2016 Ekokes Sesi 12 HW Health Equity
Health Equity
Heni Wahyuni
Universitas Gadjah Mada
• Why measuring equity?
– The concept of equity in health and health care - Data
requirements for health equity analyses
• How to measure equity?
– Concentration index and concentration curve
– Achievement index
• How to identify drivers of inequity?
– Inequity in health care delivery – decomposition of
concentration index
– Inequality versus inequity
How to identify drivers of inequity? [continued]
• Decomposition of concentration index
• How to explain poor-rich gaps in outcomes?
– Oaxaca decomposition
• How to measure the degree of financial
protection in health?
– Catastrophic payments
– Impoverishing payments
Health equity and financial protection
• Health equity
– It’s not just population averages health status that
matter—gaps between the poor and the better off persist
too
– Differences in constraints between the poor and the better
off (lower income, higher time costs, less access to health
insurance, living condition), rather than differences in
preferences
• Financial protection
– The poor in developing countries continue to increase a
large share of incomes through out-of-pocket payments
– They’re highly uncertain and potentially very large,
threatening households’ living standards, especially poor
and near-poor ones
Measurement
• Measuring health equity and financial
protection allows us to:
– Monitor trends over time
– Compare countries—benchmarking or compare
regions within a country
– Evaluate the impacts of programs
Focal variables, research questions,
and tools
•
•
•
•
Health outcomes
Health care utilization
Subsidies
Payments make for health care
Questions can be answered by health
equity analysis
• Snapshots. Do inequalities between the poor and better-off exist? How
large are they?
• Movies. Are inequalities larger now than in the 1990s?
• Cross-country comparisons. Is inequality in developing countries larger
than in developed countries?
• 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 Indonesia than Malaysia explained by greater income
inequality, given differences in health systems?
• Program impact on inequalities. To what extent is inequality in child
survival reduced by an intervention such as an immunisation program,
expanded health insurance cover or improved water supply?
Questions can be answered by
financial protection analysis
• Who pays for health care?
• Are health care payments progressive?
• Do they lead to a more equitable distribution of
disposable income?
• Who really benefits from health sector subsidies?
• What is the incidence of catastrophic and
impoverishing health expenditures?
• Who is most at risk of incurring catastrophic
payments?
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 represent
inequity.
• Drawing conclusions about health equity involves
consideration of the causes of health inequalities.
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.
The basic idea
• The poor typically lag behind the better off in
terms of health outcomes and utilization of
health services
• Policymakers would like to track progress – is the
gap narrowing? – and see how their country
compares to other countries, or region compares
to other region within the country
• Data are often presented in terms of economic or
socioeconomic groups
U5MR per 1000 live births
In which country are child deaths
distributed most unequally?
300
Poorest "quintile"
250
2nd poorest
"quintile
200
Middle "quintile"
150
2nd richest
"quintile"
100
50
Richest "quintile"
0
India
Mali
Rate ratios can be used: but don’t consider how skewed the distribution is in the
middle quintiles
Comparison made difficult by differences in average levels
How to measure health disparities?
• Borrow rank-dependent measures—Lorenz
curve and Gini Index—and their bivariate
extensions—concentration curve and
index—from income distribution literature
and apply to socioeconomic-related
inequality in health variables
Cumulative % of illness
Illness concentration curve
Here inequality
disfavors the poor:
they bear a greater
share of illness
than their share in
the population
The further the CC
is from the line of
equality, the
greater the
inequality!
75% of
disease
burden
Poorest 50% of population
Cumulative % population,
ranked in ascending order of income, wealth, etc.
Comparing too many concentration
curves is bad for your eyes!
100%
Equality
Brazil
Cote d'Ivoire
Ghana
Nepal
Nicaragua
Pakistan
Cebu
S Africa
Vietnam
cumul %
under-5 deaths
80%
60%
40%
20%
Brazil is most unequal,
but how do the rest
compare?
0%
0%
20%
40%
60%
80%
cumul % live births,
ranked by equiv consumption
100%
Cumulative proportion of illness
The concentration index
is a useful tie-breaker
Concentration index (CI) = 2 x
shaded area
75% of
disease
burden
CI lies in range (-1,1)
CI < 0 because variable
is “concentrated” among the
poor
Poorest 50% of population
Cumulative proportion of illness
The case where inequalities in illness
favor the poor
Concentration index (=2 x
shaded area, as before) is
positive in this case because
variable is “concentrated”
among the better off
25% of
disease
burden
Poorest 50% of population
Here inequality
favors the poor:
they bear a smaller
share of illness
than their share in
the population
Beware!
A negative CI doesn’t necessarily imply poor
outcomes for the poor. It depends on whether
the health variable being analyzed is a “good”
outcome or a “bad” outcome.
C and 95% conf interval
Nicaragua 1983-88
19
Brazil (NE & SE) 198792
Concentration indices for U5MR
Phillipines (Cebu)
1981-91
0.1
South Africa 1985-89
0.0
Nepal 1985-96
-0.1
Cote d’Ivoire 1978-89
-0.2
Ghana 1978-89
-0.3
Pakistan 1981-90
-0.4
-0.5
Vietnam 1982-93
Where to go from here?
• Analyses of equity in health requires data on
– Health
• Infant mortality, stunting/wasting, self-assessed health
• Chronic conditions -> reporting bias!
• Measured hypertension, grip strength, blood tests
– Socioeconomic status
• Need to be able to rank people from poor to reach
• Consumption, expenditure, wealth index
Financial Protection
Catastrophic Health Expenditure
21
Out-of-pocket spending on health
Out of Pocket Payments as share of Total Health Spending for Selected
Countries, 2008
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Source: WHO, National Health Accounts data
To what extent does the health system protect people from the
(potentially devastating) effect of out-of-pocket payments?
Groupings of countries by dominant
source of health finance
100%
Tax-financed
Denmark
75%
Finland
Sweden
Taxes as % total
Ireland
UK
Malaysia
Portugal
50%
Spain
Thailand
Hong Kong
Brazil
SHI
Sri Lanka
Kyrgyz Rep.
USA
Philippines
Italy
Japan
Switzerland
25%
Bangladesh
India (Punjab)
Indonesia
Nepal
Mexico
China
Germany
Korea
Private
Taiwan
0%
0%
Netherlands
France
25%
50%
SHI as % total
75%
100%
The basic idea (cont’d)
• Out-of-pocket expenditure (OOP) on medical care
is considered involuntary
• OOP displaces resources available for other goods
and services. It enables households to restore
well-being, not increase it
• Measures of financial protection relate OOP to a
threshold
– Classify spending as “catastrophic” if it exceeds a
certain fraction of household pre-payment income or
consumption
– Classify spending as “impoverishing” if it’s so large it
pushes households below the poverty line
What’s ‘catastrophic’ spending?
• Measure whether, and by how much, health
spending exceeds a defined threshold (e.g.
10%, 15%, 25%, 40%) of pre-payment
income/consumption
• Can define threshold as share of:
– Total consumption, or
– Non-food (i.e. discretionary) consumption. This
2nd approach can deduct either:
• Actual food consumption, or
• An estimate of the amount the household ought to
have spent on food (WHO does this—it can lead to
negative non-food consumption!)
Payments as share of expenditure
Measure of catastrophic health expenditures
(1) Catastrophic
payment
headcount: % of
households
whose health
spending
exceeds the
threshold (z)
Catastrophic
payment gap
(MPO):
Mean of the
Total catastrophic
overshoot O
Proportion H exceeding
threshold i.e. catastrophic
headcount
z
0%
100%
Cumulative % of households, ranked by decreasing payment
(2) Catastrophic
payment gap
(MPO): Average %
by which health
spending exceeds
the threshold,
among those with
catastrophic
spending
Incidence of catastrophic payments is higher
among the better-off in low-income countries
Concentration index for the catastrophic payment headcount
defined as OOP > 10% of total expenditure
Distribution-sensitive
measures of catastrophic payments
•
•
•
•
•
•
•
Given the severity of their budget constraints, the very
poor are less likely to incur catastrophic payments in
low-income countries
When incurred, catastrophic health expenditures may
have a larger impact on the welfare of the very poor
Take into account by weighting catastrophic payments
in inverse relation to position in the income distribution
Compute rank-weighted measures of catastrophic
payments
Let CE be the concentration index for the indicator of
catastrophic payments, E. CE >0 indicates the better-off
are more likely to incur catastrophic payments
Rank-weighted headcount: HW=H(1- CE), HW0
Rank-weighted overshoot: OW=O(1- Co), HW0
Fig 4: Rank-weighted catastrophic impact of OOPs
(OOP>25% of non-food)
18%
Viet
Rank-weighted cat headcount (
16%
14%
Chin
12%
10%
Ban
Nep
Indi
8%
6%
Kor
4%
HK
Taiw
Thai
2%
0%
0%
20%
40%
PhilSLK
Indo
60%
OOP as % of total finance
80%
100%
Catastrophic payments don’t get at the
degree of economic hardship caused
1200
1000
800
600
Medical spending
Non-medical spending
400
Poverty line
200
0
Richer household Poorer household
A simple example
• Depending on whether
we include OOP in the
consumption aggregate:
– We get 1 more
household in poverty,
and
– The poverty gap rises by
an amount equal to the
poorer household’s
shortfall from the
poverty line
Heni Wahyuni
Universitas Gadjah Mada
• Why measuring equity?
– The concept of equity in health and health care - Data
requirements for health equity analyses
• How to measure equity?
– Concentration index and concentration curve
– Achievement index
• How to identify drivers of inequity?
– Inequity in health care delivery – decomposition of
concentration index
– Inequality versus inequity
How to identify drivers of inequity? [continued]
• Decomposition of concentration index
• How to explain poor-rich gaps in outcomes?
– Oaxaca decomposition
• How to measure the degree of financial
protection in health?
– Catastrophic payments
– Impoverishing payments
Health equity and financial protection
• Health equity
– It’s not just population averages health status that
matter—gaps between the poor and the better off persist
too
– Differences in constraints between the poor and the better
off (lower income, higher time costs, less access to health
insurance, living condition), rather than differences in
preferences
• Financial protection
– The poor in developing countries continue to increase a
large share of incomes through out-of-pocket payments
– They’re highly uncertain and potentially very large,
threatening households’ living standards, especially poor
and near-poor ones
Measurement
• Measuring health equity and financial
protection allows us to:
– Monitor trends over time
– Compare countries—benchmarking or compare
regions within a country
– Evaluate the impacts of programs
Focal variables, research questions,
and tools
•
•
•
•
Health outcomes
Health care utilization
Subsidies
Payments make for health care
Questions can be answered by health
equity analysis
• Snapshots. Do inequalities between the poor and better-off exist? How
large are they?
• Movies. Are inequalities larger now than in the 1990s?
• Cross-country comparisons. Is inequality in developing countries larger
than in developed countries?
• 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 Indonesia than Malaysia explained by greater income
inequality, given differences in health systems?
• Program impact on inequalities. To what extent is inequality in child
survival reduced by an intervention such as an immunisation program,
expanded health insurance cover or improved water supply?
Questions can be answered by
financial protection analysis
• Who pays for health care?
• Are health care payments progressive?
• Do they lead to a more equitable distribution of
disposable income?
• Who really benefits from health sector subsidies?
• What is the incidence of catastrophic and
impoverishing health expenditures?
• Who is most at risk of incurring catastrophic
payments?
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 represent
inequity.
• Drawing conclusions about health equity involves
consideration of the causes of health inequalities.
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.
The basic idea
• The poor typically lag behind the better off in
terms of health outcomes and utilization of
health services
• Policymakers would like to track progress – is the
gap narrowing? – and see how their country
compares to other countries, or region compares
to other region within the country
• Data are often presented in terms of economic or
socioeconomic groups
U5MR per 1000 live births
In which country are child deaths
distributed most unequally?
300
Poorest "quintile"
250
2nd poorest
"quintile
200
Middle "quintile"
150
2nd richest
"quintile"
100
50
Richest "quintile"
0
India
Mali
Rate ratios can be used: but don’t consider how skewed the distribution is in the
middle quintiles
Comparison made difficult by differences in average levels
How to measure health disparities?
• Borrow rank-dependent measures—Lorenz
curve and Gini Index—and their bivariate
extensions—concentration curve and
index—from income distribution literature
and apply to socioeconomic-related
inequality in health variables
Cumulative % of illness
Illness concentration curve
Here inequality
disfavors the poor:
they bear a greater
share of illness
than their share in
the population
The further the CC
is from the line of
equality, the
greater the
inequality!
75% of
disease
burden
Poorest 50% of population
Cumulative % population,
ranked in ascending order of income, wealth, etc.
Comparing too many concentration
curves is bad for your eyes!
100%
Equality
Brazil
Cote d'Ivoire
Ghana
Nepal
Nicaragua
Pakistan
Cebu
S Africa
Vietnam
cumul %
under-5 deaths
80%
60%
40%
20%
Brazil is most unequal,
but how do the rest
compare?
0%
0%
20%
40%
60%
80%
cumul % live births,
ranked by equiv consumption
100%
Cumulative proportion of illness
The concentration index
is a useful tie-breaker
Concentration index (CI) = 2 x
shaded area
75% of
disease
burden
CI lies in range (-1,1)
CI < 0 because variable
is “concentrated” among the
poor
Poorest 50% of population
Cumulative proportion of illness
The case where inequalities in illness
favor the poor
Concentration index (=2 x
shaded area, as before) is
positive in this case because
variable is “concentrated”
among the better off
25% of
disease
burden
Poorest 50% of population
Here inequality
favors the poor:
they bear a smaller
share of illness
than their share in
the population
Beware!
A negative CI doesn’t necessarily imply poor
outcomes for the poor. It depends on whether
the health variable being analyzed is a “good”
outcome or a “bad” outcome.
C and 95% conf interval
Nicaragua 1983-88
19
Brazil (NE & SE) 198792
Concentration indices for U5MR
Phillipines (Cebu)
1981-91
0.1
South Africa 1985-89
0.0
Nepal 1985-96
-0.1
Cote d’Ivoire 1978-89
-0.2
Ghana 1978-89
-0.3
Pakistan 1981-90
-0.4
-0.5
Vietnam 1982-93
Where to go from here?
• Analyses of equity in health requires data on
– Health
• Infant mortality, stunting/wasting, self-assessed health
• Chronic conditions -> reporting bias!
• Measured hypertension, grip strength, blood tests
– Socioeconomic status
• Need to be able to rank people from poor to reach
• Consumption, expenditure, wealth index
Financial Protection
Catastrophic Health Expenditure
21
Out-of-pocket spending on health
Out of Pocket Payments as share of Total Health Spending for Selected
Countries, 2008
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Source: WHO, National Health Accounts data
To what extent does the health system protect people from the
(potentially devastating) effect of out-of-pocket payments?
Groupings of countries by dominant
source of health finance
100%
Tax-financed
Denmark
75%
Finland
Sweden
Taxes as % total
Ireland
UK
Malaysia
Portugal
50%
Spain
Thailand
Hong Kong
Brazil
SHI
Sri Lanka
Kyrgyz Rep.
USA
Philippines
Italy
Japan
Switzerland
25%
Bangladesh
India (Punjab)
Indonesia
Nepal
Mexico
China
Germany
Korea
Private
Taiwan
0%
0%
Netherlands
France
25%
50%
SHI as % total
75%
100%
The basic idea (cont’d)
• Out-of-pocket expenditure (OOP) on medical care
is considered involuntary
• OOP displaces resources available for other goods
and services. It enables households to restore
well-being, not increase it
• Measures of financial protection relate OOP to a
threshold
– Classify spending as “catastrophic” if it exceeds a
certain fraction of household pre-payment income or
consumption
– Classify spending as “impoverishing” if it’s so large it
pushes households below the poverty line
What’s ‘catastrophic’ spending?
• Measure whether, and by how much, health
spending exceeds a defined threshold (e.g.
10%, 15%, 25%, 40%) of pre-payment
income/consumption
• Can define threshold as share of:
– Total consumption, or
– Non-food (i.e. discretionary) consumption. This
2nd approach can deduct either:
• Actual food consumption, or
• An estimate of the amount the household ought to
have spent on food (WHO does this—it can lead to
negative non-food consumption!)
Payments as share of expenditure
Measure of catastrophic health expenditures
(1) Catastrophic
payment
headcount: % of
households
whose health
spending
exceeds the
threshold (z)
Catastrophic
payment gap
(MPO):
Mean of the
Total catastrophic
overshoot O
Proportion H exceeding
threshold i.e. catastrophic
headcount
z
0%
100%
Cumulative % of households, ranked by decreasing payment
(2) Catastrophic
payment gap
(MPO): Average %
by which health
spending exceeds
the threshold,
among those with
catastrophic
spending
Incidence of catastrophic payments is higher
among the better-off in low-income countries
Concentration index for the catastrophic payment headcount
defined as OOP > 10% of total expenditure
Distribution-sensitive
measures of catastrophic payments
•
•
•
•
•
•
•
Given the severity of their budget constraints, the very
poor are less likely to incur catastrophic payments in
low-income countries
When incurred, catastrophic health expenditures may
have a larger impact on the welfare of the very poor
Take into account by weighting catastrophic payments
in inverse relation to position in the income distribution
Compute rank-weighted measures of catastrophic
payments
Let CE be the concentration index for the indicator of
catastrophic payments, E. CE >0 indicates the better-off
are more likely to incur catastrophic payments
Rank-weighted headcount: HW=H(1- CE), HW0
Rank-weighted overshoot: OW=O(1- Co), HW0
Fig 4: Rank-weighted catastrophic impact of OOPs
(OOP>25% of non-food)
18%
Viet
Rank-weighted cat headcount (
16%
14%
Chin
12%
10%
Ban
Nep
Indi
8%
6%
Kor
4%
HK
Taiw
Thai
2%
0%
0%
20%
40%
PhilSLK
Indo
60%
OOP as % of total finance
80%
100%
Catastrophic payments don’t get at the
degree of economic hardship caused
1200
1000
800
600
Medical spending
Non-medical spending
400
Poverty line
200
0
Richer household Poorer household
A simple example
• Depending on whether
we include OOP in the
consumption aggregate:
– We get 1 more
household in poverty,
and
– The poverty gap rises by
an amount equal to the
poorer household’s
shortfall from the
poverty line