au
Ko
re
a
0
1 2 http://dx.doi.org/10.1787/888933280841
73
Obesity is a known risk factor for numerous health problems, including hypertension, high cholesterol, diabetes,
cardiovascular diseases, respiratory problems (asthma),
musculoskeletal diseases (arthritis) and some forms of
cancer. The rise in overweight and obesity is a major public
health concern, threatening progress in tackling cardiovascular diseases (OECD, 2015).
Estimates of obesity and overweight are derived either
from health examinations or self-reports, the former being
higher and more reliable. Based on the latest available
surveys, more than half (53.8%) of the adult population in
OECD countries are overweight or obese. In countries
where height and weight are measured (as opposed to selfreported), this proportion is even greater, at 57.5%. The
prevalence of overweight and obesity among adults
exceeds 50% in no less than 22 of 34 OECD countries. In
contrast, overweight and obesity rates are much lower in
Japan and Korea and in some European countries (France
and Switzerland), although even in these countries rates
are increasing.
The prevalence of obesity, which presents even greater
health risks than overweight, varies about six fold across
OECD countries, from a low of 5% in Japan and Korea, to
over 32% in Mexico and the United States (Figure 4.7).
Across OECD countries, 19% of the adult population are
obese. Obesity rates in men and women are similar in most
countries. However, in Chile, Mexico and Turkey, as well as
Colombia, the Russian Federation and South Africa, a
greater proportion of women are obese, while the reverse is
true in Slovenia.
The prevalence of obesity has increased over the past
decade in all OECD countries (Figure 4.8). In 2013, at least
one in five adults was obese in twelve OECD countries,
compared to one in eight a decade ago. Since 2000, obesity
rates have increased by a third or more in 14 countries. The
rapid rise occurred regardless of where levels stood a
decade ago. Obesity increased by around 45% in both
Denmark and Australia, even though the current rate in
Denmark is only half that of Australia.
The rise in obesity has affected all population groups,
regardless of sex, age, race, income or education level, but to
varying degrees. Evidence from Canada, the United Kingdom,
France, Italy, Mexico, Spain, Switzerland and the United
States shows that obesity tends to be more common in
lower educated groups, especially in women (OECD, 2014).
Rates of overweight and obesity vary by education level and
socioeconomic status, and these disparities are significant
in women while less clear-cut in men (Devaux and Sassi,
2013).
A number of behavioural and environmental factors have
contributed to the long-term rise in overweight and obesity
rates in industrialised countries, including the widespread
availability of energy dense foods and more time spent being
physically inactive. These factors have created obesogenic
environments, putting people, and especially those socially
vulnerable, more at risk of obesity.
74
A growing number of countries have adopted policies to
prevent obesity from spreading further. The policy mix
includes, for instance, public awareness campaigns, health
professionals training, advertising limits or bans on
unhealthy food, taxations and restrictions on sales of certain
types of food and beverages, and nutrition labelling. Better
informed consumers, making healthy food options available,
encouraging physical activity and focussing on vulnerable
groups are some of the areas in which progress has been
made (European Commission, 2014).
Overweight and obesity are defined as excessive
weight presenting health risks because of the high
proportion of body fat. The most frequently used
measure is based on the body mass index (BMI), which
is a single number that evaluates an individual’s weight
in relation to height (weight/height2, with weight in
kilograms and height in metres). Based on the WHO
classification (WHO, 2000), adults with a BMI from 25
to 30 are defined as overweight, and those with a BMI
of 30 or over as obese. This classification may not be
suitable for all ethnic groups, who may have equivalent levels of risk at lower or higher BMI. The thresholds
for adults are not suitable to measure overweight and
obesity among children.
For most countries, overweight and obesity rates are
self-reported through estimates of height and weight
from population-based health interview surveys.
However, around one-third of OECD countries derive
their estimates from health examinations. These differences limit data comparability. Estimates from
health examinations are generally higher, and more
reliable than estimates from health interviews. Note
that the OECD average is based on both types of estimates (self-reported and measured) and, thus, may be
underestimated.
References
Devaux, M. and F. Sassi (2013), “Social Inequalities in
Obesity and Overweight in 11 OECD Countries”, European
Journal of Public Health, Vol. 23, No. 3, pp. 464-469, June.
European Commission (2014), EU Platform on Diet, Physical
Activity and Health, 2014 Annual Report, Brussels.
http://dx.doi.org/10.1787/9789264233010-en.
OECD (2014), Obesity Update, OECD Publishing, Paris, June
2014, www.oecd.org/health/Obesity-Update-2014.pdf.
d
St
at
o1
es 1
ic
d1
li a 1
an
ex
al
M
Ze
ra
da 1
il e 1
12
13
14
14
15
16
17
16
16
15
21
22
23
25
25
24
25
26
25
25
24
23
22
21
20
20
19
18
28
30
i te
w
st
na
Ch
d
m
an
do
nl
ng
Ca
Ki
Au
d
Fi
y1
g1
d
11
13
13
15
14
14
31
31
32
35
7.0
5.7
28.5
28.3
26.5
25.8
25.7
25.1
24.9
24.8
24.4
23.6
23.6
23.0
22.7
22.3
22.2
21.0
20.9
19.6
19.6
19.0
19.0
18.3
17.5
16.9
16.6
15.8
15.7
15.4
14.5
14.2
13.7
12.4
11.7
11.1
10.3
10.3
10.0
30
Un
Ne
i te
ur
an
an
bo
rm
m
el
p. 1
a
9
10
12
12
12
11
10
10
2000
Ge
xe
Re
ni
28
9
9
9
9
10
5.0
4.7
3.7
32.4
30.6
35.3
Self-reported data
Un
Lu
h
Ic
ec
to
CD
ain
8
8
20
Es
OE
Sp
el
nd
ra
l
ce
k
ga
la
Is
r tu
Po
Po
ar
an
nm
m
ria
iu
st
lg
Fr
De
Be
Au
s
en
nd
nd
ed
la
la
ly
5
pa
Ko n
re
a
In In d
do i a
ne
s
Ch i a
No in a
rw
ay
Sw
it z It al
N e er y
th l an
er d
la
S w nds
ed
Au en
B e s tr i
l a
D e giu
nm m
Fr ar k
Po anc
r tu e
g
Is a l
r
Po ael
la
Sl S nd
ov p a
a k in
Re
B p.
Sl r a z
ov il
O E eni
CD a
Es 3 4
to
Ru G n i a
ss ree
ia ce
n
Co F e
C z l o m d.
ec bi
h a
R
I c e p.
el
Lu T and
xe ur k
m ey
bo
Ir e u r g
la
L nd
Ge at v
Co rm ia
s t any
a
Un
i te F Ric a
d inl
K i an
ng d
do
m
L i Ch
t h il e
ua
S o C a ni a
ut na
h da
A
Au fric
st a
r
N e Hu a li a
w nga
Ze r y
a
Un M l a nd
i t e ex
d ico
St
at
es
Ja
20
Cz
er
er
Sw
th
it z
ay
3
4
10
It a
rw
a1
3
10
Ne
Sw
No
n1
re
pa
Ko
Ja
4.7. Obesity among adults, 2013 (or nearest year)
Measured data
40
0
1 2 http://dx.doi.org/10.1787/888933280857
4.8. Increasing obesity among adults in OECD countries, 2000 and 2013 (or nearest years)
40
2013
0
1. Data are based on measurements rather than self-reported height and weight.
1 2 http://dx.doi.org/10.1787/888933280857
75
Children who are overweight or obese are at greater risk of
poor health in adolescence, as well as in adulthood. Among
young people, orthopaedic problems and psychosocial
problems such as low self-image, depression and impaired
quality of life can result from being overweight. Excess
weight problems in childhood are associated with an
increased risk of being an obese adult, at which point
cardiovascular disease, diabetes, certain forms of cancer,
osteoarthritis, a reduced quality of life and premature
death become health concerns (Lobstein, 2010; Currie et al.,
2012).
Overweight (including obesity) rates based on measured
(rather than self-reported) height and weight are about 24%
for boys and 22% for girls, on average, in OECD countries,
although rates are measured in different age groups in different countries (Figure 4.9). Boys tend to carry excess
weight more often than girls, with the largest gender differences observed in China, Denmark, Iceland, Korea and
Poland. In contrast, Ireland and South Africa show larger
overweight rates among girls. More than one in three children are overweight in Brazil, Chile, Greece, Italy, Mexico,
New Zealand, United Kingdom (England) and the United
States, and about one in three boys in Spain, and one in
three girls in Portugal.
Child obesity has increased in the past few decades worldwide and seems to be stabilising in high-income countries
(Ng et al., 2014; Lobstein et al., 2015). Self-reported overweight rates (including obesity) across OECD countries
slightly increased between 2001-02 and 2009-10 from 13%
to 15% in 15-year-olds (Figure 4.10). The largest increases
during this period were in the Czech Republic, Estonia,
Poland and Slovenia, all greater than 5%. Significant reductions in the proportion of overweight or obese children at
age 15 were only observed in Denmark and the United
Kingdom between 2001-02 and 2009-10, although nonresponse rates to questions about self-reported height and
weight may bias the results downward.
Childhood is an important period for forming healthy
behaviours, and the increased focus on obesity has stimulated the implementation of many community-based
initiatives in OECD countries in recent years. Studies show
that locally focussed interventions, targeting children up to
12 years of age can be effective in changing behaviours.
Schools provide opportunities to ensure that children
understand the importance of good nutrition and physical
activity, and can benefit from both. Teachers and health
professionals are often involved as providers of health and
nutrition education, and the most frequent communitybased initiatives target professional training, the social or
physical environment, and actions for parents (Bemelmans
et al., 2011).
Estimates of overweight and obesity are based on body
mass index (BMI) calculations using either measured or
self-reported height and weight, the latter possibly
under-estimating obesity and overweight. Overweight
and obese children are those whose BMI is above a set
of age- and sex-specific cut-off points (Cole et al.,
2000).
Measured data are gathered by the World Obesity
Federation (WOF, former IASO) from different national
studies, except for Germany (data come from the
2003-06 KIGGS survey) and Korea (based on the 2013
KNHANES survey). The estimates are based on
national surveys of measured height and weight among
children at various ages. Caution is therefore needed
in comparing rates across countries. Definitions of overweight and obesity among children may sometimes
vary among countries, although whenever possible
the IOTF BMI cut-off points are used.
Self-reported data are from the Health Behaviour in
School-aged Children (HBSC) surveys undertaken
between 2001-02 and 2009-10. Data are drawn from
school-based samples of 1 500 in each age group (11-,
13-and 15-year-olds) in most countries. Self-reported
height and weight are subject to under-reporting,
missing data and error, and require cautious interpretation.
References
Bemelmans, W. et al. (2011), “Overview of 71 European
Community-based initiatives against Childhood Obesity
Starting between 2005 and 2011: General Characteristics
and Reported Effects”, BMC Public Health, Vol. 14, No. 758.
Cole, T.J. et al. (2000), “Establishing a Standard Definition for
Child Overweight and Obesity Worldwide: International
Survey”, British Medical Journal, Vol. 320, pp. 1-6.
Currie, C. et al. (eds.) (2012), Social Determinants of Health and
Well-being Among Young People. Health Behaviour in Schoolaged Children (HBSC) Study: International Report from the
2009/2010 Survey, WHO Regional Office for Europe,
Copenhagen.
Lobstein, T. (2010), “The Size and Risks of the International
Epidemic of Child Obesity”, in F. Sassi (eds.), Obesity and the
Economics of Prevention: Fit Not Fat, OECD Publishing, Paris,
pp. 107-114, http://dx.doi.org/10.1787/9789264084865-en.
Lobstein, T. et al. (2015), “Child and Adolescent Obesity: Part
of a Bigger Picture”, The Lancet, Vol. 385, pp. 2510-2520.
Ng, M. et al. (2014), “Global, Regional, and National Prevalence of Overweight and Obesity in Children and Adults
during 1980–2013: A Systematic Analysis for the Global
Burden of Disease Study 2013”, The Lancet, Vol. 384,
No. 9945, pp. 766-781.
76
4.9. Measured overweight (including obesity) among children, 2013 (or nearest year)
Girls
Boys
% of children at various ages
50
40
30
20
10
In
do
ne
s
Po ia
la
Ic nd
el
B e and
lg
i
N o um
rw
a
K y
G e or e
a
C z rma
e
n
Ne ch R y
th ep
Sl er l a .
ov n d
ak s
R
E s e p.
to
ni
Ch a
i
Ru J n a
ss ap
ia an
n
Fe
d
F
Sw ra .
it z nce
er
la
Au nd
st
Sw ria
ed
F i en
nl
an
d
In
di
Is a
ra
Ir e e l
De l an
L u nm d
xe a
m rk
bo
O E ur g
C
Sl D 3 3
ov
H en
S o un i a
ut ga
h ry
A
Au fric
st a
ra
C a li a
na
d
S a
Po p ain
r tu
M gal
ex
ic
o
Ch
il e
Br
az
il
Ne
w It a
Un Z e l y
i te ala
UK d S nd
( E t ate
ng s
la
n
Gr d )
ee
ce
0
Source: World Obesity Federation (2015), KIGGS (2003-06) for Germany and KNHANES (2013) for Korea.
1 2 http://dx.doi.org/10.1787/888933280866
4.10. Change in self-reported overweight among 15-year-olds, 2001-02, 2005-06 and 2009-10
2001-02
2009-10
2005-06
% of 15-year-olds
35
30
25
20
15
10
5
es
da
at
i te
d
St
ce
na
Ca
Un
Gr
ee
ia
l
en
ga
ov
Sl
r tu
Po
ur
g
d
Lu
xe
m
bo
ly
an
el
Ic
ain
It a
y
ar
Sp
ria
ng
Hu
p.
st
Au
26
Re
h
CD
OE
ec
Cz
y
nd
an
la
Ir e
d
an
rm
nl
Fi
Ge
a
nd
ni
la
Po
en
to
Es
Sw
ed
m
y
d
Ki
ng
do
ke
m
Tu
r
Un
i te
p.
iu
Re
lg
Sl
ov
ak
la
er
it z
Sw
Be
nd
ce
k
an
Fr
d.
Fe
n
ia
De
ss
Ru
nm
nd
la
er
th
Ne
ar
s
0
Source: Currie et al. (2004); Currie et al. (2008); Currie et al. (2012).
1 2 http://dx.doi.org/10.1787/888933280866
77
5. HEALTH WORKFORCE
Doctors (overall number)
Doctors by age, sex and category
Medical graduates
International migration of doctors
Remuneration of doctors (general practitioners and specialists)
Nurses
Nursing graduates
International migration of nurses
Remuneration of nurses
The statistical data for Israel are supplied by and under the responsibility of the relevant
Israeli authorities. The use of such data by the OECD is without prejudice to the status of
the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the
terms of international law.
79
5. HEALTH WORKFORCE
The number of doctors per capita varies widely across
OECD countries. In 2013, Greece had the highest number
(with 6.3 doctors per 1 000 population), followed by Austria.
Turkey and Chile had the lowest number among OECD
countries, with slightly less than two doctors per
1 000 population. The OECD average was just over three
doctors per 1 000 population. The number of doctors per
capita is much lower in some partner countries. There was
less than one doctor per 1 000 population in Indonesia,
India and South Africa. In China, the number of doctors per
capita is still about half the OECD average, but it has grown
significantly since 2000 (Figure 5.1).
Since 2000, the number of doctors has grown in nearly all
OECD countries, both in absolute number and on a per capita basis. The growth rate was particularly rapid in some
countries which started with lower levels in 2000 (Turkey,
Korea and Mexico), but also in countries which already had
a large number such as Greece and Austria. In Greece, the
number of doctors per capita increased strongly between
2000 and 2008, but has stabilised since then. The number of
doctors has also increased strongly in Australia and the
United Kingdom (Figure 5.2), driven mainly by a strong rise
in the number of graduates from domestic medical education programmes (see indicator on medical graduates).
On the other hand, the number of physicians per capita
remained fairly stable between 2000 and 2013 in Estonia,
France, Israel and the Slovak Republic. In France, the number
of doctors increased by 10%, more or less at the same pace
as the population size.
The number of doctors has continued to grow in most
OECD countries following the 2008-09 recession, although
the growth slowed down in some countries such as Greece.
In the United Kingdom, the growth did not slow down
much; there were 15% more employed doctors in 2013 than
in 2008 (Figure 5.2).
Projecting the future supply and demand of doctors is
challenging given the high levels of uncertainty concerning
their retirement and migration patterns and their demand
(Ono, Lafortune and Schoenstein, 2013). In Australia, a
recent projection exercise based on a status quo policy
scenario estimated that there may be an over-supply of
doctors by 2017 before moving to an under-supply from
2020 to 2030. This projection exercise explored different
scenarios that may either mitigate or exacerbate these
imbalances. If the demand for doctors is growing at a
slightly slower pace than projected because of slower GDP
growth, the projected shortage in the next decade may
disappear and there may be a slight over-supply of doctors
by 2030. On the other hand, if there is a sharp reduction in
the number of immigrant doctors, a growing number of
domestic medical graduates would be required to close any
projected gap (Health Workforce Australia, 2014).
Many countries have anticipated the upcoming retirement
of a significant number of doctors by increasing their training efforts over the past decade to ensure that there would
80
be enough new doctors to replace those who will retire. In
some countries where domestic training efforts increased
(e.g., the United Kingdom and the Netherlands), there have
been recent concerns of possible surpluses of certain categories of doctors in the years ahead. This has led to recommendations to reduce slightly student intakes in medical
schools and post-graduate training programmes for certain
specialties (CfWI, 2012; ACMMP, 2014).
In many countries, current concerns focusses more specifically on shortages of general practitioners (see the indicator related to doctors by age, sex and category) or the
undersupply of doctors in rural and remote regions (see the
indicator on the geographic distribution of doctors in
Chapter 7).
The data for most countries refer to practising doctors,
defined as the number of doctors who are providing care
directly to patients. In many countries, the numbers
include interns and residents (doctors in training).
The numbers are based on head counts. The data for
Ireland are based on estimations. Several countries
also include doctors who are active in the health sector
even though they may not provide direct care to
patients, adding another 5-10% of doctors. Portugal
reports the number of physicians entitled to practice,
resulting in a larger over-estimation of the number
of practicing doctors of about 30%. Belgium and
Luxembourg set a minimum threshold of activities
for doctors to be considered to be practising, thereby
resulting in an under-estimation compared with other
countries which do not set such minimum thresholds.
Data for India are likely over-estimated as they are
based on medical registers which are not updated to
account for migration, retirement or death, nor do
they take into account doctors registered in multiple
states.
References
ACMMP (2014), The 2013 Recommendations for Medical Specialist Training, Utrecht.
CfWI – Centre for Workforce Intelligence (2012), A Strategic
Review of the Future Healthcare Workforce: Informing Medical
and Dental Student Intakes, London.
Health Workforce Australia (2014), Australia’s Future Health
Workforce – Doctors, Canberra.
Ono, T., G. Lafortune and M. Schoenstein (2013), “Health
Workforce Planning in OECD Countries: A Review of 26
Projection Models from 18 Countries”, OECD Health Working Papers, No. 62, OECD Publishing, Paris,
http://dx.doi.org/10.1787/5k44t787zcwb-en.
5. HEALTH WORKFORCE
5.1. Practising doctors per 1 000 population, 2000 and 2013 (or nearest year)
2013
2000
Per 1 000 population
7
6.3
6
5
5.0 4.9
4.3 4.3 4.3
4.1 4.0 4.0
3.9 3.8
4
3.7 3.6 3.6
3.4
3.4 3.4 3.3 3.3
3.3 3.3 3.2 3.2
3
3.0 3.0
2.8 2.8 2.8
2.7 2.6
2.6 2.6
2.3 2.2
2
2.2 2.2
1.9 1.8
1.8 1.8
1
1.7
0.8 0.7
0.3
Gr
ee
Ru Au ce¹
ss st
ia ria
n
F
N o e d.
Li r wa
th y
Po uan
r t ia
G ug
S w er m al ²
i t z an
er y
l
S w and
ed
en
It a
C z Sp l y
e c ai
h n
De Re
n m p.
Ic ar k
el
an
d
I ¹
A u sr a
Sl s el
ov tr a
a k li a
R
Ne Fr ep.¹
th an
er c e
la ¹
n
Es ds¹
t
O E oni
C a
Hu D 3
ng 4
a
La r y
t
Fi via
nl
a
Lu B el nd
xe giu
N e mb m
Un w o
i t e Z e ur g
d al
K i an
ng d
d
Ir e o m
Un Slo l a nd
i te ve
d ni a
St
Ca ates
na
d
Ja a¹
pa
Po n
la
n
Ko d
M rea
ex
ic
Ch o
il e
²
Co Br a
lo z il
m
Tu bia
rk
So C ey¹
u t hi
h na
Af
ric
a
In In d
do i a
ne
si
a
0
1. Data include not only doctors providing direct care to patients, but also those working in the health sector as managers, educators, researchers, etc.
(adding another 5-10% of doctors).
2. Data refer to all doctors licensed to practice (resulting in a large over-estimation of the number of practising doctors in Portugal, of around 30%).
1 2 http://dx.doi.org/10.1787/888933280876
5.2. Evolution in the number of doctors, selected OECD countries, 2000 to 2013 (or nearest year)
Non-European countries
Australia
Japan
European countries
Canada
United States
France
Spain
Index (2000 = 100)
170
Index (2000 = 100)
170
160
160
150
150
140
140
130
130
120
120
110
110
100
2000
2003
2006
2009
2012
100
2000
2003
Germany
United Kingdom
2006
2009
2012
1 2 http://dx.doi.org/10.1787/888933280876
81
5. HEALTH WORKFORCE
Beyond the overall number of doctors, the age and gender
composition of the medical workforce and the mix between
different categories of doctors also have important implications on the supply of medical services. The ageing of doctors in OECD countries has, for many years, raised concerns
that there may not be sufficient new recruits to replace
them, although there is evidence that the retirement of
doctors often only occurs gradually and that their retirement age is increasing (Pong, 2011). The rising share of
female doctors may affect the overall supply of medical
services, as women tend to work fewer hours than men,
although it appears that working time preferences are
becoming more similar among new generations of men
and women doctors. The growing imbalance in favour of
greater specialisation over general medicine raises concerns in many countries about access to primary care for all
the population.
In 2013, on average across OECD countries, one-third of all
doctors were over 55 years of age, up from one-fifth in 2000
(Figure 5.3). While these doctors might be expected to retire
over the next ten years, a growing number of doctors continue to practice after 65 years. In Israel and Italy, almost
half (49%) of all doctors were over 55 years of age in 2013,
but in Israel this high share may be due partly to the fact
that these numbers relate to all doctors licensed to practice
(and some of them may no longer be practicing). By contrast, only about 15% of doctors in the United Kingdom and
Korea were aged over 55 due to large numbers of new graduates entering medical practice over the past decade (see
the indicator on medical graduates).
Pension reforms, as well as a possible greater willingness
and capacity of many doctors to work longer, are likely to
have a significant impact on future replacement needs.
Several OECD countries have reformed their pension systems and increased the retirement age to take into account
longer life expectancy. While few studies have examined
the impact of these pension reforms specifically on doctors, it is likely that they will prolong working lives after age
65 in the coming years.
In 2013, 45% of doctors on average across OECD countries
were women, up from 38% in 2000 and 29% in 1990
(Figure 5.4). At least half of all doctors now are women in
10 countries. By contrast, only about one-in-five doctors in
Japan and Korea were women in 2013.
The balance between generalists and specialists has
changed over the past few decades, with the number of
specialists increasing much more rapidly, raising concerns
in many countries about shortages of general practitioners.
On average across OECD countries, generalists made up
only about 30% of all physicians in 2013; there were more
than two specialists for every generalist (Figure 5.5). Medical specialists greatly outnumber generalists in Central and
Eastern European countries and in Greece. Some countries
such as France, Canada and Australia have been able to
82
maintain a more equal balance between specialists and
generalists. In Ireland and Portugal, most generalists are
not really general practitioners, but rather non-specialist
doctors working in hospitals or other settings. In some
countries such as the United States, general internal medicine doctors are categorised as specialists although their
practice is often very similar to that of general practitioners, resulting in some underestimation of the capacity to
provide generalist care.
In most OECD countries, specialists earn more than general
practitioners, providing financial incentives for doctors to
specialise (see indicator on the remuneration of doctors). In
response to concerns about shortages of general practitioners, many countries have taken steps to improve the number of training places in general medicine. For example, in
France, about 50% of all post-graduate medical training
places are reserved for general medicine (DREES, 2014). In
Canada, the number of post-graduate training places in
family medicine more than doubled between 2000 and
2013, as part of a national effort to strengthen access to primary care for the whole population (CAPER, 2015). However, for these training policies to have lasting effects on
the composition of the medical workforce, they need to be
complemented by other measures to improve the employment and remuneration conditions of general practitioners
in order to attract and retain a sufficient number of new
doctors.
The definition of doctors is provided under the previous indicator. In some countries, the data are based
on all doctors licensed to practice, not only those
practising (e.g., Ireland and Portugal). Not all countries
are able to report all their physicians in the two broad
categories of specialists and generalists. This may be
due to the fact that specialty-specific data are not
available for doctors in training or for those working in
private practice.
References
CAPER – Canadian Post-M.D. Education Registry (2015),
Field of Post-M.D. Training by Faculty of Medicine Providing
Post-M.D. Training 2013-2014, database available at
www.caper.ca.
DREES (2014), “Les affectations des étudiants en médecine
à l’issue des épreuves classantes nationales en 2013”
[The allocations of medical students following national
ranking exams in 2013], Études et Résultats, No. 894.
Pong, R.W. (2011), Putting Up the Stethoscope for Good?, CIHI,
available at www.cihi.ca.
5. HEALTH WORKFORCE
5.3. Share of doctors aged 55 years and over, 2000 and 2013 (or nearest year)
2013
%
2000
49
49
50
33
33
nd
29
40
33
25
26
s
25
a in
30
34
34
33
28
27
27
26
26
26
41
40
38
37
36
34
45
44
43
42
21
20
15
13
10
ly
el
ra
Is
It a
a
ce
ni
an
to
Fr
Es
iu
lg
Be
Ge
rm
an
y
m
d
y
Ic
el
an
g
ar
ur
ng
bo
m
xe
Hu
p.
p.
Re
h
ec
Cz
Lu
es
Re
at
ak
St
d
ov
Sl
Un
i te
De
Sw
ed
en
k
n
ar
nm
pa
Ja
OE
la
er
it z
CD
da
il e
na
Sw
Ca
ria
Ch
ay
st
Au
No
rw
ia
d
ov
en
li a
an
ra
nl
st
Fi
Sl
th
Au
la
er
Sp
nd
d
nd
al
Ne
w
Ze
Ir e
Ne
d
Un
i te
an
a
la
re
Ko
Ki
ng
do
m
0
1 2 http://dx.doi.org/10.1787/888933280883
5.4. Share of female doctors, 2000 and 2013 (or nearest year)
2013
%
2000
80
74
60
40
38
39
40
39
40
41
41
41
41
42
43
45
45
45
46
46
47
48
50
53
55
55
56
57
62
57
22
20
20
35
34
32
38
50
an
d
Ch
B e il e
lg
i
A u um
s
S w tr a
i t z li a
er
la
nd
It a
l
Tu y
rk
e
Ca y
na
da
Gr
ee
ce
Is
ra
el
N e Ir e l
w an
Ze d
al
an
Fr d
an
Ge c e
rm
a
OE ny
CD
No 3 3
rw
a
Un
i te Au y
d s tr
Ki
ng i a
do
Sw m
e
De den
nm
ar
k
Ne Sp
th ain
er
la
n
Po ds
r tu
Cz
ec gal
h
R
H u e p.
ng
ar
P y
Sl ol a
ov nd
ak
Re
F i p.
nl
a
Sl nd
ov
en
Es ia
to
ni
a
es
at
Ic
ur
Un
i te
d
St
bo
m
xe
Lu
el
g
a
re
Ko
Ja
pa
n
0
1 2 http://dx.doi.org/10.1787/888933280883
5.5. Generalists and specialists as a share of all doctors, 2013 (or nearest year)
Generalists¹
%
Specialists²
Medical doctors not further defined
53
51
47
49
40
53
56
44
47
58
42
47
61
45
45
38
69
31
65
69
31
52
70
31
73
27
71
73
27
60
51
45
36
35
33
29
28
27
20
29
40
42
20
19
19
16
16
3
y
Sl
d
St
ar
i te
Un
Hu
ng
ce
ee
Gr
ov a te s
ak
Re
p
Po .
la
n
Sw d
ed
e
Ic n
e
C z lan
ec
d
h
R
D e e p.
nm
ar
k
Sp
a in
No
rw
Sl ay
ov
en
ia
It a
l
Es y
to
ni
a
N e Ko
re
w
Ze a
S al
Un w i t and
i t e z er
l
d
K i and
ng
do
O m
Lu ECD
xe
m 33
bo
ur
Tu g
rk
ey
Is
ra
Au el
st
r
M ia
ex
ic
Fi o
nl
an
Be d
lg
G e ium
Ne rma
th ny
er
la
Au nds
st
ra
li
Fr a
an
c
Ca e
na
da
C
Po hil e
r tu
ga
Ir e l 3
la
nd
5
12
20
0
62
77
23
58
75
22
63
80
85
15
61
86
14
54
88
61
40
58
60
12
80
52
100
1. Generalists include general practitioners/family doctors and other generalist (non-specialist) medical practitioners.
2. Specialists include paediatricians, obstetricians/gynaecologists, psychiatrists, medical, surgical and other specialists.
3. In Ireland and Portugal, most generalists are not GPs (“family doctors”), but rather non-specialist doctors working in hospitals or other settings. In
Portugal, there is some double-counting of doctors with more than one specialty.
1 2 http://dx.doi.org/10.1787/888933280883
83
5. HEALTH WORKFORCE
Medical graduates
The number of new medical graduates in a given year
reflects to a large extent government decisions taken a few
years earlier on the number of students admitted in medical schools (so-called numerus clausus policies). Since 2000,
most OECD countries have increased the number of students admitted to medical education in response to concerns about current or possible future shortages of doctors
(OECD, forthcoming), but large variations remain across
countries.
In 2013, there were on average about 12 new medical graduates per 100 000 population across OECD countries
(Figure 5.6). This proportion was highest in Ireland,
whereas Israel and Japan had the lowest number of new
medical graduates relative to their population. In Ireland,
the number of medical graduates increased strongly in
2013 due at least partly to the opening of new Graduate
Entry Programmes a few years earlier, allowing students
with an undergraduate degree in another discipline to
obtain a medical degree in four years only. In Israel, the low
number of domestic medical graduates is compensated by
the high number of foreign-trained doctors. About onethird of foreign-trained doctors in Israel are in fact people
who were born in the country but have pursued their study
abroad before coming back. The situation is quite different
in Japan, where there are very few foreign-trained doctors.
Since 2008, the Japanese government decided to increase
intakes in medical education in response to current and
projected shortages of doctors, which should lead to a
growing number of medical graduates in the coming years.
Following the expansion of the numerus clausus in most
countries over the past fifteen years, the number of medical graduates has increased, though at different paces
(Figure 5.7). In Australia, the number of medical graduates
increased by two-and-a-half times between 2000 and 2013.
Most of this growth reflects an increase in the number of
domestic students, but there has also been a growing number of international students in medical schools in Australia.
In the United Kingdom, the number of medical graduates
doubled between 2000 and 2013, reflecting an effort to
increase the domestic supply and rely less on foreigntrained doctors. Most of the increase in admission in medical schools occurred between 2000 and 2004. In 2013, the
number of graduates decreased slightly for the first time,
and so did the number of students admitted in medical
schools following a 2% reduction in medical school intakes
based on a projected oversupply of doctors in the coming
years (Department of Health, 2012).
In France, the number of medical graduates has increased
steadily since 2006 following a large increase in the numerus
clausus between 2000 and 2006. However, the number of
graduates should stabilize in the coming years, as admission quotas have remained fairly stable over the past few
years.
In the United States, the increase in admission intakes to
medical schools occurred a bit later than in several other
84
countries, mainly after 2005, so the number of medical
graduates has only started to go up recently. In addition to
the growing number of medical graduates from American
universities, there has also been a growing number of
American students who have gone to study abroad (notably
in Caribbean countries), with the intention of coming back
to complete their post-graduate training and practice in the
United States. This is expected to create additional pressures to increase the number of residency posts to allow
both domestic graduates and US foreign-trained graduates
to complete their post-graduate training.
In Nordic countries, there has been a fairly steady rise in
the number of medical graduates, with the number of graduates in Finland and Norway rising by about 50% between
2000 and 2013. Many Norwegian students also go to study
medicine abroad, notably in Germany, Poland and Hungary,
with the intention of coming back to practice in their
country.
There has also been a strong rise in the number of medical
graduates in the Czech Republic, Hungary and Poland. This
sharp increase can be explained partly by the growing
number of international students choosing these countries
to purse their medical studies. International students
accounted for about 30% of all medical graduates in the
Czech Republic in recent years.
This growing internationalisation of medical education,
combined with the international migration of already
trained doctors, makes it more difficult for national governments to set their own domestic numerus clausus policies, given that these policies may be affected by policies
and actions taken by actors in other countries (OECD, forthcoming).
Medical graduates are defined as the number of students who have graduated from medical schools in a
given year. The data for Austria and the United Kingdom
exclude foreign graduates, while other countries
include them. In Denmark, the data refer to the number of new doctors receiving an authorisation to practice, which may result in an over-estimation if these
include a certain number of foreign-trained doctors.
References
Department of Health (2012), “The Health and Education
National Strategic Exchange – Review of Medical and
Dental School Intakes in England”, UK Government.
OECD (forthcoming), Health Workforce Policies in OECD Countries: Right Jobs, Right Skills, Right Places (preliminary title),
OECD Publishing, Paris.
5. HEALTH WORKFORCE
Medical graduates
5.6. Medical graduates, 2013 (or nearest year)
25
20.3
20
19.7
15.5 15.3 15.1
15
14.4 14.3
13.9 13.6
13.2
12.8 12.7 12.7
12.2 11.9
11.5 11.4 11.4 11.2
10.9
10
10.3 10.2
9.9
9.9
9.7
9.0
8.9
8.6
8.2
7.5
7.3
6.5
6.0
5.1
5
Ic
el
a
Hu nd
Ne ng
th ar y
er
la
nd
Gr s
ee
c
Au e
s
Un P tr i a
i te or t
u
d
K i gal
n
Sl gdo
ov
ak m
C z Re
e c p.
h
Re
F i p.
nl
Ge and
rm
a
Sl ny
ov
en
OE ia
CD
Be 3 3
lg
iu
No m
rw
ay
It a
Es ly
to
n
Sw ia
ed
en
Sp
a in
M
ex
ic
Po o
Sw la
i t z nd
er
la
nd
Ne Fr a
n
w
Ze ce
al
an
d
Ch
il e
Ko
re
Un C an a
i te ad
d
a
St
at
es
Tu
rk
ey
Ja
pa
n
Is
ra
el
k¹
li a
ar
ra
st
Au
nm
De
Ir e
la
nd
0
1. In Denmark, the number refers to new doctors receiving an authorisation to practice, which may result in an over-estimation if these include
foreign-trained doctors.
1 2 http://dx.doi.org/10.1787/888933280892
5.7. Evolution in the number of medical graduates, selected OECD countries, 2000 to 2013 (or nearest year)
Non-European countries
Australia
Japan
European countries
Canada
United States
France
Netherlands
Index (2000 = 100)
250
Index (2000 = 100)
250
200
200
150
150
100
100
50
2000
2003
2006
Nordic countries
Finland
Sweden
2009
2012
50
2000
Norway
Index (2000 = 100)
250
200
200
150
150
100
100
2003
2006
2009
2003
2006
2009
Central and Eastern European countries
Czech Republic
Poland
Index (2000 = 100)
250
50
2000
Germany
United Kingdom
2012
50
2000
2003
2006
2012
Hungary
Slovak Republic
2009
2012
1 2 http://dx.doi.org/10.1787/888933280892
85
5. HEALTH WORKFORCE
The international migration of doctors and other health
workers is not a new phenomenon, but has drawn a lot of
attention in recent years because of concerns that it might
exacerbate shortages of skilled health workers in certain
countries, particularly in some developing countries that
are already suffering from critical workforce shortages. The
Global Code of Practice on the International Recruitment of
Health Personnel, adopted by the World Health Assembly
in May 2010, was designed to respond to these concerns. It
provides an instrument for countries to promote a more
ethical recruitment of health personnel, encouraging countries to achieve greater “self-sufficiency” in the training of
health workers, while recognising the basic human right of
every person to migrate.
There are significant differences across OECD countries in
the proportion of doctors trained abroad. In 2013, the share
of foreign-trained doctors ranged from less than 3% in Turkey,
Poland, Estonia, the Netherlands and the Czech Republic to
more than 40% in Israel and New Zealand (Figure 5.8). The
very high proportion of foreign-trained doctors in Israel
reflects not only the importance of immigration in this
country, but also that a large number of new licenses are
issued to people born in Israel but trained abroad (one-third
in 2013). Norway, Ireland and Australia also have a high
share of foreign-trained doctors, although in Norway
roughly half of foreign-trained doctors are people who were
born in the country but went to pursue their medical studies in another country. The share of foreign-trained doctors
in the United Kingdom, Switzerland, the United States,
Sweden and Canada varies between 23% and 30%.
Since 2000, the number and share of foreign-trained doctors has increased in many OECD countries (Figure 5.9),
contributing to the overall rise in the number and density
of doctors. In the United States and the United Kingdom,
the share has remained relatively stable over time, but the
absolute number of doctors trained abroad has continued
to increase more or less at the same pace as the number of
domestically-trained doctors (OECD, forthcoming). Sweden
has experienced a strong rise in the number and share of
foreign-trained doctors, with most of these foreign-trained
doctors coming from Germany, Poland and Iraq. The number and share of foreign-trained doctors has also increased
in France and Germany, though at a slower pace. In France,
the rise is partly due to a fuller recognition of the qualifications of foreign-trained doctors who were already working
in the country, as well as the inflow of doctors from new EU
member states, notably Romania.
In absolute numbers, the United States has by far the highest number of foreign-trained doctors, with more than
200 000 doctors trained abroad in 2013. Following the
United States is the United Kingdom with more than
48 000 foreign-trained doctors in 2014. The composition of
migration flows by country of origin depends on several
factors, including: i) the importance of migratory ties;
ii) languag e; and iii) recognition of qualifications.
Figure 5.10 provides an illustration of the distribution of the
countries of training for the two main OECD receiving
countries, the United States and the United Kingdom.
86
Nearly 50% of foreign-trained doctors in the United States
come from Asian countries, with doctors coming from
India representing by far the largest number, followed by
the Philippines and Pakistan. More than 10% of doctors
were trained in the Caribbean Islands, but in many cases
these were American students who went to study abroad
and then came back to the United States to complete their
post-graduate training and practice. Most foreign-trained
doctors in the United Kingdom also came from Asian countries, with India also leading by a wide margin, although a
growing number of foreign-trained doctors in the United
Kingdom come from other EU countries.
Even if smaller countries in Africa, Asia or Central and Eastern
Europe lose a small number of doctors in absolute terms,
this may nonetheless have a large impact on their health
systems. There is growing recognition that OECD countries
should avoid actively recruiting from countries that are suffering from acute shortages of doctors.
The data relate to foreign-trained doctors working in
OECD countries measured in terms of total stocks.
The OECD health database also includes data on the
annual flows for most of the countries shown here, as
well as by country of origin. The data sources in most
countries are professional registries or other administrative sources.
The main comparability limitation relates to differences in the activity status of doctors. Some registries
are regularly updated, making it possible to distinguish doctors who are still actively working in health
systems, while other sources include all doctors
licensed to practice, regardless of whether they are
still active or not. The latter will tend to over-estimate
not only the number of foreign-trained doctors, but
also the total number of doctors (including the
domestically-trained), making the impact on the
share unclear. The data source in some countries
includes interns and residents, while these physicians
in training are not included in other countries. Because
foreign-trained doctors are often over-represented in the
categories of interns and residents, this may result in an
under-estimation of the share of foreign-trained doctors
in countries where they are not included (e.g., France,
Hungary, Poland and Switzerland).
The data for Germany and Spain is based on nationality (or place of birth in the case of Spain), not on the
place of training.
References
Chapter on “Changing patterns in the international migration of doctors and nurses”, OECD Publishing, Paris.
5. HEALTH WORKFORCE
5.8. Share of foreign-trained doctors in OECD countries, 2013 (or nearest year)
%
60
58.5
50
43.5
40
35.8
34.2
30.5
30
28.7
27.0
25.0
24.3
23.5
19.9
20
17.3
15.2
14.4
10.7
9.4
10
9.2
8.8
7.6
5.6
4.4
3.0
2.7
2.6
2.4
1.8
a in
¹
an
Ge c e
rm
an
y
Hu ¹
ng
ar
y
De
nm
ar
k
Au
st
Sl
ov r i a
ak
Re
Cz
p.
ec
h
N e Rep
.
th
er
la
nd
s
Es
to
ni
a
Po
la
nd
Tu
rk
ey
m
0.2
Fr
Sp
iu
lg
Be
en
ia
il e
Sl
ov
d
26
Ch
CD
OE
Fi
nl
an
da
en
na
Ca
Sw
ed
at
es
nd
Un
i te
d
St
m
la
do
er
Sw
ng
Ki
d
Un
i te
Ne
it z
li a
nd
ra
st
Au
Ir e
la
ay
d
rw
No
an
al
w
Ze
Is
ra
el
0
1. In Germany and Spain, the data is based on nationality (or place of birth in Spain), not on the place of training.
1 2 http://dx.doi.org/10.1787/888933280906
5.9. Evolution in the share of foreign-trained doctors, selected OECD countries, 2000 to 2013 (or nearest year)
Canada
%
50
New Zealand
United States
40
40
30
30
20
20
10
10
0
2000
2003
2006
2009
2012
Sweden
%
50
0
2000
France
2003
2006
Germany
2009
2012
1 2 http://dx.doi.org/10.1787/888933280906
5.10. Main countries of training of foreign-trained doctors, United States and United Kingdom
United States, 2013
United Kingdom, 2014
Other, 16%
Other, 16%
Africa, 6%
India, 22%
Canada, 4%
Mexico, 5%
Asia 48%
Caribbean Isl., 13%
India, 34%
Africa, 8%
Philippines, 6%
Pakistan, 5%
China, 3%
Other EU countries, 18%
Other Asia, 12%
Pakistan, 11%
EU countries, 11%
Ireland, 4%
Other Asia, 9%
1 2 http://dx.doi.org/10.1787/888933280906
87
5. HEALTH WORKFORCE
The remuneration level for different categories of doctors
has an impact on the financial attractiveness of different
medical specialties. In many countries, governments influence the level and structure of physician remuneration by
being one of the main employers of physicians or purchaser of their services, or by regulating their fees.
OECD data on physician remuneration distinguishes
between salaried and self-employed physicians, although
in some countries this distinction is increasingly blurred,
as some salaried physicians are allowed to have a private
practice and some self-employed doctors may receive part
of their remuneration through salaries. A distinction is also
made between general practitioners and all other medical
specialists combined, though there may be wide differences in the income of different medical specialties.
As expected, the remuneration of doctors (both generalists
and specialists) is much higher than that of the average
worker in all OECD countries (Figure 5.11). Self-employed
general practitioners in Australia earned about two times
the average wage in 2013 (although this is an underestimation as it includes the remuneration of physicians in
training), whereas in Austria, Canada, Denmark, the
Netherlands, Luxembourg and the United Kingdom, selfemployed GPs earned about three times the average wage
in the country.
In most countries, GPs earn less than specialists, and in
many cases much less. In Canada and the Netherlands,
self-employed specialists earned about 4.5 times the average wage in 2013, in Germany, it was over five times, while
in Belgium and Luxembourg, they earned more than
six times the average wage (although in Belgium their
remuneration include practice expenses, thereby resulting
in an over-estimation). In France, self-employed specialists
earned almost four times the average wage, compared with
just over two times for salaried specialists and selfemployed GPs. The income gap between GPs and specialists is particularly large in Belgium and the Netherlands.
tion. This has been the case in countries such as Estonia,
France, Ireland, Italy and Slovenia, where doctors saw their
remuneration decrease in nominal terms in certain years
after the crisis. However, in more recent years, the remuneration of doctors and other health workers have started
to rise again (OECD, forthcoming).
The remuneration of doctors refers to average gross
annual income, including social security contributions and income taxes payable by the employee. It
should normally exclude practice expenses for selfemployed doctors.
A number of data limitations contribute to an underestimation of remuneration levels in some countries:
1) payments for overtime work, bonuses, other supplementary income or social security contributions
are excluded in some countries (Austria, Ireland for
salaried specialists and Italy); 2) incomes from private
practices for salaried doctors are not included in some
countries (e.g. Czech Republic, Hungary, Iceland, Ireland
and Slovenia); 3) informal payments, which may be
common in certain countries (e.g. Greece and Hungary),
are not included; 4) data relate only to public sector
employees who tend to earn less than those working in the private sector in Chile, Denmark, Greece,
Hungary, Iceland, Ireland, Norway, the Slovak Republic
and the United Kingdom; and 5) physicians in training
are included in Australia, the Czech Republic and the
United Kingdom for specialists.
The data for some countries include part-time workers,
while in other countries the data refer only to doctors
working full time.
In Belgium, the data for self-employed doctors include
practice expenses, resulting in an over-estimation.
In many OECD countries, the income gap between general
practitioners and specialists has continued to widen over
the past decade, reducing the financial attractiveness of
general practice (Figure 5.12). Since 2005, the remuneration
of specialists has risen faster than that of general practitioners in Canada, Finland, France, Hungary, Iceland, Israel,
Luxembourg and Mexico. On the other hand, in Austria,
Belgium and the Netherlands, the gap has narrowed
slightly, as the income of GPs grew faster than that of specialists.
Reference
In many OECD countries, the economic crisis which started
in 2008-09 had a significant impact on the remuneration of
doctors and other health workers. Several European countries hard hit by the recession either froze or cut down, at
least temporarily, the wages or fees of doctors in efforts to
reduce cost while protecting access to care for the popula-
Chapter on “Trends in health labour markets following
the economic crisis and current policy priorities to
address health workforce issues”, OECD Publishing,
Paris.
88
The income of doctors is compared to the average
wage of full-time employees in all sectors in the
country. The source for the average wage of workers
in the economy is the OECD Labour Force Statistics
Database.
5. HEALTH WORKFORCE
5.11. Remuneration of doctors, ratio to average wage, 2013 (or nearest year)
Specialists
General practitioners (GPs)
Salaried
Self-employed
Salaried
Australia¹
Austria
3.9
4.1
Self-employed
1.8
2.7
Belgium 2
6.1
Canada
Czech Rep.¹
4.6
2.2
2.5
2.6
2.2
3.7
5.3
2.7
1.5
1.8
2.4
Germany
2.3
2.0
3.8
2.5
4.2
3.7
2.9
4.6
1.7
1.6
n.a.
2.3
2.4
Spain
2.4
United Kingdom 3
6
4
Ratio to average wage in each country
2
4.0
n.a.
Greece
Hungary
Ireland
Israel
Italy
Luxembourg
Mexico
Netherlands
Norway
Poland
Slovak Rep.
Slovenia
3.7
6.2
2.9
n.a.
Denmark
Estonia
Finland
France
2.1
3.9
2.3
1.5
2.5
2.3
n.a.
4.6
2.8
2.9
1.7
2.8
n.a.
2.1
2.2
2.3
2.0
0
1.8
0
3.2
2
4
6
1. Physicians in training included (resulting in an underestimation).
2. Practice expenses included (resulting in an over-estimation).
3. Specialists in training included (resulting in an underestimation).
1 2 http://dx.doi.org/10.1787/888933280919
5.12. Growth in the remuneration of GPs and specialists, 2005-2013 (or nearest year)
GPs
Specialists
Average annual growth rate (%, in nominal terms)
7
6.3
6.2
6
5.5
5
4.8
4.8
4.6
4.1
4
4.1
4.2
3.7
3.4
2.9
3
3.0
2.9
2.8
2.4
2.9
2.6
2.3
2
1.5
1.2
1
0.3
s1
nd
ic
Ne
th
er
la
ex
M
ur
bo
m
xe
Lu
o
g1
el
ra
Is
Ic
el
an
d
y
Hu
ng
ar
ce
Fr
an
d
an
nl
Fi
Ca
na
da
m
iu
lg
Be
Au
st
ria
0
1. The growth rate for the Netherlands and for Luxembourg is for self-employed GPs and specialists.
1 2 http://dx.doi.org/10.1787/888933280919
89
5. HEALTH WORKFORCE
Nurses
Nurses greatly outnumber physicians in most OECD countries. Nurses play a critical role in providing health care not
only in traditional settings such as hospitals and long-term
care institutions but increasingly in primary care (especially in offering care to the chronically ill) and in home
care settings.
There are concerns in many countries about current and
possible future shortages of nurses, given that the demand
for nurses is expected to rise in a context of population ageing and the retirement of the current “baby-boom” generation of nurses. These concerns have prompted actions in
many countries to increase the training of new nurses (see
the indicator on nursing graduates), combined with efforts
to increase the retention rate of nurses in the profession.
The latter has increased in recent years in many countries
either because of the impact of the economic crisis that
have prompted more nurses to stay or come back in the
profession, or following deliberate efforts to improve their
working conditions (OECD, forthcoming).
On average across OECD countries, there were around
nine nurses per 1 000 population in 2013, up from less than
eight nurses in 2000, so the number of nurses has gone up
both in absolute terms and on a per capita basis (Figure 5.13).
In 2013, the number of nurses per capita was highest in
Switzerland, Norway, Denmark, Iceland and Finland, with
more than 14 nurses per 1 000 population. The number of
nurses per capita in OECD countries was lowest in Turkey
(with less than 2 nurses per 1 000 population), and Mexico
and Greece (with between 2 and 4 nurses per 1 000 population). With regards to partner countries, the number of
nurses per capita was generally low compared with the
OECD average. In 2013, Colombia, Indonesia, South Africa,
India and Brazil had fewer than 1.5 nurse per 1 000 population, although numbers have been growing quite rapidly in
Brazil in recent years.
The number of nurses per capita increased in almost all
OECD countries since 2000. This was the case in countries
that already had a high density of nurses in 2000 such as
Switzerland, Norway and Denmark, but also in Korea,
Portugal and France which used to have a relatively low
density of nurses but have converged towards the OECD
average (in the case of Korea and Portugal) or have now
moved beyond the OECD average (in the case of France).
The number of nurses per capita declined between 2000
and 2013 in Israel, as the size of the population grew more
rapidly than the number of nurses. It also declined in the
Slovak Republic, in both absolute numbers and on a per
capita basis.
In 2013, there were about three nurses per doctor on average across OECD countries, with about half of the countries
reporting between two to four nurses per doctor (Figure 5.14).
The nurse-to-doctor ratio was highest in Finland, Japan,
90
Ireland and Denmark (with at least 4.5 nurses per doctor). It
was lowest in Greece (with only about half a nurse per doctor) and in Turkey and Mexico (with only about one nurse
per doctor).
In response to shortages of doctors and to ensure proper
access to care, some countries have developed more
advanced roles for nurses. Evaluations of nurse practitioners from the United States, Canada, and the United Kingdom show that advanced practice nurses can improve
access to services and reduce waiting times, while delivering the same quality of care as doctors for a range of
patients, including those with minor illnesses and those
requiring routine follow-up. Existing evaluations find a
high patient satisfaction rate, while the impact on cost is
either cost-reducing or cost-neutral. The implementation
of new advanced practice nursing roles may require
changes to legislation and regulation to remove any barrier
to extensions in their scope of practice (Delamaire and
Lafortune, 2010).
The number of nurses includes those employed in
public and private settings providing services directly
to patients (“practising”) and in some cases also those
working as managers, educators or researchers.
In those countries where there are different levels of
nurses, the data include both “professional nurses”
who have a higher level of education and perform
higher level tasks and “associate professional nurses”
who have a lower level of education but are nonetheless recognised and registered as nurses. Midwives, as
well as nursing aids who are not recognised as nurses,
should normally be excluded. However, about half of
OECD countries include midwives because they are
considered as specialist nurses.
Austria reports only nurses working in hospital,
resulting in an under-estimation.
References
Delamaire, M.-L. and G. Lafortune (2010), “Nurses in
Advanced Roles: A Description and Evaluation of Experiences in 12 Developed Countries”, OECD Health Working
Paper, No. 54, OECD Publishing, Paris,
http://dx.doi.org/10.1787/5kmbrcfms5g7-en.
OECD (forthcoming), Health Workforce Policies in OECD
Countries: Right Jobs, Right Skills, Right Places (preliminary title), OECD Publishing, Paris.
5. HEALTH WORKFORCE
Nurses
5.13. Practising nurses per 1 000 population, 2000 and 2013 (or nearest year)
2000
2013
20
18
17.4
16.7
16.3
16
15.5
14.1
14
13.0
12
12.4
12.1 11.9
11.5
11.2 11.1
10.5
10
10.0
9.5 9.5 9.4
9.1
8.3 8.2
8.0 7.9 7.6
8
7.4
6.4 6.2
6.1 6.1
6
5.8 5.6
5.3 5.2 5.1
4.9
4.9
3.6
4
2.6
2.0 1.8
2
1.5 1.3 1.2
1.2 1.0
Sw
it z
er
l
No and
De r wa
nm y
Ic ar k
el
a
F i n d¹
n
Ge lan
rm d
N e Ir e a n y
th la
L u e r l n d¹
xe a n d
m s¹
b
A u our
st g
Un S r a li
i te we a
d de
St n
a
Ne J tes¹
w ap
Ze an
a
Be land
lg
i
C a um
na
Fr da
a
OE nce
CD ¹
Un
i te Slo 3 4
d ve
Ki n
C z ngd ia
e c om
h
R
Au ep
.
Li s tr i
Ru t h a ³
u
ss an
ia ia
n
Hu F e d
ng .
E s ar y
to
ni
a
Po It al
Sl r t y¹
ov ug
ak al
Re ¹
p
Ch .¹
Po il e ²
la
n
Ko d
re
Sp a
a
L a in
tv
Is i a
Gr r a e l
ee
M ce¹
ex
ic
Ch o
Tu ina
rk
e
Br y¹
a
zi
So
u t In d l
h ia
In A f r i
do c a
Co nes
lo ia
m
bi
a
0
1. Data include not only nurses providing direct care to patients, but also those working in the health sector as managers, educators, researchers, etc.
2. Data in Chile refer to all nurses who are licensed to practice (less than one-third are professional nurses with a university degree).
3. Austria reports only nurses employed in hospital.
1 2 http://dx.doi.org/10.1787/888933280929
5.14. Ratio of nurses to physicians, 2013 (or nearest year)
5
4.7
4.6
4.5
4.3 4.3 4.2
4
4.2 4.1
4.0
3.9
3.7 3.7
3.6
3.4
3.2 3.2
3.1
3
3.0 3.0
2.8 2.8 2.8
2.4 2.4
2.2
2
1
2.0
1.9
1.8 1.7 1.7
1.6 1.6 1.5
1.5 1.5
1.4 1.4 1.3
1.2 1.2
1.0
0.8
0.6 0.6
Fi
nl
an
Ja d
De pa
S w nm n
i t z ar
er k
la
L u Ic nd
xe e l a
m nd
bo
U n C ur g
i te an
d ada
St
a
Ir e t e s
la
N
Ne o nd
th r w
er ay
In l a n
N e do ds
w ne
Z e si a
a
Au lan
st d
B e r a li a
l
G e giu
rm m
Sl an
ov y
Un
en
i te
d C ia
K i hi
ng l e
do
Fr m
OE anc
C e
Sw D3 4
ed
e
Ko n
re
P
C z ol a
ec an
h d
Hu Rep
ng .
E s ar y
L i ton
th ia
ua
n
Sl I i a
ov nd
So ak ia
u t Re
h p
Af .
r
Au ic a
s
Ru L t r i a
ss at
ia via
n
Fe
d.
Po It al
r tu y
g
Is a l
ra
Sp el
a
Ch in
i
M na
ex
i
Tu co
rk
ey
C o Br a
lo z il
m
Gr b i a
ee
ce
0
Note: For those countries which have not provided data for practising nurses and/or practising physicians, the numbers relate to the same concept
(“professionally active” or “licensed to practice”) for both nurses and physicians, for the sake of consistency. The ratio for Portugal is understimated
because the number of doctors includes all licensed to practise.
1 2 http://dx.doi.org/10.1787/888933280929
91
5. HEALTH WORKFORCE
Nursing graduates
Many OECD countries have taken steps over the past
decade or so to increase the number of students admitted
in nursing schools in response to concerns about current or
possible future shortages of nurses (OECD, forthcoming).
Nonetheless, there are wide variations across countries in
training efforts of new nurses, which may be explained by
differences in the current number and age structure of the
nursing workforce (and hence the replacement needs), in
the capacity of nursing schools to take on more students,
as well as the future employment prospects of nurses.
In 2013, there were on average nearly 50 new nurse graduates per 100 000 population across OECD countries, up
from about 40 in 2003. Korea and Denmark had the highest
number of new nurse graduates relative to their population, with these two countries graduating more than
90 new nurses per 100 000 population in 2013. Mexico,
Luxembourg and the Czech Republic had the lowest number,
with less than 15 nurse graduates per 100 000 population
(Figure 5.15).
Over the past decade, the number of nursing graduates has
increased in all OECD countries, but at different rates
(Figure 5.16). In the United States, following a marked
decrease in student intakes during the 1990s, the number
of students admitted to nursing schools started to increase
strongly in the early 2000s, in response to concerns about a
potential significant shortage of nurses in the coming
years. Between 2003 and 2013, the number of nursing graduates increased by 70% (from 119 000 to over 200 000 per
year since 2010). Given this strong rise in admission and
graduation numbers, the most recent projections from the
US Department of Health and Human Services estimate
that there may be an over-supply of registered nurses and
licensed practical nurses in the United States by 2025, if
student admissions and nurse retention rates remain at
their current level (Health and Human Services, 2014).
In France, the number of graduates from nursing schools
also increased strongly over the past decade, by 50% overall
between 2003 and 2013. The numerus clausus set by the French
Ministry of Health to control entry in nursing education
programmes increased substantially since 1999, with the
number of places growing by nearly 70% (rising from
around 18 400 places in 1999 to over 31 000 in 2013). Most of
the growth occurred in the academic year of 2000/2001
when the annual quota was increased by 43%, driven by a
projected reduction in the supply of nurses resulting from
the reduction of working time to 35 hours per week, as well
as a more general concern about the anticipated retirement
of a large number of nurses.
92
In Germany, there has been a big increase in the number of
nurse graduates in recent years, related at least partly to a
greater offer of registered nurse training programmes in
several universities, in addition to the programmes traditionally offered in vocational nursing schools (CassierWoidasky, 2013).
The increase in the number of nursing graduates has been
much more modest in Japan and Norway. In Japan, the
number of nursing graduates rose by only 13% between
2003 and 2013, but this number has gone up further in 2014.
In Norway, this slow increase might be explained by a significant proportion of Norwegian students who choose to
go abroad to pursue nursing studies, and then come back to
their home country to work (see the indicator on international migration of nurses).
Nursing graduates refer to the number of students
who have obtained a recognised qualification
required to become a licensed or registered nurse.
They include graduates from both higher level and
lower level nursing programmes. They exclude graduates from Masters or PhD degrees in nursing to avoid
double-counting nurses acquiring further qualifications.
The data for Denmark and the United Kingdom are
based on the number of new nurses receiving an
authorisation to practice.
References
Cassier-Woidasky, A.-K. (2013), Nursing Education in Germany
– Challenges and Obstacles in Professionalisation, DHBW,
Stuttgart.
Health and Human Services (2014), “The Future of the
Nursing Workforce: National- and State-level Projections, 2012-2025”, US Department of Health and Human
Resources, Rockville, Maryland, United States.
Chapter on “Changes in education and training capacities for doctors and nurses: What’s happening with
numerus clausus policies?”, OECD Publishing, Paris.
5. HEALTH WORKFORCE
Nursing graduates
5.15. Nursing graduates, 2013 (or nearest year)
100
97
92
84
78
75
75
72
70
67
63
63
55
55
54
50
53
47
47
47
43
42
40
39
38
36
36
34
33
25
25
24
23
22
20
19
15
11
11
K
D e or e
n a
Sw ma
it z rk 1
er
la
Sl nd
ov
e
Au ni a
st
ra
No li a
rw
a
Ic y
el
an
Un F in d
i te lan
d
d
Sl S t a
ov te
ak s
R
G e e p.
rm
an
Au y
st
r
Ca ia
na
da
Ne
w Chil
Ze e
al
a
Be nd
lg
iu
OE m
CD
Un
3
i te J 4
d apa
Ki
ng n
do
Sw m
ed
en
Ne Fr a
th nc e
er
la
nd
Es s
to
n
Po i a
la
Hu nd
ng
ar
Ir e y
la
Po nd
r tu
g
Gr a l
ee
ce
It a
ly
Is
ra
e
Tu l
rk
ey
C z Sp a
ec in
Lu h R
xe e p
m
bo .
u
M rg
ex
ic
o
0
1. In Denmark, the number refers to new nurses receiving an authorisation to practice, which may result in an over-estimation if these include
foreign-trained nurses.
1 2 http://dx.doi.org/10.1787/888933280939
5.16. Evolution in the number of nursing graduates, selected OECD countries, 2003 to 2013 (or nearest year)
Denmark
Norway
Finland
Switzerland
France
Japan
Index (2003 = 100)
200
Index (2003 = 100)
200
150
150
100
100
50
2003
2006
2009
2012
50
2003
2006
Germany
United States
2009
2012
1 2 http://dx.doi.org/10.1787/888933280939
93
5. HEALTH WORKFORCE
In nearly all OECD countries, the proportion of foreigntrained nurses is much lower than that of foreign-trained
doctors. However, given that the overall number of nurses
is usually much greater than the number of doctors, the absolute number of foreign-trained nurses tends to be greater
than that of foreign-trained doctors (OECD, forthcoming).
OECD countries vary widely in the number and share of
foreign-trained nurses working in their health system
(Figure 5.17). While there are almost no foreign-trained
nurses working in countries such as Estonia, Turkey, Slovenia
and the Netherlands, these make up nearly 25% of the
nursing workforce in New Zealand, and between 10% and
20% of the nursing workforce in Switzerland, Australia, the
United Kingdom and Israel. The share of foreign-trained
nurses also exceeds 5% in Norway, Canada, the United States,
Germany and Italy. In absolute numbers, the United States
has by far the highest number of foreign-trained nurses
(with almost 250 000 nurses trained abroad in 2013), followed
by the United Kingdom (86 000 foreign-trained nurses in
2014) and Germany (70 000 foreign-trained nurses in 2010,
latest year available).
The number and share of foreign-trained nurses has
increased over the past ten years in several OECD countries, including New Zealand, Australia, Canada and Italy
(Figure 5.18). In Italy, the increase in the immigration of foreign-trained nurses since 2000 was primarily driven by the
arrival of a large number of nurses trained in Romania, who
are now accounting for nearly half of all foreign-trained
nurses (Figure 5.19). The movement of Romanian nurses to
Italy preceded Romania’s entry in the European Union in
2007, but has continued since then.
In the United Kingdom, in 2014, nearly half of all foreigntrained nurses came from Asian countries, mainly from the
Philippines (26%) and India (19%). But a growing number of
foreign-trained nurses also come from other EU countries,
such as Spain, Portugal, Romania and Poland. In 2014, more
than 5 600 nurses trained in Spain were working in the
U n i t e d K i n g d o m , a n d t h e re we re a l s o m o re t h a n
4 000 nurses trained in Portugal and Romania, and over
2 500 nurses trained in Poland.
In other EU countries such as France and Belgium, the percentage of nurses trained abroad remains low compared
with the United Kingdom, but their numbers have
increased rapidly. The number of foreign-trained nurses
more than doubled in France between 2000 and 2013. About
half of these foreign-trained nurses received their diploma
from Belgium (in many cases, these were French citizens
who went to study to Belgium before coming back), but
94
there has also been a strong growth in the number of
nurses trained in Portugal (with the number rising to over
1 100 in 2013, up from less than 100 in 2008) and in Spain
(rising to over 1 600 in 2013, up from 1 100 in 2008). In
Belgium, there has been a strong rise in the number of
nurses trained in Romania (exceeding 1 000 in 2014, up
from 150 in 2008), Portugal (with the number reaching 500
in 2014, up from 10 only in 2008) and to a lesser extent
Spain (with the number reaching 300 in 2014, up from
about 50 in 2008).
In 2014, more than 6 500 nurses trained in Portugal and
more than 9 200 nurses trained in Spain were working in
other EU countries, with a majority of them working in the
United Kingdom.
The data relate to foreign-trained nurses working in
OECD countries measured in terms of total stocks.
The OECD health database also includes data on the
annual flows for most of the countries shown here, as
well as by country of origin. The data sources in most
countries are professional registries or other administrative sources.
The main comparability limitation relates to differences in the activity status of nurses. Some registries
are regularly updated, making it possible to distinguish nurses who are still actively working in health
systems, while other sources include all nurses
licensed to practice, regardless of whether they are
still active or not. The latter will tend to over-estimate
the number of foreign-trained nurses, although it will
also over-estimate the total number of nurses (including the domestically-trained), so the impact on the
share is not clear.
The data for some regions in Spain is based on nationality or country of birth, not the place of training.
References
Chapter on “Changing patterns in the international
migration of doctors and nurses”, OECD Publishing,
Paris.
5. HEALTH WORKFORCE
5.17. Share of foreign-trained nurses in OECD countries, 2013 (or nearest year)
%
25
24.3
20
18.7
16.0
15
12.7
10.3
10
8.8
7.5
5.8
5.1
1.8
1.3
1.2
0.7
0.4
ia
s
th
Sl
ov
en
nd
y
la
er
ng
Hu
0.2
0.0
Ne
Un
i te
Un
ar
ar
k
d
nm
an
De
il e
Fi
nl
Ch
m
a in
¹
Sp
iu
en
lg
ed
Sw
Be
ce
l
an
Fr
ga
ly
r tu
It a
Po
Ge
rm
an
y
23
es
at
CD
OE
St
i te
d
Ca
na
da
ay
el
ra
rw
No
m
do
ng
d
Ki
Is
li a
ra
st
Au
er
it z
Sw
Ne
w
Ze
al
la
an
d
nd
0
a
2.0
ni
2.1
y
2.6
to
2.7
ke
2.7
Es
3.0
Tu
r
5.9
6.0
5
1. Data for some regions in Spain relate to foreign nationality or country of birth, not the place of training.
1 2 http://dx.doi.org/10.1787/888933280940
5.18. Evolution in the share of foreign-trained nurses, selected OECD countries, 2000 to 2013 (or nearest year)
Canada
%
25
Australia
New Zealand
20
20
15
15
10
10
5
5
0
2000
2003
2006
2009
2012
Belgium
%
25
0
2000
2003
France
2006
Italy
2009
2012
1 2 http://dx.doi.org/10.1787/888933280940
5.19. Main countries of training of foreign-trained nurses, United Kingdom and Italy
United Kingdom, 2014
Other Africa, 8%
Nigeria, 4%
South Africa, 4%
Italy, 2013
Other, 10%
Other, 15%
Spain, 6%
Portugal, 5%
India, 19%
EU countries,
29%
Latin America, 11%
Romania, 5%
Poland, 3%
Other EU, 10%
Philippines, 26%
Albania, 4%
Romania, 49%
Other EU, 11%
Poland, 11%
1 2 http://dx.doi.org/10.1787/888933280940
95
5. HEALTH WORKFORCE
The remuneration level of nurses is one of the factors
affecting their job satisfaction and the attractiveness of the
profession. It also has a direct impact on costs, as wages
represent one of the main spending items in health
systems.
The data presented in this section generally focus on the
remuneration of nurses working in hospitals, although the
data coverage differs for some countries (see the box on
“Definition and comparability”).
The data are presented in two ways. First, it is compared
with the average wage of all workers in each country, providing some indication of the relative financial attractiveness of nursing compared to other occupations. Second,
the remuneration level in each country is converted into a
common currency, the US dollar, and adjusted for purchasing power parity, to provide an indication of the relative
economic well-being of nurses compared with their counterparts in other countries.
In most countries, the remuneration of hospital nurses was
at least slightly above the average wage of all workers in
2013 (Figure 5.20). In Israel and Luxembourg, the income of
nurses was respectively 60% and 40% greater than the average wage. In Spain and the United States, it was about 30%
greater than the average wage, while in Greece, Australia
and Germany it was 20% higher. In other countries, the salary of hospital nurses is roughly equal to the average wage
in the economy. In the Slovak Republic, Hungary and
France, it is about 10% lower.
When converted to a common currency (and adjusted for
purchasing power parity), the remuneration of nurses was
at least four times higher in Luxembourg than in Hungary,
the Slovak Republic and Estonia (Figure 5.21). Nurses in the
United States also had relatively high earnings compared
with their counterparts in other countries, which explains,
at least partly, the ability of the United States to attract
many nurses from other countries.
In many countries, the remuneration of nurses has been
affected by the economic crisis in 2008, but to varying
degrees (Figure 5.22). Outside Europe, the growth in the
remuneration of nurses in countries such as the United
States, Australia and New Zealand slowed down temporarily following the economic crisis, while the crisis did not
appear to have any effect on the growth rate in nurse remuneration level in Mexico. In Europe, following the economic
crisis, the remuneration of nurses was cut down in some
countries, such as in Hungary and Italy, and has been frozen in Italy over the past few years. In Greece, the remuneration of nurses has been reduced on average by 20%
between 2009 and 2013.
Some Central and Eastern European countries have introduced a series of measures in recent years to increase the
retention of nurses and other health workers, including pay
raise despite tight budget constraints. In Hungary, a staged
increase of 20% in the salaries of nurses and doctors was
96
introduced in 2012, phased over a three-year period. In the
Czech Republic, nurses also benefitted from a pay increase
following protests of hospital workers in 2011 (although
their pay raise was lower than that for doctors), accompanied by some improvement in other aspects of their working
conditions (OECD, forthcoming).
The remuneration of nurses refers to average gross
annual income, including social security contributions and income taxes payable by the employee. It
should normally include all extra formal payments,
such as bonuses and payments for night shifts and
overtime. In most countries, the data relate specifically to nurses working in hospitals, although in Canada
the data also cover nurses working in other settings.
In some federal states, such as Australia, Canada and
the United States, the level and structure of nurse
remuneration is determined at the sub-national level,
which may contribute to variations across jurisdictions.
Data refer only to registered (“professional”) nurses in
Australia, Canada, Chile, Ireland and the United
States, resulting in an overestimation compared to
other countries where lower-level nurses (“associate
professional”) are also included. Data for New Zealand
relate to nurses employed by publically funded district
health boards, and includes registered nurses, health
assistants, nurse assistants, and enrolled nurses.
These latter three categories have a different and
significantly lower salary structure than registered
nurses.
The data relate to nurses working full time, with the
exception of Belgium where part-time nurses are also
included (resulting in an under-estimation). The data
for some countries do not include additional income
such as overtime payments and bonuses (e.g., Italy
and Slovenia). Informal payments, which in some
countries represent a significant part of total income,
are not reported.
The income of nurses is compared to the average
wage of full-time employees in all sectors in the country. The source for the average wage of workers in the
economy is the OECD Labour Force Statistics Database.
References
OECD Publishing, Paris.
5. HEALTH WORKFORCE
5.20. Remuneration of hospital nurses, ratio to average
wage, 2013 (or nearest year)
Israel
Luxembourg
United States¹
Ireland¹
Australia¹
Denmark
Belgium
Canada¹
Norway
Chile¹
New Zealand
Spain
Netherlands
Iceland
Israel
United Kingdom
Germany
OECD29
Japan
Finland
Italy
France
Turkey
Greece
Slovenia
Mexico
Poland
Czech Rep.
Estonia
Slovak Rep.
Hungary
1.6
Luxembourg
1.4
Spain
1.3
United States¹
1.3
Greece
1.2
Australia¹
1.2
Germany
1.2
Japan
1.1
Canada¹
1.1
OECD24
1.1
Belgium
1.1
Ireland¹
1.1
Czech Rep.
1.1
Denmark
1.1
Italy
1.1
United Kingdom
1.1
Poland
1.0
Estonia
1.0
Netherlands
1.0
Norway
1.0
Slovenia
1.0
Finland
1.0
Slovak Rep.
0.9
France
0.9
Hungary
0.9
0
0.5
5.21. Remuneration of hospital nurses, USD PPP, 2013
(or nearest year)
88
71
62
59
56
54
54
53
52
51
50
50
49
49
49
48
45
43
41
40
37
37
36
35
31
24
24
22
21
20
0
1.0
1.5
2.0
1. Data refer to registered (“professional”) nurses in the United States,
Australia, Canada and Ireland (resulting in an over-estimation).
1 2 http://dx.doi.org/10.1787/888933280951
40
80
USD PPP, thousands
1. Data refer to registered (“professional”) nurses in the United States,
Ireland, Australia, Canada and Chile (resulting in an over-estimation).
1 2 http://dx.doi.org/10.1787/888933280951
5.22. Evolution in the remuneration of hospital nurses, selected OECD countries, 2005-13 (or nearest year)
Belgium
Hungary
Index (2005 = 100)
150
France 1
Greece 2
Czech Rep.
Italy
Australia
New Zealand
140
130
130
120
120
110
110
100
100
90
90
2007
2009
2011
2013
1. Index for France, 2006 = 100.
2. Index for Greece, 2009 = 100.
Mexico
Index (2005 = 100)
150
140
80
2005
Canada
United States
80
2005
2007
2009
2011
2013
1 2 http://dx.doi.org/10.1787/888933280951
97
6. HEALTH CARE ACTIVITIES
Consultations with doctors
Medical technologies
Hospital beds
Hospital discharges
Average length of stay in hospitals
Cardiac procedures
Hip and knee replacement
Caesarean sections
Ambulatory surgery
99
Consultations with doctors can take place in doctors’
offices or clinics, in hospital outpatient departments or, in
some cases, in patients’ own homes. In many countries
(e.g., Denmark, Italy, Netherlands, Norway, Portugal, Slovak
Republic, Spain and United Kingdom), patients are required
or given incentives to consult a general practitioner (GP)
about any new episode of illness. The GP may then refer
them to a specialist, if indicated. In other countries,
patients may approach specialists directly.
In 2013, the number of doctor consultations per person
ranged from over 12 in Korea and Japan, to less than three
in Mexico, Finland and Sweden, as well as in South Africa
and Brazil (Figure 6.1). The OECD average was about
6.5 consultations per person per year, with most countries
reporting between five and eight consultations. Cultural
factors appear to play a role in variations across countries,
although certain health system characteristics may also be
important. Some countries where doctors receive fee-forservice tend to have above-average consultation rates (e.g.
Japan and Korea), while countries with mostly salaried doctors tend to have below-average rates (e.g. Mexico, Finland
and Sweden). However, there are examples of countries
such as Switzerland and the United States where doctors
are paid mainly by fee-for-service and where consultation
rates are below average, suggesting that other factors are
also important.
In Sweden and Finland, the low number of doctor consultations may also be explained partly by the fact that nurses
and other health professionals play an important role in
providing primary care to patients in health centres, lessening the need for consultations with doctors (Delamaire
and Lafortune, 2010).
The average number of doctor consultations per person has
increased in many OECD countries since 2000. This was
particularly the case in Korea, partly explained by the rapid
increase in the number of physicians during that period. In
some other countries, the number of consultations with
doctors per person fell. This was the case in Japan, the
Czech Republic and the Slovak Republic, although the number remains well above average in these three countries.
Information on the number of doctor consultations per
person can be used to estimate the annual numbers of consultations per doctor. This indicator should not be taken as
a measure of doctors’ productivity, since consultations can
vary in length and effectiveness, and because it excludes
the work doctors do on hospital inpatients, administration
and research. Keeping these reservations in mind, the estimated number of consultations per doctor is highest in
Korea and Japan, followed by Turkey and Hungary
(Figure 6.2). On the other hand, the estimated number of
consultations per doctor was lowest in Sweden and Finland,
where consultations with doctors in both primary care settings and hospitals tend to be concentrated more for
patients with more severe and complex cases.
100
The number and type of doctor consultations can vary
among different population groups in each country. An
OECD study found that the probability of a visit to the GP
tends to be equally distributed in most countries, but in
nearly all countries, higher income people are more likely
to see a specialist than those with low income, and also
more frequently (Devaux and de Looper, 2012).
Consultations with doctors refer to the number of
contacts with physicians, including both generalists
and specialists. There are variations across countries
in the coverage of these consultations, notably in outpatient departments of hospitals. The data come
mainly from administrative sources, although in
some countries (Ireland, Israel, Italy, Netherlands,
New Zealand, Spain, Switzerland and United Kingdom)
the data come from health interview surveys. Estimates from administrative sources tend to be higher
than those from surveys because of problems with
recall and non-response rates.
In Hungary, the figures include consultations for diagnostic exams such as CT and MRI scans (resulting in
an over-estimation). The figures for the Netherlands
exclude contacts for maternal and child care. The data
for Portugal exclude visits to private practitioners,
while those for the United Kingdom exclude consultations with specialists outside hospital outpatient
departments (resulting in an under-estimation). In
Germany, the data include only the number of cases of
physicians’ treatment according to reimbursement
regulations under the Social Health Insurance Scheme
(a case only counts the first contact over a threemonth period, even if the patient consults a doctor
more often, leading to an under-estimation). Telephone contacts are included in some countries (e.g.
Ireland, Spain and United Kingdom). In Turkey, a
majority of consultations with doctors occur in outpatient departments in hospitals.
References
Delamaire, M.-L. and G. Lafortune (2010), “Nurses in
Advanced Roles: A Description and Evaluation of Experiences in 12 Developed Countries”, OECD Health Working
Paper, No. 54, OECD Publishing, Paris,
http://dx.doi.org/10.1787/5kmbrcfms5g7-en.
Devaux, M. and M. de Looper (2012), “Income-related
Inequalities in Health Service Utilisation in 19 OECD
Countries”, OECD Health Working Papers, No. 58, OECD
Publishing, Paris,
http://dx.doi.org/10.1787/5k95xd6stnxt-en.
re
724
1923
1930
1943
1656
1794
1885
1888
1894
1622
1644
1363
1370
1294
975
921
862
1 000
1894
2 000
979
2092
1949
2.9
2.7
2.5
2.6
3.9
3.8
3.3
2.8
4.1
4.0
5.0
4.7
4.7
4.2
3.7
4
1298
2168
6
1470
2442
2277
2145
3076
2727
3 000
3010
6.8
6.6
6.5
6.2
6.2
6.2
6.4
6.4
6.0
7.1
6.8
6.5
7.4
7.4
7.1
8.1
8.2
7.7
8
2472
3244
3168
9.9
10.5
11.0
11.1
11.7
12
2540
4 000
3646
6 000
5633
Ko
re
Ja a
Hu p a n
C z ng
e ar
Sl ch R y
ov e
Ru a k p.
s s Re
i a p.
n
Ge F ed
rm .
a
Tu ny
L i r ke
th y
ua
C a ni a
n
Be ada
lg
iu
m
Sp
Au ai
st n
ra
Po li a
la
Au nd
st
ria
It a
O
Lu EC l y
xe D 3
m 3
bo
S l ur g
ov
e
E s ni a
to
n
Fr i a
an
c
N e Is e
th r a
er el
la
nd
La s
Un
tv
i t e Ic i a
d ela
Ki n
ng d
De dom
n
Co mar
lo k
m
No bia
rw
Un Por ay
i te tu
d ga
Sw St l
it z ates
er
la
N e Ir e n d
w lan
Ze d
al
an
d
Ch
S w il e
ed
M en
ex
ic
Br o
az
S o F in il
ut lan
h
Af d
ric
a
12.9
14
3407
5 000
4668
6732
7 000
Ja a
pa
Tu n
r
Hu ke y
So n
u t g ar
h
Sl A f y
ov r i c
ak a
Re
P o p.
la
C nd
C z ana
ec da
h
Co Rep
lo .
m
B e bi a
lg
Sl ium
ov
Ge eni
rm a
O an
Lu EC y
xe D 3
Ru m b 2
s s ou
ia rg
n
Au F ed
st .
ra
E s li a
to
n
L a ia
tv
i
Sp a
ain
Fr
an
ce
Un L Chil
i
t
ite h e¹
d ua
K n
N e in gd i a
t h om
er
la
nd
Is s
ra
Un Ic el
i te ela
d nd
St
at
es
It a
Ne B ly
w ra
Z e z il
al
a
Au nd
De s tr i
nm a
ar
M
S w ex k
it z ico
er
la
No nd
rw
Ir e a y
la
F i nd
nl
a
S w nd
ed
en
Ko
14.6
6.1. Number of doctor consultations per person, 2013 (or nearest year)
Annual consultations per person
16
10
2
0
1 2 http://dx.doi.org/10.1787/888933280967
6.2. Estimated number of consultations per doctor, 2013 (or nearest year)
Annual consultations per doctor
8 000
0
1. In Chile, data for the denominator include all doctors licensed to practice.
1 2 http://dx.doi.org/10.1787/888933280967
101
New medical technologies are improving diagnosis and
treatment, but they are also increasing health spending.
This section presents data on the availability and use of
two diagnostic technologies: computed tomography (CT)
scanners and magnetic resonance imaging (MRI) units.
CT and MRI exams help physicians diagnose a range of
conditions. U nlike conventional radiography and
CT scanning, MRI exams do not expose patients to ionising
radiation.
The availability of CT scanners and MRI units has
increased rapidly in most OECD countries over the past two
decades. Japan has, by far, the highest number of MRI and
CT scanners per capita, followed by the United States for MRI
units and by Australia for CT scanners (Figures 6.3 and 6.4).
Greece, Iceland, Italy, Korea and Switzerland also has
significantly more MRI and CT scanners per capita than the
OECD average. The number of MRI units and CT scanners
per population is the lowest in Mexico, Hungary, Israel and
the United Kingdom.
There is no general guideline or benchmark regarding the
ideal number of CT scanners or MRI units per population.
However, if there are too few units, this may lead to access
problems in terms of geographic proximity or waiting
times. If there are too many, this may result in an overuse
of these costly diagnostic procedures, with little if any
benefits for patients.
Data on the use of these diagnostic scanners are available
for a smaller group of countries, excluding Japan. Based on
this more limited country coverage, the number of MRI
exams per capita is highest in Turkey and the United
States, followed by France, Luxembourg and Belgium
(Figure 6.5). In the United States, the (absolute) number of
MRI exams more than doubled between 2000 and 2013. In
Turkey, it has grown even faster, by two-and-a-half times
between 2008 and 2013. In this country, there is growing
evidence that MRI exams are being systematically
prescribed for patients with various health problems,
resulting in overuse of these tests. The number of CT
exams per capita is highest in the United States, followed
by Luxembourg, France and Greece (Figure 6.6). However, in
Greece, the number of CT exams decreased by over 40%
between 2008 and 2012, while the number of MRI exams
also came down by about 30%.
There are large variations in the use of CT and MRI scanners not only across countries, but also within countries.
For example, in Belgium, there was almost a two-fold variation in MRI and CT exams between provinces with the
highest and lowest rates in 2010. In the United Kingdom
(England), the utilisation of both types of diagnostic exams
is generally much lower, but the variation across regions is
greater, with almost a four-fold difference between the
Primary Care Trusts that had the highest rates and lowest
rates of MRI and CT exams in 2010/11. In Canada, there has
been a strong rise in the use of both MRI and CT exams in
all parts of the country over the past decade, but there continues to be wide variations across provinces (OECD, 2014).
102
Clinical guidelines have been developed in several OECD
countries to promote a more rational use of MRI and CT
exams. In the United Kingdom, the National Institute for
Health and Clinical Excellence (NICE) has issued a number
of guidelines on the appropriate use of MRI and CT exams
(NICE, 2012). In the United States, a “Choosing Wisely” campaign was launched in 2012, led by professional medical
associations, to develop clear guidelines for doctors and
patients to reduce the use of unnecessary diagnostic tests
and procedures. The guidelines include, for instance,
avoiding imaging studies such as MRI, CT or X-rays for
patients with acute low back pain without specific indications (Choosing Wisely, 2015). A similar “Choosing Wisely”
campaign was launched in Canada in 2014, and work has
also started in several other OECD countries to produce
similar clear guidelines and recommendations to promote
a more proper use of diagnostic tests and other procedures.
It is still too early to tell to what extent these campaigns
will succeed in reducing the overuse of MRI and CT exams.
The data in most countries cover MRI units and CT
scanners installed both in hospitals and the ambulatory sector, but the coverage is more limited in some
countries. MRI units and CT scanners outside hospitals are not included in Belgium, Germany, Portugal
and Switzerland (for MRI units). For Australia and
Hungary, the number of MRI units and CT scanners
includes only those eligible for public reimbursement.
Similarly, MRI and CT exams performed outside
hospitals are not included in Austria, Germany, Ireland,
Portugal, Switzerland and the United Kingdom. Furthermore, MRI and CT exams for Ireland only cover public
hospitals. In Australia, the data only include exams
for private patients (in or out of hospitals), while in
Korea and the Netherlands, they only include publicly
financed exams.
References
Choosing Wisely (2015), Recommendations from the American
Society of Anesthesiologists, available at: www.choosingwisely.org/clinician-lists/american-society-anesthesiologistsimaging-studies-for-acute-low-back-pain/ .
NICE – National Institute for Health and Care Excellence (2012),
Published Diagnostics Guidance, available at
http://guidance.nice.org.uk/DT/Published.
OECD (2014), Geographic Variations in Health Care: What Do We
Know and What Can Be Done to Improve Health System
Performance?, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264216594-en.
6.3. MRI units, 2013 (or nearest year)
Japan
United States
Italy
Korea
Greece
Finland
Iceland
Switzerland¹
Austria
Denmark
Spain
OECD32
Australia²
Ireland
Luxembourg
Germany¹
Netherlands
Estonia
New Zealand
Belgium¹
Turkey
France
Canada
Slovenia
Czech Rep.
Slovak Rep.
Chile
Portugal¹
Poland
United Kingdom
Israel
Hungary²
Mexico
6.4. CT scanners, 2013 (or nearest year)
46.9
35.5
24.6
24.5
24.3
22.1
21.8
19.9
19.2
15.4
15.3
14.1
13.4
13.3
12.9
11.6
11.5
11.4
11.2
10.8
10.5
9.4
8.8
8.7
7.4
6.7
6.6
6.5
6.4
6.1
3.1
3.0
2.1
0
10
20
30
40
Japan
Australia²
United States
Iceland
Denmark
Korea
Switzerland
Greece
Italy
Austria
OECD32
Belgium¹
Luxembourg
Finland
Portugal¹
Estonia
Germany¹
Ireland
Spain
Poland
New Zealand
Slovak Rep.
Czech Rep.
Canada
France
Turkey
Chile
Slovenia
Netherlands
Israel
United Kingdom
Hungary²
Mexico
101.3
53.7
43.5
40.5
37.8
37.7
36.6
35.2
33.3
29.6
24.4
22.2
22.1
21.7
20.3
19.0
18.7
17.8
17.6
17.2
16.6
15.3
15.0
14.7
14.5
14.2
12.3
12.1
11.5
8.9
7.9
7.9
5.3
0
50
25
50
75
100
125
Per million population
Per million population
1. Equipment outside hospital not included.
2. Only equipment eligible for public reimbursement.
1 2 http://dx.doi.org/10.1787/888933280972
1. Equipment outside hospital not included.
2. Only equipment eligible for public reimbursement.
1 2 http://dx.doi.org/10.1787/888933280972
6.5. MRI exams, 2013 (or nearest year)
6.6. CT exams, 2013 (or nearest year)
Turkey
United States
France
Luxembourg
Belgium
Iceland
Spain
Greece
Switzerland¹
Denmark
Canada
OECD28
Estonia
Austria¹
Netherlands³
Slovak Rep.
Finland
Czech Rep.
United Kingdom¹
Slovenia
Hungary
Israel
Portugal¹
Australia²
Korea³
Poland
Germany¹
Ireland¹
Chile
119
107
91
81
77
75
70
68
61
60
53
52
51
50
50
46
45
45
40
36
35
31
30
28
26
23
22
16
13
0
25
50
75
100
125
1. Exams outside hospital not included (in Ireland, exams in private
hospital also not included).
2. Exams on public patients not included.
3. Exams privately-funded not included.
1 2 http://dx.doi.org/10.1787/888933280972
United States
Luxembourg
France
Greece
Belgium
Iceland
Korea³
Turkey
Denmark
Israel
Portugal¹
Austria¹
Canada
Slovak Rep.
OECD27
Australia²
Spain
Czech Rep.
Hungary
Switzerland¹
United Kingdom¹
Netherlands³
Chile
Germany¹
Ireland¹
Poland
Slovenia
Finland
240
202
193
181
179
173
145
145
142
141
141
134
132
123
120
110
96
96
92
90
76
71
71
62
59
55
55
32
0
25
50
75
100
125
1. Exams outside hospital not included (in Ireland, exams in private
hospital also not included).
2. Exams on public patients not included.
3. Exams privately-funded not included.
1 2 http://dx.doi.org/10.1787/888933280972
103
Hospital beds
The number of hospital beds provides a measure of the
resources available for delivering services to inpatients in
hospitals. This section presents data on the number of hospital beds overall and for different types of care (curative
care, psychiatric care, long-term care and other functions).
It also includes an indicator of bed occupancy rates focussing on curative care beds.
Among OECD countries, the number of hospital beds per
capita is highest in Japan and Korea, with 11 beds or more
per 1 000 population in 2013 (Figure 6.7). In Japan and Korea
hospitals have so-called “social admissions”, that is, a significant part of hospital beds are devoted to long-term care.
The number of hospital beds is also well above the OECD
average in the Russian Federation, and in Germany and
Austria. On the other hand, some of the large partner countries in Asia (India and Indonesia) have very few hospital
beds compared with the OECD average. This is also the case
for countries in Latin America (Colombia, Mexico, Chile and
Brazil).
The number of hospital beds per capita has decreased over
the past decade in most OECD countries, falling on average
from 5.5 per 1 000 population in 2000 to 4.8 in 2013. This
reduction has been driven partly by progress in medical
technology which has enabled a move to day surgery and a
reduced need for hospitalisation. In many European countries, the financial and economic crisis which started in
2008 also provided a further stimulus to reduce hospital
capacity as part of policies to reduce public spending on
health. Only in Korea and Turkey has the number of hospital
beds per capita grown since 2000.
More than two-thirds of hospital beds (69%) are allocated
for curative care on average across OECD countries
(Figure 6.8). The rest of the beds are allocated for psychiatric
care (14%), long-term care (13%) and other types of
care (4%). However, in some countries, the share of beds
allocated for psychiatric care and long-term care is much
greater than the average. In Korea, 35% of hospital beds are
allocated for long-term care. In Finland, this share is also
relatively high (27%) as local governments (municipalities)
use beds in health care centres (which are defined as
hospitals) for at least some of the needed long-term care in
institutions. In Belgium and Norway, about 30% of hospital
beds are devoted to psychiatric care.
104
In several countries, the reduction in the number of hospital
beds has been accompanied by an increase in their occupancy rates. The occupancy rate of curative care beds stood
at 77% on average across OECD countries in 2013, slightly
above the 2000 level (Figure 6.9). Israel and Ireland had the
highest rate of hospital bed occupancy at approximately
94%, followed by Norway and Canada at around 90%. In the
United Kingdom, Belgium and France, the bed occupancy
rate remained relatively stable during that period.
Hospital beds are defined as all beds that are regularly
maintained and staffed and are immediately available
for use. They include beds in general hospitals, mental
health hospitals, and other specialty hospitals. Beds
in residential long-term care facilities are excluded
(OECD, 2015).
Curative care beds are accommodating patients
where the principal intent is to do one or more of the
following: manage labour (obstetric), treat non-mental
illness or injury, and perform surgery, diagnostic or
therapeutic procedures.
Psychiatric care beds are accommodating patients
with mental health problems. They include beds in
psychiatric departments of general hospitals, and all
beds in mental health hospitals.
Long-term care beds are accommodating patients
requiring long-term care due to chronic impairments
and a reduced degree of independence in activities of
daily living. They include beds in long-term care
departments of general hospitals, beds for long-term
care in specialty hospitals, and beds for palliative care.
The occupancy rate for curative (acute) care beds is
calculated as the number of hospital bed-days related
to curative care divided by the number of available
curative care beds (multiplied by 365).
References
OECD (2015), OECD Health Statistics 2015, OECD Publishing,
Paris, http://dx.doi.org/10.1787/health-data-en.
d
i te
St
y
es
ke
at
Tu
r
ia
y
a
p.
en
ar
ni
g
ce
Re
ov
ak
d
ov
Sl
ng
l
p.
ur
to
bo
Es
m
ee
Re
ga
Long-term care beds
Hu
xe
h
r tu
Gr
ec
3.1
2.9
3.0
3.1
2.8
2.7
2.7
2.3
2.2
2.3
1.5
0.5
3.3
3.2
2.6
1.6
1.0
3.4
2.8
4.8
4.8
3.9
3.4
5.0
4.7
6.5
6.3
6.3
5.8
4.9
7.7
7.0
9.1
8.3
7.3
6.6
5.8
5.1
4.6
3.8
2.8
3
Sl
Lu
Cz
2000
Po
o
ce
ic
an
ex
a in
Psychiatric care beds
Fr
M
Sp
n
24
ly
pa
CD
It a
%
100
Ja
OE
m
y
il e
iu
an
ria
nd
Curative care beds
Ch
lg
rm
st
la
Ja
pa
n
s s Kor
ia ea
n
Ge F ed
rm .
a
Au ny
Li s tr
th ia
u
Hu a ni
ng a
ar
C z Pol y
ec an
h d
Re
F r p.
an
B
Sl el g c e
ov iu
ak m
R
L u L e p.
xe a t v
m ia
bo
E s ur g
to
F i ni a
nl
a
Gr n d
OE eec
Sw C e
it z D2
er 5
Sl l an
ov d
e
No ni a
Au r wa
st y
ra
li a
Po It al
r tu y
g
Ch al
Ic in a
el
an
Is d
De r a
nm e l
ar
Un
i te Sp k
d ain
St
a
N I te
Un ew r el a s
i te Ze nd
d ala
Ki n
ng d
d
C a om
na
Tu da
r
S w ke y
ed
So B en
ut r a
h z il
Af
ric
Ch a
M il e
C o ex i c
lo o
In m b
do i a
ne
si
In a
di
a
Ru
6
Be
Ge
Au
er
m
da
ay
do
na
ng
it z
Ki
Ca
%
100
Sw
i te
re
n
rm a
an
Au y
st
Hu r i a
ng
ar
Po y
C z lan
ec
d
h
Re
p.
Fr
an
Be ce
l
Sl giu
ov
m
Lu ak R
xe ep
m
bo .
u
Es rg
to
n
Fi ia
nl
an
Gr d
ee
OE ce
Sw CD
it z 3 3
er
la
Sl nd
ov
en
No ia
rw
Au ay
st
ra
li a
It a
Po l y
r tu
g
Ic al
el
an
d
Is
De r ael
nm
ar
Un S k
i te pa
d in
St
at
es
N e Ir e l
a
w
nd
Un
i te Zea
la
d
n
Ki
ng d
do
Ca m
na
d
Tu a
rk
Sw ey
ed
en
Ch
i
M le
ex
ic
o
Ge
Ko
pa
11.0
13.3
2000
Un
Un
nd
el
Ja
9
rw
la
ra
12
No
Ir e
Is
Hospital beds
6.7. Hospital beds per 1 000 population, 2000 and 2013 (or nearest year)
15
2013
0
1 2 http://dx.doi.org/10.1787/888933280981
6.8. Hospital beds by function of health care, 2013 (or nearest year)
Other hospital beds
80
60
40
20
0
Note: Countries ranked from highest to lowest total number of hospital beds per capita.
1 2 http://dx.doi.org/10.1787/888933280981
6.9. Occupancy rate of curative (acute) care beds, 2000 and 2013 (or nearest year)
2013
80
60
40
1 2 http://dx.doi.org/10.1787/888933280981
105
Hospital discharges
Hospital discharge rates measure the number of patients
who leave a hospital after staying at least one night.
Together with the average length of stay, they are important indicators of hospital activities. Hospital activities are
affected by a number of factors, including the capacity of
hospitals to treat patients, the ability of the primary care
sector to prevent avoidable hospital admissions, and the
availability of post-acute care settings to provide rehabilitative and long-term care services.
In 2013, hospital discharge rates were highest in Austria
and Germany, followed by Lithuania, the Russian Federation,
the Czech Republic and Hungary (Figure 6.10). They were
the lowest in Colombia, Mexico, South Africa, Brazil and
Canada. In general, those countries that have more hospital beds tend to have higher discharge rates. For example, the
number of hospital beds per capita in Austria and Germany is
more than two-times greater than in Canada and Spain,
and discharge rates are also more than two-times larger
(see indicator on “Hospital beds”).
Across OECD countries, the main conditions leading to hospitalisation in 2013 were circulatory diseases, pregnancy
and childbirth, injuries and other external causes, diseases
of the digestive system, cancers, and respiratory diseases.
Austria and Germany have the highest discharge rates for
both circulatory diseases and cancers, followed by Hungary
and Estonia for circulatory diseases (Figure 6.11), and
Greece and Hungary for cancers (Figure 6.12). While the
high rates of hospital discharges for circulatory diseases in
Hungary and Estonia are associated with lots of people
having heart and other circulatory diseases (see indicator
on “Mortality from cardiovascular diseases” in Chapter 3),
this is not the case for Germany and Austria. Similarly, cancer incidence is not higher in Austria, Germany or Greece
than in most other OECD countries (see indicator on “Cancer
incidence” in Chapter 3). In Austria, the high discharge rate
is associated with a high rate of hospital readmissions for
further investigation and treatment of cancer patients
(European Commission, 2008).
Trends in hospital discharge rates vary widely across OECD
countries. Since 2000, discharge rates have increased in
some countries where discharge rates were low in 2000 and
have increased rapidly since then (e.g. Korea and Turkey) as
well as in other countries such as Germany where it was
already above-average. In other countries (e.g. Belgium,
Czech Republic and Japan), they have remained relatively
stable, while in other countries (including Canada, Finland,
France, Italy and Spain), discharge rates fell between 2000
and 2013.
Trends in hospital discharges reflect the interaction of several
factors. Demand for hospitalisation may grow as populations’ age, given that older population groups account for a
disproportionately high percentage of hospital discharges.
However, population ageing alone may be a less important
factor in explaining trends in hospitalisation rates than
106
changes in medical technologies and clinical practices. The
diffusion of new medical interventions often gradually
extends to older population groups, as interventions
become safer and more effective for people at older ages.
But the diffusion of new medical technologies may also
involve a reduction in hospitalisation if it involves a shift
from procedures requiring overnight stays in hospitals to
same-day procedures. In the group of countries where discharge rates have decreased since 2000, there has been a
strong rise in the number of day surgeries (see indicator on
“Ambulatory surgery”).
Hospital discharge rates vary not only across countries, but
also within countries. In several OECD countries (e.g., Canada,
Finland, Germany, Italy, Portugal, Spain and the United
Kingdom), hospital medical admissions (excluding admissions for surgical interventions) vary by more than twotimes across different regions in the country (OECD, 2014).
Discharge is defined as the release of a patient who
has stayed at least one night in hospital. It includes
deaths in hospital following inpatient care. Same-day
discharges are usually excluded, with the exceptions
of Chile, the Slovak Republic, Turkey and the United
States which include some same-day separations.
Healthy babies born in hospitals are excluded from
hospital d ischarg e rates in several countries
(e.g. Australia, Austria, Canada, Chile, Estonia, Finland,
Greece, Ireland, Luxembourg, Mexico, Spain). These
comprise some 3-10% of all discharges. The data for
Canada also exclude unhealthy babies born in hospitals.
Data for some countries do not cover all hospitals. For
instance, data for Denmark, Ireland, Mexico, New
Zealand and the United Kingdom are restricted to public
or publicly-funded hospitals only. Data for Portugal
relate only to public hospitals on the mainland
(excluding the Islands of Azores and Madeira). Data
for Canada, Ireland and the Netherlands include only
acute care/short-stay hospitals. Data for France and
Japan refer to acute care hospitalisations.
References
European Commission (2008), Hospital Data Project Phase 2,
Final Report, European Commission, Luxembourg.
Know and What Can Be Done to Improve Health System Performance?, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264216594-en.
Hospital discharges
6.10. Hospital discharges, 2013 (or nearest year)
95
111
99
119
113
125
124
135
129
140
139
146
159
162
155
141
150
161
166
163
167
166
171
170
172
172
173
173
182
200
175
199
196
204
202
213
235
48
34
56
83
100
53
266
250
252
300
50
Au
s
Ge tr ia
rm ¹
L a
Ru i t h n y
ss uan
ia i
Cz n F a
ec ed
h .
Hu Rep
ng .
a
S l Gr e r y
ov e
ak ce
R ¹
S l e p.
ov ²
e
No ni a
r
F i way
n
Au lan
s t d¹
ra
li
L a¹
De a t v i
nm a
E s ar k
to
B e ni a
lg ¹
iu
S w Po m
i t z lan
er d
la
Fr nd
an
Sw ce
ed
e
Ko n
Tu rea
rk
ey
Is ²
ra
O
Ne EC el
w D
Ze 3 4
al
L u Ic a n d
xe e l a
m nd
bo
ur
g
Ch ¹
Un
i t e Ir e i n a
d l
Un K in a nd
i te gd ¹
d om
St
at
es
Ne
²
th It a
er l y
la
Po nd
r tu s
g
Ja al
pa
Sp n
a
Ch in¹
il e
Ca ¹ ²
na
d
So B a¹
ut r a
h z il
Af
M ric a
e
Co x ic
lo o¹
m
bi
a
0
1. Excludes discharges of healthy babies born in hospital (between 3-10% of all discharges).
2. Includes same-day discharges.
1 2 http://dx.doi.org/10.1787/888933280996
6.11. Hospital discharges for circulatory diseases, 2013
(or nearest year)
Germany
Austria
Hungary
Estonia
Slovak Rep.
Czech Rep.
Greece
Poland
Finland
Norway
Sweden
Slovenia
Denmark
Belgium
Italy
OECD34
France
Switzerland
United States
Luxembourg
Australia
Netherlands
Iceland
Japan
New Zealand
Portugal
Spain
Turkey
United Kingdom
Israel
Ireland
Korea
Canada
Chile
Mexico
36.9
35.8
35.6
30.1
30.0
27.7
27.1
26.6
26.3
23.8
23.6
20.1
20.1
19.8
19.6
19.3
19.2
18.2
18.2
17.8
16.5
16.4
14.4
13.8
13.7
13.3
12.9
12.7
12.2
11.8
11.3
11.3
10.3
7.2
2.4
0
10
20
30
40
1 2 http://dx.doi.org/10.1787/888933280996
6.12. Hospital discharges for cancers, 2013
(or nearest year)
Austria
Germany
Greece
Hungary
Japan
Estonia
Korea
Slovenia
Slovak Rep.
Finland
Norway
Czech Rep.
Switzerland
Denmark
OECD34
Luxembourg
Sweden
Poland
Iceland
Italy
Australia
France
Belgium
Netherlands
Portugal
Spain
United Kingdom
New Zealand
Ireland
Chile
Turkey
Israel
Canada
United States
Mexico
29.3
24.5
24.0
23.3
22.2
19.8
17.3
17.1
16.8
16.0
15.9
15.1
13.6
13.5
13.3
13.2
13.0
12.8
11.8
11.6
11.5
11.4
11.1
11.0
10.8
9.7
8.2
7.8
7.6
6.7
6.2
6.2
5.8
5.1
3.0
0
10
20
30
40
1 2 http://dx.doi.org/10.1787/888933280996
107
The average length of stay in hospitals (ALOS) is often
regarded as an indicator of efficiency. All other things being
equal, a shorter stay will reduce the cost per discharge and
shift care from inpatient to less expensive post-acute settings. However, shorter stays tend to be more service intensive and more costly per day. Too short a length of stay
could also cause adverse effects on health outcomes, or
reduce the comfort and recovery of the patient. If this leads
to a greater readmission rate, costs per episode of illness
may fall only slightly, or even rise.
In 2013, the average length of stay in hospitals for all causes
across OECD countries was about eight days (Figure 6.13).
Turkey and Mexico had the shortest stays, with about four
days (half the OECD average), whereas Japan and Korea had
the longest stays, with over 16 days (more than double the
OECD average). Across OECD countries, the average length
of stay has fallen from an average of almost 10 days in 2000
to 8 days in 2013. But there are a few exceptions to this general pattern, with the average length of stay increasing in
Korea, but also in Hungary and Luxembourg, where it is
now above the OECD average.
