Catastrophic Health Payments

1. Household Catastrophic Health Expenditure: A Multi-Country AnalysisKe XuDavid EvansKei KawabataRiadh ZeramdiniJan KlavusChristopher MurrayWorld Health OrganizationGeneva, Switzerland

2. Catastrophic Health Payments • Health expenditure is taken to be catastrophic when a household must reduce its basic expenditure over a period of time to cope with health costs. • No consensus on threshold value: in this study taken to be 40% of a household’s capacity to pay. • Widespread agreement that protecting people from catastrophic payments is a desirable objective of health policy.

3. Catastrophic Health Payments • Not much information available on prevalence of catastrophic expenditures internationally. • What is the extent of catastrophic payments across countries? • What are some characteristics of health systems that are associated with catastrophic payments?

4. Catastrophic Health Payments • Catastrophic expenditure: health expenditure at least 40% of a household’s capacity to pay. • Capacity to pay:Effective income remaining after subsistence needs have been met. • Effective income: Defined as the total consumption expenditure of the household. • Subsistence: Share of food expenditure.

5. Catastrophic Health Payments Poverty line derived as the average food expenditure of households whose food share was in the 45th and 55th range, adjusting for size of household. CTPi = EXPi - SE45-55i EqSize = HholdSize

6. Catastrophic Health Payments • Health expenditure requiring OOP payments included all types of health-related expenses at the time households received the service. • Consultation fees. • Purchase of medications. • Hospital bills. • Reimbursements from health insurance deducted.

7. Catastrophic Health Payments • 59 cross-country surveys: • LSMS. • Household budget surveys. • Household income and expenditure surveys. • Proportion of households facing catastrophic payments varied from less than 0.01% in Czech Republic and Slovakia to 10.5% in Vietnam.

8. Catastrophic Health Payments • Two groups of countries had relatively high rates of catastrophic spending: • Countries in transition, e.g., Azerbaijan, Ukraine, Vietnam, Cambodia. • Latin American countries, e.g., Argentina, Brazil, Colombia, Paraguay, Peru. • Characteristics of health systems positively associated: share of OOP in total, share population poor, share of total health expenditure in GDP.

9. Catastrophic Health Payments • Some qualifications: • Does not take into account distribution of payments within households. • Does not take into account non-financial transfers that may help households survive consequences of catastrophic payments. • OOP spending does not take into account the fact that households may choose not to seek care rather than become impoverished.

10. Cross-Population Comparability and PPPs: Using Indicators of Consumer DurablesAjay TandonAnjelica SousaEmmanuela GakidouChristopher MurrayWorld Health OrganizationGeneva, Switzerland

11. Measuring Economic Status Why measure economic status in a health survey? • Strong linkages between health status and economic status. • Poverty is a determinant of poor health. • Ill-health linked to declines in functional capacity and economic productivity. • Income inequality related to inequalities in health status. • Need information on economic status of respondents to be able to explore these linkages.

12. Measuring Economic Status • DHS, WHS modules on economic status contains a series of indicator variables on ownership of assets, consumer durables, and other economic status indicators. Problems: • How do we estimate economic status of households in any given country using these indicator variables? • How do we do it in a way so as to make the estimates cross-nationally comparable? -

13. Measuring Economic Status • Standard approach based on the construction of a weighted “asset index” derived from observed ownership of indicator variables. • WHO approach slightly different from the standard factor analysis or principal component analysis approaches employed by others [e.g., Filmer and Pritchett (2001)]. • Related more to models developed in psychometrics for educational testing using a series of questions (items).

14. Measuring Economic Status • Assume permanent income is an unobserved latent variable, yi*, which is a function of several covariates such as education, age, etc., plus a random effect. yi* = Xi + ui + ei • Observation mechanism for indicator variable a: yia = 0 if - < yi*  a yia = 1 if a < yi* <  a: Indicator-specific threshold parameter, if yi* greater than a then household is more likely to own asset indicator than not.

15. Car Measuring Economic Status Refrigerator Indicator cut-points Electricity Bicycle Permanent income (latent)

16. Measuring Economic Status • Given information on observed patterns of ownership of indicator variables for a given household -- combined with information on the likelihood of ownership at different levels of the latent variable -- yields estimates of an index measuring permanent income for the household. • High permanent income households will likely be observed to own most of the indicator variables on the ladder, whereas low permanent income households will likely own indicator variables that are on the lower end of the indicator variable ladder.

17. Measuring Economic Status • Item Independence: Can use different sets of items to estimate the latent variable (high correlation across different sets of items). Peru LSMS (2000) Variable Subset #1 Subset #2 Household Income 0.67 0.66 Household Expenditure 0.68 0.67 Household Permanent 0.93 0.93 Income (Full Set)

18. Measuring Economic Status Subset #1 Subset #2 Radio Urban property Iron Blender/food processor Gas stove Color television Refrigerator Company/business Sewing machine Bicycle Record player Telephone (fixed line) Electric fan VCR Washing machine Automobile Vacuum cleaner Telephone (mobile) Personal computer Therma Microwave oven Cable television Knitting machine Clothing dryer

19. Measuring Economic Status • Incorporates Covariates: Survey data often contain variables that are known to be correlated with economic status, e.g., age, education, rural-urban residence, etc.. Allows easy incorporation of this information -- if available -- in estimation. Greece ECHP (1994-1996) Age of household head Employment category of household head Education of household head Household size

20. Measuring Economic Status • Extensions to Multiple Indicators: The model can be extended to include indicators of permanent income that are dichotomous, polytomous, or continuous. • Y1: continuous, related to Y* with error • Y2 : 0/1 , related to Y* with error • Y3:1/2/3 , related to Y* with error • Y* = f(covariates)

21. Measuring Economic Status • Cross-National Comparability: Can estimate cross-national comparable estimates of permanent income on a latent scale. Common scale can be estimated as long as: • there are some indicator variables that are common across countries. • some of the common indicators have similar cut-points across countries (anchors).

22. B C A Car Car Car Measuring Economic Status Refrigerator Refrigerator Refrigerator Electricity Electricity Electricity Bicycle Bicycle Bicycle Permanent income (latent)

23. B C A Car Car Air-Conditioner Car Measuring Economic Status Refrigerator Refrigerator Refrigerator Motorcycle Electricity Electricity Electricity Bicycle Bicycle Bicycle Watch Permanent income (latent)

24. Measuring Economic Status Application to DHS data: • DHS surveys from 32 countries. • 20-32 indicators per country. • 7 indicators used as anchors (tradable goods such as radio, television, etc., plus others such as electricity): • Categorization: some problems with dealing with categories of floor, roof, and wall. • Collapsed some categories that did not significantly differ from each other, and assessed rankings of categories based on economic status.

25. Measuring Economic Status Application to DHS data:

26. Measuring Economic Status Application to DHS data: