Measuring and explaining Inequities in Health: Data needs and Methods Ahmad Hosseinpoor, MD PhD Health Equity Team Evidence and Information for Policy

Measuring and explaining

Inequities in Health:

Data needs and Methods

Ahmad Hosseinpoor, MD PhD

Health Equity Team

Evidence and Information for Policy

World Health Organization

TB and Poverty: Are we doing enough?Bellagio Workshop, 6-8 December 2005

World Health OrganizationEvidence and Information for Policy

Presentation outline

• Health: level vs. inequity

• The required variables for equity analysis

• Availability of data

• Measuring socioeconomic status

• Inequity measures

• Explaining health inequities: Decomposition

• Application to TB control among the poor


Health: level vs. inequity

• The conceptual distinction between:

– The determinants of level of health

– The determinants of inequities in health

The GapThe Level


Steps to implement a monitoring system in inequities in health

• Assessment of data availability

• Collection of additional data, if necessary

• Analysis, interpretation and presentation of the data

• Formulating a policy response to the results, and identifying new data needs

GOOD DATA

ACTION


The required variables for the health equity analysis

• A health measure, including health status, health care, determinants of health, …

• A measure of social position or an equity stratifier such as income or economic status, education, sex, ethnic group or geographic area.


Availability of data (Regarding analysis of health inequities)

Of 192 WHO member states

• Only 39 countries have a sufficient health information system

• 90 countries have only a census, an old household survey, or no data at all.

Bambas, 2005


Achieving standards data for the equity analysis

• Our primary recommendation is to support every country in meeting the minimal required data.

• It is also vital to plan a long-term strategy for collecting the required data for addressing health equity challenges.


Urban/rural

Occupation

Methods: measuring (socio)economic status

Principal components analysis (PCA)

Dichotomous hierarchical ordered probit

(DIHOPIT) model

House characteristics

Car

Fridge

TV

Phone

Economic status


0

0.2

0.4

0.6

0.8

1

1 2 3 4 5

Economic status Quintiles

Skill

ed B

irth

Atte

ndan

ce

Turkey 1998 Phillipines 1998 Indonesia 1997 Bangladesh 1997

Nicaragua 1998 Burkina Faso 1998 Kenya 1998 Guinea 1999


Inequity measures: absolute vs. relative

Pan

de an

d G

watk

in,

1999

0

10

20

30

40

50

60

70

80

90

100

Vietnam Mozambique

Difference (absolute)

Inequality in Infant Mortality

Vietnam Mozambique

IMR in the highest quintile 20 90

IMR in the lowest quintile 50 180

0

0.5

1

1.5

2

2.5

3

Vietnam Mozambique

Ratio (relative)


• Simple measures

– Ratios

– Differences

• More complicated measures

– Concentration index and curve

Inequity measures: simple vs. more complicated


Inequity measures: simple vs. more complicated

Lowest to highest quintiles odds ratio

Inequality in infant morality, by province. Iran, (1985-1999).

Concentration index

Hosseinpoor, 2005


Conclusions on measurement of Health Inequities

• Measuring inequity in a health variable could lead to different results based on the type of equity measure.

• Each equity measure has merits and limitations, and that different measures may be more appropriate for different settings

– Simple ones: to drive policy

– Complicated ones: to use in research settings/ to explaining equity in health

– Inequities should be measured in both absolute and relative terms in order to understand their magnitude.


•An important step for policy making:To unravel and quantify the contributions of health determinants to health inequality

• The inequity in a health variable can be decomposed to the inequities in its determinants. In other words, it demonstrates the contribution of each determinant of a health variable to its inequity.

Explaining health inequities: decomposition

Immunisation

Education

Ethnicity

SES

Others


36.2%

20.9%

13.9%

13.0%

11.9%

4.7%

2.5%

- 0.2%

- 0.8%

- 2.3%

- 5.0% 5.0% 15.0% 25.0% 35.0% 45.0%

Household economic status

M other's illiteracy

Residence in a rural area

R isky birth interval

Hygienic status of toilet

M other's age at child birth

M other's history of stillbirth

C hild sex

M other's history of abortion

Province of residence

Decomposition Analysis – Iran, 2000

Contribution of determinants of infant mortality to its economic inequality


18.6%

5.7%

0.2%

0.1%

-1.5%

-1.0%

77.8%

0.0%

-10.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%

Economic status

Residency (rural/urban)

Educatuon

Marital status

Gender

Occupation

Age

Health insurance

Inequality in seeking needed outpatient care (Iran, 2003)


Summary and application for TB

• Data needs

– Availability of datasets

– Required variables

• Methods

– Measuring socioeconomic status

– Measuring inequities in health

– Explaining inequities in health

• TB datasets

– TB control variables• TB incidence/prevalence

• TB/HIV coinfection

• Treatment success rate

• Drug resistance

– socioeconomic stratifier

• Measuring inequities in TB control variables

• Explaining inequities in TB control variables

Documents

Measuring and explaining Inequities in Health: Data needs and Methods Ahmad Hosseinpoor, MD PhD Health Equity Team Evidence and Information for Policy