The challenge of measuring need for health care in household surveys – a preliminary analysis for South Africa

F. Meheus, O. Okorafor, D. McIntyre

Inaugural Conference of the African Health Economics and

Policy Association Conference (AFHEA)

Accra, 10th-12th March 2009

Abstract n° PS06/7

SHIELD project: Strategies for Health Insurance for Equity in Less Developed Countries

Mortality and morbidity

• Indicators of health outcome (mortality and/or morbidity) are frequently used in health equity analysis.

• Mortality indicators are for example life expectancy, or overall or disease specific mortality rates.

• Morbidity indicators can be either objective or subjective:

– Objective or observed morbidity indicators are those measures that can be directly observed (e.g. physical examination or laboratory investigations);

– Subjective or self-perceived indicators are assessments of health status/illness/symptoms/functional disability by the individual.

• Morbidity indicators are usually obtained from household surveys.

• A common application of mortality/morbidity measures in health equity analysis is looking at the (socio-) economic gradient of these measures.

Need for health care

• But health equity analysis can also be concerned with the utilization of and payment for health services.

• A common application is to examine the degree of horizontal inequity in utilization of health care services relative to socio-economic status.

– This is based on the principle of "equal treatment for equal need", irrespective of other characteristics such as income, race, place of residence, etc.

– Deviations in the degree to which health care is distributed according to need are measured by an index of horizontal inequity.

• Need for health care is usually estimated using a self-perceived indicator, mainly self-assessed health or self-reported illness.

Self-perceived indicators

• There are different types of self-perceived indicators (SPI) :

– Medical, functional and subjective indicators (Blaxter 1993)

• Medical indicators measure health as a deviation from physiological norms (e.g. presence of certain diseases).

• Functional indicators: measures ill-health as a lack of ability to perform "normal" tasks or roles (e.g. days of restricted activity).

• Subjective indicators: health is defined in terms of the individual's perception of his/her health (e.g. self-assessed health).

• The most frequent indicators are self-assessed health (subjective) and self-reported illness (subjective/medical).

Are self-perceived indicators reliable?Evidence from developed countries

• They have shown to be correlated with actual health; e.g Cutler &

Richardson, 1997; Kaplan & Baron-Epel, 2003.

• They appear to be a good predictor of:

– Mortality and morbidity; e.g. Idler & Benyamini 1997;

– Subsequent use of medical care; e.g. van Doorslaer, Jones,

& Koolman, 2004;

• As such, they have often been used to describe socio-economic inequalities in health

– Various studies have observed an economic gradient in self-assessed health status; e.g. Smith 1999; Case et al 2002; Currie et al

2004; Kunst et al 1995; van Doorslaer and Koolman 2004. :

• However few evidence from developing countries:

– Greater likelihood of subsequent mortality observed in individuals who perceive their health as poor (Frankenberg & Jones (2004) in Indonesia and Yu et al. (1998) in China);

– A similar relationship was observed between self-assessed health and physical functioning (Zimmer et al 2000, Wu & Rudkin 2000; Rahman & Barsky 2003);

• Use of self-perceived indicators (SPI) remain underutilized in developing countries although included in many household surveys.

Are self-perceived indicators reliable?Evidence from developing countries

Scepticism towards SPI

• Despite being reliable, valid and a cost-effective means of health assessment, SPI are viewed with much scepticism in developing countries

– Especially when assessed in relation to socio-economic status (SES) or other covariates of health and wealth (e.g. age, sex).

• While indicators such as life expectancy or mortality have shown consistent inequalities to the disadvantage of the poor, self-perceived indicators based on self-reported illness have shown counterintuitive findings in developing countries.

• If those with lower SES report less illness given true health, then socio-economic inequality will be underestimated by using self-reported illness.

Previous findings

• Several studies have shown lower "recognition" of illness by

low-income groups than higher income groups (Baker & Van

der Gaag 1993, McIntyre & Gilson 2000, McIntyre et al.

1998, Makinen et al. 2000, Sen 2002)

• Several explanations:

– Poor cannot afford to be ill (Sauerborn et al. 1996);

– Adaptation and scale of reference bias (e.g. Groot 2000)

– Perception of own illness is contingent on social experience

(Sen 1993, 2002)

• Self-assessed health does not seem to be influenced by this.

Objective & approach

• Examine the economic gradient in self-assessed health (SAH) with self-reported illness (SRI).

Provides a useful test of the validity of self-perceived indicators in relation to socio-economic status (SES).

• Approach:

– Use own data from household survey in South Africa where both SAH and SRI are measured.