Focusing on average length of stay for specific diseases or
conditions can remove some of the effect of different case
mix and severity. Figure 6.14 shows that average length of
stay following a normal delivery was slightly less than
three days on average in 2013, down from more than threeand-a-half days in 2000. This ranged from less than
two days in Mexico, Turkey, the United Kingdom, Iceland,
Canada, New Zealand and the Netherlands, to five days or
more in the Slovak Republic and Hungary.
The average length of stay following acute myocardial
infarction was around seven days on average in 2013. It was
shortest in some of the Nordic countries (Denmark, Norway
and Sweden), Turkey and the Slovak Republic, at fewer than
five days, and highest in Korea and Germany, at more than
ten days (Figure 6.15).
Several factors can explain these cross-country variations.
Differences in the clinical need of the patient may obviously play a role, but these variations also likely reflect differences in clinical practices and payments systems. The
combination of an abundant supply of beds with the structure of hospital payments may provide hospitals with
incentives to keep patients longer. A growing number of
countries (France, Germany, Poland) have moved to prospective payment methods often based on diagnosisrelated groups (DRGs) to set payments based on the estimated cost of hospital care for different patient groups in
advance of service provision. These payment methods
have the advantage of encouraging providers to reduce the
108
cost of each episode of care. In Switzerland, the cantons
which moved from per diem payments to diagnosis-related
groups (DRG) based payments, have experienced a reduction in their hospital lengths of stay (OECD and WHO, 2011).
Most countries are seeking to reduce average length of stay
whilst maintaining or improving the quality of care. A
diverse set of policy options at clinical, service and system
level are available to achieve these twin aims. Strategic
reductions in hospital bed numbers alongside development
of community care services can be expected to shorten
average length of stay, such as seen in Denmark’s qualitydriven reforms of the hospital sector (OECD, 2013). Other
options include promoting the uptake of less invasive surgical procedures, changes in hospital payment methods,
the expansion of early discharge programmes which enable
patients to return to their home to receive follow-up care,
and support for hospitals to improve the co-ordination of
care across diagnostic and treatment pathways.
Average length of stay refers to the average number of
days that patients spend in hospital. It is generally
measured by dividing the total number of days stayed
by all inpatients during a year by the number of
admissions or discharges. Day cases are excluded. The
data cover all inpatient cases (including not only curative/acute care cases) for most countries, with the
exceptions of Canada, Japan and the Netherlands
where the data still refer to curative/acute care only
(resulting in an under-estimation).
Discharges and average length of stay of healthy
babies born in hospitals are excluded in several countries (e.g. Australia, Austria, Canada, Chile, Estonia,
Finland, Greece, Ireland, Luxembourg, Mexico, Spain),
resulting in a slight over-estimation (e.g., the inclusion of healthy newborns would reduce the ALOS by
0.5 day in Canada).
References
OECD (2013), OECD Reviews of Health Care Quality: Denmark
2013: Raising Standards, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264191136-en.
OECD and WHO (2011), OECD Reviews of Health Systems:
Switzerland 2011, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264120914-en.
6.13. Average length of stay in hospital, 2000 and 2013 (or nearest year)
2000
2013
Days
25
16.5
17.2
20
3.9
4.3
5
4.0
5.7
5.6
5.9
5.8
6.1
6.0
6.1
6.5
6.4
6.6
6.5
7.1
7.0
7.5
7.6
7.5
7.9
7.6
7.9
7.9
8.1
8.1
8.9
8.7
8.9
9.4
10.1
9.1
10
9.5
10.8
15
an
d
an
Hu c e
C z nga
ec r y
h
R
G e e p.
Lu rm
xe a n
m y
bo
P o ur g
Sw r tu
i t z gal
er
la
OE nd
CD
3
Au 4
st
Be ria
lg
iu
m
Ne
w It al
Ze y
al
a
C a nd
na
da
¹
Sp
a in
E
S st
Un lov oni a
i te ak
d Re
K i p.
ng
do
m
Po
la
n
Gr d
ee
ce
Is
ra
S
e
Ne lov l
th eni
er a
la
nd
s
Un No ¹
i te r wa
d
St y
at
e
Ir e s
la
n
Ic d
el
an
Sw d
ed
en
C
Au hil e
st
r
De a li a
nm
a
M rk
ex
ic
Tu o
rk
ey
Fr
a
Fi
nl
re
pa
Ja
Ko
n¹
0
1. Data refer to average length of stay for curative (acute) care (resulting in an under-estimation).
1 2 http://dx.doi.org/10.1787/888933281004
6.14. Average length of stay for normal delivery, 2013
(or nearest year)
Slovak Rep.
Hungary
Czech Rep.
France
Belgium
Luxembourg
Poland
Austria
Greece
Slovenia
Switzerland
Italy
Norway
Finland
OECD32
Germany
Israel
Chile
Australia
Denmark
Portugal
Korea
Spain
Sweden
Ireland
United States
Netherlands
New Zealand
Canada
Iceland
Turkey
United Kingdom
Mexico
Korea
Germany
Estonia
Chile
Finland
Austria
Hungary
Portugal
Italy
New Zealand
Slovenia
Spain
Switzerland
Ireland
United Kingdom
Belgium
Luxembourg
OECD33
Mexico
Poland
Czech Rep.
Greece
France
Israel
Netherlands
Canada
Iceland
Australia
United States
Slovak Rep.
Turkey
Sweden
Norway
Denmark
5.1
5.0
4.3
4.1
4.0
4.0
3.9
3.8
3.6
3.6
3.6
3.4
3.1
3.0
2.9
2.9
2.9
2.8
2.7
2.7
2.7
2.5
2.4
2.3
2.0
2.0
1.9
1.7
1.6
1.6
1.5
1.5
1.3
0
2
4
6.15. Average length of stay for acute myocardial
infarction (AMI), 2013 (or nearest year)
6
Days
1 2 http://dx.doi.org/10.1787/888933281004
13.1
10.3
9.1
8.9
8.3
8.1
7.9
7.9
7.8
7.8
7.4
7.3
7.3
7.1
7.1
6.9
6.9
6.8
6.6
6.2
6.1
6.1
6.0
6.0
5.6
5.5
5.5
5.4
5.4
4.9
4.9
4.7
4.0
3.9
0
5
10
15
Days
1 2 http://dx.doi.org/10.1787/888933281004
109
Cardiac procedures
Heart diseases are a leading cause of hospitalisation and
death in OECD countries (see indicator on “Mortality from
cardiovascular diseases” in Chapter 3). Coronary artery
bypass graft and angioplasty have revolutionised the treatment of ischemic heart diseases in the past few decades. A
coronary bypass is an open-chest surgery involving the
grafting of veins and/or arteries to bypass one or multiple
obstructed arteries. A coronary angioplasty is a much less
invasive procedure involving the threading of a catheter
with a balloon attached to the tip through the arterial system to distend the coronary artery at the point of obstruction; the placement of a stent to keep the artery open
accompanies the majority of angioplasties.
In 2013, Germany, Hungary and Austria had the highest
rates of coronary revascularisation procedures, while the
rates were lowest in Mexico and Chile (Figure 6.16).
A number of reasons can explain cross-country variations
in the rate of coronary bypass and angioplasty, including:
1) differences in the capacity to deliver and pay for these
procedures; 2) differences in clinical treatment guidelines
and practices; and 3) differences in coding and reporting
practices. However, the large variations in the number of
revascularisation procedures across countries do not seem
to be closely related to the incidence of ischemic heart
disease (IHD), as measured by IHD mortality (see Figure 3.6
in Chapter 3). For example, IHD mortality in Germany is
slightly below the OECD average, but Germany has the
highest rate of revascularisation procedures.
National averages can hide important variations in utilisation rates within countries. For example, in Germany, the
rate of coronary bypass surgery and angioplasty is nearly
three times higher in certain regions compared with others. There are also wide variations in the use of these revascularisation procedures across regions in other countries
such as Finland, France and Italy (OECD, 2014).
The use of angioplasty has increased rapidly over the past
20 years in most OECD countries, overtaking coronary
bypass surgery as the preferred method of revascularisation around the mid-1990s – about the same time that the
first published trials of the efficacy of coronary stenting
began to appear. On average across OECD countries, angioplasty now accounts for 78% of all revascularisation procedures (Figure 6.17), and is equal or exceeds 88% in Korea,
Estonia, France and Spain. In many OECD countries, the
growth in angioplasty was more rapid between 2000 and
110
2006 than afterwards. In the United States, the share of
angioplasty increased quickly between 2000 and 2006, but
has fallen slightly since then. Part of the explanation for
this slight reduction may be due to the fact that the data
reported by the United States do not cover the growing
number of angioplasties carried out as day cases (without
any overnight stay in hospital). In addition, the greater use
of drug-eluting stents in the United States as well as in
other countries reduces the likelihood that the same
patient will need another angioplasty (Epstein et al., 2011).
The data for most countries cover both inpatient and
day cases, with the exception of Chile, Denmark,
Iceland, Norway, Portugal, Switzerland and the United
States, where they only include inpatient cases
(resulting in some under-estimation in the number of
coronary angioplasties; this limitation in data coverage does not affect the number of coronary bypasses
since nearly all patients are staying at least one night
in hospital after such an operation). Some of the variations across countries may also be due to the use of
different classification systems and different codes
for reporting these two procedures.
In Ireland, Mexico, New Zealand and the United
Kingdom, the data only include activities in publiclyfunded hospitals, resulting in an under-estimation (it
is estimated that approximately 15% of all hospital
activity in Ireland is undertaken in private hospitals).
Data for Portugal relate only to public hospitals on the
mainland. Data for Spain only partially include activities in private hospitals.
References
Epstein, A. et al. (2011), “Coronary Revascularization Trends
in the United States, 2001-2008”, Journal of the American
Medical Association, Vol. 305, No. 17, pp. 1769-1775, May 4.
http://dx.doi.org/10.1787/9789264216594-en.
Cardiac procedures
6.16. Coronary revascularisation procedures, 2013 (or nearest year)
Coronary angioplasty
Coronary bypass
500
435
400
325
304
300
296
288
273
267
256
256
252
245
244
243
240
240
236
225
220
219
214
210
207
200
196
181
168
159
153
144
132
124
100
62
6
Ge
rm
a
Hu ny
ng
ar
A y
Ne us t
th r ia
er
la
n
B e ds
lg
iu
m
Es
to
ni
a
Is
ra
No el
rw
a
Ic y
e
C z lan
d
ec
h
S w Re
i t z p.
er
la
De nd
nm
ar
k
Fr
an
ce
It a
ly
Un Pol
i te an
d
d
St
at
es
Sw
ed
Au en
st
ra
O E li a
CD
3
Ca 1
na
d
F a
L u inl a
xe
n
m d
bo
ur
g
Sl
ov
en
ia
Ne Tur
w ke y
Ze
al
a
Un Po n d
i te r tu
d
K i gal
ng
do
m
Sp
a in
Ir e
la
nd
Ko
re
a
Ch
il e
M
ex
ic
o
0
Note: Some of the variations across countries are due to different classification systems and recording practices.
1 2 http://dx.doi.org/10.1787/888933281011
6.17. Coronary angioplasty as a share of total revascularisation procedures, 2000 to 2013 (or nearest years)
2000
%
100
2006
2013
95
90
88
88
88
86
86
85
85
84
84
83
82
80
82
81
80
80
70
79
79
78
78
78
76
76
75
75
74
72
71
66
64
63
60
50
41
40
30
20
10
It a
ly
rm
an
Ir e y
la
n
No d
rw
Sl ay
o
Ne ve
Un the ni a
i te r la
n
d
K i ds
ng
d
Cz
o
ec m
h
S w Re
i t z p.
er
la
n
Fi d
L u nl a
xe
nd
m
bo
u
OE rg
CD
3
Ic 1
el
an
d
B
N e el gi
um
w
Ze
al
a
Au nd
st
ra
li
Po a
la
Po nd
r tu
g
C a al
na
Hu d a
ng
De ar y
Un nm
i te ar
k
d
St
at
es
Ch
il e
Tu
rk
e
M y
ex
ic
o
Ge
S w el
ed
e
Au n
st
ria
a in
ra
Is
Sp
a
ce
ni
an
to
Fr
Es
Ko
re
a
0
Note: Revascularisation procedures include coronary bypass and angioplasty.
1 2 http://dx.doi.org/10.1787/888933281011
111
Significant advances in surgical treatment have provided
effective options to reduce the pain and disability associated with certain musculoskeletal conditions. Joint
replacement surgery (hip and knee replacement) is considered the most effective intervention for severe osteoarthritis, reducing pain and disability and restoring some
patients to near normal function.
and 2013. In Germany, these surgical activity rates appear
to have stabilised in recent years and even come down
slightly in 2013.
Osteoarthritis is one of the ten most disabling diseases in
developed countries. Worldwide estimates show that 10%
of men and 18% of women aged over 60 years have symptomatic osteoarthritis, including moderate and severe
forms (WHO, 2014). Age is the strongest predictor of the
development and progression of osteoarthritis. It is more
common in women, increasing after the age of 50 especially in the hand and knee. Other risk factors include obesity, physical inactivity, smoking, excess alcohol and
injuries. While joint replacement surgery is mainly carried
out among people aged 60 and over, it can also be performed among people at younger ages.
Hip replacement is a surgical procedure in which the
hip joint is replaced by a prosthetic implant. It is generally conducted to relieve arthritis pain or treat
severe physical joint damage following hip fracture.
In 2013, Switzerland, Germany and Austria had the highest
rates of hip replacement, while the United States had the
highest rate of knee replacement, followed by Austria,
Finland and Germany (Figures 6.18 and 6.19). Differences in
population structure may explain part of these variations
across countries, and age standardisation reduces to some
extent the cross-country variations. Still, large differences
persist and the country ranking does not change significantly after age standardisation (McPherson et al., 2013;
OECD, 2014).
National averages can mask important variations in hip
and knee replacement rates within countries. In Australia,
Canada, Germany, France and Italy, the rate of knee
replacement is more than two times higher in certain
regions compared with others, even after age-standardisation (OECD, 2014).
The number of hip and knee replacements has increased rapidly since 2000 in most OECD countries (Figures 6.20
and 6.21). On average, the rate of hip replacement
increased by about 35% between 2000 and 2013 and the rate
of knee replacement nearly doubled. In France, the growth
rate for both interventions was slightly lower, but still the
hip replacement rate increased by about 15% while the
knee replacement rate rose by nearly 90% between 2000
112
Knee replacement is a surgical procedure to replace
the weight-bearing surfaces of the knee joint in order
to relieve the pain and disability of osteoarthritis. It
may also be performed for other knee diseases such
as rheumatoid arthritis.
Classification systems and registration practices vary
across countries, which may affect the comparability
of the data. Some countries only include total hip
replacement (e.g. Estonia), while most countries also
include partial replacement. In Ireland, Mexico, New
Zealand and the United Kingdom, the data only
include activities in publicly-funded hospitals (it is
estimated that approximately 15% of all hospital
activity is undertaken in private hospitals). Data for
Portugal relate only to public hospitals on the mainland. Data for Spain only partially include activities in
private hospitals.
References
McPherson, K., G. Gon and M. Scott (2013), “International
Variations in a Selected Number of Surgical Procedures”,
OECD Health Working Papers, No. 61, OECD Publishing,
Paris, http://dx.doi.org/10.1787/5k49h4p5g9mw-en.
http://dx.doi.org/10.1787/9789264216594-en.
WHO (2014), “Chronic Rheumatic Conditions”, Fact Sheet,
Geneva, available at: www.who.int/chp/topics/rheumatic/en/.
6.18. Hip replacement surgery, 2013 (or nearest year)
Switzerland
Germany
Austria
Belgium
Norway
Finland
Sweden
France
Denmark
Netherlands
Luxembourg
United States
Iceland
United Kingdom
Australia
Czech Rep.
Italy
OECD33
Slovenia
Greece
New Zealand
Hungary
Canada
Ireland
Estonia
Spain
Slovak Rep.
Portugal
Poland
Israel
Turkey
Chile
Korea
Mexico
United States
Austria
Finland
Germany
Belgium
Australia
Switzerland
Luxembourg
Denmark
Canada
France
United Kingdom
Sweden
Iceland
OECD30
Netherlands
Czech Rep.
Korea
Spain
Slovenia
Italy
New Zealand
Norway
Turkey
Portugal
Hungary
Israel
Ireland
Poland
Chile
Mexico
292
283
276
246
243
242
238
236
227
216
216
204
185
183
171
170
166
161
161
152
150
137
136
127
112
107
105
88
85
63
44
33
20
8
0
100
200
300
6.19. Knee replacement surgery, 2013 (or nearest year)
226
215
202
190
187
180
176
171
167
166
145
141
135
132
121
118
115
115
112
108
104
94
89
67
62
59
53
50
26
11
3
0
400
100
1 2 http://dx.doi.org/10.1787/888933281026
6.20. Trend in hip replacement surgery, selected OECD
countries, 2000 to 2013 (or nearest years)
France
United States
Germany
200
300
1 2 http://dx.doi.org/10.1787/888933281026
6.21. Trend in knee replacement surgery, selected OECD
countries, 2000 to 2013 (or nearest years)
Italy
OECD33
France
United States
Germany
Italy
OECD30
250
350
300
200
250
150
200
100
150
50
0
100
2000
2002
2004
2006
2008
2010
2012
1 2 http://dx.doi.org/10.1787/888933281026
2000
2002
2004
2006
2008
2010
2012
1 2 http://dx.doi.org/10.1787/888933281026
113
Caesarean sections
Rates of caesarean delivery have increased in nearly all
OECD countries, although in a few countries this trend has
reversed at least slightly in the past few years. Reasons for
the increase include the rise in first births among older
women and in multiple births resulting from assisted
reproduction, malpractice liability concerns, scheduling
convenience for both physicians and patients, and the preferences of some women to have a caesarean section. Nonetheless, caesarean delivery continues to result in increased
maternal mortality, maternal and infant morbidity, and
increased complications for subsequent deliveries, raising
questions about the appropriateness of caesarean delivery
that may not be medically required.
In 2013, caesarean section rates were lowest in Nordic
countries (Iceland, Finland, Sweden and Norway), Israel
and the Netherlands, with rates ranging from 15% to 16.5%
of all live births (Figure 6.22). They were highest in Turkey,
Mexico and Chile, with rates ranging from 45% to 50%.
Caesarean rates have increased since 2000 in most OECD
countries, with the average rate going up from 20% in 2000
to 28% in 2013 (Figure 6.23). The growth rate has been particularly rapid in those countries that have the highest
rates now (Turkey, Mexico and Chile), as well as in Poland,
the Slovak Republic and the Czech Republic which used to
have relatively low rates. In some countries, however, the
growth rate has slowed down since the mid-2000s and it
has even come down slightly in Israel, Finland and Sweden.
In Italy also, caesarean rates have come down significantly
in recent years, although they remain very high. The rates
have also come down in Spain.
There can be substantial variations in caesarean rates
across regions and hospitals within the same country. In
Italy, there continues to be huge variations in caesarean
rates, driven by very large rates in the south of the country.
In Spain also, there are large variations across regions
(OECD, 2014).
In several countries, there is evidence that private hospitals
tend to perform more caesarean sections than public hospitals. In France, private for-profit hospitals authorised to
provide maternity care for pregnancies without complications have caesarean rates as high as public hospitals
which have to deal with more complicated cases (FHF,
2008). In Switzerland, caesarean sections have been found
to be substantially higher in private clinics (41%) than in
public hospitals (30.5%) (OFSP, 2013).
114
A number of countries have taken different measures to
reduce unnecessary caesarean sections. Public reporting,
provider feedback, the development of clearer clinical
guidelines, and adjustments to financial incentives have
been used to try to reduce the inappropriate use of caesareans. In Australia, where caesarean section rates are high
relative to most OECD countries, a number of States have
developed clinical guidelines and required reporting of hospital caesarean section rates, including investigation of
performance against the guidelines. These measures have
discouraged variations in practice and contributed to slowing down the rise in caesarean sections. Other countries
have reduced the gap in hospital payment rates between a
caesarean section and a normal delivery, with the aim to
discourage the inappropriate use of caesareans (OECD,
2014).
The caesarean section rate is the number of caesarean
deliveries performed per 100 live births.
In Mexico, the number of caesarean sections is estimated based on public hospital reports and data
obtained from National Health Surveys. Estimation is
required to correct for under-reporting of caesarean
deliveries in private facilities. The combined number
of caesarean deliveries is then divided by the total
number of live births as estimated by the National
Population Council.
References
FHF – Fédération hospitalière de France (2008), Étude sur les
césariennes [Study on caesareans], Paris.
http://dx.doi.org/10.1787/9789264216594-en.
OFSP – Office fédéral de la santé publique (2013), Accouchements par césarienne en Suisse [Births by Caesareans in
Switzerland], Bern.
Caesarean sections
6.22. Caesarean section rates, 2013 (or nearest year)
Per 100 live births
50
50.4
44.7 45.2
40
36.0 36.1
34.6 35.0 35.3
30.7 30.9
30
26.1 26.3 26.8
25.2 25.8
20.2 20.8
19.5 20.1
20
22.2
27.6
32.1 32.5 32.5
28.5 28.8
23.0
16.4 16.5
15.2 15.4 15.6 15.8
10
Es ia
to
n
Be ia
lg
iu
m
Fr
an
c
Un De
e
i t e nm
d
K i ar k
ng
do
m
N e Sp
a in
w
Ze
Cz alan
ec
d
h
Re
C p.
Lu an
xe a d
m a
bo
u
OE rg
CD
3
Ir e 2
la
n
A d
Sl us t
ov r i a
ak
R
G e e p.
rm
an
A
y
Un us tr
i t e a li
a
d
Sw St at
e
it z
s
er
la
nd
Po
la
Po nd
r tu
Hu g al
ng
ar
y
Ko
re
a
It a
ly
Ch
il e
M
ex
ic
o
Tu
rk
ey
ay
en
rw
ov
No
Sl
Sw
ed
d
en
s
an
nd
Fi
er
nl
la
ra
Ne
th
Ic
Is
el
an
d
el
0
1 2 http://dx.doi.org/10.1787/888933281033
6.23. Changes in caesarean section rates, 2000 to 2013 (or nearest years)
2000
2006
2013
50.4
45.2
44.7
Per 100 live births
50
16.4
36.1
36.0
35.3
35.0
34.6
32.5
32.5
32.1
30.9
30.7
28.8
28.5
27.6
26.8
26.1
25.8
25.2
23.0
22.2
20.8
20.2
16.5
15.8
15.6
15.2
15.4
20
20.1
19.5
30
26.2
40
10
Es ia
to
n
Be ia
lg
iu
m
Fr
an
Un De c e
i t e nm
d
K i ar k
ng
do
m
N e Sp
ain
w
Ze
Cz alan
ec
d
h
Re
C p.
Lu an
xe a d
m a
bo
u
OE rg
CD
3
Ir e 2
la
n
A d
Sl us t
ov r i a
ak
R
G e e p.
rm
an
A
y
Un us t
i te r al
ia
d
S
Sw t at
it z es
er
la
n
Po d
la
n
Po d
r tu
Hu g al
ng
ar
y
Ko
re
a
It a
ly
Ch
i
M le
ex
ic
o
Tu
rk
ey
en
ay
rw
ov
Sl
en
ed
No
Sw
nl
an
d
s
nd
la
er
th
Ne
Fi
el
ra
el
Ic
Is
an
d
0
1 2 http://dx.doi.org/10.1787/888933281033
115
Ambulatory surgery
In the past few decades, the number of surgical procedures
carried out on a same-day basis has increased markedly
in OECD countries. Advances in medical technologies,
particularly the diffusion of less invasive surgical
interventions and better anaesthetics, have made this
development possible. These innovations have improved
patient safety and health outcomes, and have also in many
cases reduced the unit cost per intervention by shortening
the length of stay in hospitals. However, the impact of the
rise in same-day surgeries on health spending depends not
only on changes in their unit cost, but also on the growth in
the volume of procedures performed. There is also a need
to take into account any additional cost related to postacute care and community health services following the
interventions.
due to large tonsils. Although the operation is performed
under general anaesthesia, it is now carried out mainly as a
same-day surgery in many countries, with children returning
home the same day (Figure 6.25). This is the case in Finland
(where the share of same-day surgery has increased greatly
since 2000), Canada, Belgium, the Netherlands, Sweden and
Norway, where more than half of all tonsillectomy are
now performed on a same-day basis. This proportion is
much lower in Austria (where virtually no tonsillectomy
is performed on a same-day basis), Luxembourg, Ireland
and Germany. These large differences in the share of sameday surgery may reflect variations in the perceived risks of
postoperative complications, or simply clinical traditions of
keeping children for at least one night in hospital after the
operation.
Cataract surgery and tonsillectomy (the removal of tonsils,
glands at the back of the throat, mainly performed on
children) provide good examples of high-volume surgeries
which are now carried out mainly on a same-day basis in
many OECD countries.
In some countries, there has been a strong rise however in
the share of tonsillectomy performed as day surgery since
2000. Beyond Finland which is now leading the way, the
share of same-day surgery has increased rapidly over the
past decade in the United Kingdom, Denmark, Portugal and
Italy. In France, there has virtually been no increase in the
share of day surgery for tonsillectomy since 2000, while this
share has decreased slightly in Israel and Switzerland.
There appears to be ample room for further growth in day
surgery for tonsillectomy in these countries to reduce cost
without affecting patient outcomes.
Day surgery now accounts for over 90% of all cataract
surgeries in a majority of OECD countries (Figure 6.24). In
several countries, nearly all cataract surgeries are performed
as day cases. However, the use of day surgery is still relatively
low in Poland, Hungary and the Slovak Republic, where
they still account for less than half of all cataract surgeries.
While this may be partly explained by limitations in the
data coverage of outpatient activities in hospital or outside
hospital, this may also reflect more advantageous reimbursement for inpatient stays or constraints on the development of day surgery. In Hungary, the government recently
abolished the budget cap on the number of same-day surgery
that can be performed in hospital; this is expected to lead to a
steady increase in the number of cataract and other surgeries
performed as day cases.
The number of cataract surgeries performed on a same-day
basis has grown very rapidly since 2000 in many countries,
such as Portugal and Austria (Figure 6.24). Whereas fewer
than 10% of cataract surgeries in Portugal were performed
on a same-day basis in 2000, this proportion has increased
to 92%. In Austria, the share of cataract surgeries performed as day cases increased from 1% only in 2000 to 67%
in 2013. The number of cataract surgeries carried out as day
cases has also risen rapidly in France, Ireland, Switzerland
and Luxembourg, although there is still room for further
development.
Tonsillectomy is one of the most frequent surgical procedures on children, usually performed on children suffering
from repeated or chronic infections of the tonsils or suffering from breathing problems or obstructive sleep apnea
116
Cataract surgery consists of removing the lens of the
eye because of the presence of cataracts which are
partially or completely clouding the lens, and replacing
it with an artificial lens. It is mainly performed on
elderly people. Tonsillectomy consists of removing the
tonsils, glands at the back of the throat. It is mainly
performed on children.
The data for several countries do not include outpatient cases in hospital or outside hospital (i.e., patients
who are not formally admitted and discharge), leading
to some under-estimation. In Ireland, Mexico, New
Zealand and the United Kingdom, the data only include
cataract surgeries carried out in public or publiclyfunded hospitals, excluding any procedures performed
in private hospitals (in Ireland, it is estimated that
approximately 15% of all hospital activity is undertaken in private hospitals). Data for Portugal relate only
to public hospitals on the mainland. Data for Spain only
partially include activities in private hospitals.
Ambulatory surgery
6.24. Share of cataract surgeries carried out as ambulatory cases, 2000 and 2013 (or nearest years)
2000
%
100
100
100
99
99
99
98
98
98
98
96
2013
96
96
96
95
93
92
91
89
87
83
81
80
78
77
69
67
64
60
53
47
40
37
27
20
y
nd
la
Sl
Po
p.
ov
Hu
ng
ar
y
ak
Re
o
ke
ic
ex
M
Tu
r
g
ria
ur
st
bo
m
Au
ly
nd
la
er
Lu
it z
xe
y
an
It a
rm
CD
29
ce
OE
Po
Fr
Is
an
ra
el
a
l
re
Ko
r tu
ga
nd
m
iu
la
lg
Be
ec
Ge
Sw
Ne
Cz
Ir e
d
p.
an
Re
h
al
Ze
w
Au
st
ra
li a
ay
ia
rw
en
ov
Sl
No
k
a in
ar
Sp
De
nm
d
ed
Sw
an
nl
Un
i te
d
Ne
th
Fi
er
en
s
a
ni
nd
la
to
na
Es
Ki
Ca
ng
do
m
da
0
1 2 http://dx.doi.org/10.1787/888933281044
6.25. Share of tonsillectomy carried out as ambulatory cases, 2000 and 2013 (or nearest years)
2000
%
100
2013
84
80
75
71
68
63
60
51
50
47
45
42
40
35
34
31
30
22
20
20
18
11
9
7
5
4
3
3
0.1
ria
g
ur
st
Au
nd
Lu
xe
m
bo
la
Ir e
Ge
rm
an
y
nd
a
re
la
Po
Ko
li a
ra
st
nd
la
er
it z
Au
y
ke
Tu
r
Sw
el
ra
Is
ce
an
Fr
a in
ly
It a
Sp
OE
CD
24
l
ga
r tu
Po
Ze
al
an
d
k
w
Ne
Un
De
nm
ar
m
o
do
ng
d
Ki
M
ex
ic
ay
i te
ed
rw
No
en
s
nd
la
Sw
m
iu
er
Ne
th
lg
Be
na
Ca
Fi
nl
an
d
da
0
1 2 http://dx.doi.org/10.1787/888933281044
117
7. ACCESS TO CARE
Coverage for health care
Unmet needs for medical care and dental care
Out-of-pocket medical expenditure
Geographic distribution of doctors
Waiting times for elective surgery
international law.
119
7. ACCESS TO CARE
Health care coverage through public or private health
insurance promotes access to medical goods and services,
and provides financial security against unexpected or serious
illness. However, the percentage of the population covered by
such insurance does not provide a complete indicator of
accessibility, since the range of services covered and the
degree of cost-sharing applied to those services also affects
access to care.
Most OECD countries have achieved universal (or nearuniversal) coverage of health care costs for a core set of
services, which usually include consultations with doctors
and specialists, tests and examinations, and surgical and
therapeutic procedures (Figure 7.1). Generally, dental care
and pharmaceutical drugs are partially covered, although
there are a number of countries where these services must
be purchased separately (OECD, 2015).
Three OECD countries do not have universal (or near-universal) health coverage: Greece, the United States and
Poland. In Greece, the economic crisis has reduced health
insurance coverage among people who have become longterm unemployed, and many self-employed workers have
also decided not to renew their health insurance plan
because of reduced disposable income. However, since
June 2014, uninsured people are covered for prescribed
pharmaceuticals and for services in emergency departments in public hospitals, as well as for non-emergency
hospital care under certain conditions (Eurofound, 2014). In
the United States, coverage is provided mainly through
private health insurance, and 54% of the population had this
for their basic coverage in 2014. Publicly financed coverage
insured 34.5% of the population (the elderly, people with
low income or with disabilities), leaving 11.5% of the population without insurance. The percentage of the population
uninsured decreased from 14.4% in 2013 to 11.5% in 2014,
following the implementation of the Affordable Care Act
which is designed to expand health insurance coverage
(Cohen and Martinez, 2015). In Poland, a tightening of the
law in 2012 made people lose their social health insurance
coverage if they fail to pay their contribution. However, it is
common for uninsured people who need medical care to go
to emergency services in hospital, where they will be
encouraged to get an insurance.
Basic primary health coverage, whether provided through
public or private insurance, generally covers a defined
“basket” of benefits, in many cases with cost-sharing. In
some countries, additional health coverage can be purchased
through private insurance to cover any cost-sharing left
after basic coverage (complementary insurance), add
additional services (supplementary insurance) or provide
faster access or larger choice to providers (duplicate insurance). Among the 34 OECD countries, nine have private
coverage for over half of the population (Figure 7.2).
Private health insurance offers 95% of the French population
complementary insurance to cover cost-sharing in the social
120
security system. The Netherlands has the largest supplementary market (86% of the population), followed by
Israel (83%), whereby private insurance pays for prescription drugs and dental care that are not publicly reimbursed.
Duplicate markets, providing faster private-sector access to
medical services where there are waiting times in public
systems, are largest in Ireland (45%) and Australia (47%).
The population covered by private health insurance has
increased in some OECD countries over the past decade,
whereas it has decreased in others. It increased in some
Nordic countries such as Denmark where one-third of the
population now has a private health insurance (up from
less 10% in 2005) and in Finland where the growth has been
more modest, but remains almost non-existent in other
Nordic countries. Private health insurance coverage has
also increased in Australia and Korea, but it has come
down in Ireland, New Zealand and the United Kingdom
(Figure 7.3).
The importance of private health insurance is linked to several factors, including gaps in access to publicly financed services, government interventions directed at private health
insurance markets, and historical development.
Coverage for health care is defined here as the share
of the population receiving a core set of health care
goods and services under public programmes and
through private health insurance. It includes those
covered in their own name and their dependents. Public
coverage refers both to government programmes,
generally financed by taxation, and social health
insurance, generally financed by payroll taxes. Takeup of private health insurance is often voluntary,
although it may be mandatory by law or compulsory
for employees as part of their working conditions.
Premiums are generally non-income-related, although
the purchase of private coverage can be subsidised by
government.
References
Cohen, R.A. and M.E. Martinez, M.E. (2015), Health Insurance
Coverage: Early Release of Estimates from the National Health
Interview Surve, 2014, National Center for Health Statistics, June.
Eurofound (2014), Access to Healthcare in Times of Crisis,
Dublin.
OECD (2015), “Measuring Health Coverage”, OECD, Paris,
available at: www.oecd.org/els/health-systems/measuringhealth-coverage.htm.
7. ACCESS TO CARE
7.1. Health insurance coverage for a core set of services,
2013
7.2. Private health insurance coverage, by type, 2013
(or nearest year)
Total public coverage
Primary private health coverage
Australia
Canada
Czech Rep.
Denmark
Finland
Iceland
Ireland
Israel
Italy
Japan
Korea
New Zealand
Norway
Portugal
Slovenia
Sweden
Switzerland
United Kingdom
Austria
France
Spain
Germany
Netherlands
Belgium
Mexico
Chile
Turkey
Luxembourg
Hungary
Colombia
Slovak Republic
Estonia
Poland
United States (2014)
Greece
100.0
100.0
100.0
100.0
100.0
99.8
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
99.9
99.9
99.0
88.8
99.8
99.0
91.6
79.9
98.1
96.4
96.0
91.7
94.6
93.6
91.6
France
Israel
82.9
81.3
Slovenia
72.8
Canada
67.0
Korea
61.0
United States
60.1
Australia
54.9
Luxembourg
49.7
Ireland
44.6
Austria
35.2
Denmark
33.0
Germany
33.0
New Zealand
0.9
11.0
29.7
Switzerland
27.9
Portugal
21.1
Chile
7.3
18.3
18.3
Finland
14.7
Spain
13.4
Greece
4.0
12.5
United Kingdom
10.6
Mexico
7.3
Turkey
54.0
40
86.0
Belgium
0.2
5.6
Iceland
79.0
20
95.0
Netherlands
34.5
0
Complementary
Duplicate
Primary
Supplementary
0.2
60
80
100
Percentage of total population
0
1 2 http://dx.doi.org/10.1787/888933281052
20
40
60
80
100
Note: Private health insurance can be both duplicate and supplementary
in Australia; both complementary and supplementary in Denmark and
Korea; and duplicate, complementary and supplementary in Israel and
Slovenia.
1 2 http://dx.doi.org/10.1787/888933281052
7.3. Evolution in private health insurance coverage, 2005 to 2013
2010
2005
2013
100
95.0
80
86.0
82.9
81.3
72.8
67.0
60
61.0
60.1
54.9
44.6
40
21.1
20
5.6
14.7
12.5
10.6
7.3
35.2
33.0
33.0
29.7
27.9
25.1
ce
s
an
Fr
nd
el
la
ra
Is
er
th
Ne
m
iu
ia
en
lg
Be
da
na
ov
Sl
re
a
Ca
Ko
es
at
li a
St
ra
d
st
i te
Un
la
ria
nd
Au
Ir e
y
an
st
Au
k
rm
ar
Ge
al
nm
De
an
d
nd
la
Ne
w
Ze
er
it z
Sw
l
il
az
Br
ga
d
ce
an
r tu
Po
nl
Fi
ee
m
do
ng
Ki
Un
i te
d
Gr
o
ic
ex
M
Tu
r
ke
y
0
1 2 http://dx.doi.org/10.1787/888933281052
121
7. ACCESS TO CARE
Access to health care may be prevented for a number of
reasons related either to the functioning of the health care
system itself (like the cost of a doctor visit or medical
treatment, the distance to the closest health care facility, or
waiting lists) or to personal reasons (like fear of not being
understood by the doctor or not having the time to seek
care). People who forgo health care when they need it may
jeopardize their health status. Any inequalities in unmet
care needs may result in poorer health status and increase
health inequalities.
Sweden. There were large gaps in unmet care needs
between high and low income people in the Czech Republic,
France and the United States.
Around 3% of the population on average across Europe
reported unmet needs for medical care due to cost, travelling distance and waiting lists in 2013, according to the
European Union “Statistics on Income and Living Conditions”
survey. But there are large variations across countries
(Figure 7.4). Larger shares of the population report unmet
needs in Latvia, Greece, Poland and Estonia, while less
than 1% of the population reported unmet needs in the
Netherlands, Austria, Spain, Luxembourg and the Czech
Republic. Unmet needs for medical examination are consistently higher among people in low income groups compared
with those in high income groups (Figure 7.4). The gap was
particularly large in 2013 in Latvia, Italy and Greece.
Strategies to improve access to care for disadvantaged or
underserved populations need to tackle both financial and
non-financial barriers, as well as promoting an adequate
supply and proper distribution of doctors, dentists and
other medical practitioners (see the indicator on “Geographic
distribution of doctors”).
A higher proportion of the population in European countries reports some unmet needs for dental care than for
medical care, reflecting the fact that public coverage for
dental care is generally lower in most countries. Latvia
(18.9%), Portugal (14.3%), Iceland (11.1%) and Italy (10%),
reported the highest rates of unmet needs for dental care
among European countries in 2013 (Figure 7.5). In these
countries, there were large inequalities in unmet dental
care needs between low and high income groups. On average across European countries covered under this survey,
nearly 10% of low income people reported having some
unmet needs for dental care, compared with 1.6% for high
income people.
Countries participating in the Commonwealth Fund International Health Policy Survey, and other countries using
the same survey module, collect data on unmet care needs
for doctor visits, medical care and prescribed pharmaceutical
drugs due to cost. As expected, the results from these surveys
show consistently higher unmet care needs for financial
reasons among low income people compared with high
income people (Figure 7.6). The largest proportions of
unmet care needs in 2013 were found in the United States,
while the United Kingdom had the lowest rates, followed by
122
It is important to consider self-reported unmet care needs
in conjunction with other indicators of potential barriers to
access, such as the extent of health insurance coverage and
the amount of out-of-pocket payments. For instance, the
rate of unmet care needs decreased in Germany, following
the abolition of a quarterly fee of EUR 10 charged to
patients.
Data on unmet care needs come from two main
sources. First, the European Union Statistics on
Income and Living Conditions survey (EU-SILC) ask
survey respondents whether there was a time in the
previous 12 months when they felt they needed a
medical or dental examination but did not receive it,
followed by a question as to why the need for care was
unmet (with the reasons including that care was too
expensive, the waiting time was too long, the travelling distance to receive care was too far, a lack of time,
or that they wanted to wait and see if problem got
better on its own). The data presented in Figures 7.4
and 7.5 cover unmet care needs due to cost, waiting
time and travelling distance.
The second source is the 2013 Commonwealth Fund
International Health Policy Survey which asks
whether people did not visit a doctor when they had a
medical problem, skipped a medical test, treatment,
or follow-up that was recommended by a doctor, or
did not fill prescription for medicines or skipped
doses because of cost in the past year. This survey was
carried out in eleven countries. Similar questions
were also asked in the national survey in the Czech
Republic a few years earlier (2010).
7. ACCESS TO CARE
7.4. Unmet care needs for medical examination,
by income level, 2013
High income
Average
7.5. Unmet care needs for dental examination,
by income level, 2013
Low income
High income
Average
Low income
Netherlands
Austria
Luxembourg
Czech Rep.
Germany
Slovak Rep.
United Kingdom
Belgium
Hungary
Denmark
Lithuania
Finland
Switzerland
Poland
OECD23
Sweden
France
Norway
Ireland
Spain
Estonia
Greece
Italy
Iceland
Portugal
Latvia
Netherlands
Austria
Spain
Luxembourg
Czech Rep.
Switzerland
Denmark
Norway
Germany
United Kingdom
Belgium
Slovak Rep.
Sweden
Hungary
France
Portugal
OECD23
Lithuania
Ireland
Iceland
Finland
Italy
Estonia
Poland
Greece
Latvia
0
5
10
15
20
25
%
0
Note: Unmet care needs for following reasons: too expensive, too far to
travel, or waiting time.
Source: EU-SILC 2013.
1 2 http://dx.doi.org/10.1787/888933281066
10
20
30
40
%
Note: Unmet care needs for following reasons: too expensive, too far to
travel, or waiting time.
Source: EU-SILC 2013.
1 2 http://dx.doi.org/10.1787/888933281066
7.6. Unmet care needs due to cost, by income level, 2013
Below average income
%
50
Above average income
49
40
29
30
29
27
24
23
20
20
21
21
20
19
16
16
14
12
11
10
10
9
8
5
7
6
4
3
es
d
s
at
an
St
al
Un
i te
d
Ze
Ne
w
er
th
Ne
h
ec
Cz
la
Re
nd
p.
ce
an
Fr
Ge
rm
an
y
li a
ra
st
Au
nd
la
er
it z
Sw
Ca
na
da
ay
rw
No
ed
Sw
Un
i te
d
Ki
ng
do
m
en
0
Note: Either did not visit doctor when they had a medical problem, did not get recommended care or did not fill/skipped prescription.
Source: 2013 Commonwealth Fund International Health Policy Survey, complemented with data from the national survey for the Czech Republic (2010).
1 2 http://dx.doi.org/10.1787/888933281066
123
7. ACCESS TO CARE
Financial protection through public or private health insurance substantially reduces the amount that people pay
directly for medical care, yet in some countries the burden
of out-of-pocket spending can still create barriers to health
care access and use. Households that face difficulties paying medical bills may delay or even forgo needed health
care. On average across OECD countries, 19% of health
spending is paid directly by patients (see indicator
“Financing of health care” in Chapter 9 on “Health expenditure”).
In contrast to publicly-funded care, out-of-pocket payments rely on people’s ability to pay. If the financing of
health care becomes more dependent on out-of-pocket
payments, the burden shifts, in theory, towards those who
use services more, and possibly from high to low income
households, where health care needs are higher. In practice,
many countries have policies in place to protect certain
population groups from excessive out-of-pocket payments.
These consist in partial or total exemptions for social assistance beneficiaries, seniors, or people with chronic diseases
or disabilities by capping direct payments, either in absolute
terms or as a share of income (Paris et al., 2010; OECD,
2015).
The burden of out-of-pocket medical spending can be measured either by its share of total household income or its
share of total household consumption. The share of household consumption allocated to medical spending varied
considerably across OECD countries in 2013, ranging from
less than 1.5% of total household consumption in countries
such as Turkey, the Netherlands, France and the United
Kingdom, to more than 4% in Korea, Switzerland and
Greece (Figure 7.7). On average across OECD countries, 2.8%
of household spending went towards medical goods and
services.
Health systems in OECD countries differ in the degree of
coverage for different health services and goods. In most
countries, the degree of coverage is higher for hospital care
and doctor consultations than for pharmaceuticals, dental
care and eye care (Paris et al., 2010; OECD, 2015). Taking into
account these differences and also the relative importance
of these different spending categories, there are significant
variations between OECD countries in the breakdown of
the medical costs that households have to bear themselves.
In most OECD countries, curative care (including both
inpatient and outpatient care) and pharmaceuticals are the
two main spending items for out-of-pocket expenditure
(Figure 7.8). On average, these two components account for
two-thirds of all medical spending by households, but the
importance varies between countries. In Luxembourg,
Belgium and Switzerland, household payments for inpatient and outpatient curative care account for close to 50%
of total household outlays. In other countries such as
124
Poland, the Czech Republic, Hungary and Canada, half of
out-of-pocket payments or more are for pharmaceuticals.
In some of these countries, in addition to co-payments for
prescribed pharmaceuticals, spending on over-the-counter
medicines for self-medication has been historically high.
Payments for dental treatment also play a significant part
in household medical spending, accounting for 20% of all
out-of-pocket expenditure across OECD countries. In
Estonia, Norway, Denmark and Spain, this figure reaches
30% or more. This can at least partly be explained by the
limited public coverage for dental care in these countries
compared with a more comprehensive coverage for other
categories of care. The significance of therapeutic appliances
(eye-glasses, hearing aids, etc.) in households’ total medical
spending differs widely, but is as much as 33% in the
Netherlands. The average across OECD countries was 13%.
More than half of this relates to eye-care products. In many
countries, public coverage is limited to a contribution to the
cost of lenses. Frames are often exempt from public coverage,
leaving private households to bear the full cost if they are
not covered by complementary private insurance.
Out-of-pocket payments are expenditures borne
directly by a patient where neither public nor private
insurance cover the full cost of the health good or
service. They include cost-sharing and other expenditure paid directly by private households and should
also include estimations of informal payments to
health care providers. Only expenditure for medical
spending (i.e. current health spending less expenditure
for the health part of long-term care) is presented
here, because the capacity of countries to estimate
private long-term care expenditure varies widely.
Household final consumption expenditure covers all
purchases made by resident households to meet their
everyday needs such as food, clothing, rent or health
services.
References
OECD (2015), “Measuring Health Coverage”, OECD, Paris,
available at: www.oecd.org/els/health-systems/measuringhealth-coverage.htm.
Paris, V., M. Devaux and L. Wei (2010), “Health Systems
Institutional Characteristics: A Survey of 29 OECD Countries”, OECD Health Working Paper, No. 50, OECD Publishing, Paris, http://dx.doi.org/10.1787/5kmfxfq9qbnr-en.
7. ACCESS TO CARE
7.7. Out-of-pocket medical spending as a share of final household consumption, 2013 (or nearest year)
%
5
4.7
4.5
4.1
4.0
4
4.0
3.9
3.8
3.4
3.4
3.2
3.2
3.2
3.2
3.1
3
3.0
2.9
2.9
2.8
2.8
2.8
2.6
2.6
2.5
2.4
2.3
2.2
2.2
2.1
2.0
2
1.8
1.8
1.4
1.4
1.3
1.2
1
S w Ko
it z rea
er
la
n
Gr d
ee
c
M e
ex
Hu i c o
ng
Po ar y
r tu
ga
l
Ch
S w il e
ed
en
Sp
a in
Ir e
l
Au and
Sl s tr a
ov li
ak a
Re
p.
It a
Ic l y
el
a
B e nd
lg
iu
Fi m
nl
an
Au d
st
ria
Is
ra
OE el
CD
3
Un No 4
i te r wa
d
St y
a
De tes
nm
ar
Po k
la
n
Es d
to
n
Ca ia
na
da
Ne Ja
w pa
Ze n
Cz ala
ec nd
h
R
S l e p.
ov
e
G ni a
L u er m
Un xem a n y
i te b
d ou
Ki rg
ng
do
m
Ne Fr a
th nc
er e
la
nd
s
Tu 1
rk
ey
0
Note: This indicator relates to current health spending excluding long-term care (health) expenditure.
1. The value for the Netherlands is underestimated as it excludes compulsory co-payments by patients to health insurers (if these were taken into
account this would double the share).
1 2 http://dx.doi.org/10.1787/888933281072
7.8. Shares of out-of-pocket medical spending by services and goods, 2013 (or nearest year)
Curative care 1
%
100
8
Therapeutic appliances 2
Pharmaceuticals
8
10
14
13
10
13
14
14
70
60
49
38
45
36
36
38
26
22
42
26
19
28
8
40
19
10
19
18
30
26
20
31
29
9
31
21
30
8
49
48
46
20
34
49
28
38
50
49
62
51
41
20
11
28
35
18
33
44
32
26
12
27
28
28
15
6
7
12
17
21
18
80
Other
1
3
5
5
13
90
Dental care
41
39
37
33
31
30
30
30
28
28
13
26
10
25
25
24
32
17
22
21
15
19
19
17
17
15
da
ain
Sp
nd
na
Ca
a
ni
la
Po
Es
to
p.
s
Re
h
ec
Cz
Ne
th
er
la
an
nd
y
ay
rw
rm
Ge
an
p.
d
No
el
Re
ak
ov
Sl
Ic
ar
k
en
ed
nm
De
en
ia
Sw
23
ov
Sl
ce
an
CD
OE
an
d
Fr
li a
ra
nl
Fi
st
Au
pa
n
y
Ja
ng
ar
ria
Hu
a
re
st
Au
la
er
Ko
nd
m
iu
it z
Be
lg
Sw
Lu
xe
m
bo
ur
g
0
Note: This indicator relates to current health spending excluding long-term care (health) expenditure.
1. Including rehabilitative and ancillary services.
2. Including eye care products, hearing aids, wheelchairs, etc.
1 2 http://dx.doi.org/10.1787/888933281072
125
7. ACCESS TO CARE
Access to medical care requires an adequate number and
proper distribution of doctors in all parts of the country.
Any shortage of doctors in certain regions can increase
travel times or waiting times for patients, and result in
unmet care needs. The uneven distribution of doctors is an
important policy issue in most OECD countries, especially
in those countries with remote and sparsely populated
areas, and those with deprived urban regions which may
also be underserved.
The overall number of doctors per capita varies across OECD
countries from lows of about two per 1 000 population in
Chile, Turkey and Korea, to highs of five and more in Greece
and Austria (see the indicator on “Doctors” in Chapter 5).
Beyond these cross-country differences, the number of
doctors per capita also often varies widely across regions
within the same country (Figure 7.9). A common feature in
many countries is that there tends to be a concentration of
physicians in capital cities. For example, Austria, Belgium,
the Czech Republic, Greece, Mexico, Portugal, the Slovak
Republic and the United States have a much higher density
of doctors in their national capital region.
The density of physicians is consistently greater in urban
regions, reflecting the concentration of specialised services
such as surgery and physicians’ preferences to practice in
urban settings. There are large differences in the density of
doctors between predominantly urban and rural regions in
France, Australia and Canada, although the definition of
urban and rural regions varies across countries. The distribution of physicians between urban and rural regions is
more equal in Japan and Korea, but there are generally
fewer doctors in these two countries (Figure 7.10).
Doctors may be reluctant to practice in rural regions due to
concerns about their professional life (including their
income, working hours, opportunities for career development, isolation from peers) and social amenities (such as
educational opportunities for their children and professional opportunities for their spouse).
A range of policy levers may influence the choice of practice location of physicians, including: 1) the provision of
financial incentives for doctors to work in underserved
areas; 2) increasing enrolments in medical education
programmes of students coming from specific social or
geographic background, or decentralising the location of
medical schools; 3) regulating the choice of practice location
of doctors (for new medical graduates or foreign-trained
doctors); and 4) re-organising health service delivery to
improve the working conditions of doctors in underserved
areas and find innovative ways to improve access to care
for the population.
Many OECD countries provide different types of financial
incentives to attract and retain doctors in underserved
areas, including one-time subsidies to help them set up
126
their practice and recurrent payments such as income
guarantees and bonus payments (Ono et al., 2014).
In France, the Ministry of Health launched at the end of
2012 a “Health Territory Pact” to promote the recruitment
and retention of doctors and other health workers in
underserved regions. This Pact includes a series of measures
to facilitate the establishment of young doctors in underserved areas, to improve their working conditions (notably
through the creation of new multi-disciplinary medical
homes allowing physicians and other health professionals
to work in the same location), to promote tele-medicine,
and to accelerate the transfer of competences from doctors
to other health care providers (Ministry of Health, 2015). The
first results from this programme are promising, although it is
still too early to reach any definitive conclusions on the costeffectiveness of various measures.
In Germany, the number of practice permits for new ambulatory care physicians in each region is regulated, based on
a national service delivery quota.
The effectiveness and cost of different policies to promote
a better distribution of doctors can vary significantly, with
the impact likely to depend on the characteristics of each
health system, the geography of the country, physician
behaviours, and the specific policy and programme design.
Policies should be designed with a clear understanding of the
interests of the target group in order to have any significant
and lasting impact (Ono et al., 2014).
Regions are classified in two territorial levels. The
higher level (Territorial Level 2) consists of large
regions corresponding generally to national administrative regions. These broad regions may contain a
mix of urban, intermediate and rural areas. The lower
level is composed of smaller regions classified as predominantly urban, intermediate or rural regions,
although there are variations across countries in the
classification of these regions.
References
Ministry of Health (2015), Le Pacte territoire santé [Health Territory Pact], available at: www.sante.gouv.fr/le-pacte-territoiresante-pour-lutter-contre-les-deserts-medicaux,12793.html.
Ono, T., M. Schoenstein and J. Buchan (2014), “Geographic
Imbalances in Doctor Supply and Policy Responses”,
OECD Health Working Papers, No. 69, OECD Publishing,
Paris, http://dx.doi.org/10.1787/5jz5sq5ls1wl-en.
7. ACCESS TO CARE
7.9. Physician density, by Territorial Level 2 regions, 2013 (or nearest year)
Australia
Austria
Belgium
Canada
Chile
Czech Rep.
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Israel
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Rep.
Slovenia
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
Vienna
Brussels
Prague
Copenhagen Region
Helsinki
Athens Region
Mexico city
Lisbon
Bratislava
Washington, DC.
0
1
2
3
4
5
6
7
8
9
10
Density per 1 000 population
Source: OECD Regions at a Glance 2015.
1 2 http://dx.doi.org/10.1787/888933281083
7.10. Physicians density in predominantly urban and rural regions, selected countries, 2013 (or nearest year)
Urban areas
Rural areas
Density per 1 000 population
5
4.5
4.6
4.4
4.1
4
3.6
3.3
3
2.5
2.5
2.2
2.2
2
2.1
1.7
1.4
1.0
1
Ja
pa
n
a
re
Ko
Sw
ed
en
d
an
nl
Fi
Ca
na
da
li a
ra
st
Au
Fr
an
ce
0
Note: The classification of urban and rural regions varies across countries.
Source: Australia: AIHW National Health Workforce Data Set (NHWDS) 2013; Canada: Scott’s Medical Database, 2013, Canadian Institute for Health
Information; France: RPPS médecins au 1er janvier 2015; Other: OECD Regions at a Glance 2015.
1 2 http://dx.doi.org/10.1787/888933281083
127
7. ACCESS TO CARE
Long waiting times for health services is an important policy
issue in many OECD countries (Siciliani et al., 2013). Long
waiting times for elective (non-emergency) surgery, such as
cataract surgery, hip and knee replacement, generates dissatisfaction for patients because the expected benefits of
treatments are postponed, and the pain and disability
remains. While long waiting times is considered an important
policy issue in many countries, this is not the case in others
(e.g., Belgium, France, Germany, Japan, Korea, Luxembourg,
Switzerland, United States).
Waiting times is the result of a complex interaction
between the demand and supply of health services, where
doctors play a critical role on both sides. The demand for
health services and elective surgery is determined by the
health status of the population, progress in medical technologies (including the increase ease of many procedures
like cataract which can now be performed as day surgery),
patient preferences (including their weighting of the
expected benefits and risks), and the extent of cost-sharing
for patients. However, doctors play a crucial role in converting the demand for better health from patients in a demand
for medical care. On the supply side, the availability of
different categories of surgeons, anaesthesists and other
staff involved in surgical procedures, as well as the supply
of the required medical and hospital equipment influence
surgical activity rates.
The measure used here focuses on waiting times from the
time that a medical specialist adds a patient to the waiting
list to the time that the patient receives the treatment. Both
the average waiting time and the median are presented.
Because some patients wait for very long times, the average
is usually greater than the median.
In 2013/14, the average waiting times for cataract surgery
was just over 30 days in the Netherlands, but much longer
in Chile, Estonia and Poland (Figure 7.11). In the United
Kingdom, the average waiting times for cataract surgery
was 72 days in 2013, slightly up from 66 days in 2007. In
Portugal and Spain, waiting times fell between 2007 and
2010, but has increased since then. In Finland and Estonia,
waiting times for cataract surgery has fallen steadily,
although the average waiting times remains high in
Estonia.
In 2013/14, the average waiting times for hip replacement
was just over 40 days in the Netherlands, but around
250 days in Estonia and over 300 days in Chile and Poland
(Figure 7.12). The median waiting times was around 40 days
in Denmark, 60 days in Israel, and between 75 and 90 days
in Hungary, the United Kingdom, Portugal, Canada and
New Zealand. It reached between 120 and 150 days in
Spain, Norway and Estonia, and over 200 days in Poland
and Chile. As is the case for cataract surgery, waiting times
for hip replacement fell in Portugal and Spain between 2007
and 2010, but has gone up since then.
128
Waiting times for knee replacement has come down in recent
years in the Netherlands, Denmark, Finland and Estonia,
although it remains very long in Estonia (Figure 7.13).
Over the past decade, waiting time guarantees have
become the most common policy tool to tackle long waiting
times in several countries. This has been the case in Finland
where a National Health Care Guarantee was introduced in
2005 and led to a reduction in waiting times for elective
surgery (Jonsson et al., 2013). In England, since April 2010,
the NHS Constitution has set out a right to access certain
services within maximum waiting times or for the NHS to
take all reasonable steps to offer a range of alternative
providers if this is not possible (Smith and Sutton, 2013).
These guarantees are only effective if they are enforced.
There are two main approaches to enforcement: setting
waiting time targets and holding providers accountable for
achieving these targets; or allowing patients to choose
alternative health providers (including the private sector) if
they have to wait beyond a maximum amount of time
(Siciliani et al., 2013).
There are at least two ways of measuring waiting
times for elective procedures: 1) measuring the waiting
times for patients treated in a given period; or
2) measuring waiting times for patients still on the list
at a point in time. The data reported here relate to the
first measure (data on the second measure are available
in the OECD health database). The data come from
administrative databases (not surveys). Waiting times
are reported both in terms of the average and the
median. The median is the value which separates a
distribution in two equal parts (meaning that half the
patients have longer waiting times and the other half
lower waiting times). Compared with the average, the
median minimises the influence of outliers (patients
with very long waiting times).
References
Jonsson, P.M. et al. (2013), “Finland”, Part II, Chapter 7 in
Waiting Time Policies in the Health Sector: What Works?,
OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264179080-en.
Siciliani, L., M. Borowitz and V. Moran (2013), Waiting Time
Policies in the Health Sector: What Works?, OECD Publishing,
Paris, http://dx.doi.org/10.1787/9789264179080-en.
Smith, P. and M. Sutton (2013), “United Kingdom”, Part II,
Chapter 16 in Waiting Time Policies in the Health Sector:
What Works?, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264179080-en.
7. ACCESS TO CARE
7.11. Cataract surgery, waiting times from specialist assessment to treatment, 2007 to 2014 (or 2013)
2014 (or 2013)
2010
2007
Days
450
400
350
300
250
200
150
100
50
Average
Poland
Chile
Norway
Spain
Finland
Estonia
Australia
New Zealand
Portugal
United Kingdom
Denmark
Israel
Canada
Hungary
Poland
Estonia
Chile
Norway
Spain
Israel
Finland
Portugal
Hungary
New Zealand
Denmark
United Kingdom
Netherlands
0
Median
1 2 http://dx.doi.org/10.1787/888933281097
7.12. Hip replacement, waiting times from specialist assessment to treatment, 2007 to 2014 (or 2013)
Average
Chile
Poland
Estonia
Norway
Spain
Australia
Finland
New Zealand
Canada
Portugal
United Kingdom
Hungary
Israel
Denmark
Poland
Chile
Estonia
Spain
Hungary
Norway
Portugal
Finland
Israel
New Zealand
United Kingdom
Denmark
Netherlands
2014 (or 2013)
2010
2007
Days
500
450
400
350
300
250
200
150
100
50
0
Median
1 2 http://dx.doi.org/10.1787/888933281097
7.13. Knee replacement, waiting times from specialist assessment to treatment, 2007 to 2014 (or 2013)
2014 (or 2013)
2010
2007
Days
600
500
400
300
200
100
Average
Poland
Estonia
Australia
Portugal
Norway
Spain
Chile
Finland
Hungary
New Zealand
Canada
Israel
United Kingdom
Denmark
Poland
Chile
Estonia
Hungary
Spain
Portugal
Norway
Finland
Israel
New Zealand
United Kingdom
Denmark
Netherlands
0
Median
1 2 http://dx.doi.org/10.1787/888933281097
129
8. QUALITY OF CARE
Avoidable hospital admissions
Diabetes care
Prescribing in primary care
Mortality following acute myocardial infarction (AMI)
Mortality following stroke
Waiting times for hip fracture surgery
Surgical complications
Obstetric trauma
Care for people with mental health disorders
Screening, survival and mortality for cervical cancer
Screening, survival and mortality for breast cancer
Survival and mortality for colorectal cancer
Childhood vaccination programme
Influenza vaccination for older people
Patient experience with ambulatory care
131
8. QUALITY OF CARE
Most health systems have developed a “primary level” of
care whose functions include health promotion and disease
prevention, managing new health complaints, as well as
long-term conditions and referring patients to hospital-based
services when appropriate. A key aim is to keep people well,
by providing a consistent point of care over the longer-term,
tailoring and co-ordinating care for those with multiple
health care needs and supporting the patient in selfeducation and self-management.
Asthma, chronic obstructive pulmonary disease (COPD)
and congestive heart failure (CHF) are three widely prevalent
long-term conditions. Both asthma and COPD limit the
ability to breathe: asthma symptoms are usually intermittent
and reversible with treatment, whilst COPD is a progressive
disease that almost exclusively affects current or prior
smokers. Asthma affects an estimated 235 million people
worldwide (WHO, 2013). More than 3 million people died of
COPD in 2012, which is equal to 6% of all deaths globally
that year (WHO, 2015). CHF is a serious medical condition
in which the heart is unable to pump enough blood to meet
the body’s needs. CHF is often caused by hypertension,
diabetes or coronary heart disease. Heart failure is estimated
to affect over 26 million people worldwide resulting in more
than 1 million hospitalisations annually in both the United
States and Europe.
Common to all three conditions is the fact that the evidence
base for effective treatment is well established and much of
it can be delivered at a primary care level. A high-performing
primary care system can reduce acute deterioration in people
living with asthma, COPD or CHF and prevent their admission to hospital.
improvement in t he quality of p rimary care. The
approaches countries are taking to improve the quality of
primary care have been described in a series of country
reviews undertaken by OECD. Israel’s Quality Indicators for
Community Health Care program provides an example of how
publicly reported information on care is used to incentivise
providers to develop better services (OECD, 2012).
The indicators are defined as the number of hospital
admissions with a primary diagnosis of asthma, COPD
and CHF among people aged 15 years and over per
100 000 population. Rates were age-sex standardised
to the 2010 OECD population aged 15 and over.
Disease prevalence may explain some, not all, variations in cross-country rates. Differences in coding
practices among countries and the definition of an
admission may also affect the comparability of data.
For example, while the transfer of patients from one
hospital to another is required to be excluded from
the calculations to avoid “double counting”, this cannot
be fully complied with by some countries. There is
also a risk that countries that do not have the capacity
to track patients through the system do not identify
all relevant admissions due to changes in diagnosis
coding on transfer between hospitals. The impact of
excluding admissions where death occurred has been
investigated, given these admissions are less likely to
be avoidable. The results reveal that while the impact
on the indicator rate varies across conditions (e.g. on
average, reduced asthma rates by less than 1%
whereas for CHF it was nearly 9%), the changes in the
variation of rates across countries for each condition
was minimal.
Figure 8.1 shows hospital admission rates for asthma and
COPD together, given the physiological relationship
between the two conditions. Admission rates for asthma
vary 11-fold across countries with Italy, Switzerland and
Mexico reporting the lowest rates and Korea, United States
and the Slovak Republic reporting rates over twice the
OECD average. International variation in admissions for
COPD is 17-fold across OECD countries, with Japan and Italy
reporting the lowest rates and Hungary and Ireland the
highest rates. Combined, there is a lower 8-fold variation
across countries for the two respiratory conditions. Hospital admission rates for CHF vary 7-fold, as shown in
Figure 8.2. Mexico, United Kingdom and Korea have the
lowest rates, while the Slovak Republic, Hungary and
Poland report rates at least 1.8 times the OECD average.
References
The majority of countries report a reduction in admission
rates for CHF over recent years. This may represent an
WHO (2013), “Asthma”, Fact Sheet No. 307,
www.who.int/mediacentre/factsheets/fs307/en/.
132
OECD (2012), OECD Reviews of Health Care Quality: Israel 2012:
http://dx.doi.org/10.1787/9789264029941-en.
WHO (2015), “Chronic Obstructive Pulmonary Disease
(COPD)”, Fact Sheet No. 315,
8. QUALITY OF CARE
8.1. Asthma and COPD hospital admission in adults, 2013 (or nearest year)
COPD
Asthma
Age-sex standardised rates per 100 000 population
600
500
400
300
200
100
Ja
It a
pa
n
Po l y
Sw r tu
i t z gal
er
la
n
M d
ex
ic
o
Ch
il e
Fr
an
Sl c e
Lu ove
xe ni
m a
bo
ur
Fi g
N e nl a
th nd
er
C z lan
ec ds
h
Re
S w p.
ed
e
Ic n
el
an
d
Sp
a
O E in
CD
Be 32
lg
iu
No m
rw
ay
La
tv
Ca ia
na
da
Is
ra
e
Po l
l
Un Ge and
i te rm
d
K i any
n
Sl gdo
ov
ak m
Re
p
Un K .
i t e or e
d
a
St
a
De tes
nm
a
Es rk
to
n
Au ia
st
A ria
Ne us tr
w a li
Ze a
al
a
Hu n d
ng
ar
Ir e y
la
nd
0
Note: Three-year average for Iceland and Luxembourg.
1 2 http://dx.doi.org/10.1787/888933281105
8.2. Congestive heart failure hospital admission in adults, 2008 and 2013 (or nearest years)
2008
2013
600
500
400
300
200
100
De n
n
Sw ma
it z rk
er
la
nd
Ir e
la
n
No d
rw
a
Ca y
na
d
Be a
lg
iu
Po m
r tu
ga
Ic l
Ne ela
th nd
er
la
nd
s
N e Sp
ain
w
Ze
al
an
d
Is
ra
el
Fr
an
Au c e
st
ra
O E li a
CD
30
It a
l
Fi y
nl
an
Au d
st
r
Sw ia
ed
S en
Un lov
i te eni
a
d
St
a
Ge tes
rm
a
Cz
ec ny
h
R
Sl
o v e p.
ak
Re
H u p.
ng
ar
y
Po
la
nd
a
il e
pa
Ja
Ch
m
re
Ko
do
ex
M
Ki
d
i te
Un
ng
ic
o
0
Note: Three-year average for Iceland.
1 2 http://dx.doi.org/10.1787/888933281105
133
8. QUALITY OF CARE
Diabetes care
Diabetes is a chronic disease that occurs when the body’s
ability to regulate excessive glucose levels in the blood is
lost. Across the OECD countries, diabetes is a leading cause
of cardiovascular disease, blindness, kidney failure, and
lower limb amputation. Globally it is estimated that over
380 million people had diabetes in 2014 and by 2035 it is
projected that close to 600 million people will have the condition. Diabetes caused close to 5 million deaths in 2014
(IDF, 2014). Many countries have established comprehensive approaches to diabetes care, but there are indications
that more can be done to prevent the disease (OECD, 2014).
Cholesterol-lowering drugs and medications to reduce
blood pressure are recommended in most national guidelines for the care of diabetes patients (see indicator “Prescribing in primary care” in Chapter 8)
Poor control of the level of glucose in the blood over the
short term can lead to vomiting, dehydration and even
cause coma, whereas sustained high levels of blood glucose
over a number of years can result in serious diseases with
ongoing consequences for a person’s health and wellbeing.
For example, diabetes can cause nerve damage and poor
blood circulation over time. These problems make the feet
vulnerable to skin ulcers that can deteriorate quickly and
be difficult to treat. An ulcer that does not heal can cause
severe damage to tissues and bone over time and can eventually require amputation of a toe, foot or part of a leg.
Proper diabetes management and careful foot care can prevent foot ulcers. Ongoing management of diabetes usually
involves a considerable amount of self-care, and therefore,
advice and education are central to the primary care of people with diabetes. Effective control of blood glucose levels
through routine monitoring, dietary modification and regular exercise can reduce the onset of serious complications
and the need for hospitalisation.
Figure 8.3 shows the avoidable hospital admissions for diabetes. The international variation in the rates is nearly 8fold, with Italy, Switzerland and Spain reporting the lowest
rates and Austria, Korea and Mexico reporting rates at least
two times that of the OECD average. Prevalence of diabetes
may explain some of the variation in diabetes admission
rates. A positive relationship can be demonstrated between
hospital admissions for the general population and diabetes-related hospital admissions, providing some indication
that overall access to hospital care can also play a role in
explaining the level of hospital care among the diabetic
population (OECD, 2015).
Hospital admissions for major lower extremity amputation
(i.e. surgical removal of lower limb, including leg or foot)
reflect the long-term quality of diabetes care. Figure 8.4
shows the rates of major lower extremity amputation in
adults with diabetes. In the left panel the rates based on
the general population are presented. The international
variation in rates is over 14-fold, with Korea and Italy
134
reporting rates lower than 3 per 100 000 general population
and Israel, Slovenia and Portugal reporting rates above 10.
Rates based on the estimated diabetic population are presented in the right panel. The rates based on the diabetic
population are on average 9-fold higher than for the general population and display differences in the ranking of
countries, providing an indication that differences in disease prevalence across countries may explain some, but
not all, cross-country variation.
The indicator for diabetes hospital admission is
defined as the number of hospital admissions with a
primary diagnosis of diabetes among people aged
15 years and over per 100 000 population. The indicator for major lower extremity amputation in adults
with diabetes is defined as the number of discharges
of people aged 15 years and over per 100 000 population, for the general population and the estimated diabetic population. Rates for both indicators were agesex standardised to the 2010 OECD population aged 15
and over.
Differences in data definition and coding practices
between countries may affect the comparability of
data. For example, coding of diabetes as a principal
diagnosis versus a secondary diagnosis varies across
countries. This is more pronounced for diabetes than
other conditions, given that in many cases admission
is for the secondary complications of diabetes rather
than diabetes itself. Diabetes population estimates
used to calculate amputation indicator rates were
self-reported by countries. Subject to further data
development, the use of diabetes population estimates
to standardise the indicator rates will be considered in
the future.
References
International Diabetes Federation (2014), IDF Diabetes Atlas
Sixth Edition Update 2014, https://www.idf.org/sites/default/
files/EN_6E_Atlas_Full_0.pdf.
Better Health and Quality of Care, OECD Health Policy Studies, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264233010-en.
OECD (2014), OECD Reviews of Health Care Quality: Czech
Republic 2014: Raising Standards, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264208605-en.
8. QUALITY OF CARE
Diabetes care
8.3. Diabetes hospital admission in adults, 2008 and 2013 (or nearest years)
2013
2008
450
400
350
300
250
200
150
100
50
ng d
do
er m
la
nd
No s
rw
Po ay
r tu
ga
l
Is
ra
e
Ca l
na
Hu d a
ng
a
Sw r y
ed
Sl en
ov
e
De ni a
nm
ar
Fi k
nl
an
d
La
tv
i
Ir e a
la
Au nd
st
ra
O E li a
CD
31
Lu Ja
p
xe
a
m n
bo
ur
Be g
lg
iu
m
Ne Fr a
w nc
Ze e
C z alan
ec
d
Un h
i t e Rep
d
St .
a
Ge tes
rm
Sl
ov any
ak
Re
p.
Ch
il e
Po
la
n
Au d
st
ria
Ko
re
M a
ex
ic
o
a in
an
Ki
Ne
d
i te
Un
th
el
Sp
Ic
la
er
Sw
it z
It a
ly
nd
0
1 2 http://dx.doi.org/10.1787/888933281111
8.4. Major lower extremity amputation in adults with diabetes, 2013 (or nearest year)
Korea
Italy
Switzerland
United Kingdom
Ireland
Luxembourg
Iceland
Sweden
Australia
Netherlands
Belgium
Norway
New Zealand
OECD 21/11
Spain
Canada
France
Denmark
Germany
Portugal
Slovenia
Israel
2.4
2.7
3.1
3.1
3.2
3.5
3.5
4.1
4.5
4.7
4.8
5.7
5.9
6.4
6.7
7.4
7.5
8.5
9.2
11.9
15.3
15.9
20
15
10
5
0
12.7
29.7
26.7
42.9
55.8
57.4
54.0
49.6
57.0
92.8
122.7
87.4
0
50
100
150
Age-sex standardised rates per 100 000 people with diabetes
1 2 http://dx.doi.org/10.1787/888933281111
135
8. QUALITY OF CARE
Beyond consumption and expenditure information (see
Chapter 10), prescribing can be used as an indicator of
health care quality. Antibiotics, for example, should be prescribed only where there is an evidence-based need, to
reduce the risk of resistant strains. Likewise, quinolones
and cephalosporins are considered second-line antibiotics
in most prescribing guidelines. Their use should be
restricted to ensure availability of effective second-line
therapy should first-line antibiotics fail. Total volume of
antibiotics prescribed, and second-line as a proportion of
total volume, have been validated as markers of quality in
the primary care setting. In May 2015, the World Health
Assembly endorsed a global action plan to tackle antimicrobial resistance (http://who.int/drugresistance/global_action_plan),
which is also reflected in several national strategies.
Figure 8.5 shows volume of all antibiotics prescribed in primary care, with volumes of second- line antibiotics embedded within the total amount. Total volumes vary more than
four-fold across countries, with Chile, the Netherlands and
Estonia reporting the lowest volumes, and Turkey and
Greece reporting volumes much higher than the OECD average. Volumes of second-line antibiotics vary almost 16-fold
across countries. The Nordic countries, the Netherlands and
the United Kingdom report the lowest volumes of these
antibiotics, and Korea, the Slovak Republic and Greece the
highest. Variation is likely to be explained, on the supply side,
by differences in the regulation, guidelines and incentives
that govern primary care prescribers and, on the demand
side, by cultural differences in attitudes and expectations
regarding the natural history and optimal treatment of
infective illness.
In conjunction with additional indicators of the quality of
primary care for diabetes (see “Diabetes care”), Health at a
Glance is for the first time reporting two indicators related
to the quality of prescribing in primary care for diabetic
patients. In diabetic individuals with hypertension, angiotensin-converting enzyme inhibitors (ACE-I) or angiotensin
receptor blockers (ARB) are recommended in most national
guidelines as first-line medications to reduce blood pressure, since they are most effective at reducing the risk of
cardiovascular disease and renal disease. Figures 8.6
and 8.7 suggest there is wide variability across countries in
prescribing practices for diabetes patients, with 27% of
diabetic patients in the Slovak Republic given prescriptions
for cholesterol-lowering medication, compared with 81% in
New Zealand. There is greater consistency in the proportion
of diabetic patients on antihypertensive agents with at
least one prescription for ACE-I or ARB, with the exception
of the Slovak Republic.
Benzodiazepines are often prescribed for elderly patients
for anxiety and sleep disorders, despite the risk of adverse
side effects such as fatigue, dizziness and confusion. A
meta-analysis suggests that the use of benzodiazepines in
elderly people is associated with more than double the risk
136
of developing such adverse effects compared with placebo
(Sithamparanathan et al., 2012). Figures 8.8 and 8.9 indicate
th at, ac ro s s the O EC D, o n ave rag e aroun d 2 9 p e r
1 000 elderly patients receive long-term prescriptions for
benzodiazepines and related drugs ( 365 defined daily
doses in one year), and 62 per 1 000 have received at least
one prescription for a long-acting benzodiazepine or
related drugs within the year.
To reduce the potentially harmful overuse and misuse of
medicines, diagnostic tests and procedures, the Choosing
Wisely campaign was launched in 2012. Increasingly
international, the campaign comprises evidence-based
information for clinicians and patients on when medications and procedures may be inappropriate. Appropriate
use of antibiotics and benzodiazepines is addressed
(www.choosingwisely.org).
Defined daily dose (DDD) is the assumed average
maintenance dose per day for a drug used for its main
indication in adults. DDDs are assigned to each active
ingredient in a given therapeutic class by international expert consensus. For instance, the DDD for
oral aspirin equals 3 grams, which is the assumed
maintenance daily dose to treat pain in adults. DDDs
do not necessarily reflect the average daily dose actually
used in a given country. DDDs can be aggregated
within and across therapeutic classes of the Anatomic
Therapeutic Classification (ATC). For more detail, see
www.whocc.no/atcddd.
In Figure 8.5, data for Chile include over the counter
(OTC) drugs. Data for Canada, Israel and Luxembourg
exclude drugs prescribed in hospitals, non-reimbursed
drugs and OTC drugs. Data for Iceland refer to all
sectors, not just primary care. Data for Portugal include
OTC and non-reimbursed drugs. Data for Australia
include non-reimbursed drugs. Data for Turkey refer
to outpatient health care.
Denominators comprise the population held in the
national prescribing database, rather than the general
population (with the exception of Belgian data on
benzodiazepines, which comes from a national
health survey).
References
Sithamparanathan, K., A. Sadera and L. Leung (2012),
“Adverse Effects of Benzodiazepine Use in Elderly People: A
Meta-analysis”, Asian Journal of Gerontology & Geriatrics,
Vol. 7, No. 2, pp. 107-111.
8. QUALITY OF CARE
8.5. Overall volume of antibiotics prescribed, 2013 (or nearest year)
All
2nd line (where reported)
DDS per 1 000 population, per day
45
40
35
30
25
20
15
10
5
Is
ra
e
rm l
an
No y
rw
ay
Ko
re
Au a
st
De r ia
nm
ar
Fi k
nl
a
Li nd
th
ua
ni
Ca a
C z nad
ec
a
h
Re
U n O p.
i te EC
D
d
Ki 29
ng
do
Ic m
el
an
P o d¹
r tu
Au gal
st
ra
li
Po a
Sl
la
ov nd
ak
Re
p
Ir e .
la
nd
Lu Sp
xe a in
m
bo
ur
g
It a
l
Be y
lg
iu
m
Fr
an
c
Gr e
ee
ce
Tu
rk
ey
y
ia
Sl
Ge
ov
en
ia
ar
ng
Hu
en
tv
ed
Sw
La
s
a
Es
to
ni
nd
Ne
th
er
la
Ch
il e
0
1. Data refer to all sectors (not only primary care).
Source: European Centre for Disease Prevention and OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en.
1 2 http://dx.doi.org/10.1787/888933281125
8.6. People with diabetes with a prescription of
cholesterol lowering medication in the past year, 2013
(or nearest year)
1 2 http://dx.doi.org/10.1787/888933281125
Re
p.
m
a
Sl
ov
ak
iu
11
re
lg
Be
Ko
s
nd
CD
OE
ay
la
rw
er
Ne
th
en
No
k
ed
ar
Sw
d
nd
nm
De
la
an
ga
Ne
Sl
Ir e
Sl
ov
r tu
en
ov
Re
ak
ia
p.
ia
re
en
ov
CD
Sl
OE
Ko
ay
No
rw
ga
d
an
r tu
Po
nl
ed
Fi
Sw
lg
iu
m
k
ar
Ne
Be
th
De
nm
nd
nd
er
la
la
an
al
Ir e
Ze
w
Ne
al
0
Ze
0
w
20
a
20
12
40
l
60
40
en
60
s
80
d
80
l
% of diabetic patients
100
Po
% of diabetic patients
100
8.7. People with diabetes with a prescription
of recommended antihypertensive medication in the past
year, 2013 (or nearest year)
1 2 http://dx.doi.org/10.1787/888933281125
8.8. Elderly people prescribed long-term benzodiazepines
or related drugs, 2013 (or nearest year)
8.9. Elderly people prescribed long-acting
benzodiazepines or related drugs, 2013 (or nearest year)
Per 1 000 persons aged 65 years and over
70
Per 1 000 persons aged 65 years and over
250
60
200
50
40
150
30
100
20
50
10
0
1 2 http://dx.doi.org/10.1787/888933281125
Sl
Sl
ov
ov eni a
ak
Re
p
Ir e .
la
n
Po d
r tu
g
No al
rw
O E ay
CD
1
Ca 4
na
De da
nm
a
Be rk
lg
iu
m
Is
ra
S el
Ne we
w den
Z
Ne eala
th nd
er
la
nd
Fi s
nl
an
d
a
re
Ko
re
a
s
Ko
nd
la
er
Ne
th
Fi
nl
an
d
d
an
p.
Ne
w
Ze
al
Re
da
ak
Sl
ov
Ca
na
k
ia
ar
nm
De
13
en
Sl
ov
en
CD
OE
ed
Sw
No
rw
ay
el
l
ga
ra
Is
r tu
Po
Ir e
la
nd
0
1 2 http://dx.doi.org/10.1787/888933281125
137
8. QUALITY OF CARE
Mortality due to coronary heart disease has declined substantially since the 1970s (see indicator “Mortality from
cardiovascular diseases” in Chapter 3). Advances in the
prevention such as smoking (see indicator “Tobacco consumption among adults” in Chapter 4) and treatment of
cardiovascular diseases outpaced those of many other diseases (OECD, 2015a).
A good indicator of acute care quality is the 30-day AMI
case-fatality rate. This measure reflects the processes of
care, such as timely transport of patients and effective
medical interventions. The indicator is influenced by not
only the quality of care provided in hospitals but also differences in hospital transfers, average length of stay and
AMI severity.
Figure 8.10 shows the case-fatality rates within 30 days of
admission for AMI when the death occurs in the same hospital as the initial AMI admission. The lowest rate is found
in Australia at 4.1% and the highest rate is in Mexico at 28.2%,
suggesting AMI patients do not always receive recommended
care. In Mexico, the quality of pre-hospital emergency
medical services is reportedly poor (Peralta, 2006), and the
high rates of uncontrolled diabetes may also be a contributing factor in explaining the high AMI case-fatality rates (see
indicator “Diabetes care” in Chapter 8) as patients with
diabetes have worse outcomes after AMI compared to
those without diabetes, particularly if the diabetes is poorly
controlled. In Japan, people are less likely to die of heart
disease overall, but are more likely to die once admitted
into hospital for AMI compared to other OECD countries.
One possible explanation is that the severity of patients
admitted to hospital with AMI may be more advanced
among a smaller group of people across the population, but
could also reflect underlying differences in emergency care,
diagnosis and treatment patterns (OECD, 2015b).
Figure 8.11 shows 30-day case fatality rates where fatalities
are recorded regardless of where they occur. This is a more
robust indicator because it records deaths more widely
than the same-hospital indicator, but it requires a unique
patient identifier and linked data which is not available in
all countries. The AMI case-fatality rate ranges from 7.1% in
Canada to 18.8% in Hungary and 19.1% in Latvia.
Case-fatality rates for AMI have decreased substantially
between 2003 and 2013 (Figures 8.10 and 8.11). Across the
OECD, case fatalities fell from 11.2% to 8.0% when considering same hospital deaths and from 14.3% to 9.5% when
considering deaths occurred in and out of hospital. The
rate of decline was particularly striking in the Slovak
Republic, the Netherlands and Australia for the first indicator
and in Finland and Poland for the second indicator, with
more than 6% annual average reduction per year compared
to an OECD average of respectively 3 and 4 %. Better access
to high-quality acute care for heart attack, including timely
transportation of patients, evidence-based medical inter-
138
ventions and high-quality specialised health facilities such
as percutaneous catheter intervention-capable centres
have helped to reduce 30-day case-fatality rates (OECD,
2015a). For example, Korea had higher case-fatality rates
for AMI but in 2006 it has implemented a Comprehensive
Plan for CVD, encompassing prevention, primary care and
acute CVD care (OECD, 2012). Under the Plan, specialised
services were enhanced through a creation of regional
cardio and cerebrovascular centres throughout the country,
and average waiting time from emergency room arrival to
initiation of catheterisation fell from 72.3 in 2010 to
65.8 minutes in 2011, leading to a reduction in case-fatality
(OECD, 2015a).
The case-fatality rate measures the percentage of
people aged 45 and over who die within 30 days
following admission to hospital for a specific acute
condition. Rates based on admission data refer to the
death occurred in the same hospital as the initial
admission. Admissions resulting in a transfer were
excluded for all countries except Australia, Belgium,
Denmark, Hungary, Ireland, Israel, Japan, Luxembourg,
Mexico, Netherlands, Slovak Republic and Sweden.
This exclusion generally increases the rate compared
with those countries which do not exclude these
transfers. Rates based on patient data refer to the
death occurred in the same hospital, a different hospital, or out of hospital.
Rates are age-sex standardised to the 2010 OECD
population aged 45+ admitted to hospital for a specific
acute condition such as AMI and ischemic stroke.
References
OECD (2015a), Cardiovascular Disease and Diabetes: Policies for
Better Health and Quality of Care, OECD Health Policy Studies, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264233010-en.
OECD (2015b), OECD Reviews of Health Care Quality: Japan
2015: Raising Standards, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264225817-en.
OECD (2012), OECD Reviews of Health Care Quality: Korea 2012:
http://dx.doi.org/10.1787/9789264173446-en.
Peralta, L.M.P. (2006), “The Prehospital Emergency Care System in Mexico City: A System’s Performance Evaluation”,
P re h o s p i t a l a n d D i s a s t e r M e d i c i n e , Vo l . 2 1 , N o. 2 ,
pp. 104-111.
8. QUALITY OF CARE
8.10. Thirty-day mortality after admission to hospital for AMI based on admission data, 2003 to 2013 (or nearest years)
2003
2013
2008
Age-sex standardised rate per 100 admissions of adults aged 45 years and over
30
25
20
15
10
5
Au
st
ra
Sw lia¹
ed
en
Po ¹
la
Sl nd
ov
en
ia
Un
I
i te t a
ly
d
St
De a tes
nm
ar
Ir e k ¹
la
nd
¹
N e F inl
w an
Ze d
al
an
Ca d
C z nad
ec
a
h
Re
p
Is .
ra
e l¹
No
rw
ay
L u Ic e
xe l a n
m
bo d
ur
g¹
Sl Fr a
ov n c
ak e
Re
Be p.¹
N e l gi
Un t h um
i te er la ¹
d
K i nds
¹
n
S w gdo
m
it z
er ¹
la
nd
Sp
a
O E in
CD
32
Ko
r
Ge e a
rm
a
Po ny
r tu
g
Au al
st
r
Es ia
to
ni
Ja a
p
Hu an¹
ng
ar
y¹
Ch
il e
La
tv
M ia
ex
ic
o¹
0
Note: 95% confidence intervals represented by H. Three-year average for Iceland and Luxembourg.
1. Admissions resulting in a transfer are included.
1 2 http://dx.doi.org/10.1787/888933281135
8.11. Thirty-day mortality after admission to hospital for AMI based on patient data, 2003 to 2013 (or nearest years)
2008
2003
2013
25
20
15
10
5
ia
y
ar
tv
La
a
ng
g
ni
Hu
to
Es
bo
Lu
xe
m
h
ec
Cz
ur
p.
l
Re
ga
a
re
r tu
Po
Ko
20
m
do
CD
OE
ia
ng
Ki
d
Un
i te
nd
en
ov
Sl
la
er
it z
Sw
d
el
ra
Is
en
an
nl
Fi
ed
Sw
ar
k
a in
nm
De
Sp
nd
d
Ze
Ne
w
la
an
al
la
er
Po
s
nd
ay
rw
th
Ne
It a
ly
No
Ca
na
da
0
Note: 95% confidence intervals represented by H. Three-year average for Luxembourg.
1 2 http://dx.doi.org/10.1787/888933281135
139
8. QUALITY OF CARE
Stroke and other cerebrovascular diseases accounted for
around 7% of all deaths in OECD countries in 2013. Ischemic stroke represented around 85% of all cerebrovascular
disease cases. It occurs when the blood supply to a part of
the brain is interrupted, leading to a necrosis (i.e. the cells
that die) of the affected part. Treatment for ischemic stroke
has advanced dramatically over the last decade. Clinical
trials have demonstrated clear benefits of thrombolytic
treatment for ischemic stroke as well as receiving care in
dedicated stroke units to facilitate timely and aggressive
diagnosis and therapy for stroke victims (Hacke et al., 1995;
Seenan et al., 2007).
Figure 8.12 shows the case-fatality rates within 30 days of
admission for ischemic stroke when the death occurred in
the same hospital as the initial stroke admission.
Figure 8.13 shows the case-fatality rate where deaths are
recorded regardless of where they occurred. This indicator
is more robust because it captures fatalities more comprehensively. Although more countries can report the more
partial same-hospital measure, an increasing number of
countries are investing in their data infrastructure and are
able to provide more comprehensive measures.
Across OECD countries 8.4% of patients in 2013 died within
30 days in the same hospital in which the initial admission
for ischemic stroke occurred (Figure 8.12). The case-fatality
rates were highest in Mexico (19.5%) and Latvia (18.4%).
Rates were less than 5% in Japan, Korea and the United
States. With the exception of Japan and Korea, countries
that achieve better results for ischemic stroke also tend to
report good case-fatality rates for acute myocardial
infarction (AMI). This suggests that certain aspects of acute
care may be influencing outcomes for both stroke and AMI
patients. By contrast, Japan reports the lowest rates for
ischemic stroke but high case-fatality rates for AMI. This
somewhat paradoxical result requires further investigation
but may be associated with the severity of disease in the
country that is not captured in the data (see indicator “Mortality following acute myocardial infarction” in Chapter 8
for more details).
Across the 19 countries that reported in- and out-of-hospital case-fatality rates, 10.1% of patients died within 30-days
of being admitted to hospital for stroke (Figure 8.13). This
figure is higher than the same-hospital based indicator
because it captures deaths that occur not just in the same
hospital but also in other hospitals and out-of-hospital.
140
Between 2003 and 2013, case-fatality rates for ischemic
stroke have decreased substantially (Figures 8.12 and 8.13).
Across the OECD, case fatalities fell from 10.2% to 8.4%
when considering same hospital rates and from 12.7% to
10.1% when considering in- and out-of-hospital rates. The
United Kingdom and the Netherlands for the first indicator
and the United Kingdom, Estonia and Finland for the second indicator were able to reduce their rates by an average
annual reduction of more than 6% compared to an OECD
average of respectively 2 and 2.5%. Better access to highquality stroke care, including timely transportation of
patients, evidence-based medical interventions and highquality specialised facilities such as stroke units have
helped to reduce 30-day case-fatality rates (OECD, 2015).
Despite the progress seen so far, there is still room to
improve implementation of best practice acute care for cardiovascular diseases including stroke across countries. To
shorten acute care treatment time, targeted strategies can
be highly effective. But to encourage the use of evidencebased advanced technologies in acute care, wider
approaches are needed. Adequate funding and trained professionals should be made available, and health care delivery systems should be adjusted to enable easy access
(OECD, 2015).
Case-fatality rates are defined in indicator “Mortality
following acute myocardial infarction” in Chapter 8.
References
Hacke, W. et al. (1995), “Intravenous Thrombolysis with
Recombinant Tissue Plasminogen Activator for Acute
Hemispheric Stroke. The European Co-operative Acute
Stroke Study (ECASS)”, Journal of the American Medical
Association, Vol. 274, No. 13, pp. 1017-1025.
http://dx.doi.org/10.1787/9789264233010-en.
Seenan, P., M. Long and P. Langhorne (2007), “Stroke Units
in Their Natural Habitat: Systematic Review of Observational Studies”, Stroke, Vol. 38, pp. 1886-1892.
8. QUALITY OF CARE
8.12. Thirty-day mortality after admission to hospital for ischemic stroke based on admission data, 2003 to 2013
(or nearest years)
2008
2003
2013
25
20
15
10
5
Fi
nl
an
No d
rw
ay
Is
ra
e l¹
It a
l
Au y
s
Ge tr ia
rm
a
S w ny
S w ede
n¹
it
N e z er l
a
th
er nd
la
nd
s
Fr ¹
an
ce
N e Ic el
w and
Ze
al
a
OE nd
CD
31
Ch
De il e
L u nm
U n xe m a r k¹
i te bo
d
K i ur g
ng ¹
d
Au om¹
st
ra
Be lia¹
lg
C z ium
ec
h ¹
R
H u e p.
ng
ar
Ire y¹
la
nd
¹
Sp
a in
Ca
na
Po da
Sl r tu
ov g a
ak
l
Re
p.¹
Es
to
Sl ni a
ov
en
ia
La
tv
M ia
ex
ic
o¹
a
es
St
at
re
Un
i te
d
Ja
Ko
pa
n¹
0
Note: 95% confidence intervals represented by H. Three-year average for Iceland and Luxembourg.
1. Admissions resulting in a transfer are included.
1 2 http://dx.doi.org/10.1787/888933281146
8.13. Thirty-day mortality after admission to hospital for ischemic stroke based on patient data, 2003 to 2013
(or nearest years)
2003
2013
2008
30
25
20
15
10
5
ia
tv
ia
ar
en
La
y
ov
Sl
a
ng
to
ni
Hu
ep
.
Es
hR
ec
m
xe
Lu
Cz
bo
ur
g
nd
l
ala
ga
Ze
w
r tu
Ne
om
gd
in
dK
i te
Un
Po
ar
k
19
nm
De
CD
ain
OE
Sp
da
na
Ca
ed
en
ds
lan
er
Sw
ly
It a
th
Ne
rw
ay
d
lan
er
it z
Sw
No
l
ae
Isr
d
lan
F in
Ko
re
a
0
Note: 95% confidence intervals represented by H. Three-year average for Luxembourg.
1 2 http://dx.doi.org/10.1787/888933281146
141
8. QUALITY OF CARE
The main risk factors for hip fracture are associated with
ageing – an increased risk of falling and loss of skeletal
strength from osteoporosis. With increasing life expectancy across most OECD countries, it is anticipated that hip
fracture will become a more significant public health issue
in coming years.
country. In Canada, the percentage of patients operated on
within the two day benchmark increased from 87% in 2008
to 92% in 2013, but there is considerable variation in this
indicator between provinces and hospitals (CIHI, 2015).
Portugal saw a decline of hip fracture repair within two
days of admission from 57% in 2008 to 45% in 2013.
In most instances following hip fracture, surgical intervention is required to repair or replace the hip joint. There is
general consensus that early surgical intervention maximises patient outcomes and minimises the risk of complications. General agreement is that surgery should occur
within two days (48 hours) of hospitalisation. Guidelines
in some countries call for even earlier intervention. For
example, the National Institute for Health and Care
Excellence (NICE) clinical guidelines recommend hip fracture
surgery to be performed on the day of hospital admission
or the next day (National Institute for Health and Care
Excellence, 2014).
Time to surgery for hip fracture patients is influenced by
many factors, including hospitals’ surgical theatre capacity,
flow and access. Improvement in timely surgery for patients
with a particular diagnosis or injury (e.g. hip fracture) may be
achieved at the expense of timeliness in others (e.g. hip or
knee replacements).
This is the first time Health at a Glance is reporting on the
time taken to initiate hip fracture surgery after hospital
admission. Timely surgery can be considered an indicator
of the quality of acute care received by patients with hip
fracture.
In 2013, on average across the OECD over 80% of patients
admitted for hip fracture underwent surgery within two
days (Figure 8.14). In Denmark, Iceland and the Netherlands,
the proportion was greater than 95%. Countries with the
lowest proportion of patients operated on within two days of
admission were Spain (43%), Italy (45%) and Portugal (45%).
Many patients were treated sooner than two days following
admission. In the Netherlands and the Czech Republic, for
example, over 40% of patients admitted for hip fracture
underwent surgery on the day of admission.
Figure 8.15 shows the proportion of hip-fracture repairs
occurring within two days of admission in OECD countries
between 2003 and 2013. The OECD average increased from
76% to 81% over that time. The greatest improvement was
observed in Italy, where the proportion increased from 28%
in 2008 to 45% in 2013, and in Israel, where it increased
from 70% in 2003 to 85% in 2013. A policy of comparative
public reporting of hospital indicators, including time to surgery following hip fracture, implemented by Italian authorities may partly explain the improvement observed in that
142
This indicator is defined as the proportion of patients
aged 65 years and over admitted to hospital in a
specified year with a diagnosis of upper femur
fracture, who had surgery initiated within two
calendar days of their admission to hospital. Data are
also provided for the proportion of those patients who
had surgery within one day of their admission to
hospital, and for patients who had surgery on the
same day as their hospital admission. While the
capacity to capture time of admission and surgery in
hospital administrative data varies across countries,
most countries are able to distinguish between
patients who stay overnight and have surgery within
24 hours from patients who have surgery on the day of
admission. Some countries supplied results for surgery
within two calendar days only.
References
CIHI – Canadian Institute for Health Information (2015),
Wait Times for Priority Procedures in Canada, Ottawa.
National Institute for Health and Care Excellence (2014),
“Hip Fracture: The Management of Hip Fracture in
Adults”, NICE Clinical Guideline No. 124, issued June 2011,
last modified March 2014.
8. QUALITY OF CARE
8.14. Hip fracture surgery initiation after admission to hospital, 2013 (or nearest year)
2 days
Same day
Next day
% of patients aged 65 years and over
100
90
80
70
60
50
40
30
20
10
ly
l
a in
Sp
It a
ga
ia
ia
r tu
tv
Po
La
Ne
Sl
ov
en
22
a
CD
ni
OE
d
an
to
Es
nd
al
la
w
Ze
m
iu
lg
st
Be
Cz
Sw
Ir e
ria
el
ra
Au
p.
Re
h
Is
d
an
ec
nd
la
nl
Fi
m
er
it z
ng
Un
i te
d
Ki
Ge
rm
do
an
y
y
ar
ay
Hu
ng
da
No
rw
na
en
Ca
Sw
ed
nd
d
er
Ne
th
De
Ic
el
la
an
k
ar
nm
s
0
1 2 http://dx.doi.org/10.1787/888933281152
8.15. Hip fracture surgery initiation after admission to hospital, 2003 to 2013 (or nearest years)
2003
2013
2008
% of patients aged 65 years and over being operated within two days
100
90
80
70
60
50
40
30
20
10
ly
a in
Sp
It a
l
ga
r tu
tv
ia
Po
La
ia
en
22
ov
Sl
CD
a
ni
OE
to
d
an
al
Es
nd
m
iu
la
Ne
w
Ze
Ir e
lg
st
el
ra
ria
Be
Au
Re
h
ec
Cz
Is
p.
d
an
nl
nd
la
er
Fi
m
it z
Sw
ng
Un
i te
d
Ki
Ge
rm
do
an
y
y
ar
ay
ng
Hu
rw
No
da
na
en
Ca
ed
nd
la
Ne
th
er
el
Ic
Sw
d
an
k
ar
nm
De
s
0
1 2 http://dx.doi.org/10.1787/888933281152
143
8. QUALITY OF CARE
Patient safety remains one of the most prominent issues in
health policy and public debate. High rates of error during
the delivery of medical care have been demonstrated
repeatedly, including the landmark report by the Institute
of Medicine which estimated that more people die from
medical errors than from traffic injuries or breast cancer
(Kohn et al., 2000). Robust comparison of performance with
peers is fundamental to securing improvement. Two types
of patient safety event can be distinguished for this purpose:
never events, those events that should never occur, such as
failure to remove surgical foreign bodies at the end of a
procedure; and adverse events, such as post-operative sepsis,
which can not be avoided in all cases given the high-risk
nature of some procedures, although increased incidence at
an aggregate level may indicate a systemic problem.
Figure 8.16 shows rates for two related adverse events,
pulmonary embolism (PE) or deep vein thrombosis (DVT)
after hip or knee replacement surgery. These are high risk
procedures most commonly associated with postoperative
DVT and PE complications. PE and DVT cause unnecessary
pain and in some cases death, but can be prevented by anticoagulants and other measures before, during and after
surgery. Figure 8.17 shows rates for another adverse event,
sepsis after abdominal surgery. Abdominal surgery is also a
high risk procedure. Likewise, sepsis after surgery, which
may lead to organ failure and death, can in many cases be
prevented by prophylactic antibiotics, sterile surgical techniques and good postoperative care. Figure 8.18 illustrates a
never event (events that should never occur), rates of foreign
body left in during procedure. The most common risk factors
for this never event are emergencies, unplanned changes in
procedure, patient obesity and changes in the surgical
team; preventive measures include counting instruments,
methodical wound exploration and effective communication among the surgical team.
The left panel of Figures 8.16, 8.17 and 8.18. shows the rate
of the three respective postoperative complications based
on the surgical admission, the hospital admission when
the surgery took place. The right panel of these figures
shows rates based on the surgical admission and all subsequent re-admissions to hospital within 30 days, whether at
the same hospital or in another hospital. The use of a
unique patient identifier is required to calculate the indicator rates in the right panel, which is currently not available
in some countries.
Caution is needed in interpreting the extent to which these
indicators accurately reflect international differences in
patient safety rather than differences in the way that countries report, code and calculate rates of adverse events (see
“Definition and comparability” box).
144
Surgical complications are defined as the number of
discharges with ICD codes for complication in any
secondary diagnosis field for the “surgical admission”
and any diagnosis field for any subsequent related readmission within 30 days, divided by the total number
of discharges for patients aged 15 and older. Contrary
to the data presented in Health at a Glance 2013, the
indicator rates have not been adjusted by the average
number of secondary diagnoses, given a strong positive correlation between the number of secondary
diagnoses and indicator rates reported by countries
was not evident in the most recent data.
A fundamental challenge in international comparison
of patient safety indicators centres on the quality of
the underlying data. Variations in how countries
record diagnoses and procedures and define hospital
admissions can affect calculation of rates. For example differences in the use of the present on admission
flag for diagnosis and disease (e.g. ICD-9-CM and ICD10-AM) and procedure classification systems are
known to affect data comparability. In some cases,
higher adverse event rates may signal more developed
patient safety monitoring systems and a stronger
patient safety culture rather than worse care. Recent
analysis of dispersion of postoperative PE or DVT rates
across hospitals within OECD countries revealed
extremely large variations in reported rates, including
implausibly high and low rates for hospitals in the
same country even after risk adjustment. Hence, differences in the national rates presented here are likely
to reflect differences in coding and recording practices
both between and within countries and mask true
differences in care quality. There is a need for greater
consistency in reporting of patient safety events
across countries and significant scope exists for
improved data quality within national patient safety
programs. Wider analysis of coding comparability will
inform future strategies for improvement.
References
Kohn, L.T., J.M. Corrigan and M.S. Donaldson (Editors) (2000),
To Err Is Human: Building a Safer Health System, Institute of
Medicine, National Academy Press, Washington, DC.
8. QUALITY OF CARE
8.16. Postoperative pulmonary embolism (PE) or deep vein thrombosis (DVT) in hip and knee surgeries, 2013
(or nearest year)
DVT
PE
Surgical admission based
All admission based
Poland¹
Finland¹
Spain
Italy¹
Norway
Portugal
Belgium
Sweden¹
United Kingdom
Switzerland
Ireland
United States
Israel
OECD9/9
Slovenia
Canada
Australia
New Zealand
France
2 500
2 000
1 500
Per 100 000 hospital discharges
1 000
500
0
0
500
1 000
1 500
2 000
2 500
Note: Rates have not been adjusted by the average number of secondary diagnoses.
1. The average number of secondary diagnoses is < 1.5.
1 2 http://dx.doi.org/10.1787/888933281167
8.17. Postoperative sepsis in abdominal surgeries 2013 (or nearest year)
All admission based
Poland¹
Finland¹
Korea
Canada
New Zealand
Italy¹
Sweden
Switzerland
Norway¹
Israel
Portugal
United Kingdom¹
OECD8/10
United States
Slovenia¹
Belgium
Spain
Australia
Ireland
3 500 3 000
2 500
2 000
1 500
1 000
500
0
0
500
1 000
1 500
2 000
2 500
3 000 3 500
1 2 http://dx.doi.org/10.1787/888933281167
8.18. Foreign body left in during procedure, 2013 (or nearest year)
All admission based
Poland¹
Slovenia
Italy
Norway¹
Finland¹
Belgium
Portugal
New Zealand
Israel
Spain
OECD8/9
Ireland
Sweden
United Kingdom
United States
Canada
Australia
Switzerland
25
20
15
10
5
0
0
5
10
15
20
25
1 2 http://dx.doi.org/10.1787/888933281167
145
8. QUALITY OF CARE
Obstetric trauma
Patient safety during childbirth can be assessed by looking
at potentially avoidable tearing of the perineum during
vaginal delivery. Tears that extend to the perineal muscles
and bowel wall require surgery. They are more likely to
occur in the case of first vaginal delivery, high baby birth
weight, labour induction, occiput posterior baby position,
prolonged second stage of labour and instrumental delivery. Possible complications include continued perineal pain
and incontinence.
These types of tears are not possible to prevent in all cases,
but can be reduced by employing appropriate labour management and high quality obstetric care. Hence, the proportion of deliveries involving higher degree lacerations is a
useful indicator of the quality of obstetric care. Obstetric
trauma indicators have been used by the US Joint Commission as well as by different international quality initiatives
seeking to assess and improve obstetric care (AHRQ, 2006).
Episiotomy is a surgical incision of the perineum performed to widen the vaginal opening for the delivery of an
infant. Wide variation in the use of episiotomy during vaginal deliveries currently exists across Europe, ranging from
around 70% of births in Portugal and Poland in 2010 to less
than 10% in Sweden, Denmark and Iceland (Euro-Peristat,
2013). The selective use of episiotomy to decrease severe
perineal lacerations during delivery is controversial, with
claims that there are currently inadequate data to properly
evaluate safety and effectiveness considerations (Lappen
and Gossett, 2010).
Obstetric trauma indicators are considered relatively reliable and comparable across countries, particularly given
they are less sensitive to variations in secondary diagnosis
coding practices across countries. Nevertheless, differences in the consistency with which obstetric units report
these complications may complicate international comparison. Fear of litigation, for example, may cause underreporting; conversely systems that rely on specially trained
administrative staff to identify and code adverse events
from patients’ clinical records may produce more reliable
data.
Obstetric trauma with instrument refers to deliveries using
forceps or vacuum extraction. As the risk of a perineal laceration is significantly increased when instruments are
used to assist the delivery, rates for this patient population
are reported separately. The average rate of obstetric
trauma with instrument (6.0 per 100 instrument-assisted
vaginal delivery) across 21 OECD countries in 2013 was
nearly 4 fold the rate without instrument (1.6 per
100 vaginal delivery without instrument assistance). The
rate of obstetric trauma after vaginal delivery with instrument (Figure 8.19) shows high variation across countries.
Reported rates vary from below 2% in Poland, Slovenia, Italy
146
and Israel to more than 10% in the United States, Sweden,
Denmark and Canada.
Rates of obstetric trauma after vaginal delivery without
instrument (Figure 8.20) display equally large variation
across countries, ranging from 0.3% or less in Poland and
Slovenia to 2.8% or above in the United Kingdom, Sweden
and Canada. There is a strong relationship between the two
indicators, with Poland and Slovenia reporting the lowest
rates and Sweden and Canada reporting amongst the highest rates for both indicators.
The two obstetric trauma indicators are defined as the
proportion of instrument assisted/non-assisted vaginal deliveries with third- and fourth-degree obstetric
trauma codes in any diagnosis and procedure field.
Therefore, any differences in the definition of principal and secondary diagnoses have no influence on the
calculated rates. Several differences in data reporting
across countries may influence the calculated rates of
obstetric patient safety indicators. These relate primarily to differences in coding practice and data
sources. Some countries report the obstetric trauma
rates based on administrative hospital data and others based on obstetric register data. There is some evidence that registries produce higher quality data and
report a greater number of obstetric trauma events
compared to administrative datasets (Baghestan et al.,
2007).
References
AHRQ – Agency for Health Research and Quality (2006),
Patient Safety Indicators Overview: AHRQ Quality Indicators –
February 2006, AHRQ, Rockville, United States.
Baghestan, E. et al. (2007), “A Validation of the Diagnosis of
Obstetric Sphincter Tears in Two Norwegian Databases,
the Medical Birth Registry and the Patient Administration System”, Acta Obstetricia et Gynecologica, Vol. 86,
pp. 205-209.
Euro-Peristat (2013), European Perinatal Health Report:
Health and Care of Pregnant Women and Babies in
Europe in 2010, INSERM, Paris.
Lappen, J.R. and D.R. Gossett (2010), “Changes in Episiotomy
Practice: Evidence-based Medicine in Action”, Expert
Review of Obstetrics and Gynecology, Vol. 5, No. 3,
pp. 301-309.
8. QUALITY OF CARE
Obstetric trauma
8.19. Obstetric trauma, vaginal delivery with instrument, 2013 (or nearest year)
Crude rates per 100 instrument-assisted vaginal deliveries
18
17.1
16
14
13.4
13.2
12
10.3
10
8.4
8.1
8
7.2
7.3
7.2
6.0
6
4.8
4
2
1.4
0.8
3.2
2.9
2.6
2.3
1.9
4.8
4.2
3.7
0.8
da
Ca
na
ar
De
Sw
nm
ed
at
k1
en 1
es
d
d
i te
Un
Ne
Un
i te
St
al
w
Ge
Ze
st
rm
an
an
y
li a
ra
do
Au
ng
d
Ki
it z
Sw
Ne
m
nd
er
CD
la
21
ay 1
OE
No
Ir e
nl
th
Fi
er
rw
la
an
nd
d1
s1
nd
a in
la
lg
Be
Fr
Sp
iu
m
ce
l
an
ga
el
Po
Is
r tu
ra
ly
It a
Sl
Po
ov
la
en
ia
nd
0
1. Based on registry data.
1 2 http://dx.doi.org/10.1787/888933281174
8.20. Obstetric trauma, vaginal delivery without instrument, 2013 (or nearest year)
Crude rates per 100 vaginal deliveries without instrument assistance
3.5
3.1
3.0
2.8
2.6
2.6
2.8
2.6
2.5
2.4
2.5
2.1
1.9
1.5
1.6
1.5
1.4
1.0
0.9
0.8
0.6
0.5
0.5
0.5
0.6
0.5
0.3
0.1
da
na
Ca
en 1
Sw
ed
m
Ki
Un
i te
d
w
Ne
ng
al
do
an
d
k1
Ze
nm
De
la
er
it z
la
Sw
er
th
ar
nd
s1
nd
li a
ra
st
Ne
Au
Ge
rm
an
y
nd
la
Ir e
21
OE
CD
es
at
St
d
i te
Un
No
rw
ay 1
d1
Fi
nl
an
a in
Sp
ce
an
Fr
lg
iu
m
l
ga
r tu
Po
Be
ly
It a
el
ra
Is
ia
en
ov
Sl
Po
la
nd
0
1. Based on registry data.
1 2 http://dx.doi.org/10.1787/888933281174
147
8. QUALITY OF CARE
The burden of mental illness is substantial, affecting an
estimated one in four of the OECD population at any time,
and one in two across the life course (OECD, 2014a). High
quality, timely care has the potential to improve outcomes
and may help reduce suicide and excess mortality for individuals with psychiatric disorders.
secondary prevention was sufficient (OECD, 2014a; OECD,
2014b).
High quality care for mental disorders in inpatient settings
is vital. Figure 8.21 shows rates of inpatient suicide
amongst all psychiatric hospital admissions. Inpatient suicide is a ‘never event’, which should be closely monitored
as an indication of how well inpatient settings are able to
keep patients safe from harm. Most countries report rates
below 0.1 per 100 patients; Denmark and Estonia are exceptions with rates of 0.1 and 0.3 respectively. Steps to prevent
inpatient suicide include identification and removal of
likely opportunities for self-harm, risk assessment of
patients, monitoring and appropriate treatment plans.
The inpatient suicide indicator is composed of a
denominator of patients discharged with a principal
diagnosis or first two listed secondary diagnosis code
of mental health and behavioural disorders (ICD-10
codes F10-F69 and F90-99) and a numerator of the
number of patients who committed “suicide” (ICD-10
codes: X60-X84). There are often fewer than ten inpatient suicides in a given year, meaning that reported
rates can vary. Where possible a 3-year average has
been calculated to give more stability to the indicator.
This was not possible for the Czech Republic, Portugal,
and Switzerland. The data should be interpreted with
caution due to a very small number of cases.
Suicide rate after discharge can be an indicator of the quality of care in the community, and co-ordination between
inpatient and community settings. The risk of suicide in
the first year after discharge from psychiatric inpatient
care is much greater than for the general population. Suicide rate amongst patients who had been hospitalised in
the previous year was 0.43 per 100 patients, compared to a
suicide rate of 0.01 per 100 for the general population in
2012 across OECD countries for which these data are available. Patients with a psychiatric illness are particularly at
risk immediately following discharge from hospital; in all
countries suicide within 30 days of discharge amounted to
at least one quarter of all suicides within the first year following discharge (Figure 8.22). Good discharge planning
and follow-up, and enhanced levels of care immediately
following discharge can help reduce suicide in the high-risk
days immediately following discharge (OECD, 2014a).
Individuals with a psychiatric illness have a higher mortality rate than the general population. An ‘excess mortality’
value that is greater than one implies that people with
mental disorders face a higher risk of death than the rest of
the population. Figures 8.23 and 8.24 show the excess mortality for schizophrenia and bipolar disorder, which is
above two in all countries. A higher rate of physical illness
and chronic disease related to risk factors such as smoking,
drug and alcohol abuse, side effects of psychotropic treatment and poor physical health care and increased risk of
suicide contribute to excess mortality. A multifaceted
disease-related approach is needed to reduce this excess
mortality, including primary care prevention of physical ill
health among people with mental disorders, better integration of physical and mental health care, behavioural interventions, and changing professional attitudes. For
example, Sweden monitors the use of inpatient physical
care for patients with a mental disorder diagnosis that
could have been avoided if primary care and/or primary or
148
Suicide within 30 days and within one year of discharge is established by linking discharge following
hospitalisation with a principal diagnosis or first two
listed secondary diagnosis code of mental health and
behavioural disorders (ICD-10 codes F10-F69 and
F90-99), with suicides recorded in death registries
(ICD-10 codes: X60-X84). In cases with several admissions during the reference year, the follow-up period
starts from the last discharge.
For the excess mortality indicators the numerator is
the overall mortality rate for persons aged between 15
and 74 years old ever diagnosed with schizophrenia or
bipolar disorder. The denominator is the overall mortality rate for the general population aged between 15
and 74 years old. The relatively small number of people
with bipolar disorder dying in any given year can
cause substantial variations from year to year in some
countries. The available data in most countries did
not allow the calculation of 2-year averages.
The data have been age-sex standardised to the 2010
OECD population structure, to remove the effect of
different population structures across countries.
References
OECD (2014a), Making Mental Health Count. The Social and Economic Costs of neglecting mental health care, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264208445-en.
OECD (2014b), OECD Reviews of Health Care Quality: Norway:
http://dx.doi.org/10.1787/9789264208469-en.
8. QUALITY OF CARE
8.21. Inpatient suicide amongst patients with
a psychiatric disorder, 2013 (or latest year)
8.22. Suicide following hospitalisation for a psychiatric
disorder, within 30 days and one year of discharge, 2012
Within 30 days of discharge
Age-sex standardised rates per 100 patients
0.30
Within one year of discharge
0.25
Age-sex standardised rates per 100 patients
1.2
1.0
0.20
0.8
0.15
0.6
0.02
0.02
0.02
0.00
0.04
0.03
0.05
0.08
0.08
0.07
0.06
0.05
0.10
0.09
0.12
0.25
0.4
0.2
0
ia
k
Ne
Sl
ov
en
ar
d
nm
De
Fi
nl
ed
an
en
el
ra
Is
al
Sw
an
d
ia
tv
w
Ze
Ch
h
ec
La
il e
p.
Re
m
do
ng
Cz
Ki
d
i te
Un
Cz
ec
h
Re
p.
S w Sp a
i t z in
e
Sl r l an
ov
ak d
Re
p
No .
Ne
r
w way
Ze
al
a
Po nd
r tu
ga
Un
l
C
i te an
a
d
d
Ki
a
ng
do
m
Fi
nl
an
Be d
lg
iu
m
Is
ra
De el
nm
ar
Es k
to
ni
a
0
Note: Three-year average for most countries.
1 2 http://dx.doi.org/10.1787/888933281184
1 2 http://dx.doi.org/10.1787/888933281184
8.23. Excess mortality from schizophrenia, 2013
(or latest year)
8.24. Excess mortality from bipolar disorder, 2013
(or latest year)
Women
Men
Women
Men
Ratio
7
6.1
6.6
Ratio
7
6
5.2
4.9
5.2
5.3
5.6
6
5
Note: Excess mortality is compared to the mortality rate for the general
population.
1 2 http://dx.doi.org/10.1787/888933281184
3.7
3.0
3.3
3.3
2.6
2.6
2.7
2.5
an
nl
Fi
an
al
w
Ze
d
d
a
re
Ko
Ne
De
nm
ar
k
ay
rw
No
ed
en
el
ra
Is
d
an
nl
Fi
No
rw
ay
a
re
Ko
en
ed
an
al
Ze
Ne
w
Sw
k
ar
De
nm
ra
Is
tv
d
0
el
0
ia
1
Sw
1.7
2
1
La
2.6
2.4
3
2.8
3
2
4.5
4.2
3.9
3.8
4
3.2
3.3
3.5
3.6
4
3.9
4.4
4.5
5
Note: Excess mortality is compared to the mortality rate for the general
population.
1 2 http://dx.doi.org/10.1787/888933281184
149
8. QUALITY OF CARE
Cervical cancer is highly preventable if precancerous
changes are detected and treated before progression
occurs. The main cause of cervical cancer, which accounts
for approximately 95% of all cases, is exposure to the
human papilloma virus (HPV) through sexual activity
(IARC, 2005).
Countries follow different policies with regards to the prevention and early diagnosis of cervical cancer. About half of
OECD countries have cervical cancer screening organised
through population-based programmes but their periodicity
and target age groups vary (OECD, 2013). Some countries
with low cervical cancer incidence such as Israel and
Switzerland do not have an organised screening programme but women in the eligible age group can have a
Pap smear test performed every three years for free. WHO
recommends HPV vaccinations as part of national immunisation programmes, primarily for girls aged 9-13 years, in
countries where the prevention of cervical cancer is a public health priority, the introduction is feasible and financially sustainable, and cost-effectiveness has been
evaluated (WHO, 2014). Nowadays, most OECD countries
have HPV vaccination programmes.
Screening rates for cervical cancer ranged from 20.7% in
Mexico to 84.5% in the United States in 2013 and have
increased from 57.0% to 61.6% on average across OECD
countries over the past decade (Figure 8.25). The coverage
increase was particularly large in Korea where the screening programme was rolled out nationwide in the mid2000s. In about half of OECD countries, however, screening
coverage declined, which may be related to the introduction of HPV vaccinations, starting from the late 2000s
(OECD, 2013).
Cancer survival is one of the key measures of the effectiveness of cancer care systems, taking into account both early
detection of the disease and the effectiveness of treatment.
Five-year relative cervical cancer survival ranges widely
from 45.3% in Chile to 81.2% in Norway in recent years
(Figure 8.26). Some countries with relatively high screening
coverage such as the United States, Austria, the United
Kingdom, New Zealand and Ireland have lower survival,
but four of the five countries have low mortality. During the
past decade, five-year relative survival for cervical cancer
improved in many countries.
Mortality rates reflect the effect of cancer care over the past
years and the impact of screening, as well as changes in
incidence. The mortality rates for cervical cancer declined
i n m o s t O E C D c o u n t r i e s b e t we e n 2 0 0 3 a n d 2 0 1 3
(Figure 8.27). In Greece, however, the mortality rate from
cervical cancer increased substantially by 47% during the
same period, although it is still below the OECD average.
The incidence is low and decreasing over time and it is
likely that Greece can control the increasing burden of cervical cancer by providing more effective cervical cancer
treatment.
150
Screening rates are based on surveys or encounter
data, which may influence the results. Survey-based
results may be affected by recall bias. Programme data
are often calculated for monitoring national screening
programmes and differences in target population and
screening frequency may also lead to variations in
screening coverage across countries.
Relative survival is the ratio of the observed survival
experienced by cancer patients over a specified period
of time after diagnosis to the expected survival in a
comparable group from the general population in
terms of age, sex and time period. Survival data for
Chile, Germany and Italy are based on a sample of
patients. The number of countries which monitor and
report cancer survival has been increasing in recent
years and an international study (Allemani et al.,
2015) also shows that a wide range of countries have
cancer registries which enable international comparisons of cancer survival.
Countries use either period analysis or cohort analysis
to calculate cancer survival. Period analysis gives an
up-to-date estimate of cancer patient survival using
more recent incidence and follow-up periods than
cohort analysis which uses survival information of a
complete five-year follow-up period. The reference
periods for diagnosis and follow-up years vary across
countries.
Cancer survival presented have been age-standardised
using the International Cancer Survival Standard
(ICSS) population.
See indicator “Mortality from cancer” in Chapter 3 for
definition, source and methodology underlying cancer
mortality rates.
References
Allemani, C. et al. (2015), “Global Surveillance of Cancer
Survival 1995-2009: Analysis of Individual Data for
25 676 887 Patients from 279 Population-based Registries
in 67 Countries (CONCORD-2)”, The Lancet, Vol. 385,
pp. 977-1010.
IARC – International Agency for Research on Cancer (2005),
“Cervix Cancer Screening”, IARC Handbooks of Cancer Prevention, Vol. 10, International Agency for Research on
Cancer, Lyon.
OECD (2013), Cancer Care: Assuring Quality to Improve Survival, OECD Publishing, Paris, http://dx.doi.org/10.1787/
9789264181052-en.
WHO (2014), “Human Papillomavirus Vaccines: WHO Position Paper, October 2014”, Weekly Epidemiological Record,
No. 43, 89, 465–492, Geneva.
8. QUALITY OF CARE
8.25. Cervical cancer screening in women aged 20-69,
2003 to 2013 (or nearest years)
2013
8.26. Cervical cancer five-year relative survival, 1998-2003
and 2008-2013 (or nearest periods)
2008-2013
2003
United States²
Austria 2
Sweden1
United Kingdom1
New Zealand1
Ireland1
Switzerland2
Norway1
France 2
Canada 2
Slovenia1
Greece 2
Poland 2
Spain 2
Finland1
Turkey1
Netherlands1
Denmark1
Iceland*1
OECD24
Chile1
Belgium1
Australia1
Luxembourg1
Portugal2
Germany2
Czech Rep.1
Korea1
Estonia1
Slovak Rep.1
Lithuania1
Japan 2
Italy1
Hungary1
Latvia1
Mexico 1
1998-2003
Norway1
Korea1
Italy1
Japan 2
Denmark 3
Finland1
Iceland*2
Estonia 3
Sweden 2
Israel 2
Australia1
Canada 2
France 2
OECD22
Czech Rep. 2
New Zealand1
Germany1
Netherlands2
Belgium1
Portugal2
Austria 2
Ireland1
United States 2
United Kingdom 2
Latvia 2
Slovenia1
Poland1
Chile 2
0
25
50
75
100
% of women screened
1. Programme. 2. Survey. * Three-year average.
1 2 http://dx.doi.org/10.1787/888933281196
0
25
50
75
100
Age-standardised survival (%)
1. Period analysis. 2. Cohort analysis. 3 Different analysis methods used
for different years. * Three-period average. 95% confidence intervals
represented by H.
1 2 http://dx.doi.org/10.1787/888933281196
8.27. Cervical cancer mortality, 2003 to 2013 (or nearest years)
2003
2013
18
16
14
12
10
8
6
4
2
Ic I t a l y
el
an
d
S w F in *
i t z land
er
Au l an
st d
ra
Tu lia
rk
Ca ey
na
Fr da
an
Gr c e
ee
Ne S ce
th pa
er in
la
n
Ne I ds
w sr a
Lu Ze el
Un xem a l a
i te b nd
d ou
Un Kin rg*
i t e gdo
d m
St
a
Be tes
lg
i
N o um
rw
Au ay
Ge s tr i
rm a
Po an
r tu y
S w gal
De ede
nm n
ar
Ja k
Sl p a n
ov
O E eni
CD a
3
Ko 4
re
C z Ir e l a a
ec nd
h
R
H e
S l u n g p.
ov a
ak r y
Re
p
Br .
az
Ru P o i l
ss la
ia nd
n
Fe
d
Ch .
E s il e
C o to
s t ni a
aR
ic
La a
Li t v
th ia
ua
M ni a
C o ex i c
lo o
m
bi
a
0
* Three-year average.
1 2 http://dx.doi.org/10.1787/888933281196
151
8. QUALITY OF CARE
Breast cancer is the most prevalent form of cancer in
women across OECD countries. One in nine women will
have breast cancer at some point in their life and one in
thirty will die from the disease. Risk factors that increase a
person's chance of getting this disease include age, family
history of breast cancer, genetic predisposition, reproductive factors, oestrogen replacement therapy, and lifestyles
including obesity, physical inactivity, diet and alcohol consumption.
Most OECD countries have adopted breast cancer screening
programmes as an effective way for detecting the disease
early, though the periodicity and population target groups
vary across countries (OECD, 2013). Due to recent progress
in treatment outcomes and concerns about false-positive
results, over-diagnosis and overtreatment, breast cancer
screening recommendations have been re-evaluated in
recent years. Taking account of recent research findings,
WHO recommends organised population-based mammography screening if women are able to make an informed
decision based on the benefits and risks of mammography
screening (WHO, 2014).
Screening rates ranged from less than 20% in Mexico to
over 80% in Finland, Slovenia, Denmark and the United
States in 2013 (Figure 8.28). Screening coverage increased
substantially among countries with low rates a decade ago.
Mexico and Chile had an increase of more than ten-fold,
Korea an over four-fold increase, and the Slovak Republic
and Lithuania a three-fold rise. On the other hand, countries that had the highest screening rates in the early 2000s
experienced some reductions, including Finland, the
United States, the Netherlands, Ireland and Norway. In
Ireland, the screening programme, which was commenced
on a phased basis in 2000, completed its nationwide rollout in 2009, but it is still at a stage too early to evaluate the
coverage trend over time.
Breast cancer survival reflects early diagnosis as well as
improved treatments. All OECD countries have attained
five-year relative breast cancer survival of 80% except Estonia, Poland and Chile (Figure 8.29). Relative survival of people with cervical and colorectal cancers is also the lowest
for Poland and Chile (see indicators “Screening, survival
and mortality for cervical cancer” and “Survival and mortality for colorectal cancer”). In both countries, access to
care is limited due to fewer numbers of cancer care centres
and radiotherapy facilities. In Chile, some cancer drugs and
other medical technologies are not widely available, and
there are not enough specialised professionals, resulting in
a long waiting time for cancer treatment (OECD, 2013).
Over the last decade, the five-year relative breast cancer
survival has improved in all OECD countries. Relative survival
has increased considerably in some Eastern European coun-
152
tries such as Estonia, the Czech Republic and Latvia,
although survival after breast cancer diagnosis is still
below the OECD average. The improvement may be related to
strengthening of cancer care governance in these countries.
For instance, the Czech Republic intensified its effort to detect
breast cancer patients early through the introduction of a
screening programme in 2002 and implemented a National
Cancer Control Programme in 2005 to improve the quality
of cancer care and cancer survival. Cancer care delivery
was reorganised by reducing the number of comprehensive
cancer centres while aiming to optimise the population
coverage of each centre, and skilled professionals and necessary investment were allocated at each centre. The current
cancer care delivery model is considered to be well organised
and distributed adequately around the country, and, partly
due to the more equal access, variations in cancer survival
across regions have been reduced (OECD, 2013; OECD, 2014).
Mortality rates have declined in most OECD countries over
the past decade (Figure 8.30). The reduction is a reflection
of improvements in early detection and treatment of breast
cancer. Improvements were substantial in the Czech
Republic, Norway and the Netherlands with a decline of
over 20% in a decade. Denmark also reported a considerable decline, but its mortality rate was still the highest in
2013. On the other hand, in Korea, Turkey and Japan, the
mortality rate from breast cancer increased over the past
decade, although it remains the lowest among OECD countries, and the incidence of breast cancer has doubled or
more in the past decade.
Screening rates and survival are defined in indicator
“Screening, survival and mortality for cervical cancer”
in Chapter 8. See indicator “Mortality from cancer” in
Chapter 3 for definition, source and methodology
underlying cancer mortality rates.
References
OECD (2014), OECD Reviews of Health Care Quality: Czech
Republic 2014: Raising Standards, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264208605-en.
OECD (2013), Cancer Care: Assuring Quality to Improve
Survival, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264181052-en.
WHO (2014), “WHO Position Paper on Mammography
Screening”, Geneva.
8. QUALITY OF CARE
8.28. Mammography screening in women aged 50-69,
2003 to 2013 (or nearest years)
2013
8.29. Breast cancer five-year relative survival, 1998-2003
and 2008-2013 (or nearest periods)
2008-2013
2003
Finland1
Slovenia1
Denmark1
United States 2
Austria 2
Netherlands1
Spain 2
United Kingdom1
Norway1
Portugal2
New Zealand
Canada 2
Germany2
Israel1
Ireland1
Korea1
Italy1
Luxembourg*1
OECD27
Iceland*1
Czech Rep.1
Poland 2
Belgium1
Australia1
Estonia1
France 1
Greece 2
Switzerland2
Hungary1
Japan 2
Slovak Rep.1
Lithuania1
Turkey1
Latvia1
Chile1
Mexico 1
1998-2003
Sweden 2
United States 2
Norway1
Finland1
Australia1
Portugal2
Israel 2
Canada 2
Japan 2
Iceland*2
Denmark 3
New Zealand
Belgium1
Korea1
Italy1
Germany1
Netherlands 2
OECD22
Latvia 2
France 2
Slovenia1
Austria 2
Ireland1
United Kingdom 2
Czech Rep. 2
Chile 2
Poland1
Estonia 3
0
25
50
75
100
% of women screened
1. Programme. 2. Survey. * Three-year average.
1 2 http://dx.doi.org/10.1787/888933281202
0
20
40
60
80
100
represented by H.
1 2 http://dx.doi.org/10.1787/888933281202
8.30. Breast cancer mortality in women, 2003 to 2013 (or nearest years)
2003
2013
45
40
35
30
25
20
15
10
5
Ko
r
Tu ea
rk
e
Ja y
p
M an
C o ex i c
lo o
m
bi
a
Ch
il e
B
Co r
st a zi
aR l
ic
Sp a
No a in
r
P w
So or ay
t
ut ug
h al
Af
r
Fi ic a
nl
S w and
ed
Un Aus en
i te tr a
d li a
St
a
Po tes
OE land
CD
C 3
C z an 4
ec ad
h a
L i Re
t h p.
ua
n
S w Gr e i a
it z ec
er e
la
Au nd
st
ria
It
Ru L a l y
ss at v
Sl i an i a
ov F e
a k d.
Re
F r p.
an
Ne Es ce
w to
L u Z e ni a
Un xem a l a
i te b nd
d ou
Ki rg
ng *
Ge dom
rm
a
N e Is n y
th r a
er el
la
Ic n d s
el
a
Sl nd*
ov
e
Ir e n i a
la
B e nd
lg
Hu ium
ng
De ar
nm y
ar
k
0
1 2 http://dx.doi.org/10.1787/888933281202
153
8. QUALITY OF CARE
Colorectal cancer is the third most commonly diagnosed
form of cancer after prostate and lung cancers for men, and
the second most common cancer after breast cancer for
women, across OECD countries. Colorectal cancer incidence
is high in Korea, the Slovak Republic, Hungary, Denmark and
the Netherlands at 40 or more cases per 100 000 population
while it is low in Mexico, Greece, Chile and Turkey at less
than half this rate. Incidence is significantly higher for men
than women across countries. There are several factors
that place certain individuals at increased risk for the disease, including age, ulcerative colitis, a personal or family
history of colorectal cancer or polyps, and lifestyle factors
such as a diet high in fat and low in fibre, lack of physical
activity, obesity, and tobacco and alcohol consumption.
Following screening for breast and cervical cancers,
colorectal cancer screening has become available, and an
increasing number of countries have introduced free population-based screening, targeting people in their 50s
and 60s (OECD, 2013). Partly because of uncertainties about
the cost-effectiveness of screening (Lansdorp-Vogelaar et
al., 2010), countries are using different methods (i.e. faecal
occult blood test, colonoscopy and flexible sigmoidoscopy).
Multiple methods are also available within the screening
programme in some countries. In most countries that
provide faecal occult blood test, screening is available every
two years. The screening periodicity schedule is less frequent
with colonoscopy and flexible sigmoidoscopy, generally
every ten years, making it difficult to compare screening
coverage across countries.
Advances in diagnosis and treatment of colorectal cancer
including improved surgical techniques, radiation therapy
and combined chemotherapy and their wider and timelier
access have contributed to increased survival over the last
decade. All OECD countries showed improvement in fiveyear relative survival for colorectal cancer. On average, fiveyear colorectal cancer survival improved from 55.8% to
62.2% for people with colorectal cancer during 1998-2003 to
2008-2013 respectively (Figure 8.31). Poland, Estonia and
the Czech Republic also had a considerable improvement,
but cancer survival in these countries is still the lowest
among OECD countries at less than 55%. Korea and Israel
had the highest survival at over 70%.
In most OECD countries, colorectal cancer survival is higher
for women but in Chile, Korea, Israel, Japan, Portugal, Austria
and the Netherlands, men have a slightly higher survival
(Figure 8.32). The gender difference is the largest in Estonia
with the five-year relative survival of 48.4% for males and
154
55.9% for females. Slovenia, Latvia and Sweden also have a
comparatively large difference.
Most countries experienced a decline in mortality of
colorectal cancer in recent years, with the average rate
across OECD countries falling from 27.4 to 24.2 deaths per
100 000 population between 2003 and 2013 (Figure 8.33).
The decline was particularly large in the Czech Republic,
Austria and Australia with a reduction of over 25%. The
main exceptions to this general trend were Turkey, Brazil,
Chile and Mexico where the mortality rate from colorectal
cancer increased by more than 10% over the last decade,
although the rate remains much lower than the OECD average. Despite some progress, Central and Eastern European
countries, particularly Hungary, the Slovak Republic, Slovenia
and the Czech Republic, continue to have higher mortality
rates than other OECD countries.
Across countries, colorectal cancer continues to be an
important cause of cancer death for both men and women
(see indicator “Mortality from cancer” in Chapter 8) and
countries will need to make further effort to promote not
only early diagnosis and effective treatment but also
healthy lifestyles to reduce its risk factors (see Chapter 8
“Non-medical determinants”).
Survival and mortality rates are defined in indicator
“Screening, survival and mortality for cervical cancer”
in Chapter 8. See indicator “Mortality from cancer” in
Chapter 3 for definition, source and methodology
underlying cancer mortality rates. Survival and mortality rates of colorectal cancer are based on ICD-10
codes C18-C21 (colon, rectosigmoid junction, rectum,
and anus).
References
Lansdorp-Vogelaar, I., A.B. Knudsen and H. Brenner (2010),
“Cost-effectiveness of Colorectal Cancer Screening – An
Overview”, Best Practice & Research Clinical Gastroenterology, Vol. 24, pp. 439- 449.
OECD (2013), Cancer Care: Assuring Quality to Improve Survival, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264181052-en.
8. QUALITY OF CARE
8.32. Colorectal cancer, five-year relative survival
by gender, 2008-13 (or nearest periods)
8.31. Colorectal cancer, five-year relative survival,
1998-2003 and 2008-13 (or nearest periods)
2008-2013
Men
1998-2003
Women
Korea1
Israel 2
Iceland*2
Australia1
Japan 2
Belgium1
Sweden 2
Finland1
Austria 2
United States 2
Germany1
Netherlands 2
Italy1
Norway1
Canada 2
New Zealand1
OECD25
Portugal2
Slovenia1
Denmark 3
Ireland1
Latvia 2
United Kingdom 2
Czech Rep. 2
Estonia 3
Poland1
Chile 2
Korea1
Israel 2
Australia1
Japan 2
Belgium1
Sweden 2
Finland1
Austria 2
United States 2
Germany1
Netherlands 2
Italy1
Norway1
Canada 2
New Zealand1
OECD21
Portugal2
Slovenia1
Denmark 3
Ireland1
Latvia 2
France 2
United Kingdom 2
Czech Rep. 2
Estonia 3
Poland1
0
20
40
0
60
80
100
1. Period analysis, 2. Cohort analysis. 95% confidence intervals
represented by H.
1 2 http://dx.doi.org/10.1787/888933281219
20
40
60
80
100
represented by H.
1 2 http://dx.doi.org/10.1787/888933281219
8.33. Colorectal cancer mortality, 2003 to 2013 (or nearest years)
2003
2013
50
45
40
35
30
25
20
15
10
5
So Me
ut xi
h co
Af
ric
B a
Co r a z
l o il
m
bi
Co Turk a
st ey
aR
Gr i c a
e
Un F i e c e
i t e nl a
d nd
St
at
e
Sw C s
i t z hil
er e
Au l an
st d
Ic r a li a
el
an
Au d*
st
r
Ko i a
Un
i te F r e a
d r an
Ki c
ng e
d
B e om
lg
iu
m
It a
Sw ly
Ge ede
rm n
a
C a ny
na
d
Ja a
pa
Lu
xe Is n
m r ae
bo l
u
OE rg*
C
Li D 3
th 4
ua
n
N e Ir e i a
th lan
er d
la
n
E s ds
to
Ru S n i a
ss pa
a in in
F
N o e d.
rw
Po a
r tu y
Ne Po gal
w la
Ze nd
a
De lan
C z nm d
ec ar
h k
Re
L a p.
S tv
Sl lov i a
ov en
ak ia
Hu Rep
ng .
ar
y
0
1 2 http://dx.doi.org/10.1787/888933281219
155
8. QUALITY OF CARE
All OECD countries have established vaccination programmes based on their interpretation of the risks and
benefits of each vaccine. Figures 8.34 and 8.35 show that
the overall vaccination of children against measles and
diphtheria, tetanus and pertussis (DTP) is high in OECD
countries. On average, 95% of children receive the recommended DTP vaccination and 94% receive measles vaccinations in accordance with national immunisation schedules.
Rates for DTP vaccinations are below 90% only in Indonesia,
Austria, Mexico, India and South Africa. Rates for measles
vaccinations are below 90% in Denmark, France, Mexico,
Indonesia, Austria, India and South Africa.
While national coverage rates are high in many countries,
some parts of the population remain exposed to certain
diseases. For example, the United States reported 189 measles
cases between 1 January and 18 Septembre 2015. Most of
these cases were linked to an amusement park in
California. The Centers for Disease Control and Prevention
reported that most of the measles cases in 2015 were in
unvaccinated people. In the previous year, over 650 cases of
measles were reported in the United States, the highest
number of cases since measles elimination was documented in 2000. Many of the cases were associated with a
large outbreak that originated in the Philippines (Centers
for Disease Control and Prevention, 2015). In July 2015, the
first death related to measles since 2003 was reported in
the United States (Washington State Department of Health,
2015).
Parts of Europe also reported large number of measles
cases in 2015. During the 12 months to June 2015, more
than 4 000 cases were reported across 30 countries. More
than half the cases were in Germany, with over 400 cases
reported in Italy. The measles-related death of an
18-month toddler in Germany was reported in February
2015. Most of the cases across Europe were among unvaccinated people (European Centre for Disease Prevention and
Control, 2015). Catch-up programmes in older children may
be needed to avoid the risk of, or respond to, measles outbreaks. Such a campaign was conducted in the United
Kingdom in 2013.
Figure 8.36 shows the percentage of children aged one year
vaccinated for hepatitis B. The hepatitis B virus is transmitted by contact with blood or body fluids of an infected person. A small proportion of infections become chronic, and
these people are at high risk of death from cancer or cirrhosis
of the liver. A vaccination has been available since 1982 and
is considered to be 95% effective in preventing infection
and its chronic consequences. Since a high proportion of
chronic infections are acquired during early childhood, the
WHO recommends that all infants should receive their first
dose of hepatitis B vaccine as soon as possible after birth,
preferably within 24 hours (WHO, 2015).
Most countries have followed the WHO recommendation to
incorporate hepatitis B vaccine as an integral part of their
156
national infant immunisation programme. Across the
OECD, the average immunisation coverage for hepatitis B
for children aged one year old is 92%. In countries such as
China, the Czech Republic and Korea, it reaches 99%. However, a number of countries do not require children to be
vaccinated, and consequently the rates for these countries
are significantly lower than other countries. For example,
in Denmark, Sweden and the United Kingdom, vaccination
against hepatitis B is not part of the general infant vaccination programme, but is provided to high-risk groups such as
children with mothers who are infected by the hepatitis B
virus. Other OECD countries that do not include vaccination
against hepatitis B in their infant programmes are Iceland,
Finland, Hungary, Japan, Slovenia and Switzerland. In
Canada, not all jurisdictions immunise infants against
hepatitis B, with some doing this at school age.
Vaccination rates reflect the percentage of children
that receives the respective vaccination in the recommended timeframe. The age of complete immunisation
differs across countries due to different immunisation
schedules. For those countries recommending the
first dose of a vaccine after age one, the indicator is
calculated as the proportion of children less than two
years of age who have received that vaccine. Thus,
these indicators are based on the actual policy in a
given country.
Some countries administer combination vaccines (e.g.
DTP for diphtheria, tetanus and pertussis) while others administer the vaccinations separately. Some
countries ascertain vaccinations based on surveys
and others based on encounter data, which may influence the results.
References
Centers for Disease Control and Prevention (2015), Measles
Cases and Outbreaks, available at:
www.cdc.gov/measles/cases-outbreaks.html (accessed 12/
10/2015).
European Centre for Disease Prevention and Control (2015),
“Surveillance Report: Measles and Rubella Monitoring”,
July 2015.
Washington State Department of Health (2015), “Measles
Led to Death of Clallam Co. Woman; First in US in a
Dozen Years”, available at www.doh.wa.gov/Newsroom/
2015NewsReleases/15119WAMeaslesRelatedDeath.
WHO (2015), “Hepatitis B”, Fact Sheet No. 204, Geneva.
8. QUALITY OF CARE
8.34. Vaccination against diphteria, tetanus and
pertussis, children aged 1, 2013
8.35. Vaccination against measles, children aged 1, 2013
Brazil
China
Czech Rep.
Greece
Hungary
Korea
Poland
Portugal
Russian Fed.
Slovak Rep.
Turkey
Finland
Germany
Israel
Sweden
Latvia
Netherlands
Canada
Japan
Luxembourg
Spain
United Kingdom
OECD34
Australia
Estonia
Slovenia
Ireland
Lithuania
Norway
Switzerland
Belgium
Colombia
New Zealand
Iceland
Costa Rica
United States
Chile
Italy
Denmark
France
Mexico
Indonesia
Austria
India
South Africa
Belgium
China
Czech Rep.
France
Greece
Hungary
Korea
Luxembourg
Poland
Finland
Japan
Portugal
Slovak Rep.
Sweden
Turkey
Italy
Netherlands
Russian Fed.
Canada
Germany
Ireland
Spain
Switzerland
United Kingdom
OECD34
Brazil
Costa Rica
Latvia
Slovenia
Denmark
Estonia
Israel
Norway
United States
Lithuania
New Zealand
Australia
Chile
Iceland
Colombia
Indonesia
Austria
Mexico
India
South Africa
0
25
50
75
100
% of children vaccinated
Source: WHO/UNICEF.
1 2 http://dx.doi.org/10.1787/888933281226
0
25
50
75
100
Source: WHO/UNICEF.
1 2 http://dx.doi.org/10.1787/888933281226
8.36. Vaccination against hepatitis B, children aged 1, 2013
100
80
60
40
20
C z Chi
ec na
h
Re
p
Ko .
re
Be a
lg
iu
m
Gr
ee
ce
Is
r
Po ael
r
Sl tu
ov g a
ak l
Re
p.
Ru
ss It al
ia y
n
Fe
d
Tu .
rk
ey
Po
la
nd
Br
az
Ir e i l
la
nd
Ne L at
th v ia
er
la
nd
s
S
p
Co a
s t in
Lu a R
xe i c
m a
bo
u
Es rg
to
L i ni a
N e t hu
w a ni
Ze a
al
a
O E nd
CD
Au 24
st
C o r a li a
lo
m
bi
a
Un
C
i t e hi
le
d
St
a
Ge tes
rm
In a n
do y
ne
s
Au ia
st
r
M ia
ex
ic
Ca o
na
d
Fr a
an
ce
S o In
u t di
a
h
Af
ric
a
0
Source: WHO/UNICEF.
1 2 http://dx.doi.org/10.1787/888933281226
157
8. QUALITY OF CARE
Influenza is a common infectious disease affecting 5%-10%
of adults and 20%-30% of children. There are an estimated
3 to 5 million cases of severe influenza-related illness
worldwide each year, and 250 000 to 500 000 deaths (WHO,
2014). Influenza can also have a major impact on health
care systems. In the United States, it is estimated that each
year, more than 200 000 people are hospitalised for respiratory and heart condition illnesses associated with seasonal
influenza virus infections (Thompson et al., 2004). At certain
times of the year, influenza can place health systems under
significant stress. For example, in Ontario, Canada, the
average annual rate of emergency department visits
attributable to seasonal influenza is 500 per 100 000 population. This rate increased to an estimated 1 000 per
100 000 population during the H1N1 pandemic in 2009
(Schanzer et al., 2013).
In 2003, countries participating in the World Health Assembly committed to the goal of attaining vaccination coverage
against influenza of at least 50% of the elderly population
by 2006 and 75% by 2010. Figure 8.37 shows that in 2013, the
OECD average influenza vaccination rate for people aged 65
and over was 48%. Vaccination rates are as low as 1.1% in
Estonia, where influenza vaccination is recommended but
not free. Only four countries have attained the 75% target:
Mexico, Korea, Chile and the United Kingdom. Australia
came close to meeting the target.
Figure 8.38 indicates that between 2003 and 2013, the vaccination rate against influenza among the elderly population
has remained stable on average among the group of OECD
countries that have trend data over this period, but with no
uniform trend across countries. In some countries, such as
New Zealand, Israel, Germany, Denmark, the Czech Republic
and the United Kingdom, the percentage of the population
aged 65 and over vaccinated against influenza has
increased, while it has come down in other countries such
as the Netherlands, Spain, France, the Slovak Republic and
Slovenia.
In June 2009, the WHO declared an influenza pandemic.
The H1N1 influenza virus (also referred to as “swine flu”)
infected an estimated 11% to 18% of the global population
(Kelly et al., 2011). Mexico was at the centre of the pandemic, being among the first countries where swine flu was
detected and also where mortality rates were reportedly
higher than those in many other countries. The high rate of
seasonal vaccinations that are still being observed in Mexico
may come as a result of the H1N1 experiences in that country. In other countries, however, the take-up rate of H1N1
vaccine was lower than expected, despite the vaccine being
158
included in most 2009-10 vaccination programmes. In part,
this may be due to the easing of concerns about the threat
of H1N1 amongst the general population by the time the
vaccine became available. Studies have shown that the
most important determinant for individuals to take-up
H1N1 vaccine was previous exposure to seasonal flu vaccine,
leading some researchers to argue that higher vaccination
rates for seasonal flu may help take-up during potential
future pandemics (Nguyen et al., 2011).
Influenza vaccination rate refers to the number of
people aged 65 and older who have received an annual
influenza vaccination, divided by the total number of
people over 65 years of age. In some countries, the
data are for people over 60 years of age. The main limitation in terms of data comparability arises from the
use of different data sources, whether survey or programme, which are susceptible to different types of
errors and biases. For example, data from population
surveys may reflect some variation due to recall errors
and irregularity of administration.
References
Kelly, H. et al. (2011), “The Age-specific Cumulative Incidence of Infection with Pandemic Influenza H1N1 2009
Was Similar in Various Countries Prior to Vaccination”,
PLoS One, Vol. 6, No. 8:e21828.
Nguyen, T. et al. (2011), “Acceptance of A Pandemic Influenza Vaccine: A Systematic Review of Surveys of the
General Public”, Infection and Drug Resistance, Vol. 4,
pp. 197-207.
Schanzer, D.L., B. Schwartz and M.J. Mello (2013), “Impact of
Seasonal and Pandemic Influenza on Emergency Department Visits, 2003-2010, Ontario, Canada”, Academic Emergency Medicine, Vol. 20, No. 4, pp. 388-397.
Thompson, W.W. et al. (2004), “Influenza-Associated Hospitalizations in the United States”, Journal of American
Medical Association, Vol. 292, No. 11, pp. 1333-1340.
WHO (2014), “Influenza (Seasonal)”, Fact Sheet No. 211, available at: www.who.int/mediacentre/factsheets/fs211/en/
(accessed 23/06/2015).
8. QUALITY OF CARE
8.37. Influenza vaccination coverage, population aged 65 and over, 2013 (or nearest year)
%
100
90
80
78.5 77.4
76.5 75.5
74.6
69.0 68.8
70
66.5
64.1
61.2
60
59.2 58.6 58.0
56.4
54.2
51.9
50
50.0 49.9
48.0
46.0 45.8 45.6
43.3
40
41.4 41.0
36.7 36.1
30
22.1 20.8
20
15.6
13.2 13.0 12.1
10
1.1
ex
Un
M
Ko
ic
o
re
a
i te
d Chi
Ki
l
e
ng
d
Au om
Ne s tr
w a li
a
Z
Ne eala
th nd
Un er l a
i te nd
s
d
St
at
es
Ca
na
da
Is
ra
Ir e e l
l
Ge and
rm
a
B e ny
lg
iu
m
Sp
a in
It a
l
Fr y
an
ce
Ja
pa
Po n
r tu
OE gal
Sw CD
it z 3 3
er
la
S w nd
ed
De en
L u nm
xe a r
m k
bo
ur
Gr g
ee
c
Fi e
nl
an
Hu d
ng
ar
Au y
C z s tr
ec ia
h
Re
N o p.
Sl r w
ov
a
ak y
Re
p
Tu .
rk
Sl e y
ov
en
i
Po a
la
nd
Es
to
ni
a
0
1 2 http://dx.doi.org/10.1787/888933281235
8.38. Influenza vaccination coverage, population aged 65 and over, 2003 and 2013 (or nearest years)
2013
2003
%
90
80
70
60
50
40
30
20
10
ia
p.
en
ov
Sl
p.
Re
Re
ak
h
ec
ov
Sl
y
ria
Au
st
Cz
d
ar
ng
Hu
an
nl
Fi
ur
bo
m
xe
Lu
De
nm
ar
g
k
en
l
Sw
ed
n
ga
r tu
Po
ce
pa
Ja
ly
26
an
Fr
CD
It a
OE
m
a in
Sp
iu
Be
lg
an
y
nd
rm
Ge
el
da
ra
la
Ir e
Is
es
na
Ca
s
at
Un
i te
d
St
d
nd
an
er
th
Ne
w
Ze
al
la
li a
m
ra
Ne
do
st
Au
il e
Un
i te
d
Ki
ng
Ch
Ko
re
a
0
1 2 http://dx.doi.org/10.1787/888933281235
159
8. QUALITY OF CARE
Delivering health care that is responsive and patient-centered
is playing a greater role in health care policy across OECD
countries. Measuring and monitoring patient experience
empowers patients and the public, involves them in decisions on health care delivery and governance, and provides
insight into the extent to which they are health-literate and
have control over the treatment they receive. Across countries, using the health care user as a direct source of information is becoming more prevalent for health system
monitoring, planning and decision making, and efforts to
measure and monitor patient experiences have actually led
to health care quality improvements (Fujisawa and Klazinga,
forthcoming).
Since the mid-1990s, there have been efforts to institutionalise measurement and monitoring of patient experiences.
In many countries, responsible organisations have been
established or existing institutions have been taking charge
of measuring and reporting patient experiences. They
developed survey instruments for regular collection of
patient experience data and standardised procedures for
analysis and reporting. An increasing number of countries
collect not only Patient-Reported Experience Measures
(PREMs) but also Patient-Reported Outcome Measures
(PROMs) which collect patients’ perception on their specific
medical conditions and general health, including mobility,
pain/discomfort and anxiety/depression, before and after a
specific medical intervention such as hip and knee replacement.
A growing number of countries are using patient-reported
data to drive quality improvements in health systems.
Patient experience data are reported in periodic national
health system reports or on public websites, showing differences across providers, regions and over time. Korea,
Norway, Sweden and the United Kingdom use patient experience measures in payment mechanisms or for fund allocations to promote quality improvement and patientcentred care, and Australia, Canada, the Czech Republic,
Denmark and France use them to inform health care regulators for inspection, regulation and/or accreditation.
Patient-reported measures are also used in some Canadian
jurisdictions, Denmark, France and the Netherlands to provide specific feedback for providers’ quality improvement.
In England, PROMs and patients’ feedback about their
experience are used to inform patient choice and to incentivise service improvement. For example, PROMs data for
patients undergoing some procedures such as hip and knee
replacement are used for benchmarking hospitals. The use
of PROMs can also enable the potential shift from a volume-based to a value-based model of health system
resource management (Canadian Institute for Health Information, 2015).
Patients generally report positive experiences when it
comes to communication and autonomy in the ambulatory
health care system. Across countries, the majority of
160
patients report positive experiences with regards to time
spent with the doctor (Figure 8.39), easy-to-understand
explanations (Figure 8.40), opportunities to ask questions
or raise concerns (Figure 8.41), as well as involvement in
care and treatment decisions (Figure 8.42). For all four
aspects of patient experience, Belgium and Luxembourg
score high at above 95% of patients reporting positive experiences. Poland has lower rates with fewer than one in two
patients reporting having been given the opportunity to ask
questions or been involved in their care and treatment during consultation. The proportion of patients with positive
experience has decreased since 2010 in Australia, France,
the Netherlands and Switzerland, but countries with
lower rates such as Sweden and Poland have improved
some aspects of patient experiences in recent years (Commonwealth Fund, 2010).
In order to measure and monitor general patient
experience in the health system, the OECD recommends collecting data on patient experience with any
doctor in ambulatory settings. An increasing number of
countries have been collecting patient experience data
based on this recommendation through nationally representative population surveys while Japan and Portugal
collect them through nationally-representative service
user surveys. Some countries, however, collect data
on patient experience with a regular doctor. For about
half the countries presented, the Commonwealth
Fund's International Health Policy Surveys 2010 and 2013
were used, even though there are critiques relating to
the sample size and response rates. Data from this
survey refer to patient experience with a regular doctor rather than any doctor.
Rates are age-sex standardised to the 2010 OECD population, to remove the effect of different population
structures across countries.
References
“CIHI Proms Forum, Background Document”,
www.cihi.ca/proms.
Commonwealth Fund (2010), “2010 International Health
Policy Survey in Eleven Countries”, Chartpack, Commonwealth Fund, New York.
Fujisawa, R. and N. Klazinga (forthcoming), “Measuring
Patient Experiences (PREMs): Progress Made by the OECD
and its Member Countries 2006-2015”, OECD Health Working Papers, Paris.
8. QUALITY OF CARE
8.39. Doctor spending enough time with patient
in consultation, 2013 (or nearest year)
8.40. Doctor providing easy-to-understand explanations,
2013 (or nearest year)
Belgium1
97.5
Belgium1
97.8
Czech Rep.1
97.2
Luxembourg1
97.5
95.6
Portugal1, 2
96.3
Japan1
96.3
Luxembourg1
New Zealand1
92.6
Portugal1, 2
89.6
Czech Rep.1
2
88.2
New Zealand1
Estonia1
Germany
96.2
90.9
86.9
Germany2
90.7
Australia1
86.5
United Kingdom 2
89.5
United Kingdom 2
86.3
OECD19
87.9
Netherlands 2
85.1
Estonia1, 2
87.4
OECD18
84.9
Switzerland2
83.6
Israel1
81.8
2
80.9
France 2
80.0
United States
Norway2
79.6
Canada 2
79.3
Sweden 2
78.3
Poland1, 2
20
86.8
86.3
Australia 2
85.9
Canada 2
85.4
Norway2
84.1
France 2
83.7
Israel1
83.2
Switzerland2
81.9
Sweden 2
59.6
0
Netherlands2
United States 2
81.8
Poland1, 2
40
60
80
100
Age-standardised rates per 100 patients
69.5
Note: 95% confidence intervals represented by H.
1. National sources. 2. Data refer to patient experiences with regular doctor.
Source: Commonwealth Fund International Health Policy Survey 2013 and
other national sources.
1 2 http://dx.doi.org/10.1787/888933281241
40
60
80
100
Source: Commonwealth Fund International Health Policy Survey 2013
and other national sources.
1 2 http://dx.doi.org/10.1787/888933281241
8.41. Doctor giving opportunity to ask questions or raise
concerns, 2013 (or nearest year)
8.42. Doctor involving patient in decisions about care and
treatment, 2013 (or nearest year)
Luxembourg
20
97.7
Luxembourg1
95.5
95.3
Belgium1
95.1
Belgium1
1
0
Switzerland2
94.4
Portugal1
Czech Rep. 2
94.0
New Zealand1
Germany2
94.0
United Kingdom 2
88.0
2
92.5
Germany2
87.7
Netherlands
90.9
88.2
United Kingdom 2
92.2
Australia 2
New Zealand 2
92.0
United States 2
Portugal1
91.8
Netherlands2
83.9
Norway2
83.3
Canada
2
88.3
86.0
83.9
Australia 2
88.3
Canada 2
83.0
United States2
86.7
Czech Rep.1
81.8
OECD19
85.0
Switzerland2
81.4
83.4
OECD19
81.3
Estonia1, 2
83.2
Sweden 2
80.5
2
82.8
Israel1
79.7
Norway
France
2
Israel1
78.4
Sweden 2
75.8
Japan1
Poland
69.8
1, 2
78.8
67.4
Spain1, 2
62.1
Poland1, 2
33.6
0
France 2
Estonia1, 2
20
40
60
80
100
1 2 http://dx.doi.org/10.1787/888933281241
47.9
0
20
40
60
80
100
1 2 http://dx.doi.org/10.1787/888933281241
161
9. HEALTH EXPENDITURE AND FINANCING
Health expenditure per capita
Health expenditure in relation to GDP
Health expenditure by function
Financing of health care
Expenditure by disease and age
Capital expenditure in the health sector
international law.
163
The amount that each country spends on health, for both
individual and collective services, and how this changes
over time can be the result of a wide array of social and economic factors, as well as the financing and organisational
structures of a country's health system.
In 2013, the United States continued to outspend all other
OECD countries by a wide margin, with the equivalent of
USD 8 713 for each US resident (Figure 9.1). This level of
health spending is two-and-a-half times the average of all
OECD countries (USD 3 453) and nearly 40% higher than the
next biggest spender, Switzerland (adjusted for the different purchasing powers – see “Definition and comparability”
box). Compared with some other G7 countries, the United
States spends around twice as much on health care per
person as Germany, Canada and France. Countries spending less than half the OECD average include many of the
central European members of the OECD, such as Hungary
and Poland, together with Chile. The lowest per capita
spenders on health in the OECD were Mexico and Turkey
with levels of less than a third of the OECD average. Outside
of the OECD, among the key partner countries, China and
India spent 13% and 4% of the OECD average on health in
per capita terms in 2013.
Figure 9.1 also shows the breakdown of per capita spending
on health into public and private sources (see the indicator
on “Financing of health care”). In general, the ranking
according to per capita public expenditure remains comparable to that of total spending. Even if the private sector in
the United States continues to play the dominant role in
financing, public spending on health per capita is still
greater than that in all other OECD countries, with the
exception of Norway and the Netherlands.
Per capita spending on health across the OECD edged up
slightly in 2013 continuing a trend of recent years. This slow
rise comes after health spending growth ground to a halt in
the wake of the global financial and economic crisis. Between
2009 and 2013, average annual health spending growth across
the OECD was 0.6%, in contrast to the 3.4% in the period
between 2005 and 2009 (Figure 9.2). There has been a difference of health spending growth between Europe and the rest
of the OECD with some European countries facing dramatic
reductions in health spending from 2010 onwards.
There have been some significant changes in the annual
growth rates in health spending in the years before and
during the financial crisis in a number of countries. Annual
increases have been reversed in Greece (5.4% vs. -7.2%) and
Ireland (5.3% vs. -4.0%) and have slowed down in the vast
majority of OECD countries. Only six countries – Hungary,
Mexico, Switzerland, Israel, Japan and Chile – recorded
higher average growth following the crisis than pre-2009.
Chile, Korea and Turkey saw health spending increase by
more than 5% in real terms in 2013. For Chile and Korea,
164
this level of spending growth has been constant since 2009.
Preliminary estimates for 2014 point towards a slight slowdown in health spending in Japan, after recent strong
growth.
In the United States, health spending grew by 1.5% in 2013,
less than half the average annual growth rate prior to 2009.
The latest forecasts from the Centers for Medicare and
Medicaid Services point to faster growth in 2014 as more
Americans gain health insurance coverage (Keehan et al.,
2015).
Canada has seen a sustained period of low growth since
2010. This is in contrast to the average 3.5% growth per year
between 2005 and 2009. With health spending growth estimated to have continued below economic growth, health
spending as a share of GDP has also declined from a high of
10.6% in 2009 to 10.2% in 2013.
Expenditure on health measures the final consumption of health goods and services (i.e. current health
expenditure). This includes spending by both public
and private sources on medical services and goods,
public health and prevention programmes and
administration, but excludes spending on capital formation (investments).
To compare spending levels between countries, per
capita health expenditures are converted to a common currency (US dollar) and adjusted to take account
of the different purchasing power of the national currencies. Economy-wide (GDP) PPPs are used as the
most available and reliable conversion rates.
For the calculation of growth rates in real terms, economy-wide GDP deflators are used. In some countries
(e.g. France and Norway) health-specific deflators
exist, based on national methodologies, but these are
not used due to limited comparability.
Note: Ireland is currently implementing a project to report
increased detail on health expenditure and financing data in
accordance with international guidelines. Data for 2013 is
therefore not available and revisions to this and the following
indicators will be made available on completion of the project.
References
Keehan, S.P. et al. (2015), “National Health Expenditure Projections, 2014-24: Spending Growth Faster Than Recent
Trends”, Health Affairs, Vol. 34, No. 8, pp. 1407-1417.
L u Gr e
xe e c
m e
bo
ur
Ir e g
la
Po nd
r tu
ga
Sp l
ain
It a
De l y
nm
a
Ic r k
el
an
Sl
o d
C v
Un z e eni a
c
i te h
d Re
K i p.
ng
do
Ca m
na
d
Es a
to
n
Be ia
lg
iu
m
Sl O E
ov C D
Ne ak R
w
e
Z e p.
al
an
Hu d
ng
ar
Fr y
an
c
Au e
st
r
Sw ia
ed
e
Tu n
rk
No ey
rw
ay 1
Un F in
l
i te an
d
d
Ne S t a
th tes
er
la
nd
M s
ex
Au ico
st
r
Ge a li a
rm
an
P y
S w ola
i t z nd
er
la
nd
Is
ra
e
Ja l
pa
n
Ko
re
a
Ch
il e 2
-7.2
-5
-0.1
-0.2
3.6
3.2
3.9
5.4
5.9
6.4
9.0
649
215
293
1 573
1 471
1 530
1 542
1 380
941
2 010
1 653
1 121
864
2 275
1 719
1 216
2 428
2 366
3 677
3 453
3 328
3 077
2 514
4 256
3 663
3 235
4 371
4 351
3 866
3 442
2 898
2 511
2 040
1 606
1 048
1 000
2.5
1.9
1.3
8.4
10
2.3
11.3
2005-09
2.9
2.0
2.8
2.0
3.3
6.7
4 553
4 553
4 124
3 713
4 904
4 819
5 862
8 713
Public
1.7
1.7
1.7
5.0
5.4
2 000
1.2
1.9
1.2
1.7
1.3
2.3
1.5
0.8
1.5
0.9
2.2
1.0
1.7
1.0
4.1
3.4
3.2
3.5
3.6
3.2
3 000
0.6
0.6
0.6
0.5
0.3
0.3
0.4
3.4
4 000
-2.3
0
-0.3
-0.4
-0.8
0.5
5
3.5
5 000
-1.6
-1.7
1.3
5 131
6 000
-3.0
5.3
6 325
7 000
-4.0
5.4
8 000
-4.3
-0.4
i te
Sw d St
it z ate
er s
la
Ne No nd
th r w
er ay
la
Sw nds
Ge ede
rm n
De an
nm y
Lu A ar
xe u s k
m tr i
bo a
u
Ca rg 2
n
B e ad
lg a
iu
F m
Au r an
st ce
ra
li
Ja a 2
Ic p a n
el
Ir e a n d
la
n
OE d 2
Ne Fi CD
Un w nl
i te Ze and
d al
K i an
ng d
do
m
It a
S ly
Po p ai
r tu n
Sl g a
ov l
en
Is i a
r
Gr a e l
ee
c
C z Ko e
e r
Sl ch e a
ov Re
a k p.
Ru H u R e p
ss ng .
i a ar
n y
Fe
d. 1
L i Chi
th le
ua
E s ni a
to
Po ni a
la
C o Br nd
st a zi
aR l 1
ic
So
a
ut L at 1
h vi
Af a
r
M ic a 1
ex
ic
Co Turk o
lo ey
m
bi
a
In C h i 1
do n a 1
ne
si
In a 1
di
a1
Un
9.1. Health expenditure per capita, 2013 (or nearest year)
USD PPP
9 000
Private
0
Note: Expenditure excludes investments, unless otherwise stated.
1. Includes investments.
2. Data refers to 2012.
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en; WHO Global Health Expenditure Database.
1 2 http://dx.doi.org/10.1787/888933281252
9.2. Annual average growth rate in per capita health expenditure, real terms, 2005 to 2013 (or nearest years)
Annual average growth rate (%)
15
2009-13
-10
1. Mainland Norway GDP price index used as deflator. 2. CPI used as deflator.
1 2 http://dx.doi.org/10.1787/888933281252
165
The change in how much a country spends on healthcare
in relation to spending on all the other goods and services
in the economy can depend on both fluctuations in the rate
of health spending itself as well as growth in the economy
as a whole. The 2000s were characterised by a period of
health spending growth above that of the overall economy
so that health expenditure as a share of GDP rose sharply in
many OECD countries. However, the economic crisis that
took hold in 2008 resulted in an initial rise followed by a
reduction in the health spending to GDP ratio across many
OECD countries.
Health spending accounted for 8.9% of GDP (excluding
investment) on average across OECD countries in 2013,
unchanged from 2012 and up marginally from 8.8% in 2011
(Figure 9.3). Including capital spending (see the indicator
on “Capital expenditure in the health care sector”), expenditure on health as a share of GDP is estimated to have been
9.3% on average in 2013.
In 2013, the United States spent 16.4% of GDP on health,
remaining well above the OECD average and more than five
percentage points above a group of high-income countries all
at around 11%, which include the Netherlands, Switzerland,
Sweden, Germany and France. Almost half of OECD countries spend in a band between 8 and 10% of GDP on health
services. Among OECD countries, Mexico and Estonia
devoted around 6% of GDP to health – around two-thirds of
the OECD average, while Turkey reported the lowest share
at just over 5% of GDP. Among the key partner countries,
China and India spent 5.6% and 4.0% of GDP respectively
in 2013, while Brazil (9.1%) and South Africa (8.9%) spent
close to the OECD average (all including investment).
The health spending to GDP ratio jumped sharply in 2009 to
reach 9.0% on average – up from 8.3% in 2008 as overall economic conditions rapidly deteriorated but health spending
continued to grow or was maintained in many countries.
In the subsequent context of reducing public deficits, the
subsequent reductions in (public) spending on health
have resulted in the share of GDP first falling and since
stabilising as health expenditure growth has become
aligned to economic growth in many OECD countries
(Figures 9.4, and 9.5).
166
The United States has seen its health spending to GDP ratio
remain consistent at 16.4% since 2009, in contrast to the
earlier steep rise whereby the share increased almost two
percentage points between 2005 and 2009. Canada also
experienced a steady rise through the second half on the
2000s to reach a peak in 2009. Since then, with health
spending growth lower than economic growth, the share of
GDP has gradually decreased. Japan, on the other hand, has
seen its health spending share of GDP rise steadily from the
OECD average in 2005 to continue increasing to more than
10% of GDP by 2013 as a result of a deliberate policy to
increase public spending on health.
In Europe, France and Germany also have seen their health
spending to GDP ratio stabilise since 2009 as health spending growth has aligned with economic growth. Other European countries, such as Portugal and Ireland saw health
spending growth decline much more than GDP, resulting in
a rapidly decreasing health spending to GDP ratio, after significant increase prior to 2009, as health spending significantly outpaced economic growth. Greece, where there
have been significant cuts in health spending, has seen the
health spending to GDP ratio fluctuate but overall remain at
a similar level to the mid-2000s as the overall economy has
suffered to the same extent.
See indicator “Health expenditure per capita” for a
definition of expenditure on health.
Gross domestic product (GDP) = final consumption +
gross capital formation + net exports. Final consumption of households includes goods and services used
by households or the community to satisfy their individual needs. It includes final consumption expenditure of households, general government and nonprofit institutions serving households.
In countries, such as Ireland and Luxembourg, where
a significant proportion of GDP refers to profits
exported and not available for national consumption,
GNI may be a more meaningful measure than GDP.
9.3. Health expenditure as a share of GDP, 2013 (or nearest year)
Public
Private
16.4
% GDP
18
16
4.0
5.3
5.1
6.0
5.6
6.2
6.1
6.5
6
6.4
6.8
6.6
7.1
6.9
7.4
7.3
7.5
8.1
8
7.6
8.6
8.5
8.7
8.7
8.8
8.8
8.9
8.8
8.9
8.9
9.1
9.1
9.5
9.2
10.1
9.9
10.2
10
10.2
10.4
10.2
11.0
10.9
11.1
11.0
12
11.1
14
2.9
4
2
Un
i te
Ne d S
th t at
S w er l es
i t z and
er s
S w l and
Ge ede
rm n
a
Fr ny
De anc
nm e
a
Ja r k
Be pan
lg
C a ium
na
Co Au da
s
s
Ne t a tr ia
w Ric
Ze a 2
al
a
Gr n d
Po eec
rt e
So B uga
ut r a l
h zi
Af l 2
r
No ic a 2
rw
ay
Au OEC
st D
ra
li a 1
It a
Sp l y
Ic ain
e
Sl l an
ov d
Un
i te F eni a
d inl
K i an
ng d
do
S l Ir e l m
ov an
ak d 1
Re
I s p.
Hu r ae
ng l
a
Cz C r y
e c hil
h e
Re
p
C o Ko .
Lu lo re
x m a
Ru e m b b i a 2
s s ou
ia rg
1
n
Fe
P o d. 2
l
M and
L i ex i c
th o
ua
E s ni a
to
n
Ch i a
in
La a 2
t
Tu via
rk
e
In In d y
do i a
ne 2
si
a2
0
Note: Excluding investments unless otherwise stated.
1. Data refers to 2012.
2. Including investments.
1 2 http://dx.doi.org/10.1787/888933281263
9.4. Health expenditure as a share of GDP, selected G7
countries, 2005-13
% GDP
18
9.5. Health expenditure as a share of GDP, selected
European countries, 2005-13
Canada
France
Estonia
Greece
Germany
Japan
Ireland
Portugal
United States
OECD34
Spain
OECD34
% GDP
12
16
10
14
8
12
10
6
8
4
6
2005
2007
2009
2011
2013
1 2 http://dx.doi.org/10.1787/888933281263
2005
2007
2009
2011
2013
1 2 http://dx.doi.org/10.1787/888933281263
167
Spending on inpatient care and outpatient care combined
covers the major part of health expenditure across OECD
countries – almost two-thirds of current health expenditure on average in 2013 (Figure 9.6). A further 20% of health
spending was allocated to medical goods (mainly pharmaceuticals), while 12% went towards long-term care and the
remaining 6% on collective services, such as public health
and prevention services as well as administration.
Greece has the highest share of spending on inpatient care
(including day care in hospitals) among OECD countries: it
accounted for 42% of total health spending in 2013, up from
36% in 2009, as a consequence of larger decreases in spending for outpatient care and pharmaceuticals. In Poland,
France and Austria, the hospital sector also plays an important role, with inpatient spending comprising more than a
third of total costs. While the United States consistently
reports the highest share of outpatient care (and by consequence the lowest inpatient share), it should be noted that
this figure includes remunerations of physicians who independently bill patients for hospital care. Other countries
with a high share of outpatient spending include Portugal
and Israel (48% and 46%).
The other major category of health spending is medical
goods. In the Slovak Republic and Hungary, medical goods
represent the largest spending category at 36% and 33% of
all health expenditure, respectively. With around 30%, the
share is also high in Greece and Mexico. In Denmark and
Norway, on the other hand, spending on medical goods
represents only 10-11% of total health spending.
There are also differences between countries in their
expenditure on long-term care (see the indicator on “Longterm care expenditure” in Chapter 11). Countries such as
Norway, the Netherlands, Sweden and Denmark which
have established formal arrangements for the elderly and
the dependent population, allocate around a quarter or
more of total health spending to long-term care. In many
southern or central European countries with a more informal long-term care sector, the expenditure on formal longterm care services accounts for a much smaller share of
total spending.
The slowdown in health spending experienced in many
OECD countries in recent years has affected all spending
categories, but to varying degrees (Figure 9.7). Expenditure
for pharmaceuticals has been cut annually by nearly 2%
after recording positive annual increases of 2% in the precrisis years – still down on previously strong growth in pharmaceutical spending in the 1990s and early 2000s (see the
indicator on “Pharmaceutical expenditure” in Chapter 10).
Despite initially ring-fencing and protecting public health
budgets, prevention spending turned negative in around
half of OECD since 2009. Overall, spending on preventive
care contracted by -0.3% on an annual basis, after recording
very high growth rates during the period 2005-09 (5.6%).
Part of the reversal in spending growth can be explained by
168
the H1N1 influenza epidemic, which led to significant oneoff expenditure for vaccination in many countries around
2009.
While spending on long-term, outpatient and inpatient
care have continued to grow, the rates have also significantly reduced since 2009. Expenditure growth for outpatient care was reduced by more than half overall (1.7% vs.
3.9%), but has still remained positive in three quarters of
OECD countries. Some governments decided to protect
expenditure for primary care and front-line services whilst
looking for cuts elsewhere in the health system. The
annual average growth rate for hospital care dropped to a
quarter of its previous growth rate, down from 2.4%, and
was negative between 2009 and 2013 in a dozen OECD
countries. Reducing wages in public hospitals, postponing
staff replacement and delaying investment in hospital
infrastructure were among the most frequent measures
taken in OECD countries to balance health budgets.
The System of Health Accounts (OECD, 2000; OECD,
Eurostat and WHO, 2011) defines the boundaries of
the health care system. Current health expenditure
comprises personal health care (curative care, rehabilitative care, long-term care, ancillary services and
medical goods) and collective services (prevention
and public health services as well as health administration). Curative, rehabilitative and long-term care
can also be classified by mode of production (inpatient, day care, outpatient and home care). Concerning long-term care, only the health aspect is normally
reported as health expenditure, although it is difficult
in certain countries to separate out clearly the health
and social aspects of long-term care. Some countries
with comprehensive long-term care packages focusing on social care might be ranked surprisingly low
based on SHA data because of the exclusion of their
social care. For example, an ongoing review of Japanese
long-term care boundaries concerning SHA will likely
lead to a significant increase in health spending based
on SHA2011 to be released in 2016. Thus, estimations
of long-term care expenditure are one of the main factors limiting comparability across countries.
References
OECD (2000), A System of Health Accounts, OECD Publishing,
OECD, Eurostat and WHO (2011), A System of Health Accounts,
2011 Edition, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264116016-en.
9.6. Current health expenditure by function of health care, 2013 (or nearest year)
Inpatient care*
%
100
4
5
90
5
11
Outpatient care**
4
5
6
8
4
6
Long-term care
4
5
5
15
20
8
2
6
70
16
22
13
9
5
4
16
22
24
23
5
5
8
33
20
20
22
14
14
34
34
35
22
22
21
4
50
48
46
8
16
12
22
26
25
22
29
32
21
30
36
37
40
7
14
30
14
10
8
29
25
12
10
11
20
24
36
9
3
5
16
19
12
Collective services
13
20
1
60
8
24
23
15
9
6
4
8
13
20
23
32
6
10
12
80
6
Medical goods
29
32
52
35
37
25
33
34
28
30
33
30
40
30
35
28
34
30
42
20
10
36
31
30
28
26
26
35
32
35
28
28
28
25
30
30
28
29
28
24
18
29
23
s
nd
la
er
th
Ne
Be
lg
iu
m
o
a
ic
da
re
ex
M
Ko
ay
na
rw
Ca
No
y
en
ar
Hu
Sw
ed
p.
ng
y
Re
ov
ak
ia
Sl
Ge
rm
an
d
en
ov
Sl
ce
an
el
Ic
nd
an
er
it z
Sw
Fr
27
la
k
CD
OE
ar
Lu
De
nm
ur
g
a in
bo
xe
m
ce
ee
Gr
Sp
n
ria
st
Au
nd
pa
Ja
la
Po
Un
i te
Cz
ec
h
Re
p.
d
a
an
ni
nl
to
Fi
at
es
¹
Es
d
St
l
ga
ra
Is
Po
r tu
el
0
Note: Countries are ranked by curative-rehabilitative care as a share of current expenditure on health. * Refers to curative-rehabilitative care in
inpatient and day care settings. ** Includes home-care and ancillary services.
1. Inpatient services provided by independent billing physicians are included in outpatient care for the United States.
1 2 http://dx.doi.org/10.1787/888933281277
9.7. Growth rates of health spending for selected functions per capita, OECD average, 2005-13
2005-09
2009-13
Average annual growth rates in real terms, %
7
5.9
6
5.6
5
3.9
4
3
2.7
2.4
2
1
3.8
1.9
1.7
0.8
0.7
0
-0.3
-1
-2
-1.8
-3
Inpatient care
Outpatient care
Long-term care
Pharmaceuticals
Prevention
Administration
1 2 http://dx.doi.org/10.1787/888933281277
169
Across all OECD countries, health care is financed by a mix
of public and private spending. In some countries, public
health spending is mostly confined to spending by the government using general revenues. In others, social insurance funds finance the bulk of health expenditure. Private
financing of health care consists mainly of payments by
households (either as standalone payments or as part of
co-payment arrangements) as well as various forms of private health insurance.
In nearly all OECD countries, the public sector is the main
source of health care financing. Around three-quarters of
health care spending was publicly financed in 2013
(Figure 9.8). In Denmark, Sweden and the United Kingdom,
central, regional or local governments financed more than
80% of all health spending. In the Czech Republic, France,
Luxembourg, Japan and Germany, social health insurance
financed 70% or more of all health expenditure. Only in
Chile and the United States was the share of public spending on health below 50%. In these countries, a great proportion of health spending is financed either directly by
households (Chile) or by private insurance (United States).
Health care is competing for public resources with different
sectors such as education, defence and housing. The size of
the public budget allocated to health is determined by a
number of factors including the type of health and longterm care system, the demographic composition of the
population and the relative budget priorities. On average,
15% of total government expenditure was dedicated to
health care in 2013 (Figure 9.9). There are, however, important variations across OECD member states. Whereas a
fifth of government spending is allocated to health care in
countries such as New Zealand and Switzerland, this falls
to around 10% in Hungary and Greece.
Developments in overall health spending are largely driven
by the trends in public spending. Strong pre-crisis growth
resulted in average public expenditure on health increasing
at an annual rate of almost 4% (Figure 9.10). In 2010, growth
in public health spending came to a halt with reductions in
many countries. Since then spending growth has been very
slow, often in line with overall economic growth.
After public financing, the main source of funding tends to
be out-of-pocket payments. On average private households
directly financed 19% of health spending in 2013. The share
of out-of-pocket payments was above 30% in Mexico, Korea,
Chile and Greece and 10% or lower in France and the United
Kingdom. Out-of-pocket spending has continued to grow
since 2009, albeit at a slower rate, partly as a result of costsharing measures introduced in a number of countries.
Measures taken include increasing co-payments and raising reimbursement thresholds for pharmaceuticals, reducing benefits for dental treatment, increasing user charges
for hospital care, introducing cost-sharing for certain activities in primary care and removing entitlements for public
coverage for particular groups of the population.
170
Private health insurance (PHI) can play different roles in
health systems. Whereas PHI provides primary health care
coverage for large population groups in the United States
and Chile, it complements or supplements public coverage
for the vast majority of the population in countries such as
France, Belgium and Slovenia. In other countries, such as
Australia and Ireland, it serves as duplicate insurance providing access to a larger group of providers. Spending for
PHI accounts for only 6% of overall health spending in the
OECD, but it represents a sizeable share in a number of
countries, particularly in the United States (35%) and Chile
(20%). While health spending growth through private
health insurance slowed down significantly in the period
2009-11, spending grew by 2.9% between 2011 and 2013 –
also as a response to some cost-shifting and loss of coverage in some countries.
The financing of health care can be analysed from the
point of view of the sources of funding (households,
employers and the state), financing schemes (e.g.
compulsory or voluntary insurance) and financing
agents (organisations managing the financing
schemes). Here “financing” is used in the sense of
financing schemes as defined in the System of Health
Accounts (OECD, 2000; OECD, Eurostat and WHO, 2011).
Public financing includes expenditure by the general
government and social security funds. Private financing covers households’ out-of-pocket payments, private health insurance and other private funds (NGOs
and private corporations). Out-of-pocket payments
are expenditures borne directly by patients. They
include cost-sharing and, in certain countries, estimations of informal payments to health care providers.
Total government expenditure is used as defined in
the System of National Accounts and includes as
major components intermediate consumption, compensation of employees, interest, social benefits,
social transfers in kind, subsidies, other current
expenditure and capital expenditure payable by central, regional and local governments as well as social
security funds.
References
OECD (2000), A System of Health Accounts, OECD Publishing,
OECD, Eurostat and WHO (2011), A System of Health Accounts,
2011 Edition, OECD Publishing, Paris, http://dx.doi.org/
10.1787/9789264116016-en.
9.8. Expenditure on health by type of financing, 2013 (or nearest year)
General government
% of current health expenditure
1
2
100
90
6
5
80
15
15
14
15
3
10
2
14
Social security
5
5
18
12
1
4
14
17
13
18
22
5
13
17
22
7
11
9
2
6
4
23
19
70
3
3
27
20
17
26
31
4
6
11
20
28
23
37
1
5
14
14
7
Other
13
24
13
28
9
13
24
8
5
4
15
19
Private insurance
35
45
1
33
60
36
45
50
80
78
84
40
73
84
58
74
83
66
75
74
67
77
72
70
68
68
67
61
30
47
61
69
68
67
40
4
56
47
45
65
29
53
48
20
37
31
20
10
7
10
6
19
11
9
11
7
4
7
25
16
10
3
9
42
22
11
Ne
th
er
la
nd
No s¹
rw
De ay
n
C z ma
ec rk
h
Re
Un
i t e S w p.
d
e
K i de
ng n
do
m
Lu Ja ²
xe p a
m n
bo
u
Ic r g
Ne ela
w nd
Ze ²
al
an
Fr d
an
c
Tu e
rk
Be ey
lg
iu
Es m
to
ni
a
I
Ge t al y
rm
an
Au y
st
r
F ia
Sl inl a
ov n
ak d
Re
O E p.
CD
34
Sp
Sl a in
ov
en
Ca ia
na
d
Po a
l
Au and
st
ra
Ir e l i a
la
Po nd ²
Sw r tu
i t z gal
er
la
n
Gr d
ee
Hu c e
ng
ar
y
Is
ra
el
Ko
re
Un M a
i t e ex i
c
d
St o
at
es
³
Ch
il e
0
12
1. The Netherlands report compulsory cost-sharing in health care insurance and in Exceptional Medical Expenses Act under social security rather
than under private out-of-pocket, resulting in an underestimation of the out-of-pocket share.
2. Data refer to total health expenditure (= current health expenditure plus capital formation).
3. Social security reported together with general government.
1 2 http://dx.doi.org/10.1787/888933281280
9.9. Health expenditure as share of total government
expenditure, 2013 (or nearest year)
New Zealand
Switzerland
Netherlands
United States
Japan¹
Germany
Canada
Sweden
Norway
Australia
United Kingdom¹
Iceland
Denmark
Austria
France
OECD34
Belgium
Chile
Spain
Czech Rep.
Slovak Rep.
Italy
Ireland
Luxembourg
Korea
Portugal
Estonia
Mexico
Finland
Israel
Poland
Turkey
Slovenia
Greece
Hungary
22
22
9.10. Growth of health spending by financing, OECD
average, 2005-2013
2005-07
2007-09
2009-11
2011-13
21
20
20
3.7
19
18
17
17
16
16
16
15
15
15
15
15
14
14
14
14
13
13
12
12
12
12
11
11
11
11
10
10
10
10
0
5
15
20
25
% total government expenditure
1. Data refer to total health expenditure (= current health expenditure
plus capital formation).
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en;
OECD National Accounts; Eurostat Statistics Database; IMF World
Economic Outlook Database.
1 2 http://dx.doi.org/10.1787/888933281280
4.3
General government/
Social security
0.2
0.5
2.8
0.7
0.7
1.2
4.9
4.0
Private insurance
1.8
2.9
10
0
2
4
6
Annual growth rates per capita in real terms (%)
1 2 http://dx.doi.org/10.1787/888933281280
171
Attributing health care expenditure by disease and age is
important for health policy makers in order to analyse
resource allocations in the health care system. This information can also play a role in assessing the impact of population ageing and changing disease patterns on spending.
Furthermore, the linking of health expenditures by disease
to appropriate measures of outputs (e.g. hospital discharges by disease) and outcomes (e.g. survival rates after
heart attack or cancer) helps in monitoring the performance of health care systems at a disease-based level
(Heijink et al., 2006).
Figure 9.11 shows the distribution of hospital inpatient
expenditure according to seven main diagnostic categories.
These categories account for between 60% and 80% of all
inpatient acute care expenditure across the group of countries. Circulatory diseases account for the highest share of
inpatient spending in each of the countries except for
Korea and the Netherlands, where spending on cancer and
mental and behavioural disorders is the largest category,
respectively. The differences between countries can be
influenced by a number of factors, including demographic
structure and disease patterns, as well as institutional
arrangements and clinical guidelines for treating different
diseases. For example, in the Netherlands, mental and
behavioral disorders account for around 23% of all inpatient spending – around twice the level as that of Germany,
Finland and Japan. This may be partly explained by the
large number of acute mental health hospitals with very
long average lengths of stay (OECD, 2015). Similarly, longer
than average lengths of stay in Japan for some of the specific circulatory diseases such as cerebrovascular disease
(stroke) might explain why more than 22% of hospital inpatient expenditures are allocated to the treatment of circulatory diseases. Discharges related to circulatory diseases
only account for 12% of all discharges in Japan – a proportion similar to other countries.
Figure 9.12 compares expenditure by hospital discharge for
circulatory diseases and cancers. Generally, the cost per
discharge between these two main disease categories is
similar in all countries, apart from Japan where spending
per discharge for circulatory diseases is more than twice
that of cancer. Japan has the highest expenditure per discharge compared to the other countries for circulatory disease, again due to the much longer lengths of stay, while
the Netherlands has the highest expenditure per discharge
for cancer treatment.
Different cost patterns can also be due partly to demographic
factors. The allocation of current health spending by age
group in the Czech Republic, Korea and the Netherlands in
172
Figure 9.13 shows that the share of spending increases with
age after an initial peak of spending linked to birth and
early childhood illnesses. The share of current health
spending remains relatively constant until around the 50 to
54 age group before increasing sharply as people grow
older. As a result, a significant share of current health
spending is consumed by elderly population. Those aged 65
and above consume around 60% of the current health
spending on average in all three countries. In addition, in
Korea and the Netherlands more than 20% of current
health spending is accounted for by those aged 85 years
and above, while in the Czech Republic the share is much
lower. This may be explained by a lower level of long-term
care spending in Czech Republic.
Definition and deviations
Expenditure by disease and age allocates current
health expenditure by patient characteristics. Guidelines developed propose disease categories according
to ICD-10. To ensure comparability between countries,
expenditures are also linked to the System of Health
Accounts (SHA) framework and a common methodology is proposed advocating primarily a top-down allocation of expenditures based on principal diagnosis.
The main comparability issues relates to the treatment of non-allocated and non-disease-specific
expenditures. In the former case this is due to data
limitations (often in outpatient and pharmaceutical
expenditure) and in the latter case mainly prevention
and administration expenditure.
Note that the charts cover allocated spending only
and the following country limitations apply. Canada
excludes Quebec and mental health hospitals; the
Czech Republic refers to expenditure by the Health
Insurance Fund only; Germany refers to total hospital
expenditure; and the Netherlands refers to curative
care in general and specialty hospitals.
References
Heijink, R., M.A. Koopmanschap and J.J. Polder (2006), International Comparison of Cost of Illness, RIVM, Bilthoven.
OECD (2015), Addressing Dementia: The OECD Response, OECD
Health Policy Studies, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264231726-en.
9.11. Share of hospital inpatient expenditures by main diagnostic category, 2011 (or nearest year)
Circulatory diseases
%
25
Cancer
Mental and behavioural disorders
Respiratory system
Injury, poisoning and other consequences of external causes
Digestive diseases
Diseases of musculoskeletal system and connective tissue
Japan
(2010)
Netherlands
(2011)
20
15
10
5
0
Canada
(2008)
Czech Rep.
(2011)
Finland
(2010)
Germany
(2008)
Hungary
(2006)
Korea
(2009)
Slovenia
(2011)
Sweden
(2011)
Switzerland
(2011)
Source: OECD Expenditure by Disease, Age and Gender Database.
1 2 http://dx.doi.org/10.1787/888933281298
9.12. Expenditure per hospital discharge for two
diagnostic categories, 2011 (or nearest year)
Circulatory disease
Cancer
9.13. Share of current health spending by age group, 2011
(or nearest year)
Czech Rep.
%
Korea
Netherlands
30
8 558
Switzerland
10 925
5 753
6 444
Sweden
4 773
5 162
Slovenia
11 962
Netherlands
20
14 809
4 742
5 081
Korea (2009)
17 366
Japan (2010)
8 001
10
1 817
1 586
Hungary (2006)
5 616
5 819
Germany (2008)
4 881
4 847
Finland (2010)
2 909
2 406
4
510 9
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
-7
75 4
-7
80 9
-8
4
85
+
0
0-
Czech Rep.
5 800
5 776
Canada (2008)
0
5 000
10 000
15 000
USD PPP
1 2 http://dx.doi.org/10.1787/888933281298
1 2 http://dx.doi.org/10.1787/888933281298
173
Knowing how much a health system is investing in hospitals, medical technology and other equipment is very relevant for policy making and analysis. Although health
systems remain a highly labour-intensive sector, capital
has been increasingly important as a factor of production
of health services over recent decades. This is illustrated,
for example, by the growing importance of diagnostic and
therapeutic equipment or the expansion of information,
computer and telecommunications technology in health
care over the last few years. The availability of statistics on
capital expenditure is essential to the analysis of the health
system’s production capacity (that is, whether capacity is
appropriate, deficient or excessive), which is needed in
turn to inform policy implementation (for example, if
excess capacity exists, the marginal cost of expanding coverage will be lower than if the health care system is already
straining to fill current demand).
On average, OECD countries invested around 0.45% of their
GDP in 2013 in terms of capital spending in the health sector. This compares with 8.9% of GDP on average across the
OECD for current spending on health care services and medical goods (see the indicator on “Health expenditure in relation to GDP”). As with current spending, there are both
differences in the current levels of investment expenditure
between countries and in the recent trends observed.
At the higher end of the scale, Belgium spent more than
0.8% of GDP on capital investment in 2013, followed by a
group of countries, including France, Germany and the
United States, all spending more than 0.6% of GDP. Around
half the OECD countries are in a relatively narrow band of
plus or minus 25% of the average ranging from the United
Kingdom to Australia. At the lower end, Turkey, Chile and
Hungary spent around half the OECD average, while
Greece, Iceland and Mexico spent around 0.1% of GDP on
capital infrastructure and equipment in the health care
sector.
Data from National Accounts provides an idea of the type
of assets and capital spending. While capital spending can
fluctuate from year to year, overall in the health sector
there is an even split between spending on construction
(i.e. building of hospitals and other health care facilities)
and spending on equipment (medical machinery, ambulances, as well as ICT equipment). Together they account
for 85% of capital expenditure. The remaining 15% is
accounted for by intellectual property products – the result
of research, development or innovation. This can vary significantly between countries.
174
In parallel with current health spending, capital spending
has been affected by the global economic crisis with outlays on health system infrastructure and equipment
often being a prime target for reduction or postponement. Overall, capital spending grew strongly in the
period up to 2008 – annual capital expenditure was 22%
higher than in 2005 in real terms on average. During the
next three years, the annual outlay fell back by almost
15%. Since 2011, there has been a return to growth in capital spending (Figures 9.15 and 9.16).
The country differences also mirror the trends in current
spending. Outside of Europe, investment in the health sector has been generally less affected by the economic downturn. Australia and Korea, for example, report capital
spending more than 40% higher in 2013 compared with
2005.
A number of European countries have seen severe reductions in capital spending. Figures for Greece show that the
outlay was less than 40% of the 2005 level in 2013, with an
acceleration of the fall in 2010. Similarly, Spain experienced
a sharp reversal after 2008, with capital spending in 2012 at
half the level of 2005.
Gross fixed capital formation in the health care system is measured by the total value of the fixed assets
that health providers have acquired during the
accounting period (less the value of the disposals of
assets) and that are used repeatedly or continuously
for more than one year in the production of health
services. The breakdown by assets includes infrastructure (e.g. hospitals, clinics, etc.), machinery and
equipment (including diagnostic and surgical machinery, ambulances, and ICT equipment), as well as software and databases.
Gross fixed capital formation is reported by many
countries under the System of Health Accounts. It is
also reported under the National Accounts broken
down by industrial sector according to the International Standard Industrial Classification (ISIC) Rev. 4
using Section Q: Human health and social work activities or Division 86: Human health activities. The former is normally broader than the SHA boundary while
the latter is narrower.
9.14. Gross fixed capital formation in the healthcare sector as a share of GDP, 2013 (or nearest year)
Public
% GDP
1.0
Private
Total (no breakdown)
0.8
0.6
0.4
0.2
es
D
N e enm
w
a
Ze rk
al
an
d2
Fr
an
Ge c e
rm
a
Au ny 1
st
ra
S li a
Ne we
th de
er n
la
nd
s1
Fi
nl
an
Ca d
na
da
Sp
a in
2
Po
r tu
ga
No l
rw
a
Es y
to
ni
a
It a
ly 1
OE
CD
Po
Cz la
ec nd
h
Re
S l p. 1
o
Lu ve
xe ni
m a
bo
ur
Un
g
i te
I
d sr a
Ki
el
ng
do
m
Ir e
la
nd
Sl Kor
ov
e
ak a
Re
p
Tu .
rk
ey
Ch
Hu il e
ng
a
Gr r y
ee
c
Ic e 1
el
an
d1
M
ex
ic
o
ria
at
St
i te
d
st
Au
Un
Be
lg
iu
m1
0
1. Refers to gross fixed capital formation in ISIC 86: Human health activities (ISIC Rev. 4).
2. Refers to gross fixed capital formation in ISIC Q: Human health and social work activities (ISIC Rev. 4).
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en; OECD National Accounts Database.
1 2 http://dx.doi.org/10.1787/888933281305
9.15. Gross fixed capital formation, selected
non-European countries, 2005-13
Australia
Canada
United States
OECD34
9.16. Gross fixed capital formation, selected European
countries, 2005-13
Korea
2005 = 100
160
Ireland
Greece
Spain
OECD34
Portugal
2005 = 100
160
140
140
120
120
100
100
80
80
60
60
40
40
20
20
2005
2007
2009
2011
2013
1 2 http://dx.doi.org/10.1787/888933281305
2005
2007
2009
2011
2013
1 2 http://dx.doi.org/10.1787/888933281305
175
10. PHARMACEUTICAL SECTOR
Financing of pharmaceutical expenditure
Pharmacists and pharmacies
Pharmaceutical consumption
Share of generic market
Research and development in the pharmaceutical sector
international law.
177
Pharmaceuticals play a vital role in the health system and
policy makers must balance the access of patients to new
effective medicines with limited health care budgets, while
providing the right incentives to manufacturers to develop
new generations of drugs. After inpatient and outpatient
care, pharmaceuticals represent the third largest expenditure item of health care spending and accounted for more
than a sixth (17%) of health expenditure on average across
OECD countries in 2013, not taking into account spending
on pharmaceuticals in hospitals.
The total retail pharmaceutical bill across OECD countries
was around USD 800 billion in 2013. However, there are
wide variations in pharmaceutical spending per capita
across countries, reflecting differences in volume, patterns
of consumption and pharmaceutical prices (Figure 10.1).
With more than USD 1 000 in 2013, the United States spent
far more on pharmaceuticals than any other OECD country
on a per capita basis, and double the OECD average. Japan
(USD 752), Greece (USD 721) and Canada (USD 713) also
spent significantly more on medicines than other OECD
countries. At the other end of the scale, Denmark (USD 240)
had relatively low spending levels, less than half the average across OECD countries. It is important to note that
these figures refer only to retail pharmaceuticals, that is,
pharmaceuticals dispensed directly to patients with a
medical prescription or over-the-counter purchases. Pharmaceuticals can also be administered to patients when
they are in hospital, but these are not taken into account
here. Figures available for a small number of OECD countries suggest that this can add another 10-20% on average
to the retail spending, but can vary according to different dispensing and budgetary practices (Belloni et al., forthcoming).
Around 80% of total retail pharmaceutical spending is for
prescribed medicines; the rest being spent on over-thecounter (OTC) medicines. OTC medicines are pharmaceuticals that can be bought without prescription and their costs
are generally borne by patients. In some cases, however,
OTC drugs can also be reimbursed by public payers.
Depending on country-specific legislation, OTC pharmaceuticals can be sold outside of pharmacies, for example, in
supermarkets, other retail stores or via the internet. In Australia, Spain and Poland, the share of OTC medicines is relatively high – in the latter case accounting for half of
pharmaceutical spending.
During the 1990s and early 2000s, increasing spending on
retail pharmaceuticals acted as a major contributor in driv-
178
ing up overall health expenditure (Figure 10.2). Average real
annual growth in pharmaceutical spending outpaced overall health spending growth – more than 5% on average each
year between 1990 and 2004, compared with average health
spending growth of less than 4% per year. However, in the
second half of the 2000s there was a significant drop in
average pharmaceutical spending growth which then
intensified following the global economic crisis. In this
period, policy makers in many OECD countries were concerned about reining in public pharmaceutical spending in
an effort to limit total public spending (see Indicator
“Financing of pharmaceutical expenditure”). Thus, a number of countries introduced a series of measures: price cuts
(achieved through negotiations with the pharmaceutical
manufacturers, introduction of reference pricing, application of compulsory rebates, decrease of pharmacy margins,
reductions of the value added tax applicable for pharmaceuticals), promoting the use of generics, reduction of
package sizes, reduction in coverage (excluding pharmaceuticals from reimbursement) and increases in co-payments by
households.
Pharmaceutical expenditure covers spending on prescription medicines and self-medication, often
referred to as over-.the-counter products. In some
countries, other medical non-durable goods are also
included. Pharmaceuticals consumed in hospitals and
other health care settings are excluded. Final expenditure on pharmaceuticals includes wholesale and retail
margins and value-added tax. It also includes pharmacists’ remuneration when the latter is separate
from the price of medicines. Total pharmaceutical
spending refers in most countries to “net” spending,
i.e. adjusted for possible rebates payable by manufacturers, wholesalers or pharmacies.
References
Belloni, A., D. Morgan and V. Paris (forthcoming), “Pharmaceutical Expenditure and Policies: Past Trends and
Future Challenges”, OECD Working Paper, OECD Publishing, Paris.
10.1. Expenditure on pharmaceuticals per capita, 2013 (or nearest year)
Prescribed medicines
Over-the-counter medicines
Total (no breakdown)
USD PPP
1 200
1 026
1 000
800
752
721
713
678
666
652
603
600
596
590
572
536
533
526
515
503
481
459
459
436
397
400
396
392
381
367
364
326
287
273
240
200
a
k
ni
ar
De
nm
el 1
to
Es
nd
ra
Is
g2
la
Po
ay
ur
bo
m
Lu
xe
No
rw
p.
l1
ec
h
Re
d
ga
r tu
Po
Cz
s1
an
el
Ic
a
la
er
th
Ne
Sl
nd
en
re
Ko
d
ed
Sw
ia
Fi
nl
an
y
en
ov
Sl
29
ar
ng
CD
OE
Hu
p. 1
a in
Sp
ria
st
Re
ak
Au
ov
li a
ly 1
ce
ra
Au
st
m
an
Fr
It a
1
iu
nd
lg
la
Be
Ir e
y
nd
an
la
er
Sw
it z
da
na
rm
Ca
Ge
n
ce 1
pa
ee
Gr
at
St
Un
i te
d
Ja
es
0
1. Includes medical non-durables (resulting in an over-estimation of around 5-10%).
2. Excludes spending on over-the-counter medicines.
1 2 http://dx.doi.org/10.1787/888933281318
10.2. Average annual growth in pharmaceutical and total health expenditure per capita, in real terms, average across
OECD countries, 1990 to 2013
Health expenditure (including pharmaceutical spending)
%
8
6
4
2
0
-2
-4
1990
1995
2000
2005
2010
2013
1 2 http://dx.doi.org/10.1787/888933281318
179
In all OECD countries, pharmaceuticals are financed by a
mix of public and private spending. Tax-funded schemes or
social health insurance cover a significant amount of prescribed pharmaceuticals in most countries, sometimes
complemented by private health insurance. Patients typically have to cover some part of the cost of prescription
drugs themselves, although exemptions often exist for vulnerable segments of the population such as children, the
elderly and patients suffering from certain chronic illnesses. Over-the-counter (OTC) pharmaceuticals are normally financed entirely by private households.
Pharmaceutical spending represents around 1.4% of GDP
on average across OECD countries ranging from 0.5% in
Denmark to 2.8% in Greece (Figure 10.3). Public funds represent slightly less than 60% on average – just under 1% of
GDP across OECD countries. However, this share is significantly higher in Japan (1.5%) and Greece (1.9%) and much
lower in Denmark and Norway (both 0.3%). The proportion
of private expenditure in GDP is highest in Hungary and the
United States (both 1.3%), and also high in Canada (1.0%).
Public protection against the costs of pharmaceuticals is
not as developed as for other health services, such as inpatient and outpatient care (Figure 10.4). On average across
OECD countries, the public sector covered a much higher
proportion of the costs of health services (79%) compared
with pharmaceuticals (57%) in 2013. This is true for all
countries with the exception of Greece where public coverage for pharmaceuticals is higher (67% vs. 64%). Public coverage for pharmaceuticals is high in countries such as
France, Japan and Germany where coverage by public
financing schemes accounts for 70% or more of total costs.
Private sources have to cover more than half of the total
pharmaceutical bill in eight OECD countries, with public
coverage being the lowest in Poland (32%), the United
States (34%) and Canada (36%). However, in the United
States and Canada, private health insurance plays a significant role in covering parts of the pharmaceutical costs for
patients. Poland reports large spending on privately
financed OTC pharmaceuticals.
The growth in public spending on pharmaceuticals has
remained below total health spending growth over the last
decade (see Indicator “Pharmaceutical expenditure”) with
recent growth rates in sharp decline as compared to precrisis years (Figure 10.5). Between 2009 and 2013, public
expenditure on pharmaceuticals dropped by 3.2% on average across OECD countries while it increased by 2.7% each
year in the 2005-09 period. The reduction was particularly
steep in Portugal (-11.1%), Denmark (-10.4%) and Iceland
(-9.9%). Greece and the Netherlands have also seen signifi-
180
cant reversals in growth of public pharmaceutical spending
following the crisis compared to the pre-crisis period. The
reduction in public spending on pharmaceuticals has not
been restricted to Europe. Public spending also came down
in Canada and Australia (both -2.1%). Japan, on the other
hand, continues to see substantial annual increases (4.9%).
Reduction of public pharmaceutical spending in most
OECD countries was achieved by a wide range of policy
measures (see Indicator “Pharmaceutical expenditure”),
including reforms that have aimed to shift some of the
burden of pharmaceutical spending away from the public
purse to private payers. These measures included the delisting of products (i.e. excluding them from reimbursement)
and the introduction or increase of user charges for retail
prescription drugs (Belloni et al., forthcoming). In recent
years, measures of this kind have been taken by around a
dozen OECD countries. Ireland, for example, introduced a
50-cent prescription fee for Medical Card holders in 2010
which was subsequently increased. At the same time, the
monthly drug reimbursement threshold was raised by 20%
to EUR 120 for non-Medical Card holders, followed by
subsequent increases. As a result of these policy measures,
the share of private financing of pharmaceuticals has
increased substantially in a number of countries. In Spain,
39% of pharmaceutical costs were covered out-of-pocket in
2013, up from 24% in 2009. In Greece and Iceland, the proportion of pharmaceutical spending paid for by households
directly went up by 10 percentage points or more since
2009.
See indicator on pharmaceutical expenditure for definition of what is included and possible limitations.
See indicator on financing of health care for definition
of “public” and “private” spending on health.
Health services refer to inpatient and outpatient care
(including day cases), long-term health care and
auxiliary services.
References
Belloni, A., D. Morgan and V. Paris (forthcoming), “Pharmaceutical Expenditure and Policies: Past Trends and
Future Challenges”, OECD Working Paper, OECD Publishing, Paris.
10.4. Public share of expenditure on health services
and goods, 2013 (or nearest year)
10.3. Expenditure on pharmaceuticals as a share of GDP,
2013 (or nearest year)
Public
Greece 1
Hungary
Japan
Slovak Rep.1
United States
Slovenia
Canada
Italy1
Spain
France
Germany
Belgium
Ireland1
Portugal1
OECD29
Poland
Australia
Czech Rep.
Korea
Austria
Switzerland
Finland
Estonia
Sweden
Israel1
Iceland
Netherlands1
Norway
Luxembourg 2
Denmark
Pharmaceuticals1
Private
Luxembourg
Netherlands2
Germany
Japan
France
Austria
Slovak Rep.
Greece
Belgium
Switzerland
Czech Rep.
Spain
Norway
OECD26
Portugal
Korea
Estonia
Finland
Sweden
Australia
Slovenia
Hungary
Denmark
Iceland
Canada
2.8
2.2
2.1
2.0
1.9
1.7
1.7
1.6
1.6
1.6
1.5
1.4
1.4
1.4
1.4
1.4
1.3
1.3
1.3
1.2
1.2
1.2
1.1
1.0
1.0
0.9
0.9
0.6
82
80
75
71
69
68
67
67
66
65
62
61
58
57
55
55
54
53
52
49
48
43
43
38
36
United States
Poland
0.6
0.5
0
1
2
Health services
34
32
0
3
% GDP
50
100
%
2. The shares for the Netherlands are overestimated as they include
compulsory co-payments by patients to health insurers.
1 2 http://dx.doi.org/10.1787/888933281325
2. Excludes spending on over-the-counter medicines.
1 2 http://dx.doi.org/10.1787/888933281325
10.5. Average annual growth in public pharmaceutical expenditure1 per capita, in real terms, 2005-09 and 2009-13
(or nearest periods)
10.3
2.2
2.7
5.4
4.9
5.5
2.1
0.8
1.2
-0.3
-5.8
-0.7
-0.5
-1.1
-1.5
1.1
-1.9
-2.1
-2.1
-2.5
-2.9
-3.2
0.5
0.8
3.4
3.4
2.8
3.8
5.4
2.7
-0.6
-3.2
-3.6
-1.4
-3.9
-6.6
-4.2
-5.3
-6.4
-6.4
-7.7
-9.6
-9.9
-10.4
-11.1
-5.7
-5
-10
-0.3
1.6
-1.2
-1.1
0.1
0.1
5
3.8
5.0
7.8
8.5
10.3
11.6
10
0
2009-13
2005-09
%
15
n
pa
Ja
es
at
i te
d
St
a
ay
ni
rw
No
Un
nd
to
Es
a
re
ria
la
it z
er
Ko
Sw
y
st
Au
an
ce
Ge
rm
m
an
Fr
d
iu
an
lg
Be
Fi
nl
da
li a
na
Ca
nd
st
ra
p.
la
Re
Ir e
Au
ov
ak
28
Sl
OE
CD
en
ia
ed
Sw
y
ly
en
ov
Sl
It a
s
ar
ng
Hu
nd
nd
th
er
la
la
Po
Ne
p.
a in
Sp
ur
Cz
ec
h
Re
g
ce
Lu
xe
m
bo
d
ee
Gr
k
an
Ic
el
ar
nm
De
Po
r tu
ga
l
-15
1 2 http://dx.doi.org/10.1787/888933281325
181
Pharmacists assist people in obtaining medication and
ensuring that these are used in a safe and proper fashion.
The role of the pharmacists has changed over the recent
years. Although their main role is still to dispense medications
in community pharmacies, pharmacists are increasingly
providing direct care to patients (e.g. flu vaccinations in
Ireland), both in community pharmacies and as part of integrated health care provider teams.
OECD countries generally have between 50 and 130 pharmacists per 100 000 population. Japan has by far the highest
density of pharmacists, at twice the OECD average, while
the density of pharmacists is low in Turkey, Chile and the
Netherlands (Figure 10.6). Between 2000 and 2013, the number of pharmacists per capita has increased in nearly all
OECD countries, with the exception of Switzerland. It
increased most rapidly in Portugal, Ireland, Japan, Spain and
Hungary.
In Japan, the strong increase in the number of pharmacists
can be attributed to a large extent to government’s efforts to
separate more clearly drug prescribing from drug dispensing.
Traditionally, the vast majority of prescription drugs in
Japan were dispensed directly by doctors. Over the years,
the Japanese government has taken steps to encourage the
separation of drug prescribing from dispensing. The Medical
Service Law was first amended in 1997 and then in 2006 to
recognise community pharmacies as facilities providing
health goods and services. Following these amendments,
the percentage of prescriptions dispensed by pharmacists
rose from less than 40% of all prescriptions in 2000 to 67% in
2013, while the number of community pharmacies
increased from 48 252 to 57 071 (Japanese Pharmaceutical
Association, 2015).
Most pharmacists work in community pharmacies, but
some also work in hospital, industry, research and academia
(FIP, 2015). For instance, in Canada in 2012, more than
three-quarters of practising pharmacists worked in a community pharmacy while about 25% worked in hospitals and
other health care facilities (CIHI, 2013). In Japan, around
55% of pharmacists worked in community pharmacies in
2012, while around 20% worked in hospitals or clinics and
the other 25% worked in other settings (Japanese Pharmaceutical Association, 2015).
including branch pharmacies and supplementary pharmacy units attached to the main pharmacy (Vogler et al.,
2012).
The number of community pharmacies varies widely
across OECD countries (Figure 10.7). This big variation can
be explained by the more or less active planning role of
governments and agencies; by the remuneration model
used in the country, as well as by different dispensing
channels of medicines. In addition to community pharmacies, medicines can be dispensed through hospital pharmacies (serving both inpatients and outpatients) or can be
provided directly by doctors in a few countries. For example,
the relatively low number of community pharmacies in the
Netherlands may be explained partly by the fact that
patients can also purchase their prescription drugs directly
from some doctors (Vogler et al., 2012). There are about
400 GPs who are selling medicines in the Netherlands, providing access to drugs especially in rural areas where the
nearest pharmacy may be quite far away (RIVM, 2014).
Denmark has few, but large, community pharmacies
References
182
The range of products and services provided by the pharmacies varies across countries. In most European countries, for
example, pharmacies can also sell cosmetics, food supplements, medical devices and homeopathic products and in a
few countries pharmacies can also sell reading glasses and
didactic toys (Martins et al., 2015). Depending on countries’
legislation, pharmacies can provide services such as vaccination, medication use review, unit dose dispensing,
generic substitution, point of care testing, medication
administration, needle exchange programme, take back
medicines (disposal of medicines), etc.
Practicing pharmacists are defined as the number of pharmacists who are licensed to practice and provide direct
services to clients/patients. They can be either salaried
or self-employed, and work in community pharmacies,
hospitals and other settings. Assistant pharmacists and
the other employees of pharmacies are normally
excluded.
In Ireland, the figures include all pharmacists registered
with the Pharmaceutical Society of Ireland, possibly
including some pharmacists who are not in activity. In
addition they include assistant pharmacists, pharmaceutical assistants, and doctors who are dispensing
medication (approximately 140 in 2007), resulting in an
over-estimation compared with the data provided by
other countries. Assistant pharmacists are also
included in Iceland.
Community pharmacies are premises which in accordance
to the local legal provisions and definitions may operate
as a facility in the provision of pharmacy services in the
community settings. The number of community pharmacies reported are the number of premises where
dispensing of medicines happened under the supervision
of a pharmacist.
Pharmacist Workforce, 2012 – Provincial/Territorial Highlights, Ottawa, Canada.
FIP – International Pharmaceutical Federation (2015), Global
Trends Shaping Pharmacy – Regulatory Frameworks, Distribution of Medicines and Professional Services, The Hague.
Japan Pharmaceutical Association (2015), Annual Report of
JPA 2014-2015, Tokyo.
Martins, S.F. et al. (2015), “The Organizational Framework of
Community Pharmacies in Europe”, International Journal
of Clinical Pharmacy, May 28.
RIVM – National Institute for Public Health and the Environment (2014), The Dutch National Atlas of Public Health,
Bilthoven.
Vogler, S. et al. (2012), “Impact of Pharmacy Deregulation
and Regulation in European Countries”, Vienna.
10.6. Practising pharmacists, 2000 and 2013 (or nearest year)
2000
2013
180
161
160
140
127
119
120
114
112
111
108 106
97
100
92
91
83
80
80
80
77
77
76
75
73
72
72
70
69
66
65
64
62
61
60
58
54
50
40
35
35
21
20
Be
Ir e
la
nd
²
Sp
a in
Ic
el
an
Gr d
ee
ce
Fr ¹
an
ce
It a
ly
Un C a ¹
i te nad
d
St a
a
Au tes¹
st
r
Un O a li a
i te EC
D
d
Ki 33
ng
d
Po om
r tu
g
S w al
ed
H e
N e un n
w ga
Ze r y
al
an
d¹
L u Is
xe r a e
m
bo l
ur
Po g
la
n
Au d
st
r
No ia
rw
a
Es y
to
ni
a
Ko
Ge r e a
Sl r ma
ov ny
ak
C z Re
e c p.
h
R
S l e p.
o
v
Sw e
i t z ni a
er
l
De and
nm
a
Tu rk
rk
ey
¹
Ne Ch
th il e ²
er
la
nd
s
d
iu
lg
an
pa
nl
Fi
Ja
m
n
0
1. Data include not only pharmacists providing direct services to patients, but also those working in the health sector as researchers, for
pharmaceutical companies, etc.
2. Data refer to all pharmacists licensed to practice (resulting in a large over-estimation of the number of practising pharmacists).
1 2 http://dx.doi.org/10.1787/888933281337
10.7. Community pharmacies, 2015 (or nearest year)
50
47.2
45.0
45
43.9
41.8
40
37.5
35.7
35
34.0
31.5
29.9
30
28.0
26.7
25.1
25.2
25
23.6
23.1
22.1
21.3
20.2
20
15.4
15
15.0
14.9
13.3
11.7
10.1
10
6.0
5
3.9
ar
k
el
ra
es
*
nm
De
d
Is
s*
St
at
nd
la
er
th
i te
Un
d
en
*
Ne
an
ed
Sw
ay
nl
Fi
ria
st
rw
No
d*
Au
nd
la
an
el
Ic
m
er
Sw
it z
li a
Un
i te
d
Ki
ng
do
y
ra
st
ar
Au
y
25
ng
Hu
CD
OE
Ge
rm
an
da
l
ga
na
Ca
y
ly
r tu
Po
It a
ce
ke
Tu
r
nd
la
nd
an
Fr
Po
a
re
la
Ir e
Ko
m
iu
pa
n
lg
Be
Ja
Sp
a in
0
* Estimates.
Source: FIP (2015), Global Trends Shaping Pharmacy – Regulatory Frameworks, Distribution of Medicines and Professional Services. 2013-1015.
1 2 http://dx.doi.org/10.1787/888933281337
183
In general, pharmaceutical consumption continues to
increase, partly driven by a growing demand for drugs to
treat ageing-related and chronic diseases and by changes
in clinical practice. This section examines consumption of
four categories of pharmaceuticals: antihypertensive, cholesterol-lowering, antidiabetic and antidepressant drugs.
Consumption is measured in defined daily doses (DDD)
(see the box on “Definition and comparability”).
Consumption of antihypertensives has nearly doubled in
OECD countries between 2000 and 2013. It has more than
tripled in Estonia and quadrupled in Luxembourg
(Figure 10.8). It is highest in Germany and Hungary, almost
five-fold the level of Korea and Turkey. These variations
reflect both differences in the prevalence of high-blood
pressure and in clinical practice. In 2008, 16% of the Korean
population had high blood pressure, against 26% in
Germany and 37% in Hungary, while the average number of
DDD prescribed per patient with high blood pressure was
lower in Korea (0.5) than in Hungary (1.1) and Germany (1.2)
(OECD, 2015).
The use of cholesterol-lowering drugs has more than tripled in OECD countries between 2000 and 2013 (Figure 10.9).
The Slovak Republic, the United Kingdom and Australia
had the highest consumption per capita in 2013, with levels
over 40% higher than the OECD average. Prescription clinical guidelines for anti-cholesterol treatments have been
updated several times since the 1990s, recommending
wider screening, earlier treatments, and higher dosages.
This explains part of the high growth observed during the
period.
The use of antidiabetics has almost doubled in OECD countries between 2000 and 2013 (Figure 10.10). This growth can
be explained by a rising prevalence of diabetes, largely
linked to increases in the prevalence of obesity (see
indicator on overweight and obesity in Chapter 4), a major
risk factor for the development of type-2 diabetes. In 2013,
the consumption of antidiabetics was highest in Finland,
Germany and the United Kingdom.
Consumption of antidepressants has increased considerably in most OECD countries since 2000 (Figure 10.11). This
might reflect some narrowing of the treatment gap for
depression. However, there is significant variation in consumption of antidepressants between countries. Iceland
reported the highest level of consumption of antidepressants in 2013, twice the OECD average, followed by Australia,
Portugal and Canada. Chile, Korea and Estonia reported low
consumption levels.
The level of antidepressants consumption depends on the
prevalence of depression in each country, and on how
depression is diagnosed and treated. This, in turn, depends
on other available therapies, local guidelines, and prescribing behavior (OECD, 2014; Moore et al., 2009). These factors
vary between countries. In England and in France, the
increase in antidepressants consumption has been associated with a longer duration of drug treatment (Grandfils
and Sermet, 2009; Moore et al., 2009).
184
Where antidepressants consumption is very low – Korea,
Chile, Estonia – there may be a case for addressing unmet
needs. In other countries with particularly high antidepressants consumption, there is a need to assess the appropriateness of prescribing patterns, and the availability of
alternative treatments for depression.
Defined daily dose (DDD) is the assumed average
maintenance dose per day for a drug used for its main
indication in adults. DDDs are assigned to each active
ingredient(s) in a given therapeutic class by international expert consensus. For instance, the DDD for
oral aspirin equals 3 grams, which is the assumed
maintenance daily dose to treat pain in adults. DDDs
do not necessarily reflect the average daily dose actually used in a given country. DDDs can be aggregated
within and across therapeutic classes of the AnatomicTherapeutic Classification (ATC). For more detail, see
www.whocc.no/atcddd.
The volume of hypertension drugs consumption presented in Figure 10.8 refers to the sum of five ATC2
categories which can all be prescribed against hypertension (antihypertensives, diuretics, beta-blocking
agents, calcium channel blockers and agents acting
on the renin-angiotensin system).
Data generally refer to outpatient consumption only,
except for the Czech Republic, Estonia, Italy and Sweden
where data also include hospital consumption. The
data for Canada relate to three provinces only (British
Columbia, Manitoba and Saskatchewan). The data for
Spain refer to outpatient consumption for prescribed
drugs covered by the National Health System (public
insurance). Data for Luxembourg are underestimated
due to incomplete consideration of products with
multiple active ingredients.
References
Grandfils, N. and C. Sermet (2009), “Evolution 1998-2002 of
the Antidepressant Consumption in France, Germany
and the United Kingdom”, Document de travail IRDES,
No. 21, Paris.
Moore, M. et al. (2009), “Explaining the Rise in Antidepressant Prescribing: A Descriptive Study Using the General
Practice Research Database”, British Medical Journal,
Vol. 339:b3999.
http://dx.doi.org/10.1787/9789264233010-en.
OECD (2014), Making Mental Health Count: The Social and Economic Costs of Neglecting Mental Health Care, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264208445-en.
10.8. Antihypertensive drugs consumption,
2000 and 2013 (or nearest years)
2000
Turkey
Korea
Austria
Greece
Israel
Luxembourg
Australia
Portugal
France
Spain
Iceland
Norway
Netherlands
Canada
OECD26
Estonia
Belgium
Sweden
United Kingdom
Denmark
Italy
Slovak Rep.
Slovenia
Finland
Czech Rep.
Hungary
Germany
10.9. Cholesterol-lowering drugs consumption,
2000 and 2013 (or nearest years)
2013
2000
124
141
184
194
217
223
239
250
266
269
274
279
303
311
318
323
328
368
380
398
399
410
414
427
442
543
575
0
100
Chile
Turkey
Estonia
Korea
Austria
Germany
Italy
Sweden
Iceland
France
OECD27
Spain
Canada
Portugal
Finland
Greece
Czech Rep.
Hungary
Israel
Slovenia
Netherlands
Luxembourg
Norway
Denmark
Belgium
Australia
United Kingdom
Slovak Rep.
200
300
400
500
600
Defined daily dose, per 1 000 people per day
2013
10
26
44
45
69
73
83
86
91
92
95
96
99
102
102
103
103
105
109
110
112
117
120
126
130
134
135
153
0
30
60
90
120
150
180
1 2 http://dx.doi.org/10.1787/888933281342
1 2 http://dx.doi.org/10.1787/888933281342
10.10. Antidiabetic drugs consumption, 2000 and 2013
(or nearest years)
10.11. Antidepressant drugs consumption, 2000 and 2013
(or nearest years)
2000
Chile
Austria
Iceland
Norway
Denmark
Estonia
Israel
Sweden
Turkey
Canada
OECD27
Australia
Portugal
Luxembourg
Belgium
Korea
Slovak Rep.
France
Greece
Spain
Italy
Slovenia
Netherlands
Hungary
Czech Rep.
United Kingdom
Germany
Finland
2013
9
40
43
49
52
55
55
56
56
58
62
62
63
64
65
65
66
66
66
67
67
73
75
78
80
82
83
86
0
20
40
60
80
100
1 2 http://dx.doi.org/10.1787/888933281342
2013
2000
Chile
Korea
Estonia
Hungary
Turkey
Slovak Rep.
Israel
Italy
Netherlands
Greece
Czech Rep.
France
Germany
Slovenia
Luxembourg
Norway
OECD28
Austria
Spain
Finland
Belgium
New Zealand
Denmark
United Kingdom
Sweden
Canada
Portugal
Australia
Iceland
13
20
21
28
35
35
42
43
43
44
49
50
53
53
54
56
58
59
65
69
72
73
80
82
84
85
88
96
118
0
20
40
60
80
100
1 2 http://dx.doi.org/10.1787/888933281342
185
All OECD countries see the development of generic markets
as a good opportunity to increase efficiency in pharmaceutical spending but many do not fully exploit the potential of
generics (Figure 10.12). In 2013, generics accounted for more
than three-quarters of the volume of pharmaceuticals sold in
the United States, the United Kingdom, Chile, Germany and
New Zealand, while they represented less than one-quarter of
the market in Luxembourg, Switzerland, Italy, and Greece.
Some of the differences in generic uptake can be explained by
market structures, notably the number of off-patent medicines, and by prescribing practices, but generic uptake also
very much depends on policies implemented by countries
(EGA, 2011; Vogler, 2012). Several countries have expanded
their efforts to encourage generic uptake since the onset of
the economic crisis in 2008.
Prescribing in International Non-proprietary Name (INN) is
permitted in two-thirds of OECD countries and is mandatory
in a few countries (e.g. Estonia since 2010, Portugal and Spain
since 2011, and France since 2015). Similarly, pharmacists are
allowed to substitute brand-name drugs with generics in a
majority of OECD countries. While generic substitution is
mandatory in some countries (e.g., Denmark, Finland, Spain,
Sweden, Italy), New Zealand and the United Kingdom have
high generic penetration without any substitution mandate.
Financial incentives for physicians, pharmacists and patients
have been implemented to boost the development of generic
markets. For instance, France (in 2009 and 2012) introduced
incentives for GPs to prescribe generics through a pay-for-performance scheme while Japan (in 2012) increased the share of
generics in total prescribing leading to a bonus.
Pharmacies are often paid through mark-ups based on the
price of medicines. This disincentive to substitute a generic
for a more expensive drug has been addressed in some countries. France guarantees pharmacists an equivalent mark-up,
while in Switzerland, pharmacists receive a fee for generic
substitution. In several countries, pharmacists have the obligation to inform patients about the possibility of a cheaper
alternative.
Patients have a financial interest to choose cheaper drugs
when their co-payment is lower for generic drugs than for its
equivalent. This is generally the case in all systems using
reference prices (or fixed reimbursement amount) for clusters
of products. In Greece, patients choosing originator over
generic drugs are now required to pay for the difference. In
France, since 2010, patients refusing generic substitution have
to pay in advance for their drugs and are reimbursed later.
These policies, associated with patent expiries of several
blockbusters in recent years, have contributed to the
increase in generic market share observed over the past
decade (Figure 10.13). In Portugal, the generic reimbursed
market grew from virtually zero in 2000 to 39% in volume
and 23% in value in 2013. In Spain, the generic reimbursed
market share reached 47% in volume and 21% in value in
2013, up from 3% in 2000. Beyond encouraging generic takeup, it is also important to promote the lowest possible price
for generics. Figure 10.12 suggests, for instance, that the
186
differential price between brand-name and generic drugs is
much higher in the United Kingdom than in Germany.
One way to exert pressure on generic prices is tendering,
which has been used in New Zealand, the Netherlands and
Germany with some success. Many countries, however, prefer
regulating the price of generics at market entry by reference
to the price of the originator (a practice known as “generic
price linkage”). Several countries have recently increased this
gap. In Canada, several provinces have introduced or reduced
the reimbursement prices of generics included in public
plans’ formularies since 2010. As a result, generic price caps
are around 25% of brand name products’ price (PMPRB, 2015).
France and Greece also increased the gap between originator
and generic prices to 40% and 60% respectively (Belloni et al.,
forthcoming).
A generic is defined as a pharmaceutical product
which has the same qualitative and quantitative composition in active substances and the same pharmaceutical form as the reference product, and whose
bioequivalence with the reference product has been
demonstrated. Generics can be classified in branded
generics (generics with a specific trade name) and
unbranded generics (which use the international nonproprietary name and the name of the company).
In many countries, the data cover all pharmaceutical
consumption. However, several countries provide data
covering only the community pharmaceutical market or
the reimbursed pharmaceutical market.
The share of generic market expressed in value can be
the turnover of pharmaceutical companies, the
amount paid for pharmaceuticals by third-party payers,
or the amount paid by all payers (third-party and
consumers). The share of generic market in volume
can be expressed in DDDs or as a number of packages/
boxes or standard units.
References
Belloni, A., D. Morgan and V. Paris (forthcoming), “Pharmaceutical Expenditure and Policies: Past Trends and Future
Challenges”, OECD Working Paper, OECD Publishing, Paris.
EGA – European Generic Medicines Association (2011), Market Review – The European Generic Medicines Markets, EGA.
PMPRB – Patented Medicine Prices Review Board (2015),
NPDUIS CompassRx: Annual Public Drug Plan Expenditure
Report 2012/13, 1st Edition.
Vogler, S. (2012), “The Impact of Pharmaceutical Pricing and
Reimbursement Policies on Generic Uptake: Implementation of Policy Options on Generics in 29 European
Countries – An Overview”, Generics and Biosimilars Initiative Journal, Vol. 1, No. 2, pp. 44-51.
10.12. Share of generics in the total pharmaceutical market, 2013 (or nearest year)
Value
%
90
84
Volume
83
80
80
77
72
70
70
59
60
55
54
50
48
46
47
46
45
41
40
39
35
34
33
30
40
37
37
32
29
28
24
16
24
30
29
28
23
21
19
17
17
14
20
17
16
16
14
19
16 17
15
11
11
8
4
g1
ur
nd
Lu
xe
m
bo
ly
la
It a
Sw
it z
er
ce 1
n
pa
nd 1
ee
Gr
Ja
la
ce 1
an
Fr
Ir e
m1
a
ni
iu
Be
lg
l1
ga
to
Es
p.
Po
r tu
d
Cz
ec
h
Re
an
ay
rw
nl
Fi
No
ia 2
1
en
ov
Sl
Sp
a in
1
26
ria
CD
OE
k1
st
Au
De
nm
ar
ke
y
s1
nd
Tu
r
da
Ne
Sl
th
er
la
na
ov
ak
Ca
Re
p.
d1
y1
Ne
w
Ze
al
an
an
il e 2
Ge
rm
Ch
do
ng
d
Un
i te
Un
i te
Ki
d
St
at
es
m1
0
1. Reimbursed pharmaceutical market.
2. Community pharmacy market.
1 2 http://dx.doi.org/10.1787/888933281352
10.13. Trend in share of generics in the reimbursed pharmaceutical market, selected countries, 2000 to 2013
In value
In volume
Germany
Spain
%
40
Portugal
United Kingdom
Germany
Spain
%
90
Portugal
United Kingdom
75
30
60
20
45
30
10
15
0
0
2000
2002
2004
2006
2008
2010
2012
2000
2002
2004
2006
2008
2010
2012
1 2 http://dx.doi.org/10.1787/888933281352
187
The pharmaceutical industry devotes significant resources
to research and development (R&D). In 2011, the industry
spent USD 92 billion on R&D (OECD, 2015). This represents
10-15% of industry revenues.
While pharmaceutical and biotechnology companies are
the greatest contributors to pharmaceutical R&D, pharmaceutical R&D financing is a complex mix of private and
public funding. The industry receives R&D tax credits in
many countries, and the development of medicines draws
heavily on knowledge and innovation derived from other
sectors including higher education and NGOs (Kezselheim
et al., 2015).
Worldwide, most pharmaceutical R&D activity takes place
in OECD countries. In 2011, the pharmaceutical industry
spent close to USD 50 billion for R&D in the United States,
11.5 billion in Japan, 5.2 in Germany and 3.7 in France. As a
share of GDP, pharmaceutical industry R&D spending is
highest in Switzerland (0.63%), Belgium (0.45%), Slovenia
(0.45%) and Denmark (0.36%) (Figure 10.14). In the United
States and Japan, the percentages were 0.30 and 0.26
respectively.
In some countries, pharmaceutical R&D accounts for onefourth to one-third of total private R&D expenditure,
reflecting a high degree of specialisation. This is the case in
Belgium (31%), Switzerland (30%), the United Kingdom
(28%), Hungary (26%) and Slovenia (25%). Sixteen and ten
per cent of private R&D was spent on pharmaceuticals in
the United States and Japan respectively.
Expenditure on R&D in the pharmaceutical industry in
OECD countries doubled in real terms between 2000 and
2011 (Figure 10.15). Expenditure growth was the highest in
the United States (+85%), followed by Japan (+76%) and
Europe (+38%). Outside the OECD, China has seen pharmaceutical R&D spending increase by 3.4-fold during that
time.
Is this increase in R&D spending associated with a higher
output or productivity? In the United States, the world’s
largest developer of pharmaceuticals, the annual number
of approved new drugs, formulations or indications has
more than doubled since 1970 (Figure 10.16). However,
when compared with R&D spending over that period
(adjusted for inflation), the number of approvals per
billion USD spent on R&D has reduced by a factor of 15
(Figure 10.16).
The reasons for this observation are likely to be complex.
Growing requirements to obtain regulatory approval have
increased development costs. Higher failure rates and an
ever-increasing “back catalogue” of effective drugs may
also be a factor. More fundamental problems with the current R&D model and development pipeline have also been
suggested (Scannell et al., 2012). Risk-benefit decisions
made by industry regarding early R&D targets may also be
a function of the regulator, payer and the community
response to the eventual product. Of course, the downward
188
trend may reverse in the coming years due to changes in
the R&D model, or the emergence of new technology (e.g.
precision medicine).
Business enterprise expenditure on R&D (BERD) covers R&D activities carried out in the private sector by
performing firms and institutes, regardless of the origin
of funding. This includes all firms, organisations and
institutions whose primary activity is the production
of goods and services for sale to the general public at
an economically significant price, and the private and
not-for-profit institutions serving them. BERD will
register in the country where the R&D activity took
place, not the country of origin of the organisations
funding the activity.
Data are provided by participating countries using a
survey. When assessing changes in BERD over time, it
is necessary to take account of changes in methods
and breaks in series, notably in terms of the extension
of survey coverage, particularly in the services sector,
and the privatisation of publicly owned firms. Identifying new and occasional R&D performers is also a challenge and OECD countries take different approaches in
their BERD surveys.
Gross domestic product (GDP) = final consumption +
gross capital formation + net exports. Final consumption of households includes goods and services used
by households or the community to satisfy their individual needs. It includes final consumption expenditure of households, general government and nonprofit institutions serving households. In countries,
such as Ireland and Luxembourg, where a significant
proportion of GDP refers to profits exported and not
available for national consumption, GNI may be a
more meaningful measure than GDP.
References
Kezselheim, A., Y. Tan and J. Avorn (2015), “The Roles of
Academia, Rare Diseases, and Repurposing in the Development of the Most Transformative Drugs”, Health
Affairs, Vol. 34, pp. 286-293.
OECD (2015), Main Science and Technology Indicators Database,
online, available at: www.oecd.org/sti/msti.htm [accessed
8 July 2015].
Scannell, J. et al. (2012), “Diagnosing the Decline in Pharmaceutical R&D Efficiency”, Nature Reviews Drug Discovery,
pp. 191-200.
10.14. Business expenditure on R&D (BERD) in pharmaceutical industry as a proportion of GDP and of total BERD, 2011
(or nearest year)
% GDP
% total BERD
% BERD
35
0.6
30
0.5
25
0.4
20
0.3
15
0.2
10
0.1
5
0
0
Sw
it z
er
la
B e nd
lg
iu
Sl m
ov
e
Un De ni a
i t e nm
d
K ar k
Un in g
i t e dom
d
St
at
e
Ic s
el
an
d
Ja
p
Sw an
ed
Hu en
ng
ar
Fr y
an
Ge c e
rm
an
y
Is
ra
el
Ir e
la
nd
Sp
a in
Ko
re
Fi a
nl
an
A d
Ne us
th tr ia
er
la
n
Po ds
r tu
ga
l
It a
ly
Gr
ee
c
Ca e
na
da
M
ex
Au ico
st
C z r al
ec ia
h
Re
N o p.
rw
a
E y
Sl s to
ov ni a
ak
Re
p
Tu .
rk
e
Po y
la
nd
Ch
in
a
% GDP
0.7
Source: OECD Main Science and Technology Indicators Database.
1 2 http://dx.doi.org/10.1787/888933281362
10.15. Business expenditure on R&D in the pharmaceutical sector by region in 2000, 2005 and 2011 (or nearest years)
in 2005 USD PPP
Europe
United States
Other OECD
Japan
China
2005 USD PPP (billions)
90
80
70
60
50
40
30
20
10
0
2000
2005
2011
Source: OECD Main Science and Technology Indicators Database.
1 2 http://dx.doi.org/10.1787/888933281362
10.16. Annual FDA pharmaceutical approvals, per USD billion R&D spend (indexed to 2008 USD)
FDA approvals (3-year average)
FDA approvals per USD billion R&D spend (3-year average)
FDA approvals
140
FDA approvals per USD billion R&D spend
30
120
25
100
20
80
15
60
10
40
5
20
0
0
1970
1972
1974
1976
1978
1980
1982 1984
1986
1988
1990
1992
1994
1996 1998
2000
2002
2004
2006
2008
2010
Source: Pharmaceutical Research and Manufacturers of America (PhRMA); Food and Drug Administration (FDA); Scannell et al (2012).
1 2 http://dx.doi.org/10.1787/888933281362
189
11. AGEING AND LONG-TERM CARE
Demographic trends
Life expectancy and healthy life expectancy at age 65
Self-reported health and disability at age 65
Dementia prevalence
Recipients of long-term care
Informal carers
Long-term care workers
Long-term care beds in institutions and hospitals
Long-term care expenditure
international law.
191
Demographic trends
Longer life expectancies (see “Life expectancy” indicator in
Chapter 3) and declining fertility rates mean that older
people make up an ever-increasing proportion of the populations of OECD countries.
On average across OECD countries, the share of the population aged over 65 years has increased from less than 9% in
1960 to 15% in 2010 and is expected to nearly double in the
next four decades to reach 27% in 2050 (Figure 11.1, left
panel). In about two thirds of OECD countries, at least onequarter of the population will be over 65 years of age by
2050. This proportion is expected to be especially large in
Japan, Korea and Spain where nearly 40% of the population
will be aged over 65 years by 2050. Population ageing will
also occur rapidly in China where the share of the population over 65 is expected to triple between 2010 and 2050, to
reach a level just below the OECD average. Conversely,
Israel, the United States and Mexico will see a more gradual
increase in the share of the elderly population due to significant inflows of migrants and higher fertility rates.
The growth in the share of the population aged 80 years
and over will be even more dramatic (Figure 11.1, right
panel). On average across OECD countries, 4% of the population were 80 years old and over in 2010. By 2050, the percentage will increase to 10%. In Japan, Spain and Germany,
the proportion of the population aged over 80 is expected to
nearly triple between 2010 and 2050 (rising from 6% to 16%
in Japan and from 5% to 15% in Spain and Germany). The
rise will be even faster in Korea where the share of the population aged over 80 years will grow from 2% to 14% over
the next four decades. China will see similarly rapid ageing,
with the share of the population aged over 80 rising from
1% to 8%.
Population ageing is a phenomenon affecting most countries around the world, but the speed of the process varies
(Figure 11.2). The speed of population ageing is particularly
rapid in the European Union, where the share of the population aged 80 years and over increased from 1.5% in 1960 to
192
nearly 5% in 2010, and is expected to rise to 11% by 2050.
The pace of population ageing has been slower in other
parts of the world, although it is expected to accelerate in
coming decades. In large partner countries including Brazil,
China, India, Indonesia and South Africa, only 2% of the
population was 80 years and over in 2010, but this share is
expected to reach around 5% by 2050.
Although the pressure that this growing proportion of people aged 65 and 80 over will put on long-term care systems
will depend on the health status of people as they reach
these ages, population ageing is likely to lead to greater
demand for elderly care. As the share of the economically
active population is expected to decline, it will also affect
the financing of social protection systems and the potential
supply of labour in the economy. On average across OECD
countries, there were slightly more than four people of
working age (15-64 years) for every person 65 years and
older in 2012. This rate is projected to halve from 4.2 in 2012
to 2.1 on average across OECD countries over the next
40 years (OECD, 2013).
Data on the population structure have been extracted
from the OECD Historical Population Data and Projections (1950-2050). The projections are based on the
most recent “medium-variant” population projections
from the United Nations, World Population Prospects
– 2012 Revision.
References
OECD (2013), OECD Pensions at a Glance 2013: OECD and G20
Indicators, OECD Publishing, Paris,
http://dx.doi.org/10.1787/pension_glance-2013-en.
Demographic trends
11.1. Share of the population aged over 65 and 80 years, 2010 and 2050
2010
2050
2010
Population aged 65 years and over
Japan
Korea
Spain
Germany
Italy
Greece
Czech Rep.
Portugal
Slovak Rep.
Slovenia
Poland
Estonia
Switzerland
Hungary
Austria
OECD34
Netherlands
Finland
Ireland
France
New Zealand
Lithuania
Canada
Belgium
Sweden
United Kingdom
China
Denmark
Iceland
Norway
Latvia
Brazil
Luxembourg
Chile
Russian Fed.
United States
Australia
Turkey
Israel
Mexico
Indonesia
India
South Africa
23
11
17
21
20
19
15
18
13
17
13
17
17
17
18
15
15
17
11
17
13
16
14
17
18
16
8
17
12
15
18
7
14
9
13
13
14
7
10
6
5
5
5
0
Japan
Spain
Germany
Korea
Italy
Switzerland
Austria
Netherlands
Finland
France
Czech Rep.
Portugal
Slovenia
New Zealand
Greece
Estonia
United Kingdom
OECD34
Canada
Belgium
Poland
Slovak Rep.
Sweden
Denmark
Norway
Iceland
Luxembourg
Lithuania
Ireland
Hungary
United States
Australia
Chile
Latvia
China
Brazil
Israel
Russian Fed.
Turkey
Mexico
Indonesia
India
South Africa
39
37
36
33
33
33
32
32
31
31
30
29
28
28
28
27
27
27
26
26
26
26
25
25
24
24
24
24
23
23
23
23
22
22
21
21
21
21
17
16
16
13
11
10
20
30
2050
Population aged 80 years and over
40
6
5
5
2
6
5
5
4
5
5
4
5
4
3
5
4
4
4
4
5
3
3
5
4
5
3
4
4
3
4
4
4
2
4
1
1
3
3
1
1
1
1
1
16
15
15
14
14
12
12
11
11
11
11
11
11
11
11
10
10
10
10
10
10
9
9
9
9
8
8
8
8
8
8
7
7
7
7
6
6
6
5
4
3
2
2
0
5
10
15
20
%
%
Source: OECD Historical Population Data and Projections Database, 2015.
1 2 http://dx.doi.org/10.1787/888933281371
11.2. Trends in the share of the population aged over 80 years, 1960-2050
%
EU28
OECD
Japan
Partner countries1
World
18
16
14
12
10
8
6
4
2
0
1960
1970
1980
1990
2000
2010
2020
2030
2040
2050
1. Partner countries include Brazil, China, India, Indonesia, Latvia, Lithuania, Russia and South Africa.
Source: OECD Historical Population Data and Projections Database, 2015.
1 2 http://dx.doi.org/10.1787/888933281371
193
Life expectancy at age 65 has increased significantly for
both men and women over the past few decades in OECD
countries, rising by 5.5 years on average since 1970
(Figure 11.3). Some of the factors explaining these gains in
life expectancy at age 65 include advances in medical care
combined with greater access to health care, healthier lifestyles and improved living conditions before and after people reach age 65.
Japan and Korea have achieved the highest gains in life
expectancy at age 65 since 1970, with an increase of almost
eight years. The gains have been much more modest in
Hungary, the Slovak Republic and Mexico, with an increase
of only about three years.
In 2013, people at age 65 in OECD countries could expect to
live another 19.5 years: 21 years for women and 18 years for
men (Figure 11.4). This gender gap of three years on average across OECD countries has been fairly stable over time.
In 2013, life expectancy at age 65 was highest in Japan for
women (24 years) and in Switzerland for men (nearly
20 years), followed by France in both cases. Among OECD
countries, it was lowest in Hungary for both women and
men.
Countries’ relative positions with respect to life expectancy
at age 65 mirror closely their relative positions with regard
to life expectancy at age 80. Life expectancy at age 80 in
2013 was highest in France and Japan for women (who can
expect to live an additional 11.5 years) and highest in
France and Spain for men (who can expect to live more
than 9 years).
Increased life expectancy at age 65 does not necessarily
mean that the extra years lived are in good health. In
Europe, an indicator of disability-free life expectancy
known as “healthy life years” is calculated regularly, based
on a general question about disability in the European
Union Survey of Income and Living Conditions (EU-SILC).
Given that this indicator has only recently been developed,
long-time series are not yet available and efforts continue
to improve its comparability.
Among European countries participating in the survey, the
average number of healthy life years at age 65 was almost
the same for women and men, at 9.5 years for women and
9.4 years for men in 2013 (Figure 11.5). The absence of any
significant gender gap in healthy life years means that
many of the additional years of life that women experience
relative to men are lived with some type of activity limitation. Nordic countries (with the exception of Finland) had
the highest number of healthy life years at age 65 in 2013,
with women and men in Iceland and Norway expecting to
live an additional 15 years free from disability on average.
194
Life expectancy and healthy life expectancy at age 65 years
vary by educational status. For both men and women,
highly educated people are likely to live longer and in better health. Differences in life expectancy by education level
are particularly large in Central and Eastern European
countries, especially for men. In the Czech Republic,
65-year-old men with a high level of education could expect
to live seven years longer than those with a low education
level in 2012. By contrast, differences in life expectancy by
education level are much smaller (less than two years) in
Nordic countries (Denmark, Finland, Norway and Sweden)
and Portugal (Eurostat Database 2015).
Life expectancy measures how long on average a person of a given age can expect to live, if current death
rates do not change. However, the actual age-specific
death rate of any particular birth cohort cannot be
known in advance. If rates are falling, as has been the
case over the past decades in OECD countries, actual
life spans will be higher than life expectancy calculated using current death rates. The methodology
used to calculate life expectancy can vary slightly
between countries. This can change a country’s estimates by a fraction of a year.
Disability-free life expectancy (or “healthy life years”)
is defined as the number of years spent free of activity
limitation. In Europe, this indicator is calculated
annually by Eurostat for EU countries and some EFTA
countries. The disability measure is the Global Activity Limitation Indicator (GALI) which comes from the
European Union Statistics on Income and Living Conditions (EU-SILC) survey. The GALI measures limitation in usual activities due to health problems. While
healthy life years is the most comparable indicator to
date, there are still problems with translation of the
GALI question, although it does appear to satisfactorily reflect other health and disability measures
(Jagger et al., 2010).
References
Jagger, C. et al. (2010), “The Global Activity Limitation Indicator (GALI) Measured Function and Disability Similarly
across European Countries”, Journal of Clinical Epidemiology,
Vol. 63, pp. 892-899.
Un
d
Sl
ov
ak
Re
a
y
4
4
4
5
6
6
6
8
7
7
7
8
7
8
8
9
12
14
14
13
16
15
15
15
19
19
19
21
21
21
21
21
21
22
20
20
20
20
19
19
22
21
21
21
21
21
22
22
21
21
20
19
18
17
17
16
16
15
16
17
18
22
19.9
18.3
19.1
19.2
19.3
19.5
19.5
19.6
19.7
19.7
19.7
19.8
19.9
17.8
18.0
17.7
16.5
16.7
16.8
17.5
16.1
16.2
13.9
20.0
20.1
20.1
20.2
20.2
20.3
20.3
20.5
20.7
20.8
20.9
21.3
21.5
21.5
20.0
17.7
15.3
15
p.
ni
ar
to
ng
6
Es
Hu
y
ia
ly
an
en
rm
ov
Ge
Sl
7
7
15
It a
ce
nd
8
ee
la
8
18
17
18
23
22
22
21
23
22
Ja
p
Fr an
an
c
S w Sp e
i t z ain
er
la
nd
I
Au t al
Lu st y
xe r a
m li a
bo
u
Ca rg
na
d
Is a
ra
e
Ko l
re
N e Gr a
w ee
Ze ce
al
S w and
ed
Ic en
el
a
No nd
rw
F i ay
nl
Un
a
i te Au nd
d st
Ki ri
ng a
d
P o
Ne or t m
th ug
er al
l
Ge and
rm s
B e any
lg
O E ium
CD
Ir e 3 4
la
Un Slo nd
i te ve
d ni a
St
De a te
nm s
ar
k
Ch
il e
Br
a
E s z il
to
n
Po i a
la
M nd
C z ex
ec ico
Sl h R
ov e
a k p.
Re
Tu p.
Hu r ke y
ng
ar
La y
Li t vi
Ru t h u a
ss an
So ian ia
ut Fe
h d.
Af
ric
a
2013
Gr
Po
d
Note: Countries are ranked in descending order of life expectancy for the whole population.
an
9
9
9
9
18
18
18
18
18
19
18
18
19
19
19
19
19
18
19
19
19
19
19
19
24
24
Men
nl
p.
ria
10
11
12
13
Men
Re
st
9
10
9
9
9
10
9
10
11
10
11
11
11
11
11
11
11
12
15
19
19
19
Fi
h
Au
s
a in
nd
Sp
la
l
24
ga
CD
er
ec
th
Cz
Ne
OE
r tu
ce
nd
m
g
10
Po
la
an
er
Fr
it z
do
ur
m
nd
iu
bo
ng
m
Ki
xe
Sw
i te
Lu
lg
k
13
14
Ja
p
Fr an
an
c
S w Sp e
i t z a in
er
la
nd
I
Au t al
Lu s t y
xe r a
m li a
bo
u
Ca rg
na
d
Is a
ra
e
Ko l
re
N e Gr a
w ee
Ze ce
al
S w and
ed
Ic en
el
a
No nd
rw
F i ay
nl
Un
a
i te Au nd
d st
Ki ri
ng a
d
P o
Ne or t m
th ug
er al
l
Ge and
rm s
Be any
lg
O E ium
CD
Ir e 3 4
la
Un Slo nd
i te ve
d ni a
St
De a te
nm s
ar
k
Ch
il e
Br
a
E s z il
to
n
Po ia
la
M nd
C z ex
ec ico
Sl h R
ov e
a k p.
Re
Tu p.
Hu r ke y
ng
ar
La y
Li t vi
Ru t h u a
ss an
So ian ia
ut Fe
h d.
Af
ric
a
20
Be
la
ar
12
Ir e
nm
en
14
De
ed
15
15
15
Years
16
Sw
d
20
ay
an
Years
25
rw
el
Years
25
No
Ic
11.3. Life expectancy at age 65, 1970 and 2013 (or nearest years)
1970
10
5
0
1 2 http://dx.doi.org/10.1787/888933281383
11.4. Life expectancy at age 65 by sex, 2013 (or nearest year)
Women
10
5
0
1 2 http://dx.doi.org/10.1787/888933281383
11.5. Healthy life years at age 65, European countries, 2013
Women
2
0
Note: Countries are ranked in descending order of healthy life expectancy for the whole population.
Source: Eurostat Database 2015.
1 2 http://dx.doi.org/10.1787/888933281383
195
Most OECD countries conduct regular health surveys which
allow respondents to report on different aspects of their
health. These surveys often include a question on self-perceived health status, along the lines of: “How is your health
in general?”. Although these questions are subjective, indicators of perceived general health have been found to be a
good predictor of future health care use and mortality
(DeSalvo, 2005; Bond et al., 2006). However, cross-country
differences may be difficult to interpret, as survey questions may differ slightly and cultural factors can affect
responses.
Keeping these limitations in mind, more than half of the
population aged 65 years and over report being in good
health in 13 of the 34 OECD countries (Figure 11.6). The
highest rates are in New Zealand, Canada and the United
States, where more than three-quarters of older people
report good health, but the response categories offered to
survey respondents in these three countries are different
from those used in most other OECD countries, introducing
an upward bias in the results (see box on “Definition and
comparability” below). Among European countries, older
people in Sweden, Switzerland, Norway and Ireland report
the best health status, with more than 60% assessing their
health to be good.
At the other end of the scale, less than 20% of over-65s in
Portugal, Hungary, Estonia, Poland, Turkey, the Slovak
Republic and Korea report being in good health. In nearly
all countries, men over 65 were more likely than women to
rate their health to be good. On average across OECD countries, 47% of men aged over 65 rated their health to be good
or better, while 41% of women did so.
The percentage of the population aged 65 years and over
who rate their health as being good or better has remained
fairly stable over the past 30 years in most countries where
long time series are available. There has been significant
improvement however in the United States, where the
share has increased from 65% in 1982 to 77% in 2013.
Measures of disability are not yet standardised across
countries, limiting the possibility for comparisons. In
Europe, based on the EU Statistics on Income and Living
Conditions survey, half of all over-65s reported that they
were limited either to some extent or severely in their usual
daily activities because of a health problem in 2013
(Figure 11.7). This ranged from a proportion of less than
25% in Norway and Iceland up to nearly 75% in the Slovak
Republic and close to 70% in Estonia. On average across
25 European OECD countries, most limitations reported
were moderate, 18% of the population aged 65 and over
reported severe limitations, which often correspond to
needs for long-term care.
Women were more likely than men to report severe activity
limitations due to a health problem in all European countries covered by this survey, with the exception of Poland.
The proportion of people aged 65 and over reporting some
severe activity limitations was highest in Greece and the
Slovak Republic, followed by Italy and Estonia (Figure 11.8).
196
Self-reported health reflects people’s overall perception of their own health, including both physical and
psychological dimensions. Typically, survey respondents are asked a question such as: “How is your
health in general? Very good, good, fair, poor, very
poor”. OECD Health Statistics provides figures related to
the proportion of people rating their health to be
“good/very good” combined.
Caution is required in making cross-country comparisons of perceived health status, for at least two reasons. First, people’s assessment of their health is
subjective and can be affected by cultural factors. Second, there are variations in the question and answer
categories used to measure perceived health across
surveys/countries. In particular, the response scale
used in Australia, Canada, New Zealand and the
United States is asymmetric (skewed on the positive
side), including the following response categories:
“excellent, very good, good, fair, poor”. The data
reported in OECD Health Statistics refer to respondents
answering one of the three positive responses (“excellent, very good or good”). By contrast, in most other
OECD countries, the response scale is symmetric, with
response categories being: “very good, good, fair, poor,
very poor”. The data reported from these countries
refer only to the first two categories (“very good,
good”). Such difference in response categories biases
upward the results from those countries that are
using an asymmetric scale.
Perceived general disability is measured in the EUSILC survey through the question: “For at least the
past six months, have you been hampered because of
a health problem in activities people usually do? Yes,
strongly limited/Yes, limited/No, not limited”. Persons
in institutions are not surveyed, resulting in an underestimation of disability prevalence. Again, the measure is subjective, and cultural factors may affect survey responses.
References
Bond, J. et al. (2006), “Self-rated Health Status as a Predictor
of Death, Functional and Cognitive Impairments: A Longitudinal cohort Study”, European Journal of Ageing, Vol. 3,
pp. 193-206.
DeSalvo, K.B. et al. (2005), “Predicting Mortality and Healthcare Utilization with a Single Question”, Health Services
Research, Vol. 40, pp. 1234-1246.
11.6. Perceived health status in adults aged 65 years and over, 2013 (or nearest year)
Women
Men
Total
Un
Ne
w
Ze
al
an
Ca d 1
n
i te ad
a1
d
St
a
Au tes 1
st
ra
l
Sw ia 1
Sw ed
i t z en
er
la
No nd
rw
ay
N e Ir e l
a
th nd
er
la
De nds
nm
ar
Un
i t e Ic e k
la
d
K i nd
ng
do
Be m
Lu lg
xe ium
m
bo
ur
Is g
ra
OE el 1
CD
3
M 4
ex
ic
Au o
s
Ge tr ia
rm
an
y
Ch
il e 1
Fr
an
ce
Sp
a in
Fi
nl
an
Gr d
ee
Sl c e
ov
en
ia
It a
ly
C z Jap
ec an
h
Re
p.
Sl Ko
ov r e
ak a
Re
p
Tu .
rk
ey
Po
la
n
Es d
to
n
Hu i a
ng
Po ar y
r tu
ga
l
% of population aged 65 years and over reporting to be in good or very good health
100
90
80
70
60
50
40
30
20
10
0
1. Results not directly comparable with other countries due to methodological differences (resulting in an upward bias).
1 2 http://dx.doi.org/10.1787/888933281398
11.7. Limitations in daily activities in adults aged 65 years and over, European countries, 2013
Limited strongly
Limited to some extent
80
70
60
50
40
30
20
10
Re
ni
p.
a
ly
y
ak
ov
Hu
Es
to
It a
ng
ee
ar
ce
y
Ge
Gr
rm
an
ia
y
en
ov
Sl
Tu
r
ke
nd
a in
Po
la
d
an
Fi
nl
Sp
l
ria
st
ga
Po
Au
s
25
r tu
CD
Sl
Ne
Cz
th
OE
er
la
an
Fr
nd
ce
p.
nd
h
ec
it z
Sw
xe
Lu
Re
g
er
bo
m
ng
Ki
d
Un
i te
la
ur
m
m
do
iu
nd
lg
Be
De
Ir e
nm
la
ar
k
en
d
ed
an
el
Ic
No
Sw
rw
ay
0
1 2 http://dx.doi.org/10.1787/888933281398
11.8. Strong limitations in daily activities in adults aged 65 years and over, European countries, 2013
Men
Women
40
35
30
25
20
15
10
5
ce
Gr
ee
p.
ly
Sl
ov
ak
Re
It a
a
m
ni
to
Es
do
nd
ng
Ki
d
Un
i te
ga
la
l
Po
ria
st
r tu
Po
en
ia
Au
ov
Sl
Hu
ng
ar
y
y
ce
ke
Tu
r
an
an
y
Fr
25
rm
Ge
CD
g
OE
ur
m
iu
bo
m
xe
Lu
an
d
lg
Be
a in
el
Ic
d
an
Sp
nl
la
nd
Fi
p.
Re
Ir e
en
Cz
ec
h
ed
nd
s
Sw
ay
Ne
th
er
la
rw
No
la
er
it z
Sw
De
nm
ar
k
nd
0
Note: Countries are ranked in ascending order of percentage with strong limitations in daily activities for the whole population.
1 2 http://dx.doi.org/10.1787/888933281398
197
Dementia prevalence
Dementia describes a variety of brain disorders which progressively lead to brain damage and cause a gradual deterioration of the individual’s functional capacity and social
relations. Alzheimer’s disease is the most common form of
dementia, representing about 60% to 80% of cases. There is
currently no cure or disease modifying treatment, but better policies can improve the lives of people with dementia
by helping them and their families adjust to living with the
condition and ensuring that they have access to high quality health and social care.
According to WHO, 47.5 million people around the world
live with dementia in 2015. With populations ageing and
the effectiveness of preventive strategies still unclear, this
number is expected to rise to 75.6 million by 2030 and
almost triple by 2050, reaching 135.5 million (WHO, 2015). The
global cost of dementia was estimated at USD 604 billion in
2010 (Wimo et al., 2013) and as prevalence increases this
cost will grow.
In 2015, there were an estimated 18 million people living
with dementia in OECD countries, equivalent to more than
one in every 70 people. Although some people develop
early-onset dementia, the vast majority of those with
dementia are older people and across all OECD countries
more than one in every 16 people aged over 60 are living
with the condition. Prevalence varies between countries:
Italy, Japan and Germany all have more than 20 people with
dementia per 1 000 population, while the Slovak Republic,
Korea, Mexico and Turkey have fewer than ten (Figure 11.9).
Much of the variation in prevalence is due to the age structures of the populations in different countries, since dementia is strongly linked to age. Across all OECD countries, around
1.3% of people aged 60-64 have dementia, compared to nearly
45% of those aged over 90 (Figure 11.10). Age-specific prevalence is similar across most countries, although studies in
Latin America have found higher rates than in other
regions (Prince et al., 2013). While this may be partly due to
differences in study design, it has also been suggested that
low educational levels among older people and high vascular risk could be contributing to increased rates of dementia (Rizzi et al., 2014).
If the age-specific prevalence of dementia remains the
same, ageing populations mean that it will become more
common in the future. Prevalence will rise more quickly in
countries that are ageing rapidly. For example, the next
20 years will see prevalence in Japan rise from 21 to nearly
37 per 1 000 people; and in Korea prevalence will more than
double from 8 to 20 per 1 000 people (Figure 11.9). The overall number of people living with dementia in OECD countries will rise from 18 million in 2015 to nearly 31 million in
2035, with the oldest people (aged over 90) accounting for
an increasing share (Figure 11.11). However, there is some
evidence that the age-specific prevalence of dementia may
be falling in some countries (Matthews et al., 2013) and it
may be possible to reduce the risk of dementia through
healthier lifestyles and preventive interventions. If such
198
efforts are successful, the rise in prevalence may be less
dramatic than these numbers suggest.
There has recently been a renewed international focus on
tackling dementia and the OECD has been at the forefront
of this work, supporting countries to develop better policies. Finding a cure must be the long-term goal, but this will
require greater investment and a more collaborative
approach to research, harnessing the potential of big data.
However, any cure is likely to take several years to develop
and in the meantime countries need to act to improve the
lives of the millions of people living with dementia now.
This must include promoting timely diagnosis, delivering
high quality health and long-term care and providing support for families and carers (OECD, 2015).
The prevalence estimates in Figure 11.9 are taken
from Prince et al. (2013), which is the latest and most
comprehensive systematic review of studies of
dementia prevalence around the world. Prevalence by
country has been estimated by applying these agespecific prevalence rates for the relevant region of the
world to population estimates from the UN (World
Population Prospects: The 2012 Revision). Although
gender-specific prevalence rates were available for some
regions, the overall rates were used in this analysis.
Prevalence rates are assumed to be constant over
time.
References
Matthews, F.E. et al. (2013), “A Two-decade Comparison of
Prevalence of Dementia in Individuals Aged 65 Years and
Older from Three Geographical Areas of England: Results
of the Cognitive Function and Ageing Study I and II”, The
Lancet, Vol. 382, No. 9902.
OECD (2015), Addressing Dementia: The OECD Response, OECD
Health Policy Studies, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264231726-en.
Prince, M. et al. (2013), “The Global Prevalence of Dementia:
A Systematic Review and Metaanalysis”, Alzheimer’s &
Dementia, Vol. 9, No. 2, pp. 63-75.
Rizzi, L. et al. (2014), “Global Epidemiology of Dementia:
Alzheimer’s and Vascular Types”, BioMed Research International, Vol. 2014, Article ID 908915, 8 pages.
WHO (2015), “Dementia”, Fact Sheet No. 362,
Wimo, A. et al. (2013), “The Worldwide Economic Impact of
Dementia 2010”, Alzheimer's & Dementia, Vol. 9, No. 1,
pp. 1-11.
Dementia prevalence
11.9. Estimated prevalence of dementia per 1 000 population, 2015 and 2035
2015
2035
40
35
30
25
20
15
10
5
It a
Ja l y
Ge pa
rm n
a
Fr ny
an
Gr c e
ee
c
Sp e
S w a in
ed
F i en
nl
B e and
lg
P o ium
r tu
A gal
Un S w i us t
t
i te ze ria
d rla
Ki n
ng d
De dom
N e nm
th ar
er k
la
n
No ds
rw
OE ay
CD
Lu Ca 3 4
xe n a
m da
b
A u o ur
st g
Sl r a li a
ov
en
Ne Es ia
w ton
Ze ia
al
a
Un Hun nd
i te ga
d ry
St
a
I te
C z c ela s
ec nd
h
Re
p
Ch .
Ir e i l e
la
Po nd
la
Ru I n d
s s sr a
i
Sl an el
ov F e
a k d.
Re
p
Ko .
re
Br a
a
M z il
ex
i
Tu co
rk
ey
In C h i n
do a
ne
si
a
So
u t In d
h ia
Af
ric
a
0
Source: OECD analysis of data from Prince et al. (2013) and the United Nations.
1 2 http://dx.doi.org/10.1787/888933281401
11.10. Age-specific prevalence of dementia across all OECD countries, 2015
% of population
50
40
30
20
10
0
60-64
65-69
70-74
75-79
80-84
85-89
90+
1 2 http://dx.doi.org/10.1787/888933281401
11.11. Estimated number of people with dementia in all OECD countries, by age, 1995, 2015 and 2035
Million population
10
2035
8
6
2015
4
1995
2
0
60-64
65-69
70-74
75-79
80-84
85-89
90+
1 2 http://dx.doi.org/10.1787/888933281401
199
As people age, they are more likely to develop disabilities
and need support from family, friends and long-term care
(LTC) services. As a result, while LTC services are delivered
to younger disabled groups, the majority of LTC recipients
are older people. On average across the OECD, more than
half of all LTC recipients are aged over 80 and nearly four in
five are aged over 65 (Figure 11.12). Rising life expectancies
mean that older people make up an increasing proportion
of the populations of OECD countries. The risk of dementia
(see indicator on “Dementia prevalence”) and other debilitating conditions increases with age, so demand for LTC
services is likely to increase – although this effect may be
partially offset by improving health in old age. As a result,
the average proportion of the population receiving LTC in
OECD countries has risen from 1.9% in 2000 to 2.3% in 2013.
While population ageing is a significant driver of the
growth in LTC users over time, it explains relatively little of
the cross-country variation. For example, Portugal has a
relatively old population but only a small proportion receiving formal LTC. By contrast, Israel has one of the youngest
populations in the OECD but a greater than average proportion receiving LTC. A more important driver is the availability of publicly funded LTC services. Countries with strong
public provision, such as the Netherlands and Nordic countries, report the greatest number of LTC recipients as a
share of their populations, while countries with limited
public provision, such as the United States, Portugal and
Poland, report much smaller numbers. However, data for
people receiving care outside of public systems are more
difficult to collect and may be underreported, meaning that
figures for countries that rely more heavily on privatelyfunded care may be artificially low. Cultural norms around
the degree to which families look after older people may
also be an important driver of the utilisation of formal services (see indicator on “Informal carers”).
In response to most people’s preference to receive LTC services at home, many OECD countries have over the past
decade implemented programmes and benefits to support
home-based care, in particular for older people. In most
countries for which trend data are available, the proportion of LTC recipients aged 65 and over receiving longterm care at home has increased over the past ten years
(Figure 11.13), with particularly large increases in Sweden,
France and Korea. Often this is the result of specific policies: for example, Sweden has reduced its institutional care
capacity in an effort to encourage community care; while
France has adopted a multi-year plan to increase home
nursing care capacity to 230 000 by 2025 (Colombo et al.,
2011).
While the proportion of LTC recipients living at home has
increased over the past decade in most OECD countries, it
has declined from 69% to 60% in Finland. However, this
does not represent an increase in the use of traditional
institutions, but an increase in the use of “service housing”
200
– where older people move into specially adapted houses
where 24/7 care is available. This model of care allows people with relatively severe needs to retain more independence and autonomy than they would in a traditional care
institution.
LTC recipients are defined as persons receiving longterm care by paid providers, including non-professionals receiving cash payments under a social programme. They also include recipients of cash benefits
such as consumer-choice programmes, care allowances or other social benefits which are granted with
the primary goal of supporting people with long-term
care needs. LTC institutions refer to nursing and residential care facilities which provide accommodation
and long-term care as a package. LTC at home is
defined as people with functional restrictions who
receive most of their care at home. Home care also
applies to the use of institutions on a temporary basis,
community care and day-care centres and specially
designed living arrangements. Data for Iceland and
Canada are only available for people receiving care in
institutions, so the total number of recipients will be
underestimated.
Concerning the number of people receiving LTC in
institutions, the estimate for Ireland is underreported. Data for Japan underestimate the number of
recipients in institutions because hospitals also provide LTC. In the Czech Republic, LTC recipients refer to
recipients of the care allowance (i.e., cash allowance
paid to eligible dependent persons). Data for Poland
only refer to services in nursing homes. Data in Spain
only refer to a partial coverage of facilities or services.
In Australia, the data do not include recipients who
access the Veterans’ Home Care Program and those
who access services under the National Disability
Agreement, as it is currently unknown how many of
these people could be included in LTC recipients.
Australia collects data on users of aged care, but this
does not distinguish those using services on a longterm basis, so the figures presented here are estimated. With regard to the age threshold in chart 11.13,
data for France refer to people aged over 60.
References
Colombo, F. et al. (2011), Help Wanted? Providing and Paying
for Long-Term Care, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264097759-en.
11.12. Proportion of population receiving long-term care, 2013 (or nearest year)
0-64
65-79
80+
Total for all ages
Total for all ages in 2000 (or nearest year)
% of total population
5
4.5
4.3
4.2
4
3.8
3.3
3.2
3.0
3
2.9
2.9
2.8
2.7
2.7
2.7
2.5
2.3
2.2
2.2
2.0
2
2.0
1.6
1.5
1.2
1
0.8
0.7
0.7
0.4
0.5
0.4
0.3
nd
l
la
Po
Po
r tu
ga
es
nd
i te
d
St
at
da
la
Un
Sl
Lu
Ir e
na
Ca
Ic
el
an
d
a
m
re
Ko
iu
Be
Es
lg
ni
a
ain
to
ly
Sp
ria
It a
p.
st
Au
li a
Re
ra
ak
st
Au
ov
g
21
OE
CD
ur
d
xe
m
bo
el
Fi
nl
an
n
Is
ra
p.
pa
Ja
ia
ec
h
Re
en
ov
Sl
Cz
w
Ze
al
an
ar
d
k
y
Ne
De
nm
y
ar
ng
Hu
an
ay
Ge
rm
en
rw
ed
No
Sw
la
er
it z
th
Ne
Sw
er
la
nd
s
nd
0
1 2 http://dx.doi.org/10.1787/888933281419
11.13. Share of long-term care recipients aged 65 years and over receiving care at home, 2000 and 2013 (or nearest year)
2013
2000
% of total LTC recipients aged 65 years and over
90
80
70
77.5
69.4
76.6
71.2
70.8
70.1
70.0
67.9 69.1
65.7
65.0
66.3
69.3
67.8
64.9
63.0
61.3
58.7
60
60.0
58.9
55.9
55.4
52.2
51.1
50
48.7
40.8
40
40.0
42.1
30
20
10
es
at
li a
St
Un
i te
d
st
Au
ur
bo
m
xe
Lu
ra
g
d
an
nl
Fi
Fr
an
ce
a
re
Ko
13
CD
OE
an
rm
Ge
la
er
Ne
th
y
s
nd
en
ed
la
er
Sw
it z
No
Sw
nd
ay
rw
n
pa
Ja
Hu
ng
ar
y
0
1 2 http://dx.doi.org/10.1787/888933281419
201
Informal carers
Family and friends are the most important source of care
for people with LTC needs in OECD countries. Because of
the informal nature of care that they provide, it is not easy
to get comparable data on the number of people caring for
family and friends across countries, nor on the frequency
of their caregiving. The data presented in this section come
from national or international health surveys and refer to
people aged 50 years and over who report providing care
and assistance to family members and friends.
On average across OECD countries, around 15% of people
aged 50 and over provided care for a dependent relative or
friend in 2013 (Figure 11.14). There is significant variation
between countries, with nearly 20% of over-50s in Belgium
and Estonia providing informal care, compared to just over
10% in Israel and Australia. Rates of informal care are negatively correlated with the proportion of older people
receiving formal services (see indicator on “Recipients of
informal care”) and the density of LTC workers (see indicator on “Long-term care workers”). Countries such as Estonia
and the Czech Republic, with relatively few LTC workers and
recipients of formal services, have higher rates of informal
care; while countries such as Israel and Sweden, with large
number of LTC workers and many older people receiving LTC
services, have lower rates of informal care. The causality
here is not clear: it could be that strong public provision
means families do not have to care for older people with LTC
needs, or it could be that a strong tradition of family support
reduces the need for extensive public provision.
The majority of informal carers are women in all OECD
countries and on average more than 60% of carers are
women. This ranges from a high of 70% in Slovenia to a low
of 55% in Sweden (Figure 11.15).
On average across OECD countries, 74% of informal carers
provide care on a daily basis, while the remaining 26% provide care only on a weekly basis. However, there is wide
variation across countries in the intensity in caregiving
(Figure 11.16). In countries with comprehensive public
LTC systems, such as the Netherlands, Switzerland and
Nordic countries, family and friends provide less intensive
care. The highest intensity of care is reported in Spain,
Slovenia and Israel – although these countries actually
have relatively few people providing informal care
(Figure 11.14). Taking the total number of carers into
account, Estonia, Belgium, the Czech Republic and France
have greatest proportion of over-50s providing daily care to
family or friends, suggesting that informal care is particularly important in these countries.
Intensive caregiving is associated with a reduction in
labour force attachment for caregivers of working age,
202
higher poverty rates, and a higher prevalence of mental
health problems. Many OECD countries have implemented
policies to support family carers with a view to mitigating
these negative impacts. These include paid care leave (e.g.,
Belgium), flexible work schedules (e.g., Australia and the
United States), respite care (e.g., Austria, Denmark and
Germany) and counselling/training services (e.g., Sweden).
Moreover, a number of OECD countries provide cash benefits to family caregivers or cash-for-care allowances for
recipients which can be used to pay informal caregivers
(Colombo et al., 2011).
Declining family size, increased geographical mobility and
rising participation rates of women in the labour market
mean that there is a risk that fewer people will be willing
and able to provide informal care in the future. This could
have two consequences. Firstly, those that do provide informal care may be required to provide higher-intensity care.
This will make the support that they receive even more
important if negative health and employment outcomes
are to be avoided. Secondly, a reduction in the supply of
informal care would put increasing pressure on public LTC
systems. These systems will need adequate funding and
infrastructure in place to cope with increased demand, otherwise people could be left without access to the services
they need.
Family carers are defined as people providing daily or
weekly help to family members, friends and people in
their social network living in their household or outside of the household who require help for Activities
of Daily Living (ADL) and Instrumental Activities of
Daily Living (IADL). The data relate only to the population aged 50 and over, and are based on national or
international health surveys. Survey results may be
affected by reporting biases or recall problems. Data
for Australia are limited to those providing assistance
with mobility, self-care, and communication, so may
be underestimated relative to other countries.
References
http://dx.doi.org/10.1787/9789264097759-en.
Informal carers
11.14. Population aged 50 and over reporting to be informal carers, 2013 (or nearest year)
%
30
25
19.7
20
19.0
18.1
17.5
16.5
15.9
15.7
15.4
15
14.7
14.9
14.4
13.5
12.9
12.3
11.4
11.0
10.8
10.4
10
5
Sl
el
Is
ra
li a
Au
st
ov
ra
en
ia
en
De
Sw
nm
ed
ar
k
nd
la
er
it z
Sw
Ge
OE
rm
Sp
an
a in
y
17
CD
ur
bo
m
xe
Lu
Ne
d
Un
i te
g
ly
st
It a
ria
s
Au
la
th
Ki
er
Fr
ng
an
nd
ce
m
do
Re
h
ec
Cz
Be
Es
lg
to
iu
ni
p.
a
m
0
Source: OECD estimates based on 2013 HILDA survey for Australia, 2012-13 Understanding Society survey for the United Kingdom and 2013 SHARE
survey for other European countries.
1 2 http://dx.doi.org/10.1787/888933281423
11.15. Share of women among all informal carers aged 50 and over, 2013 (or nearest year)
%
100
90
80
70
60
50
40
30
20
10
0
70.1
Lu
De
Be
en
ed
Ne
th
54.8
k
ar
iu
lg
la
er
Au
55.9
m
s
nd
ria
st
an
rm
Ge
56.1
Sw
57.9
nm
58.5
y
g
ur
m
OE
bo
CD
Re
h
ec
Cz
d
59.0
Un
i te
60.0
17
p.
el
ra
Is
do
Sp
61.0
61.1
xe
61.5
m
a in
61.7
Ki
Au
Fr
st
61.9
ly
ce
an
ra
la
er
it z
Sw
63.2
ng
63.3
li a
nd
a
ni
Es
ov
Sl
63.5
It a
64.5
to
en
ia
64.8
Source: OECD estimates based on 2013 HILDA survey for Australia, 2012-13 Understanding Society survey for the United Kingdom and 2013 SHARE
survey for other European countries.
1 2 http://dx.doi.org/10.1787/888933281423
11.16. Frequency of care provided by informal carers, 2013
Daily
Weekly
% of carers
100
14
90
14
16
21
21
22
23
23
26
24
28
80
29
33
38
39
41
62
61
59
70
60
50
86
40
86
84
79
79
78
77
77
74
76
72
30
71
67
20
10
en
ed
la
Sw
it z
er
la
er
th
Ne
Sw
nd
s
nd
k
ar
nm
De
Ge
rm
an
y
m
iu
lg
Be
15
CD
OE
g
m
xe
Lu
Cz
ec
h
bo
Re
ur
p.
ly
It a
ce
an
Fr
ria
st
Au
Es
to
ni
a
el
ra
Is
ia
en
ov
Sl
Sp
ain
0
Source: OECD estimates based on 2013 SHARE survey.
1 2 http://dx.doi.org/10.1787/888933281423
203
Long-term care (LTC) is a labour-intensive service. Formal
LTC workers are defined as paid staff, typically nurses and
personal carers, providing care and/or assistance to people
limited in their daily activities at home or in institutions,
excluding hospitals. Formal care is complemented by informal, usually unpaid, support from family and friends,
which accounts for a large part of care for older people in
all OECD countries (see indicator on “Informal carers”).
Relative to the population aged 65 and over, Sweden and
the United States have the most LTC workers and Turkey
and Portugal the least (Figure 11.17). In all countries except
for Israel, Japan, Estonia and Korea, the majority of LTC
staff work in institutions, even though the majority of
recipients usually receive care at home (see indicator on
“Care recipients”). This reflects the fact that those in institutions often have more severe needs and require more
intensive care.
Most LTC workers are women and work part-time. Over
90% of LTC workers are women in Canada, Denmark, the
Czech Republic, Ireland, Korea, New Zealand, the Slovak
Republic, the Netherlands, Norway and Sweden. Foreignborn workers also play an important role in LTC provision,
although their presence is uneven across OECD countries.
While Germany has very few foreign-born LTC workers,
nearly one in four care workers in the United States is foreign-born (Colombo et al., 2011). The recruitment of foreign-born workers can help respond to growing demand for
LTC, but growing migrant inflows have raised issues around
the management of irregular migration, and paid work
which is undeclared for tax and social security purposes.
The LTC sector represents a small but growing share of
total employment in OECD countries, averaging just over
2%. This share has increased over the past decade in many
countries, with the broadening of public provision and
increased demand for services. In Japan, the number of LTC
workers has more than doubled since 2001, following the
implementation of a universal LTC insurance programme
in 2000 and government policies to professionalise LTC
work, while there was a slight decrease in total employment over this period. Similarly, LTC employment in
Germany has outstripped the growth in total employment
since 2001. In contrast, LTC employment in Sweden and the
Netherlands – countries which already had comprehensive
LTC systems and high employment in the sector in the
early 2000s – has roughly followed trends in overall employment (Figure 11.18).
On average, around 30% of LTC workers are nurses and the
other 70% are personal care workers (also referred to
as nursing aides, health assistants in institutions or homebased care assistants) with less formal training. Since quality of care depends on all staff having appropriate skills,
many OECD countries have set educational and training
requirements for personal care workers, although these
vary substantially, especially where home-based care is
concerned (OECD/European Commission, 2013).
204
Increasing demand for LTC services and a possible decline
in the availability of family caregivers mean that demand
for LTC workers is likely to rise. Responding to increasing
demand will require policies to improve recruitment (e.g.
encouraging more unemployed people to consider training
and working in the LTC sector); improve retention (e.g.
enhancing pay and work conditions); and increase productivity (e.g. through reorganisation of work processes and
more effective use of new technologies) (Colombo et al.,
2011; European Commission, 2013).
Long-term care workers are defined as paid workers
who provide care at home or in institutions (outside
hospitals). They include qualified nurses and personal
care workers providing assistance with ADL and other
personal support. Personal care workers include different categories of workers who may be called under
different names in different countries. They may have
some recognised qualification or not. Because personal care workers may not be part of recognised
occupations, it is more difficult to collect comparable
data for this category of LTC workers across countries.
LTC workers also include family members or friends
who are employed under a formal contract either by
the care recipient, an agency, or public and private
care service companies. They exclude nurses working
in administration. The numbers are expressed as
head counts, not full-time equivalent.
The data for Italy exclude workers in semi-residential
long-term care facilities. The data for Japan involve
double-counting (as some workers may work in more
than one home). The data for Ireland refer only to the
public sector. The data for Australia are estimates
drawn from the 2011 National Aged Care Workforce
Census and Survey, and underrepresent the numbers
of people who could be considered LTC workers.
References
http://dx.doi.org/10.1787/9789264097759-en.
European Commission (2013), “Long-term Care in Ageing
Societies – Challenges and Policy Options”¸ Commission
Staff Working Document, SWD 41, Brussels.
OECD and European Commission (2013), A Good Life in Old
Age? Monitoring and Improving Quality in Long-term Care,
OECD Health Policy Studies, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264194564-en.
11.17. Long-term care workers per 100 people aged 65 and over, 2013 (or nearest year)
Institutions
Home
Institutions + home
Workers per 100 people aged 65+
14
12
2.6
10
8
3.9
12.3
2.7
3.3
9.9
2.7
2.9
9.4
4
1.1
4.5
5.5
2
5.8
5.2
4.3
4.2
3.2
1.6
3
2.8
4.2
2.1
0.8
1
1.1
1.6
1.4
0.1
1.3
0.1
l
y
ke
Tu
r
r tu
Po
Re
ga
p.
y
ak
Sl
ov
Hu
h
Cz
ec
ng
Re
ar
p.
a
re
Ko
ain
Sp
ria
st
y
an
rm
Ge
Au
nd
la
a
n
ni
to
Es
Ja
pa
14
CD
li a
OE
nd
ra
st
Au
la
Sw
it z
er
nm
ar
k
s
nd
la
Ne
De
el
ra
th
er
St
d
Is
at
es
1
en 1
ed
i te
Un
Sw
1.7
1.4
0.8
0
5.2
Ir e
6
1. In Sweden, Spain and the Slovak Republic, it is not possible to distinguish LTC workers in institutions and at home.
1 2 http://dx.doi.org/10.1787/888933281433
11.18. Trends in long-term care employment and total employment, selected OECD countries, 2001-13
Long-term care
Total employment
Japan
Index (2000 = 100)
250
200
200
150
150
100
100
50
50
0
2001
2003
2005
2007
2009
2011
2013
Netherlands
Index (2000 = 100)
250
0
2001
200
150
150
100
100
50
50
2003
2005
2007
2003
2005
2009
2011
2013
0
2001
2007
2009
2011
2013
2009
2011
2013
Sweden
Index (2003 = 100)
250
200
0
2001
Germany
Index (2001 = 100)
250
2003
2005
2007
1 2 http://dx.doi.org/10.1787/888933281433
205
The number of beds in long-term care (LTC) institutions
and in LTC departments in hospitals provides a measure of
the resources available for delivering LTC services to individuals outside of their home.
On average across OECD countries, there were 45 beds in
LTC institutions and five beds in LTC departments in hospitals per 1 000 people aged 65 and over in 2013 (Figure 11.19).
Belgium had the highest number of LTC beds in 2013, with
around 72 beds per 1 000 people aged 65 and over in LTC
institutions. On the other hand, there were fewer than
20 beds per 1 000 people aged 65 and over in LTC institutions or in hospitals in Italy and Poland.
On average across all OECD countries, there has been a slight
increase in the number of LTC beds per 1 000 population
over 65 since 2000 (Figure 11.20). This increase consists
entirely of beds in LTC institutions, with the number of
hospital beds remaining constant on average. However, this
masks a lot of variation. At one extreme, some countries
with well-established, comprehensive LTC systems have
been reducing residential LTC capacity. Sweden has
reduced the number of LTC beds by 2.4 per year per 1 000
population over 65, as part of a drive to move LTC out of
residential facilities and into the community (Colombo et
al., 2011). The Netherlands, Denmark and Norway have also
made significant reductions in the number of beds available. At the other end of the scale, Korea has seen a massive increase in capacity since 2000, adding 4.5 beds per
year per 1 000 population over 65, with the increase particularly marked since the introduction of a public LTC insurance scheme in 2008. In contrast to many other countries, a
significant proportion of the LTC beds added in Korea are in
hospitals. Spain has also increased its number of LTC beds
significantly, although all of the additional beds are in LTC
institutions rather than hospitals.
While most countries allocate very few beds for LTC in hospitals, others still use hospital beds quite extensively for
LTC purposes. Despite recent increases in the number of
beds in LTC institutions in Korea, the majority of LTC beds
are still in hospitals. In Japan many hospital beds are used
for long-term care, but recently the number has been
decreasing. Some European countries, such as Finland,
Hungary and Estonia, still have a significant number of LTC
beds in hospitals, but in general there has been a move
towards replacing hospital beds with institutional facilities,
which are often cheaper and provide a better living environment for people with LTC needs. Finland, France and
Iceland have all seen significant increases in LTC beds in
institutions and decreases in hospital LTC beds since 2000 –
although in the case of Iceland, this is partly due to
changes in how beds are categorised.
Providing LTC in institutions can be more efficient than
community care for people with intensive needs, due to
economies of scale and the fact that care workers do not
206
need to travel to each person separately. However, from the
point of view of public budgets, it often costs more, since
informal carers make less of a contribution and LTC systems often pick up board and lodging costs as well as care
costs. Moreover, LTC users generally prefer to remain at
home and most countries have taken steps in recent years
to support this preference and promote community care
(see Figure 11.13). However, depending on individual circumstances, a move to LTC institutions may be the most
appropriate option, for example for people living alone and
requiring round the clock care and supervision (Wiener et
al., 2009) or people living in remote areas with limited
home-care support. It is therefore important that countries
retain an appropriate level of residential LTC capacity, and
that care institutions develop and apply models of care that
promote dignity and autonomy.
Long-term care institutions refer to nursing and residential care facilities which provide accommodation
and long-term care as a package. They include specially designed institutions or hospital-like settings
where the predominant service component is longterm care for people with moderate to severe functional
restrictions. Beds in adapted living arrangements for
persons who require help while guaranteeing a high
degree of autonomy and self-control are not included.
For international comparisons, they should not
include beds in rehabilitation centers.
However, there are variations in data coverage across
countries. Several countries only include beds in publicly-funded LTC institutions, while others also
include private institutions (both profit and non-forprofit). Some countries also include beds in treatment
centers for addicted people, psychiatric units of general or specialised hospitals, and rehabilitation centers. Australia does not collect data on the numbers of
beds provided for LTC. Data on Australian LTC beds in
institutions are estimated from aged care database.
References
http://dx.doi.org/10.1787/9789264097759-en.
Wiener, J. et al. (2009), “Why Are Nursing Home Utilization
Rates Declining?”, Real Choice System Change Grant
Program, US Department of Health and Human Services,
Centres for Medicare and Medicaid Services, available at
www.hcbs.org/files/160/7990/SCGNursing.pdf.
11.19. Long-term care beds in institutions and hospitals, 2013 (or nearest year)
Institutions
Hospitals
Per 1 000 population aged 65 and over
80
72.1 71.1
70.6
70
67.6
67.4
65.5
59.5 59.0
58.1
60
56.6 55.8
54.5 54.3 54.2 54.0 53.7
53.1
50.2 49.7 49.5
48.9 48.9 47.9
50
45.6 45.0
38.8
40
36.6
35.1
28.8
30
23.4
18.9 18.0
20
10
d
nd
ak s
Re
p.
Ne Fr a
n
w
ce
Z
Lu eala
xe
n
m d
bo
ur
Hu g
ng
a
Sl r y
ov
en
No ia
rw
ay
Ko
r
e
Au
a
st
ra
li a
Ca
na
Ge da
rm
an
Ir e y
la
Un O nd
i te EC
D
d
Ki 21
ng
d
De om
nm
ar
Es k
to
ni
a
Sp
a in
Au
C z s tr
ec ia
Un h R
e
i te
d p.
St
at
Li es
tu
an
ia
Ja
pa
n
Is
ra
el
La
tv
ia
It a
l
Po y
la
nd
la
er
th
ov
nd
la
it z
Fi
er
nl
an
en
Ne
Sl
ed
Sw
Sw
an
el
Ic
Be
lg
iu
m
d
0
1 2 http://dx.doi.org/10.1787/888933281448
11.20. Trends in long-term care beds in institutions and in hospitals, 2000-13 (or nearest year)
Hospitals
Institutions
Net annual change
Average annual change in beds per 1 000 population aged 65 and over
5
4.5
4
3.7
3
2
1.4
1.3
1.2
0.9
1
0.6
0.5
0.4
0.4
0.3
0.2
0.2
-0.1
0
-0.1
-0.1
-0.3
-0.3
-0.5
-0.5
-0.5
-0.7
-0.8
-0.9
-1.1
-1
-2.3
-2
en
s
ed
nd
la
ar
er
th
Ne
Sw
k
ay
nm
rw
nd
No
De
Sw
it z
er
la
na
da
es
d
i te
Ca
ia
St
at
p.
an
Un
Re
tu
Li
nd
h
ec
Cz
m
iu
la
Po
pa
el
n
lg
Be
Ja
ce
ra
Is
Fr
an
d
y
an
an
nl
Fi
ia
ly
y
17
rm
Ge
CD
OE
tv
La
It a
d
ar
Hu
ng
a
an
el
Ic
ni
to
Es
Lu
xe
m
bo
ur
g
li a
ra
a in
st
Au
Sp
Ko
re
a
-3
Note: The OECD average includes only countries with data for both institutions and hospitals.
1. Australia, Germany, Luxembourg, the Netherlands, Norway and Switzerland do not report any long-term care beds in hospital.
1 2 http://dx.doi.org/10.1787/888933281448
207
Long-term care (LTC) expenditure has risen over the past
few decades in most OECD countries and is expected to rise
further in the coming years, with population ageing leading
to more people needing ongoing health and social care, rising incomes leading to higher expectations of quality of life
in old age, the supply of informal care potentially shrinking
and productivity gains difficult to achieve in such a labourintensive sector (De La Maisonneuve and Oliveira Martins,
2013).
Many OECD countries have expanded the availability of
home care services in order to allow people receiving LTC to
remain more independent and part of their community.
Between 2005 and 2013, the annual growth rate of public
spending on home care matched spending growth for care in
institutional care settings – at 4.3% per year (Figure 11.23).
However, there were significant increases in home care
spending of more than 7% per year in Korea, Estonia, Japan
and France.
A significant share of LTC services is funded from public
sources. Total public spending on LTC (including both the
health and social care components) accounted for 1.7%
of GDP on averag e across OECD countries in 2013
(Figure 11.21). The highest spender was the Netherlands,
where public expenditure on long-term care was two and a
half times greater than the OECD average, at 4.3% of GDP. At
the other end of the scale, the Slovak Republic, Greece,
Estonia, Hungary, the Czech Republic, Poland and Israel
allocated less than 0.5% of their GDP to public provision of
long-term care. This variation partly reflects differences in
population structure, but mostly the development of formal LTC systems, as opposed to more informal arrangements based mainly on care provided by unpaid family
members. Despite the problems of underreporting, privately-funded LTC expenditure plays a relatively large role
in Switzerland (0.6% of GDP), Germany (0.6%) and Belgium
(0.4%). As a share of total spending on LTC (including private and public health and social components), private
spending accounts for more than a third in the United
States (43%), Germany (37%) and Spain (36%). Most private
spending is out-of-pocket, since private LTC insurance does
not play an important role in any country.
Projection scenarios suggest that public resources allocated
to LTC as a share of GDP could double or more by 2060
(Colombo et al., 2011; De La Maisonneuve and Oliveira
Martins, 2013). One of the main challenges in many OECD
countries in the future will be to strike the right balance
between providing appropriate social protection to people
with LTC needs and ensuring that this protection is fiscally
sustainable.
The boundaries between health and social LTC spending
are still not fully consistent across countries, with some
reporting particular components of LTC as health care,
while others view it as social spending. The Netherlands,
Sweden, Norway and Denmark spend over 2% of GDP on
the health part of LTC, which is double the OECD average.
Finland has the highest level of public spending on social
LTC, reaching 1.6% of GDP, much higher than the OECD
average of 0.5%. The Netherlands and Japan spend more
than 1% of GDP on social LTC, but this accounts for less
than 0.1% of GDP in Korea, Spain and Luxembourg.
Public spending on LTC has grown rapidly in recent years in
some countries (Figure 11.22). The annual growth rate in
public expenditures on LTC was 4.0% between 2005 and
2013 across OECD countries, which is above the growth in
health care expenditures over the same period. Countries
such as Korea and Portugal have implemented measures to
expand the comprehensiveness of their LTC systems in
recent years and so have among the highest public spending growth rates since 2005, although spending in both
countries remains relatively low as a share of GDP.
208
LTC spending comprises both health and social support services to people with chronic conditions and
disabilities needing care on an on-going basis. Based
on the System of Health Accounts (SHA), the health
component of LTC spending relates to nursing and
personal care services (i.e. assistance with activities of
daily living (ADL)). It covers palliative care and care
provided in LTC institutions or at home. LTC social
expenditure primarily covers assistance with instrumental activities of daily living (IADL). Countries’
reporting practices between the health and social
components of LTC spending may differ. In addition,
publicly-funded LTC expenditure is more suitable for
international comparisons as there is significant variation in the reporting of privately-funded LTC expenditure across OECD countries.
Data for the United States refer to institutional care
only, so underestimate the total amount of public
spending on long-term care services.
References
http://dx.doi.org/10.1787/9789264097759-en.
De La Maisonneuve, C. and J.O. Martins (2013), “Public
Spending on Health and Long-term Care: A New Set of Projections”, OECD Economic Policy Papers, No. 6, OECD Publishing, Paris, http://dx.doi.org/10.1787/5k44t7jwwr9x-en.
11.21. Long-term care public expenditure (health and social components), as share of GDP, 2013 (or nearest year)
Health LTC
Social LTC
% GDP
5
4.3
4
3.2
3
2.4
2.3
2.2
2.1
1.9
2
1.9
1.8
1.8
1.7
1.3
1.2
1.2
1.0
1
0.9
0.7
0.7
0.5
0.5
0.4
0.4
0.3
0.2
0.2
0.0
0.0
ic
pu
Re
ak
ov
ec
Sl
Cz
Un
bl
ce
a
Gr
ee
ni
y
ar
to
Es
ic
Hu
pu
h
Re
ng
bl
nd
el
Po
la
ra
Is
ga
l
es 1
r tu
at
St
d
i te
Po
a
n
re
ai
Ko
Sp
ia
y
en
Sl
ov
an
da
na
rm
Ge
Ca
g
ur
st
Au
bo
xe
m
ria
11
d
CD
OE
Lu
nd
an
el
la
er
it z
Sw
Ic
m
ce
Be
lg
an
pa
Fr
Ja
nl
Fi
iu
n
d
k
an
ar
ay
rw
nm
No
De
en
ed
Sw
Ne
th
er
la
nd
s
0
Note: The OECD average only includes the eleven countries that report health and social LTC.
1. Figures for the United States refer only to institutional care.
1 2 http://dx.doi.org/10.1787/888933281455
11.22. Annual growth rate in public expenditure
on long-term care (health and social), in real terms,
2005-13 (or nearest year)
Korea
Switzerland
Portugal
Estonia
Belgium
Japan
Czech Rep.
Norway
OECD22
Spain
Netherlands
Luxembourg
Finland
Austria
Germany
Poland
Slovenia
Canada
United States
Denmark
Iceland
Sweden
Hungary
Slovak Rep.
11.23. Annual growth rate in public expenditure on
long-term care (health), by setting, in real terms, 2005-13
(or nearest year)
Institution LTC
36.1
Home LTC
13.8
10.8
7.6
Estonia
8.1
2.2
Japan
6.1
5.6
4.3
4.5
6.4
3.7
Norway
4.0
5.4
6.6
5.1
4.3
4.3
Belgium
3.9
3.8
OECD18
3.8
Switzerland
3.4
Germany
1.4
2.4
Canada
1.6
2.3
Czech Rep.
2.2
1.8
2.6
-1.4
2.1
2.0
1.6
Netherlands
1.4
2.3
0.6
Slovenia
1.2
-0.3
Luxembourg
0.5
-0.5
Poland
0.5
0
10
20
30
40
Average annual growth rate (%)
Note: The OECD average excludes Korea (due to the extremely high
growth rate).
1 2 http://dx.doi.org/10.1787/888933281455
5.6
4.7
Denmark
1.8
3.7
3.2
2.8
Hungary
1.4
12.8
3.8
Austria
1.8
5.7
3.5
Spain
3.9
7.0
-3.4
Finland
15.6
8.7
France
5.0
-10
36.3
57.2
Korea
-10
0
3.8
5.3
14.3
10
20
Average annual growth rate (%)
Note: The OECD average excludes Korea (due to the extremely high
growth rate).
1 2 http://dx.doi.org/10.1787/888933281455
209
© OECD 2015
ANNEX A
Additional information on demographic and economic
context, and health expenditure and financing
211
ANNEX A.
ADDITIONAL INFORMATION ON DEMOGRAPHIC AND ECONOMIC CONTEXT, AND HEALTH EXPENDITURE AND FINANCING
Table A.1. Total population, mid-year, 1970 to 2014
Thousands
Australia
1970
1980
1990
2000
2010
2011
2012
2013
2014
23 524
12 507
14 695
17 065
19 028
22 032
22 340
22 724
23 132
Austria
7 467
7 549
7 678
8 012
8 363
8 392
8 430
8 479
8 503
Belgium
9 656
9 859
9 967
10 251
10 896
11 048
11 128
11 183
11 284
Canada
21 745
24 518
27 691
30 687
34 127
34 484
34 880
35 317
35 540
Chile
9 570
11 174
13 179
15 398
17 094
17 248
17 403
17 557
17 819
Czech Republic
9 858
10 304
10 333
10 255
10 474
10 496
10 511
10 514
10 527
Denmark
4 929
5 123
5 141
5 340
5 548
5 571
5 592
5 615
5 597
Estonia
1 360
1 477
1 569
1 397
1 331
1 327
1 323
1 318
1 316
Finland
4 606
4 780
4 986
5 176
5 363
5 388
5 414
5 439
5 460
France
50 772
53 880
56 709
59 062
62 918
63 223
63 514
63 790
64 360
Germany1
61 098
61 549
63 202
82 212
81 777
81 798
80 426
80 646
80 925
8 793
9 643
10 157
10 917
11 153
11 103
11 037
10 948
11 381
10 338
10 711
10 374
10 211
10 000
9 972
9 920
9 893
9 843
Iceland
204
228
255
281
318
319
321
324
327
Ireland
2 957
3 413
3 514
3 805
4 560
4 577
4 587
4 598
4 610
Greece
Hungary
Israel
2 958
3 878
4 660
6 289
7 624
7 766
7 910
8 057
8 186
Italy
53 822
56 434
56 719
56 942
59 277
59 379
59 540
60 234
60 789
Japan
103 721
117 061
123 613
126 927
128 058
127 799
127 515
127 296
127 083
Korea
32 241
38 124
42 869
47 008
49 410
49 779
50 004
50 220
50 424
339
364
382
436
507
518
531
543
556
Mexico
50 628
66 737
87 065
100 896
114 256
115 683
117 054
118 395
119 713
Netherlands
13 039
14 150
14 952
15 926
16 615
16 693
16 755
16 804
16 858
New Zealand
2 828
3 170
3 390
3 858
4 366
4 404
4 433
4 472
4 388
Norway
3 876
4 086
4 241
4 491
4 889
4 953
5 019
5 080
5 137
Poland
32 664
35 574
38 111
38 259
38 043
38 063
38 063
38 040
38 037
Portugal
8 680
9 766
9 983
10 290
10 573
10 558
10 515
10 457
10 375
Slovak Republic
4 538
4 980
5 299
5 389
5 391
5 398
5 408
5 413
5 416
Slovenia
1 725
1901
1998
1989
2049
2053
2057
2060
2062
33 815
37 439
38 850
40 263
46 577
46 743
46 773
46 620
45 943
Sweden
8 043
8 311
8 559
8 872
9 378
9 449
9 519
9 600
9 699
Switzerland
6 181
6 319
6 716
7 184
7 825
7 912
7 997
8 089
8 188
35 294
44 522
56 104
67 393
73 142
74 224
75 176
76 148
76 903
Luxembourg
Spain
Turkey
United Kingdom
55 663
56 314
57 248
58 893
62 766
63 259
63 700
64 107
64 091
United States
205 052
227 225
249 623
282 162
309 326
311 583
313 874
316 129
318 892
OECD (total)
870 967
965 259
1 052 204
1 155 498
1 236 028
1 243 502
1 249 052
1 256 518
1 264 123
96 078
118 563
146 593
171 280
193 253
194 933
196 526
198 043
199 492
814 423
984 122
1 165 429
1 280 429
1 359 822
1 368 440
1 377 065
1 385 567
1 393 784
Partners
Brazil
China (People’s Rep.)
Colombia
..
..
34 130
40 296
45 510
46 045
46 582
47 121
47 662
Latvia
2 359
2 512
2 663
2 368
2 098
2 060
2 034
2 013
1 994
Lithuania
3 140
3 413
3 698
3 500
3 097
3 028
2 988
2 958
3 163
India
555 064
698 721
868 891
1 042 262
1 205 625
1 221 156
1 236 687
1 252 140
1 267 402
Indonesia
114 080
145 510
178 633
208 939
240 677
243 802
246 864
249 866
252 812
Russian Federation
130 392
138 655
147 969
146 597
142 849
142 961
143 207
143 507
143 787
22 502
29 077
36 793
44 846
51 452
51 949
52 386
52 776
53 140
South Africa
1. Population figures for Germany prior to 1991 refer to West Germany.
1 2 http://dx.doi.org/10.1787/888933281510
212
ANNEX A.
Table A.2. Share of the population aged 65 and over, 1970 to 2014
1970
Australia
1980
1990
2000
2010
2011
2012
2013
2014
8.3
9.6
11.1
12.4
13.6
13.8
14.2
14.4
14.7
Austria
14.0
15.5
14.8
15.4
17.6
17.6
17.8
18.0
18.3
Belgium
13.3
14.3
14.8
16.7
17.1
17.0
17.3
17.5
17.7
Canada
7.9
9.4
11.3
12.6
14.2
14.5
14.9
15.2
15.6
Chile
5.0
5.5
6.1
7.2
9.0
9.3
9.5
9.8
10.0
Czech Republic
12.0
13.6
12.5
13.8
15.3
15.6
16.2
16.8
17.3
Denmark
12.1
14.3
15.6
14.8
16.3
16.8
17.3
17.8
18.3
Estonia
11.6
12.5
11.6
14.9
17.5
17.5
17.7
18.1
18.4
Finland
9.0
11.9
13.3
14.8
17.0
17.5
18.1
18.7
19.3
France
12.8
14.0
13.9
16.0
16.8
16.9
17.3
17.7
..
Germany
13.0
15.5
15.2
16.2
20.7
20.6
21.0
21.1
20.8
Greece
11.1
13.0
13.6
16.4
19.0
19.3
19.8
20.2
19.7
Hungary
11.5
13.5
13.2
15.0
16.6
16.8
16.9
17.2
17.6
Iceland
8.7
9.8
10.5
11.5
12.0
12.3
12.6
12.9
13.1
Ireland
11.1
10.7
11.4
11.1
11.2
11.5
11.9
12.2
12.6
Israel
6.7
8.6
9.1
9.8
9.9
10.0
10.3
10.7
10.9
Italy
10.7
13.1
14.7
18.1
20.4
20.5
20.8
21.0
21.4
Japan
7.1
9.1
12.1
17.4
23.0
23.3
24.1
25.1
26.0
Korea
3.1
3.8
5.1
7.2
11.0
11.4
11.8
12.2
12.7
12.4
13.6
13.3
14.2
13.8
13.7
13.8
13.8
14.1
4.6
4.3
4.3
5.2
6.2
6.3
6.4
6.5
6.7
10.1
11.4
12.7
13.5
15.3
15.5
16.2
16.8
17.3
Luxembourg
Mexico
Netherlands
New Zealand
8.4
9.7
11.2
11.8
13.0
13.3
13.8
14.2
14.8
Norway
12.8
14.6
16.3
15.2
14.8
15.0
15.3
15.6
15.8
Poland
8.2
10.1
9.9
12.1
13.6
13.6
14.0
14.4
14.9
Portugal
9.2
11.1
13.2
16.0
18.3
18.7
19.1
19.4
19.3
Slovak Republic
9.1
10.5
10.2
11.4
12.4
12.6
12.8
13.1
13.5
Slovenia
9.5
11.3
10.6
13.8
16.5
16.5
16.8
17.1
17.5
Spain
9.5
10.8
13.4
16.7
16.8
17.1
17.4
17.7
18.4
Sweden
13.5
16.2
17.7
17.3
18.0
18.4
18.7
19.0
19.3
Switzerland
11.2
13.8
14.5
15.2
16.7
16.8
17.1
17.3
17.5
4.3
4.7
4.2
5.3
7.0
7.2
7.3
7.5
7.7
12.9
14.9
15.7
15.8
16.2
16.4
16.7
17.1
17.6
Turkey
United Kingdom
United States
9.8
11.3
12.5
12.4
13.1
13.3
13.7
14.1
14.5
OECD34
9.8
11.4
12.0
13.4
15.0
15.2
15.5
15.9
16.2
Brazil
3.5
4.0
4.4
5.4
6.8
7.0
7.2
7.4
7.6
China (People’s Rep.)
4.0
5.1
5.8
6.9
8.4
8.5
8.7
8.9
9.1
..
..
5.0
5.7
6.7
6.9
7.0
7.2
7.3
India
3.3
3.6
3.9
4.4
5.1
5.1
5.2
5.3
5.4
Indonesia
3.3
3.6
3.8
4.7
5.0
5.1
5.1
5.2
5.3
11.9
13.0
11.8
14.9
18.3
18.5
18.7
18.9
18.7
Lithuania
9.9
11.3
10.8
13.8
17.6
18.0
18.2
18.3
17.2
Russia
7.7
10.2
10.0
12.4
12.8
12.7
12.9
13.0
13.3
South Africa
3.4
3.1
3.2
3.4
5.2
5.3
5.4
5.5
5.6
Partners
Colombia
Latvia
1 2 http://dx.doi.org/10.1787/888933281523
213
ANNEX A.
Table A.3. GDP per capita in 2013 and average annual growth rates, 1970 to 2013
GDP per capita in
USD PPP
Average annual growth rate per capita, in real terms
2013
1970-80
1980-90
1990-2000
2000-10
2010-2013
Australia
44 976
1.3
1.5
2.4
1.6
1.3
Austria
45 082
3.5
2.1
2.2
1.1
0.9
Belgium
41 573
3.2
1.9
2.0
0.9
-0.2
Canada
42 839
2.8
1.4
1.8
0.8
1.1
Chile
22 178
..
..
5.0
3.1
4.5
Czech Republic
28 739
..
..
0.6
3.0
0.0
Denmark
43 782
1.9
2.0
2.3
0.3
-0.4
Estonia
25 823
..
..
..
3.9
5.2
Finland
39 869
3.4
2.7
1.9
1.3
-0.5
France
37 671
3.0
2.0
1.7
0.6
0.4
Germany1
43 887
2.8
2.1
1.3
1.0
1.8
Greece
25 854
3.6
0.2
1.7
1.5
-5.9
Hungary
23 336
..
..
..
2.2
1.0
Iceland
42 035
5.2
1.6
1.6
1.5
1.8
Ireland
45 677
3.2
3.3
6.3
0.6
0.6
Israel
32 502
..
1.9
2.9
1.4
1.6
Italy
35 075
3.3
2.3
1.6
-0.1
-1.8
Japan
36 236
3.2
4.1
0.9
0.7
1.2
Korea
33 089
7.4
8.6
6.0
3.9
2.4
Luxembourg
91 048
1.9
4.5
3.6
1.1
-0.8
Mexico
16 891
3.7
-0.9
2.0
0.6
1.9
Netherlands
46 162
2.3
1.7
2.5
0.9
-0.6
New Zealand
34 899
1.0
1.2
1.7
1.3
1.5
Norway
65 640
4.1
1.2
4.0
0.9
1.6
Poland
23 985
..
..
3.7
4.0
2.7
Portugal
27 509
3.5
3.0
2.6
0.5
-2.1
Slovak Republic
26 497
..
..
..
4.8
1.8
Slovenia
28 859
..
..
1.9
2.4
-1.2
Spain
33 092
2.6
2.6
2.4
0.7
-1.3
Sweden
44 646
1.6
1.9
1.8
1.5
0.4
Switzerland
56 940
1.0
1.6
0.5
1.0
0.5
Turkey
18 508
..
..
1.8
3.0
3.6
United Kingdom
38 255
2.0
2.7
2.1
1.1
0.6
United States
53 042
2.1
2.4
2.2
0.7
1.3
OECD
38 123
2.9
2.3
2.4
1.6
0.7
Brazil
16 192
..
-0.6
0.8
2.4
1.9
China (People's Rep.)
11 661
..
7.7
9.3
9.9
7.7
Colombia
12 695
..
1.5
1.0
2.8
4.0
Costa Rica
13 872
..
..
..
2.6
3.0
India
5 406
..
3.3
3.5
5.9
4.8
Indonesia
10 023
..
3.4
2.6
3.9
4.4
Latvia
22 958
..
..
..
5.2
6.1
Lithuania
25 715
..
..
.
5.4
6.0
Russian Federation
25 247
..
..
..
5.1
2.8
South Africa
12 553
..
-0.8
-0.1
1.9
1.0
Partners
1. Data prior to 1991 refers to Western Germany.
Source: OECD Health Statistics 2015, http://dx.doi.org/10.1787/health-data-en. International Monetary Fund, World
Economic Outlook Database, April 2015.
1 2 http://dx.doi.org/10.1787/888933281533
214
ANNEX A.
Table A.4. Health expenditure per capita in 2013, average annual growth rates,
2009 to 2013
Health expenditure
per capita in USD
PPP
Annual growth rate per capita in real terms1
2013
2009/10
2010/11
2011/12
Australia2
3 866
-0.6
3.8
2.9
..
2.5
Austria
4 553
1.5
0.5
2.3
-0.3
1.6
Belgium
4 256
-0.8
2.7
0.1
0.1
1.8
Canada
4 351
2.0
-1.3
0.3
0.1
1.9
Chile3
1 606
6.0
5.1
6.1
8.3
5.9
Czech Republic
2 040
-3.1
2.5
-0.1
-0.2
2.5
Denmark
4 553
-1.4
-1.4
0.2
-0.5
1.3
Estonia
1 542
-4.3
0.8
..
4.4
3.9
Finland
3 442
1.6
2.3
0.8
0.2
1.5
France
4 124
0.8
..
0.6
1.2
1.2
Germany
4 819
3.0
0.8
2.7
1.7
2.4
Greece
2 366
-10.9
-2.8
-12.2
-2.5
-2.3
Hungary
1 719
4.4
1.9
-2.2
-0.6
-0.8
Iceland
3 677
-6.1
0.1
1.3
3.4
0.0
Ireland2
3 663
-8.7
-4.1
1.1
..
1.2
Israel
2 428
3.1
2.9
5.7
2.8
2.7
Italy
3 077
1.1
-0.9
-3.0
-3.5
-0.6
Japan
3 713
5.2
4.9
3.0
..
3.7
Korea
2 275
8.1
4.0
4.4
5.3
7.2
Luxembourg2
4 371
-2.2
-5.8
-5.0
..
-2.1
Mexico
1 048
1.3
-2.1
5.9
2.0
1.7
Netherlands
5 131
2.3
1.7
3.2
-0.3
2.5
New Zealand
3 328
0.4
0.8
2.7
-1.3
2.4
Norway4
5 862
-0.1
2.6
1.9
0.6
1.6
Poland
1 530
..
2.0
1.2
3.8
5.8
Portugal
2 514
1.1
-4.8
-5.0
-3.2
-0.9
Slovak Republic
2 010
..
-2.4
4.4
0.0
6.7
Slovenia
2 511
0.9
0.1
-0.8
-1.4
1.4
Spain
2 898
-0.1
-0.6
-2.4
-3.8
1.0
Sweden
4 904
-0.3
..
1.4
2.0
1.4
Switzerland
6 325
..
2.1
3.5
1.9
1.9
941
-1.2
1.2
-0.7
5.4
3.0
United Kingdom
3 235
-1.3
-0.1
0.3
0.6
1.7
United States
8 713
1.9
1.0
1.6
1.5
1.9
OECD
3 453
0.1
0.6
0.8
0.9
2.0
1 471
7.7
2.4
..
..
4.2
649
6.1
12.3
12.5
..
12.0
Turkey
2012/13
2005-13
Partners
Brazil5
China (People's Rep.)5
Colombia5
864
-1.0
1.9
7.4
..
5.9
1 380
..
..
..
..
..
India5
215
..
..
..
..
..
Indonesia5
293
9.2
3.8
11.8
5.3
6.2
Latvia
1 216
-1.8
-1.8
2.5
3.7
1.7
Lithuania
1 573
-3.7
3.4
1.9
1.3
4.9
Russian Federation 5
1 653
-4.0
1.4
-0.3
1.8
6.3
South Africa5
1 121
1.9
2.0
5.3
1.3
2.1
Costa Rica5
1. Using national currency units at 2005 GDP price level.
2. Latest year 2012.
3. CPI is used as deflator.
4. GDP deflator refers to Mainland Norway.
5. Including investment.
1 2 http://dx.doi.org/10.1787/888933281543
215
ANNEX A.
Table A.5. Expenditure on health, percentage of GDP, 1980-2013
1980
1990
2000
Australia
5.8
6.5
Austria
7.0 |
7.7
Belgium
6.2
7.1 |
8.0 |
Canada
6.6
8.4
8.3
Chile
..
..
6.4
Czech Republic
..
3.8 |
8.0
Denmark
8.4
2010
2011
2012
7.6
8.5
8.6
9.2
10.1
9.9
10.1
10.1
9.9
10.1
10.2
10.2 e
10.6
10.3
10.2
10.2
6.7
6.7
7.0
7.3
5.7 |
6.9
7.0
7.1
7.1
8.1 |
10.4
10.2
10.4
10.4
6.0
8.8 e
2013
..
Estonia
..
..
5.2
6.1
5.7 |
5.8
Finland
5.9
7.2 |
6.7
8.2
8.2
8.5
8.6
France
6.7
8.0 |
9.5 |
10.8 |
10.7
10.8
10.9
Germany
8.1
8.0 |
9.8
11.0
10.7
10.8
11.0
6.0
7.2
9.2 e
9.7 e
9.1 e
9.2 e
6.8 |
7.7
7.6
7.5
7.4
8.7
Greece
..
Hungary
..
.. |
Iceland
5.8
7.4
9.0
8.8
8.6
8.7
Ireland
7.5
5.6
5.6
8.5
8.0
8.1
Israel
7.0
6.6
6.8
7.0
7.0
7.4 e
Italy
7.0 |
7.6
8.9
8.8
8.8
Japan
6.4
..
5.8
7.4
9.5
10.0
10.1
Korea
..
7.5 e
8.8
10.2 e
3.5
3.7
4.0
6.5
6.5
6.7
Luxembourg
..
..
5.9
7.2
6.8
6.6
..
Mexico
..
4.3 |
4.9 |
6.2
5.9
6.1
6.2
Netherlands
6.6
7.1 |
7.0 |
10.4
10.5
11.0
11.1
New Zealand
5.7
6.7
7.5 |
9.7 e
9.7 e
9.8 e
9.5 e
Norway
5.4
7.1 |
7.7 |
8.9
8.8
8.8
8.9
Poland
..
4.3
5.3 |
6.5
6.3
6.3
6.4
5.5 |
8.3
9.8
9.5
9.3
9.1
Portugal
4.8
6.9
Slovak Republic
..
..
5.3 |
7.8
7.5
7.7
7.6
Slovenia
..
..
8.1 |
8.6
8.5
8.7
8.7
5.0
6.1 |
6.8 |
9.0
9.1
9.0
8.8
..
7.3 |
7.4 |
8.5 |
10.6
10.8
11.0
Switzerland
6.6
7.4 |
9.3 |
10.5
10.6
11.0
11.1
Turkey
2.4
2.5 |
4.7
5.3
5.0
5.0
5.1
United Kingdom
5.1
5.1
6.3
8.6
8.5
8.5
8.5
United States
8.2
11.3
12.5
16.4
16.4
16.4
16.4
OECD
6.1
6.5
7.2
8.8
8.8
8.9
8.9
Brazil1
..
..
7.0
8.7
8.7
8.9
9.1
China (People's Rep.)1
..
..
4.6
5.0
5.1
5.4
5.6
Colombia1
..
..
5.9
6.8
6.5
6.8
6.8
Costa Rica1
..
..
7.1
9.7
10.2
10.1
9.9
India1
..
..
4.3
3.8
3.9
3.9
4.0
Indonesia1
..
..
1.8
2.7
2.7
2.9
2.9
Latvia
..
..
..
6.1
5.6
5.4
5.3
Lithuania
..
..
..
6.8
6.5
6.3
6.1
Russian Federation1
..
..
5.4
6.9
6.7
6.5
6.5
South Africa1
..
..
8.3
8.7
8.6
8.9
8.9
Spain
Sweden
Partners
| Break in series.
e: Preliminary estimate.
1. Including investment.
1 2 http://dx.doi.org/10.1787/888933281551
216
ORGANISATION FOR ECONOMIC CO-OPERATION
AND DEVELOPMENT
The OECD is a unique forum where governments work together to address the economic,
social and environmental challenges of globalisation. The OECD is also at the forefront of efforts to
understand and to help governments respond to new developments and concerns, such as
corporate governance, the information economy and the challenges of an ageing population. The
Organisation provides a setting where governments can compare policy experiences, seek answers
to common problems, identify good practice and work to co-ordinate domestic and international
policies.
The OECD member countries are: Australia, Austria, Belgium, Canada, Chile, the Czech
Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel,
Italy, Japan, Korea, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal,
the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the
United States. The European Union takes part in the work of the OECD.
OECD Publishing disseminates widely the results of the Organisation’s statistics gathering and
research on economic, social and environmental issues, as well as the conventions, guidelines and
standards agreed by its members.
OECD PUBLISHING, 2, rue André-Pascal, 75775 PARIS CEDEX 16
(81 2015 07 1 P) ISBN 978-92-64-23257-0 – 2015
OECD INDICATORS
This new edition of Health at a Glance presents the most recent comparable data on the performance of health
systems in OECD countries. Where possible, it also reports data for partner countries (Brazil, China, Colombia,
Costa Rica, India, Indonesia, Latvia, Lithuania, Russian Federation and South Africa). Compared with the
previous edition, this new edition includes a new set of dashboards of health indicators to summarise in a clear
and user-friendly way the relative strengths and weaknesses of OECD countries on different key indicators of
health and health system performance, and also a special focus on the pharmaceutical sector. This edition also
contains new indicators on health workforce migration and on the quality of health care.
Contents
Chapter 1. Dashboards of health indicators
Chapter 2. Special focus: Pharmaceutical spending trends and future challenges
Chapter 3. Health status
Chapter 4. Non-medical determinants of health
Chapter 5. Health workforce
Chapter 6. Health care activities
Chapter 7. Access to care
Chapter 8. Quality of care
Chapter 9. Health expenditure and financing
Chapter 10. Pharmaceutical sector
Chapter 11. Ageing and long-term care
Consult this publication on line at http://dx.doi.org/10.1787/health_glance-2015-en.
This work is published on the OECD iLibrary, which gathers all OECD books, periodicals and statistical databases.
Visit www.oecd-ilibrary.org for more information.
ISBN 978-92-64-23257-0
81 2015 07 1 P
9HSTCQE*cdcfha+

Health at a Glance 2015: OECD Indicators

Transcript

Documenti analoghi

In It Together …in Italy

164 The recovery continues to rely on private demand

Overview of B2020 browser tool

Italian Speaking Recruitment Consultant

Bloggingstocks.com Ore 16.00 Closing Bell Enzon Pharmaceuticals

Denis`s CV - TU Dresden

even more - Case Western Reserve University

RS200910_læge - speciallæger_2

A Reduced Organic Carbon Component in Martian

The $2.6 Billion Pill—Methodologic and Policy Considerations

the Italian Strategy for Digital Schools

Eucotax Wintercourse 2008

pdf english

AnatOnMe - Waypoint