health equity in jamaica measurement and results · 2018. 9. 14. · jamaica results: 2007 table...

Health Equity in JamaicaHealth Equity in Jamaica

Measurement and Results

E. Scott and K. Theodore (2010)

Measuring Disparities in Health:

The Tools• The tools used are borrowed from the income distribution

literature and applied to health variables.

• are rank-based measures of inequality. That is, they are based on ranking individuals or households by the measure of living standards and then evaluating the cumulative distribution of the health variable across this

2E. Scott and K. Theodore (2010)

cumulative distribution of the health variable across this ranking.

• Advantage: they take account of the full distribution and so are not dependent upon arbitrary dichotomisation or categorisation.

• The Concentration curve and Concentration Index.

• Are akin to the Lorenz curve and Gini Index

In general, the Concentration curve plots the cumulative % of

health variable against the cumulative % of population ranked

by socioeconomic status

L(s)

cumulativeproportion

1

Must be possible to sum

health variable.

Living standards variable only

needs to provide a ranking.

3cumulative proportion of population

proportion

of ill-health

0 1

ranked by socioeconomic status

Curve above the diagonal �

concentration among the poor

Curve below the diagonal �

concentration among the rich

Curve on the diagonal = equality

Graphing the Ccurve: grouped data

1. Rank indvs/HHs by living standards

variable, into quintiles (or deciles)

2. Obtain for each quintile the mean of

variable of interest and # relevant cases


variable of interest and # relevant cases

3. form cumulative % relevant cases and

corresponding cumulative % of total value

of variable of interest; graph concentration

curve using xy chart

Graphing the Ccurve: micro data

• Grouped data produces only an approx. to

the Ccurve

• Better to compute the cumulative

frequencies from individual level data to get


frequencies from individual level data to get

a picture of the complete distribution

Curve versus IndexConcentration curves can be used to identify

• whether socioeconomic inequality in some health sector variable exists and

• whether it is more pronounced at one point in time than another or in one country than another.


• difficult to compare many concentration curves, particularly when they lie close together

• does not give a measure of the magnitude of inequality that can be compared conveniently across many time periods, countries, regions, etc.

The Concentration index • quantifies the degree of socioeconomic-related

inequality in a health variable (Kakwani, Wagstaff, and van Doorslaer 1997; Wagstaff, van Doorslaer, and Paci 1989).

• It has been used, for example, to measure and to compare the degree of socioeconomic-related inequality


compare the degree of socioeconomic-related inequality in

� child mortality (Wagstaff 2000),

� child immunization (Gwatkin et al. 2003),

� child malnutrition (Wagstaff, van Doorslaer, and Watanabe 2003),

� adult health (van Doorslaer et al. 1997),

� health subsidies (O’Donnell et al. 2007),

� health care utilization (van Doorslaer et al. 2006).

50%

75%

100%

cum

. %

of

hea

lth

va

ria

ble

C = 2 x area between 450 line and concentration curve= A/(A+B)

C>0 (<0) if health variable is disproportionately concentrated on rich (poor)

Concentration index defined


0%

25%

50%

0% 25% 50% 75% 100%

cum. % population ranked by income

cum

. %

of

hea

lth

va

ria

ble

rich (poor)

C=0 if distribution in proportionate

C lies in range (-1,1)

C=1 if richest person has all of the health variableC=-1 of poorest person has all of the health variable

A

B( )h

L p

Concentration index defined

• Note that although a proportionate distribution

(i.e. the concentration curve lying on top of the

45o line) is sufficient for the index to take a

value of zero, it is not necessary.


value of zero, it is not necessary.

• The concentration curve could cross the 45o line

and give a concentration index of zero.

• Hence it is always advisable to examine the

index in conjunction with the curve.

Concentration index formulae

( )1

0

1 2 hC L p dp= − ∫

2 1n

If the living standards variable is discrete:

where n is sample size, h the


1

2 11

n

i i

i

C h rn nµ =

= − −∑where n is sample size, h the

health variable, µ its mean and

r the fractional rank by income

( )2

cov ,C h rµ

=

For computation, this is more convenient:

Concentration index defined• The formulae makes it clear that C reflects the

correlation between health variable and the rank in the living standards distribution In fact, it is the covariance between these two variables scaled by 2 divided by the mean of the health variable.


scaled by 2 divided by the mean of the health variable.

• Note that C depends only on the relationship between the health variable and the rank of the living standards variable and not on the variation in the living standards variable itself. A change in the degree of income inequality need not affect the concentration index measure of income-related health inequality.

Properties of the concentration index

• Strictly, requires ratio scaled, non-negative

variable

• Invariant to multiplication by scalar

• But not to any linear transformation

• So, not appropriate for interval scaled


• So, not appropriate for interval scaled

variable with arbitrary mean

• This can be problematic for measures of

health that are often ordinal

Interpreting the concentration index

• How “bad” is a C of 0.10?

• Koolman & van Doorslaer (2004) 75C = % of health variable that must be (linearly) transferred from richer to poorer half of pop. to arrive at distribution with a C of zero


of pop. to arrive at distribution with a C of zero

– But this ensures equality of health predicted by income rank, and not equality per se

Computing C with grouped data

)(...)()( 1123321221 −− −++−+−= TTTT LpLpLpLpLpLpC

• In the formula, pt is the cumulative

percentage of the sample ranked by

economic status in group t, and L is the


economic status in group t, and Lt is the

corresponding concentration curve ordinate

• C is the sum of the differences of their cross

products from adjacent groups

Estimating C from micro data• Use covariance formula C=2cov(h,r)/µ

– Weights applied in computation of mean, covar and rank

• Equivalently, use regression

22 ih

rσ α β ε

= + +


– OLS estimate of β is the estimate of C

22 ir i i

hrσ α β ε

µ

= + +

Standardization• Seek a more refined description of the relationship between

health and SES

• Want to examine socioeconomic-related inequality in health conditional on other factors, e.g. age/sex

• Standardization necessary in case where these factors are correlated with both health and SES

• Direct standardization � distribution if all SES groups had same age/sex structure


same age/sex structure

• Indirect standardization � corrects distribution by comparing it with that expected given actual age/sex

• Direct standardization requires grouping

• Both methods can be implemented by regression

• Can include other (control) variables to reduce bias in the estimated effects of the confounding variables (age/sex) on health

Standardization

• for health status/morbidity variables we

standardize by age/sex groups

• for health care utilization we standardize for


• for health care utilization we standardize for

need which include demographic and health

status/morbidity variables

Direct standardization

Group (g) specific regression:

i g jg ji kg ki i

j k

y x zα β γ ε= + + +∑ ∑

Standardized health:x sample means


ˆˆ ˆˆ ˆDS DS

i g g jg j k kg

j k

y y x zα β γ= = + +∑ ∑j

x sample means

kgz group-specific

means

Immediately gives standardized distribution of health

across (e.g., income) groups

Indirect standardization

ki j ji ki i

j k

y x zα β γ ε= + + +∑ ∑ yi – health, xji – age/sex,

zki – control vbl. e.g. education

Predicted values from:

ˆˆ ˆˆ Xy x zα β γ= + +∑ ∑ ˆˆ ˆ, ,α β γ are OLS estimates


ˆˆ ˆˆ X

i j ji k k

j k

y x zα β γ= + +∑ ∑ ˆˆ ˆ, ,α β γ are OLS estimates

kz are sample means

Standardized health:

ˆ ˆIS X

i i iy y y y= − +Sample mean is added to ensure

standardized = actual mean

Standardization of the concentration

index

• Can use either method of standardization to

compute the C index for the standardized

distribution

• If want to standardized for the total correlation


• If want to standardized for the total correlation

with demographic confounding variables (x),

then can do in one-step

• OLS estimate of β2 is indirectly standardized

concentration index 2

2 22 ir i j ji i

j

hr xσ α β δ υ

µ

= + + +

∑

Equity in health care

• Policies are usually geared towards an equitable distribution of health care

• In health care, most attention given to Horizontal Equity “equal treament for equal medical need irrespective of income, race, etc”

• Specifying a vertical equity norm


• Specifying a vertical equity norm

– assume that, on average, differential utilisation across different levels of need is appropriate

• Given this, assess whether there is equal utilisation for the same level of need

• Methods to measure and explain horizontal inequity in health care utilisation

(1) Identifying HI through the need-

standardized distribution of health care• Care is unequally distributed by income

• But so is need

• To assess whether care is equitably distributed:

• Either compare actual distribution of care (by

income) with distribution of need

22

income) with distribution of need

• Or assess (in)equality in the need-

standardized distribution of care

• Income-related distribution of:

• Actual use describes inequality

• Need-standardized use describes inequity

(2) Measuring HI through

DecompositionFor any linear model:

the concentration index for y can be written as:

( )β µ γ µ µ= + +∑ ∑( / ) / /C x C z C GC

α β γ ε= + + +∑ ∑i j ji k ki ij k

y x z

23

The index of horizontal inequity (HI) can be obtained

directly from this decomposition:

That is, inequity is total inequality minus need-related

inequality

( ) εβ µ γ µ µ= + +∑ ∑( / ) / /j j j k k kj kC x C z C GC

( )/jj j j

HI C x Cβ µ∑= −

(2) Explaining HI through

DecompositionCan decompose total income-related inequality in

observed use into:

(a) “acceptable”, or need-induced, inequality

(b) inequity, or non-need related inequality, due to


(b) inequity, or non-need related inequality, due to

i. direct contribution of income

ii. contribution of other, non-need variables (e.g.

education, health insurance, location)

iii. contribution of residuals (unexplained inequality)

HI = C - (a) = (i) + (ii) + (iii)

More Details

• “Analyzing Health Equity Using Household

Survey Data: A Guide to Techniques and

their Implementation” 2008. Owen


their Implementation” 2008. Owen

O’Donnell, Eddy van Doorslaer, Adam

Wagstaff and Magnus Lindelow. The World

Bank, Washington DC.

Jamaica Data

• SLC 2007 & 2004

• Ranking variable: adult equivalent per

capita household consumption


capita household consumption

• Sample of individuals aged 18+

• Health Status: P(ill/injured) & Duration

• Utilization: P(HPvisit) & #HPvisit,

P(hospitalized) & Days hospitalized.

Jamaica DataStandardizing variables

• Age-gender categories: male and female dummy variables for age categories 18-34, 35-44, 45-64, 65-74, 75+. Reference category = male 18-34 years.

• Self-assessed health (SAH): dummy variables for very good, good, fair, poor, very poor. Reference category =


good, good, fair, poor, very poor. Reference category = very good

• Activity/physical limitation: dummy variables for moderate, severe, none. Reference category = no limitation

• Chronic disease: dummy variable for the presence of a chronic problem, defined here as illness of more than 4 weeks duration.

Jamaica Data

Control (non-standardizing) variables include

dummies for

• education

• economic activity status


• economic activity status

• urban-rural location

• marital status

• private insurance coverage

• household size.

Jamaica Results: 2007Table A.2: Quintile Distributions of Actual and Standardized Illness and Duration:

Jamaica 2007

Injured or IllInjured or Ill

(stndrdized)Days ill

Days ill

(stndrdized)

Quintiles of

adult eqv


adult eqv

consumption

1 0.1706 0.1638 9.7192 9.3202

2 0.1582 0.1605 7.4138 6.9792

3 0.1491 0.1529 5.9726 5.9985

4 0.1504 0.1562 6.1222 6.5100

5 0.1493 0.1547 3.4921 3.8157

Total 0.1555 0.1575 6.5193 6.4924

CI / SCI -0.0255 -0.0119 -0.1815 -0.1535

Jamaica Results: 2007Concentration Curves Injured or Ill (actual and standardized)

60

70

80

90

100C

um

ula

tive %

Outc

om

e


0

10

20

30

40

50

60

0 2 4 6 8 10

Population Consumption Deciles

Cum

ula

tive %

Outc

om

e

Inj/Ill (S)