• Compare degree of inequality of both measures by looking at proportions.

• Examine degree of inequality by calculating concentration index.

Data sources

• Data from South Africa

• Carried out own household survey in 2008 (CASE-survey)

• Comprised all nine South African provinces and is nationally

representative

• Sample size = 4,800 households

Data was collected as part of a larger project (SHIELD –

Strategies for Health Insurance and Equity in less

Developed countries) examining the financing and benefit

incidence in three African countries, with specific focus

on health insurance.

Indicators included in the study

• Self-assessed health (SAH)– Question: "How would you describe your current health status?"

– 5 ordered-response categories:

• (1) Excellent; (2) Good; (3) Average; (4) Poor; (5)Very poor

– Ordinal variable

– Low scores point to good self-assessed health

• Self-reported illness (SRI)– Question: "During the past month, were you/was … ill or

injured"

– 2 possible answers:• (1) yes; (2) No

– Dichotomous variable

Socio-economic status

• We used household per capita consumption expenditure.

– Is superior to income, since income suffers from

underreporting; it can be seasonally variable and does not

reflect long-term income or permanent wealth (Makinen et al.

2000).

• Households are categorised into quintiles

– 5 groups of equal size.

Economic gradient (i)

IndicatorHousehold expenditure quintiles means

Total Poorest 2 3 4 Richest

Illness or injury (4 weeks)

0,286 0,336 0,280 0,277 0,292 0,245

Poor SAH 0,174 0,217 0,251 0,195 0,153 0,057

• Responses from SAH were recoded into a dichotomous

variable (1 = bad health when responses very poor & poor; 0

= good health for other responses).

Economic gradient (ii)

Self-assessed health Self-reported illness

• Both show downward trend, i.e. respondents in highest income

groups report bettere health and fewer illness. But distribution for

SRI is more erratic.

0,00

0,10

0,20

0,30

0,40

Q1 Q2 Q3 Q4 Q5

0,00

0,10

0,20

0,30

0,40

Q1 Q2 Q3 Q4 Q5

Concentration index

Indicator

Concentration index

Normal Standardized

Illness or injury (4 weeks) -0,0475 -0,066

Very poor SAH -0,0775 -0,080

Poor SAH -0,1912 -0,231

• CIs reveal inequality gradients disadvantageous to the poor for

both SAH and SRI.

• But magnitude of inequality is much higher for SAH than SRI.

Race

Self-assessed health Self-reported illness

0,00

0,05

0,10

0,15

0,20

0,25

0,30

African Coloured Indian White

0,00

0,10

0,20

0,30

0,40

0,50

African Coloured Indian White

• SAH was recoded (1 = poor health)

• SAH in line with expected distribution; SRI counterintuitive

– "White" report as many illness as "Africans"

Gender

Self-assessed health Self-reported illness

0,00

0,10

0,20

0,30

Male Female0,00

0,10

0,20

0,30

0,40

Male Female

Age and location

0,00

0,10

0,20

0,30

0,40

16-24 25-44 45-64 ≥ 65

0,00

0,10

0,20

0,30

0,40

Urban Rural

Self-assessed health Self-reported illness

Age

Location

0,00

0,10

0,20

0,30

0,40

16-24 25-44 45-64 ≥ 65

0,00

0,10

0,20

0,30

0,40

Urban Rural

Limitations

• No age-sex standardization done.

• Dichotomization of SAH variable arbitrary; alternatives:

– Use scoring algorithm (e.g. Canadian HUI)

– Assume functional form for the distribution, e.g. standard

lognormal distribution (Wagstaff & van Doorslaer 1994)

– Use interval regression methods

• Need more findings from other African countries; findings for

Ghana and Tanzania soon available.

Lessons

• Most evidence from developed countries

– Urgently need more evidence from developing countries.

• Given findings from other studies, and what we observed,

self-reported illness should be avoided in inequality analysis

or as a proxy for need.

• Household surveys in Africa should include both SAH and

SRI.

SHIELD partners

• This study is part of the SHIELD project; funded by the European Union DG Research Sixth Framework Programme/INCODEV

• For more informaton visit: http://web.uct.ac.za/depts/heu/SHIELD/about/about.htm

Thank you

Contributors

• Filip MeheusInstitute of Tropical Medicine, Antwerp (Belgium) &

Royal Tropical Institute, Amsterdam (The Netherlands)

fmeheus@itg.be

• Okore Okorafor

Health Economics Unit, University of Cape Town, South Africa

• Prof. Di McIntyre

Health Economics Unit, University of Cape Town, South Africa