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Equity in health and health care: lessons from an Asian comparative study. Eddy van Doorslaer Erasmus School of Economics & Erasmus Medical Centre Rotterdam Merck Foundation Lecture London School of Economics, 16 March 2007. 2. Introduction - background. - PowerPoint PPT Presentation
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Equity in health and health care:lessons from an Asian comparative
studyEddy van Doorslaer
Erasmus School of Economics & Erasmus Medical Centre
Rotterdam
Merck Foundation Lecture London School of Economics, 16 March 2007
Introduction - background
Inequality and inequity in the distribution of– Health– Health care– Health care payments
Some analogies with measurement of income inequality, but not perfect
Approaches to study equity in health care finance and delivery in high-income (EU/OECD) countries, developed in ECuity Project
Usefulness for low-to-middle income countries, examined in EQUITAP Project (Asian-Pacific)
2
Draws on several papers
1. Van Doorslaer, E, O O’Donnell, R Rannan-Eliya et al (20 authors) Effect of payments for health care on poverty estimates in 11 countries in Asia: an analysis of household survey data, The Lancet, 2006, 368, Dec
2. O’Donnell, O, E van Doorslaer, R Rannan-Eliya et al (18 authors) The incidence of public spending on health care: comparative evidence from Asia, World Bank Economic Review, (Published online, Jan 2007)
3. Van Doorslaer, E, O O’Donnell, R Rannan-Eliya et al (23 authors) Catastrophic expenditures on health care in Asia, Health Economics, (Published online, Feb 2007)
I. Distribution of health care payments
In OECD context: mostly publicly financed health care systems with universal coverage have disconnected receipt of care from payment for care
Payments according to ability to pay Tools of progressivity and redistribution can be used to
examine health payments by income Descriptive x-country comparative results informative
on potential consequences of alternative health care financing choices
E.g out-of-pocket payments most regressive source
The health care financing triangle - OECD(Wagstaff, Van Doorslaer et al, JHE, 1999)
US
UK
Switzerland
Sweden
Spain Portugal
Netherlands
Italy
Ireland
Germany
France
Finland
Denmark
Belgium
0%
20%
40%
60%
80%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% social insurance
% g
en t
ax
0% private finance
Overall progressivity hc finance — OECD(Wagstaff, Van Doorslaer et al, JHE, 1999)
-0.15 -0.10 -0.05 0.00 0.05 0.10 Kakwani index
Denmark (1987)Finland (1990)France (1989)
Germany (1989)Ireland (1987)
Italy (1991)Netherlands (1992)
Portugal (1990)Spain (1990)
Sweden (1990)Switzerland (1992)
United Kingdom (1992)United States (1987)
Distribution of health care finance
In low-income settings: high shares of out-of-pocket spending, small pre-paid shares
And receipt of care linked to payment for care Payments largely according to use Tools of progressivity and redistribution can be
very misleading Rich pay more, even in proportion to their income Interest in (lack of) prepayment and protection
against income loss
Health care financing triangle – Asia(O’Donnell, Van Doorslaer et al, 2006)
Thailand
Taiwan
Sri Lanka
Punjab Philippines
Nepal
Kyrgyz Rep.
Korea Rep.
Japan Indonesia
Hong Kong
China
Bangladesh
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% Out-of-pocket
% G
en T
ax R
evR
Inequality and progressivity – total health care payments, Asia (O’Donnell et al, 2006)
-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6
Japan Taiwan
Korea Rep. Kyrgyz Rep.
China Punjab Nepal
Sri Lanka Philippines Hong Kong Indonesia
Thailand Bangladesh
Concentration index Kakwani index
Measures of income protection of interest High health expenditure out-of-pocket (OOP) can affect
household welfare through– Effect on health care use: see BIA– Effect on living standards: income protection
Wagstaff and van Doorslaer (2003) proposed to look at measures of ‘impact’ of OOPs on– Incidence and intensity of high or catastrophic
expenditure– Incidence and intensity of poverty changes
Impoverishment
Most health spending in low-income settings not discretionary
Standard poverty headcount and gap measures do not account for health needs
Variability and unpredictability of OOPs makes adjustment of poverty line for health needs difficult
How many more individuals are below the poverty line – and by how far – if OOP spending is deducted from total spending?
Change in headcount and gap makes ‘hidden poor’ visible
Pen’s Parade and OOPs in China, 2000
Pen' s parade - China 2000
0
5
10
15
20
25
30
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
HHs ranked by pre-payment expendi ture per capi ta
HHs
expe
ndit
ure
per
capi
ta a
s m
ulti
ple
of$1
.08
PL
PL1($1.08 ) pre-payment exp. post-payment exp. PL2($2. 15 )
Poverty headcount (at $1) increase by OOP share
Vietnam
Thailand Sri Lanka
Philippines
Nepal
Malaysia Kyrgyz
Indonesia
India
China
Bangladesh
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
0% 20% 40% 60% 80% 100%
OOP share of total health expenditure
Cha
nge in
head
co
unt ($
1 p
ov
lin
e)
Poverty headcount (at $1) increase by population at risk
Vietnam
Thailand Sri Lanka
Philippines
Nepal
Malaysia Kyrgyz
Indonesia
India
China
Bangladesh
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
0% 10% 20% 30% 40% 50% 60%
% with pre-payment consumption between $1 and $2
Change in
headcount ($
1 p
overt
y li
ne)
Some EQUITAP Project findings – Asia(Van Doorslaer et al, Lancet, 2006)
– Estimated increase in poverty headcount from 19.3% to 22% of total population (so plus 2.7% of population)
– or an extra 78.25 million Asian people poor– who are currently not counted as poor because their
OOP health spending lifts them above poverty line– Largest relative increases in Bangladesh, India, China
and Vietnam– Magnitude clearly linked to OOP finance share and to
population share at risk– But again, even at similar OOP share levels, some
countries appear more successful at protecting living standards than others. So not just a question of affordability
II. Distribution of health care utilisation in rich countries (Van Doorslaer et al, CMAJ, 2006)
In OECD context: horizontal equity (HE) concern requires equal treatment for equal need, irrespective of income
Measurement of inequality in need-standardised distribution of use by income
Norm = average relationship between use and need Many systems close to achieving HE, but varies by type
of careE.g.: – GP care often pro-poor– specialist care everywhere pro-rich– more pro-rich when private options available
Access to health care in Asia?
In low-income countries: self-reported health measures often show less pro-poor gradient in LDCs
Unlike more objective indicators of need Need measurement problematic Therefore: assume need is equal or at least
not pro-rich and look at distribution of actual utilization, i.e.
unadjusted for need
Poor kids more likely to die before age five 21
But poor kids and mothers less likely to receive health services
22
Who makes use of public health services? Benefit-incidence analysis (BIA)
Important to realize: Even if benefits not distributed according to needs In-kind transfers still achieve a welfare
redistribution even if incidence itself is pro-rich if incidence of public expenditure less
concentrated among the rich than income itself
Use formal tests of stochastic dominance to compare concentration curves of public health
subsidy with diagonal and Lorenz curves
Example: concentration of public health subsidy in Malaysia and India
BIA of public health subsidy – Asia (O’Donnell, Van Doorslaer et al, WBER, 2007)
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Concentration index
Kakwani index
Pro-rich
Pro-poor
Inequality-increasing
Inequality-reducing
Cross-country dominance results – Asia(O’Donnell, Van Doorslaer et al, WBER, 2007)
Malaysia Thailand Sri Lanka Vietnam Bangladesh Indonesia India Gansu Heilongjiang Nepal
Hong Kong SAR D* D* D* D D* D* D* D* D* D*
Malaysia ns ns D D D* D* D* D* D*
Thailand ns D D D* D* D D* D*
Sri Lanka ns ns D D D D* D*
Vietnam D D* D D* D D*
Bangladesh ns ns ns ns ns
Indonesia ns D ns D
India D ns D
Gansu (China) ns ns
Heilongjiang (China) ns
D= Stochastic Dominance (more pro-poor) ; * = strict D= Stochastic Dominance (more pro-poor) ; * = strict dominance ; ns = not signif 5%dominance ; ns = not signif 5%
Cross-country dominance results – Asia(O’Donnell, Van Doorslaer et al, WBER, 2007)
Very pro-poor: Hong Kong Mildly pro-poor: Malaysia, Thailand, Sri Lanka Mildly pro-rich: Vietnam Very pro-rich: Bangladesh, Indonesia, India, Gansu,
Heilongjiang and Nepal
Two questions
Does public policy fail in low-income countries where poor do not even get their fair (pop) share of public subsidy? Subsidy still narrows rich-poor gap, and poor may benefit more from same subsidy.
Why is public health subsidy more pro-poor in Malaysia, Thailand, Sri Lanka (and Vietnam)?All three emphasize universality, minimize user charges, seek to exempt the poor and spend more.And private sector offering attractive alternative seems to lead to better targeting and redistribution.
Distributions of public and private inpatient care(O’Donnell et al, 2006)
Thailand
Sri Lanka
Malaysia
IndonesiaIndia
Hong KongBangladesh
0.0
0.2
0.4
0.6
0.8
-0.6 -0.4 -0.2 0 0.2 0.4
Concentration index of public care
Con
centr
ati
on index o
f pri
vate
car
e
III. Health inequality
Ultimately of greatest concern Which inequality is inequitable? Application of rank-based measures such as
concentration indices and curves less analogous for health measures, and dependent on measurement scale of health
Welfare basis for income-related health inequality rests on strong assumptions (Bleichrodt & V Doorslaer, 2006)
Two issues here:– Reporting heterogeneity– Decomposition analysis
Heterogeneous health reporting?
Socioeconomic gradients in self-reported health often not found in LDC survey data (e.g. LSMS illness questions)
Suspicion of systematic heterogeneity, i.e. at same true but unobserved health, poor report better health
Implicit health scale cutpoints may vary (eg by SES) Proposed fix: identify any reporting biases (eg by SES)
from the evaluation of health vignettes Then use this information to ‘correct’ health scale
cutpoints And use corrected cutpoints when analyzing individual’s
own health (eg with hierarchical ordered probit model)
Example from Asia(taken from Bago d’Uva et al, 2005)
WHO Multi-Country Survey Data for Indonesia, India (Andrah Pradesh) and 3 Chinese provinces
Ratings for 6 health domains For own health and vignettesReporting effects by income: are poor more likely to report
same condition as very good? Yes in India and China (some domains), not in Indonesia (Fig)
Does heterogeneity correction ‘resurrect’ the SE gradients? Yes, for some domains, and some countries. Not for others.
Reporting heterogeneity evidence: rich-poor disparities in China example
Ratio of top to bottom quintile of prob of reporting very good health (on the vignettes): rich have higher expectations
Gansu, Henan & Shan-dong (China)
0.92
0.94
0.96
0.98
1.00
1.02
mobility cognition pain self usual affect
Effect of heterogeneity correction on rich-poor disparities in China example
Gansu, Henan & Shang-dong (China)
1.00
1.05
1.10
1.15
1.20
mobility cognition pain self usual affect
Ratio of top to bottom quintile of prob of being in very good health (own)
Beyond measurement: decomposition of health inequality
In general, for any linear additive explanatory model
such as :
where y is health, X is a vector of determinants, and is
a disturbance term, one can write (Wagstaff et al,
2003):
And in (time) differences:
i k ki iky x
( / ) /k k kkC x C GC
ttktktk ktk ktkttk GCCCCC /111
Example: Child malnutrition in Vietnam 93-98 Decomposing change in CI of haz (Wagstaff et al, 2003)
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
1993 1998 changeResidual
Commune
Mum educ.
Head educ.
Sanitation
Water
Consumption
Sex
Age
Lessons from EQUITAP: methods
Egalitarian based equity measurement approaches require some reconsideration in low-income, low-coverage contexts
Payments: focus on income protection rather than redistribution
Health care use: examine redistribution through (public) subsidies in absence of need adjustment
Health: inequality decomposition studies using ‘objective’ indicators need to be complemented with studies using self-reported indicators, after purging any reporting biases
In general, descriptive x-country work on measurement and explanation needs to be complemented with well-controlled evaluation work on distributional impact.
Lessons from EQUITAP: findings
Some countries in Asia achieve far better distributional outcomes than others, even at similar income levels
Unfortunately, it is often the larger countries that perform worse (China, India, Indonesia, …)
What explains relative success of the good examples? (Sri Lanka, Thailand, Malaysia)Income and growth helps, but is not sufficientTargeted protection of worst off also helpsInterestingly, a combination of (near) universal public provision, linited user charges and good geographic dispersion of services, coupled with an attractive private alternative sometimes leads to best protection.