Equality

Inj/Ill

Jamaica Results: 2007Concentration Curves Days of Illness (actual and standardized)

70

80

90

100C

um

ula

tive %

Outc

om

e


0

10

20

30

40

50

60

0 2 4 6 8 10


Cum

ula

tive %

Outc

om

e

Equality

Days Ill (S)

Days Ill

Jamaica Results: 2007

Probability

HP Visit

Probability HP

Visit

(stndrdized)

Number of

Visits

Number of Visits

(stndrdized)

Quintiles of

adult eqv


adult eqv

consumption

1 0.0945 0.0734 0.1146 0.0823

2 0.0954 0.0945 0.1323 0.1300

3 0.1028 0.1020 0.1575 0.1572

4 0.1095 0.1246 0.1608 0.1820

5 0.1023 0.1168 0.1420 0.1654

Total 0.1009 0.1024 0.1410 0.1433

CI / HIWV 0.0294 0.1000 0.0468 0.1236

Jamaica Results: 2007Concentration Curve HP Visit (actual and standardized)

80

100

Cum

ula

tive %

Outc

om

e


0

20

40

60

0 2 4 6 8 10


Cum

ula

tive %

Outc

om

e

HPvisit (S)

Equality

HPvisit

Jamaica Results: 2007Concentration Curve Number Visits to HP (actual and

standardized)

80

100


0

20

40

60

0 2 4 6 8 10


Cum

ula

tive %

Outc

om

e

# visits to GP

# visits to GP (S)

Equality


P(Hospitalized)P(Hospitalized)

(standardized)

Days

Hospital

Days Hospital

(standardized)

Quintiles of

adult eqv


adult eqv

consumption

1 0.0041 0.0033 0.0199 0.0160

2 0.0049 0.0047 0.0248 0.0241

3 0.0049 0.0048 0.0320 0.0302

4 0.0079 0.0083 0.0363 0.0327

5 0.0091 0.0092 0.0739 0.0777

Total 0.0062 0.0061 0.0374 0.0364

CI / HIWV 0.1803 0.2253 0.2842 0.3340


Concentration Curves Hospitalization (actual and standardized)

70

80

90

100


0

10

20

30

40

50

60

70

0 2 4 6 8 10


Cum

ulative Pct.

Hospitalization (P) (S)

Equality

Hospitalization (P)


Concentration Curves Nights Hospitalized (actual and standardized)

70

80

90

100


0

10

20

30

40

50

60

70

0 2 4 6 8 10


Cum

ula

tive P

ct.

Equality

# Nights Hospitalized

# Nights Hospitalized (S)

2004-2007

Health Status

Prob(Injured or Ill)Days Ill

2004 2007 2004 2007


2004 2007 2004 2007

Concentration

index, CI0.0244 -0.0255 -0.0154 -0.1815

Standardized CI 0.0161 -0.0119 -0.0267 -0.1535

2004-2007

Utilization: Visits

Prob(GP Visit) Number GP Visits


2004 2007 2004 2007

Concentration index, CI 0.0368 0.0294 0.0355 0.0468

Standardized CI (HIWV) 0.0691 0.1000 0.0691 0.1236

Inequity* 0.0696 0.0941 0.0743 0.1207

2004-2007

Utilization: Hospitalization

Prob(Hospitalized) Days Hospitalized


2004 2007 2004 2007

Concentration index, CI 0.0812 0.1803 0.3326 0.2842

Standardized CI (HIWV) 0.1091 0.2253 0.2877 0.3340

Inequity* 0.1427 0.1834 0.2510 0.2227

“Hefty Price for Free Health Care”

• Jamaica Gleaner, April 10, 2010

• Revenue foregone: J$4b (2 years)


• Revenue foregone: J$4b (2 years)

• Increased utilization 16-23%

• long wait for treatment

• unavailability of several prescription drugs

health equity in jamaica measurement and results · 2018. 9. 14. · jamaica results: 2007 table...

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