Transcript www.worldbank.org/analyzinghealthequity Book published by the World Bank in 2008. Presentations accompany the book and are designed as a course on health equity analysis. The book contents can.

www.worldbank.org/analyzinghealthequity
Book published by the
World Bank in 2008.
Presentations accompany
the book and are
designed as a course on
health equity analysis.
The book contents can be
downloaded from the
website above.
Analyzing Health Equity Using
Household Survey Data
Lecture 1
Introduction
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Huge rich-poor inequalities in health
outcomes and health care in Bolivia
100
120
100
% babies delivered
by a medically trained person
risk of newborn dying before
5th birthday (per 1000)
90
80
60
40
20
80
70
60
50
40
30
20
10
0
poorest
2nd
20%
poorest
20%
middle
20%
second
richest
20%
richest
20%
0
poorest
2nd
20%
poorest
20%
middle
20%
second
richest
20%
richest
20%
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
And Bolivia is not an isolated
example in Latin America and the
Caribbean….
180
Under-five mortality rate
160
140
120
100
80
60
40
20
0
1
2
3
4
5
Haiti 1994/95
Haiti 2000
Guatemala 1998/99
Guatemala 1995
Nicaragua 1997/98
Colombia 1995
Paraguay 1990
Bolivia 2003
Colombia 2000
Colombia 2005
Dominican Republic 2002
Nicaragua 2001
Dominican Republic 1996
Bolivia 1998
Peru 1996
Peru 2000
Brazil 1996
Wealth quintile (poorest to richest)
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Health inequality and inequity
• Rich-poor inequalities in health largely, if not
entirely, derive from differences in constraints
(e.g. incomes, time costs, health insurance,
environment) rather than in preferences.
• Hence they are often considered to represent
inequities.
• But in high-income countries the poor often use
more health care and this may not be represent
inequity.
• Drawing conclusions about health equity involves
consideration of the causes of health inequalities.
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Equity of what?
• Health outcomes, e.g. infant mortality, child
growth, disability, incidence of illness, general
health, life expectancy.
• Health care utilisation, e.g. doctor visits, inpatient
stays, vaccinations, maternity care.
• Subsidies received through use of public health
care.
• Payments for health care (both direct and indirect).
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Equity in relation to what?
• Equity in health, health care and health payments
could be examined in relation to gender, ethnicity,
geographic location, education, income….
• This course focuses on equity by socioeconomic
status, usually measured by income, wealth or
consumption.
• Many of the techniques are applicable to equity in
relation to other characteristics but they often
require that individuals can be ranked by that
characteristic.
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Course objectives
• To provide a step-by-step practical guide to the
measurement of various aspects of health equity.
• To introduce the relevant theory and literature.
• To provide hands-on experience of computing
health equity measures using Stata®.
• To illustrate the interpretation of results through
worked examples.
• To stimulate analysis that will facilitate more
comprehensive monitoring of health equity,
especially in developing countries.
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Questions addressed by health equity
analyses
• Snapshots. Does inequality in child survival between the poor and
better-off exist? How large is it?
• Movies. Is inequality in child survival smaller now than in the 1990s?
• Cross-country comparisons. Is inequality in child survival larger in
Brazil than in Cuba?
• Decompositions. To what extent is inequality in child survival
explained by inequalities in education, health insurance cover, access
to maternal and child health care, etc?
• Cross-country detective exercises. To what extent is greater inequality
in child survival in Brazil than Cuba explained by greater income
inequality, given differences in health systems?
• Program impact evaluations. To what extent is inequality in child
survival reduced by an intervention such as a vaccination program,
expanded health insurance cover or improved water supply?
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Ingredients of health equity analysis
• Data usually from a household survey.
• Measurement of the key variables health/health care/health payments and of
socioeconomic status.
• Quantitative measures of inequality or
inequity.
• Multivariate methods to explain inequality,
identify its causes and the impact on it of
interventions
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Course content
• The course is structured around these four ingredients, plus
applications.
• Part I – Data
– data requirements and sources (lecture 2)
– measurement of the key variables – child survival,
anthropometrics, adult health & living standards (3-6).
• Part II – Tools
– measures of socioeconomic inequality in health
variables (7-9)
– multivariate analysis (10-11)
– decomposition methods (12-13).
• Part III – Applications
– to health care utilisation (14-15)
– to health payments (16-19).
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Concentration curves (lecture 7) are
central to the assessment of
socioeconomic inequality in health
cumul. % under-five deaths
100%
90%
80%
70%
equality
Haiti 1994/95
60%
Guatemala 1995
Bolivia 2003
50%
40%
Bolivia 1998
Brazil 1996
30%
20%
10%
0%
0%
20%
40%
60%
80%
100%
cumul. % pop. (poorest first)
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Concentration index (lecture 8) is a tiebreaker, but rests on certain value
judgments
Brazil 1996
Peru 2000
Peru 1996
Bolivia 1998
Dominican Republic 1996
Nicaragua 2001
Dominican Republic 2002
Colombia 2005
Colombia 2000
Bolivia 2003
Paraguay 1990
Colombia 1995
Nicaragua 1997/98
Guatemala 1995
Guatemala 1998/99
Haiti 2000
Haiti 1994/95
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
concentration index
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Multivariate methods
• Estimation and inference with complex survey
data (lecture 10)
• Nonlinear models for health and medical
expenditure data (11)
• Explaining differences between groups: Oaxaca
decomposition (12)
• Explaining socioeconomic-related health
inequality: Decomposition of the concentration
index (13)
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Lecture 14: Who (really) benefits from
health care subsidies?
Often the better off!
Hong Kong SAR
Malaysia
Sri Lanka
Thailand
Bangladesh
Vietnam
Indonesia
India
Heilongjiang, China
Gansu, China
Nepal
0
5
10
15
20
25
30
35
40
45
share of govt. health subsidies accruing to poorest 20%
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Lecture 16: Who (really) pays for
health care?
Bangladesh
Thailand
Indonesia
Hong Kong
Philippines
Sri Lanka
Nepal
Punjab , India
China
Kyrgyz Rep.
Korea Rep.
Taiwan
Japan
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
Kakwani index of progressivity
(+=progressive; -=regressive)
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Lecture 18: Incidence of catastrophic
out-of-pocket payments varies across
countries
VIETNAM
BANGLADESH
CHINA
INDIA
KYRGYZ REP.
NEPAL
KOREA REP.
SRI LANKA
INDONESIA
PHILIPPINES
HONG KONG SAR
THAILAND
TAIWAN
MALAYSIA
0%
5%
10%
15%
20%
25%
30%
35%
% pop. with out-of-pocket payments exceeding 15% non-food consumption
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Lecture 19: Health care payments
and poverty
Pre-payment and post-payment consumption, Bangladesh 2000
HH cons as multiple of pov line PL
16
14
12
10
8
6
4
2
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
HHs ranked by total consumption
P o verty line
P o st payment co nsumptio n
P re payment co nsumptio n
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Acknowledgements
• Participants in the ECuity and Equitap projects
• The European Commission INCO-DEV programme (ICA4CT-2001-10015) that funded the Equitap project.
• The Dutch government for financial support for the production
and printing of the book and these slides through Reaching the
Poor II
• Davidson Gwatkin and Abdo Yazbeck for encouragement and
support during the production and market-testing of the
technical notes, and the production of the volume
• John Didier, Tanya Ringland, Chialing Yang and others at the
World Bank Institute for managing the production and
dissemination of the book
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity