Lecture 1:
Inequalities and inequities in health
and health care utilization
Concentration curve and
concentration index
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.
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.
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
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
•
Data are often presented in terms of economic or
socioeconomic groups
In which country are child deaths
distributed most unequally?
0
Rate ratios can be used: but don’t consider how skewed the distribution is in the middle quintiles
In which country is child stunting distributed most unequally? Which country has made the largest
How to measure health disparities?
• Measures of dispersion like the variance, coefficient of variation, or Theil’s entropy inform of total, not
socioeconomic-related health inequality
• Relative risk ratios, e.g. mortality in top to bottom occupation class, do not take account of group sizes
• Rate ratios of top to bottom quintiles do not reflect the complete distribution
• 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
Illness concentration curve
Poorest 50% of population 75% of
Cumulative % population,
ranked in ascending order of income, wealth, etc.
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
Comparing too many concentration
curves is bad for your eyes!
0%
cumul % live births, ranked by equiv consumption
c
The concentration index
is a useful tie-breaker
Poorest 50% of population 75% of
Concentration index (CI) = 2 x shaded area
CI lies in range (-1,1)
CI < 0 because variable
The case where inequalities in illness
favor the poor
Poorest 50% of population 25% of
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
0%
cum. % population ranked by income
cu
between 450 line and concentration
curve
= 2A=2(0.5 - B) = 1 - 2B
C>0 (<0) if health variable is
disproportionately concentrated on rich (poor)
C=0 if distribution is proportionate (unless crossing diagonal) C lies in range (-1,1)
C=1 if richest person has all of the health variable
C=-1 of poorest person has all of the health variable
Concentration index defined
A
B
( )
h
Some formulae for the concentration index
If the living standards variable is discrete:
where
n
is sample size,
h
the
health variable,
μ
its mean and
r
the fractional rank by income
(
)
Properties of the concentration index
• depend on the measurement characteristics of the health variable of interest.
• Strictly, requires ratio scaled, non-negative variable • Invariant to multiplication by scalar
• But not to any linear transformation
• So, not appropriate for interval scaled variable with arbitrary mean
• This can be problematic for measures of health that are often ordinal
• If variable is dichotomous, C lies in the interval (μ-1, 1-μ) (Wagstaff, 2005):
– So interval shrinks as mean rises.
Erreygers’ (2009) has normalized the
concentration index for bounded variables
•
This satisfies the following axioms:
– Level independence: E(h*)=E(h), h*=k+h
– Cardinal consistency: E(h*)=E(h), h*=k+gH, k>0, g>0
Total inequality in health and
socioeconomic-related health inequality
0%
cum % of pop, ranked by health or income
c
diagonal Lorenz curve Conc curve
By definition, the health Lorenz curve must lie below the concentration curve.
Total inequality in health is larger than
socioeconomic-related health inequality
Gini index of total health inequality
Then
Thus, G = C + R, where R>=0 and measures the outward move from the health concentration curve to the health Lorenz curve, or the re-ranking in moving from the SES to the health distribution
“even if the social class gradient was magically eliminated,
dispersion in health outcomes in the population would remain very much the same”
Smith J, 1999, Healthy bodies and thick wallets”, J Econ Perspectives
µ
Estimating the concentration index from
micro data
• Use “convenient covariance” formula C=2cov(h,r)/μ
– Weights applied in computation of mean, covar and rank
• Equivalently, use “convenient regression”
– Where the fractional rank (r) is calculated as follows if there are weights (w)
– OLS estimate of β is the estimate of the concentration index
Sensitivity of the concentration index to
the living standards measure
• C reflects covariance between health and rank in the living standards distribution
• C will differ across living standards measures if re-ranking of individuals is correlated with health
(Wagstaff & Watanabe, 2003)
2
From OLS estimate of
where is the re-ranking and ∆ =ri r1i − r2i 2
r
σ∆ its variance,
Evidence on sensitivity of concentration
index
Wagstaff & Watanabe (2003) – signif. difference b/w C estimated from consumption and assets index in only 6/19 cases for
underweight and stunting
But Lindelow (2006) finds greater sensitivity in concentration indices for health service utilization in Mozambique
--
--
-! -
-C
Cote d’Ivoire 1978-89
Nepal 1985-96
South Africa 1985-89
Phillipines (Cebu) 1981-91
Nicaragua 1983-88
Brazil (NE & SE) 1987-92
Taking account of the level and the
distribution of health
If there is concern for the level of health, and not only
socioeconomic-related inequality in its distribution, then may want a summary statistic to reflect mean health in addition to this inequality.
Might refer to such a measure as an index of ‘health achievement’.
An index of health achievement can be obtained by taking a weighted average of levels of health, rather than of health shares, as follows:
That is simply the product of the mean and one minus the extended CI. So, for a desirable health variable, increases in the mean may be traded-off
against increases in pro-rich inequality
For a non-desirable health variable, decreases in the mean can be traded-off against increases in its concentration on the poor.
Mean and inequality-weighted
mean of medically attended births
Deliveries by a medically-trained person
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
