1. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Analyzing Health Equity Using Household Survey Data Lecture 2 Data for Health Equity Analysis: Requirements, Sources and Sample Design

2. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Data requirements: Health outcomes Murray and Chen (1992) classification of morbidity measures Self-Perceived Symptoms and impairmentsOccurrence of illness or specific symptoms during a defined time period Functional disabilityAssessment of ability to carry out specific functions and tasks, or restrictions on normal activities (activities of daily living, e.g., dressing, preparing meals, or performing physical movement) HandicapSelf-perceived functional disability within a specifically defined context Observed Physical and vital signsAspects of disease or pathology that can be detected by physical examination (e.g., blood pressure and lung capacity) Physiological and pathophysiological indicators Measures based on laboratory examinations (e.g., blood, urine, feces, and other bodily fluids), body measurements (anthropometry) Physical testsDemonstrated ability to perform specific functions, both physical and mental (e.g., running, squatting, blowing up a balloon, or performing an intellectual task) Clinical diagnosisAssessment of health status by a trained health professional based on an examination and possibly specific tests

3. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Data requirements: Health-related behavior Health care utilization Payments for health care Smoking, drinking, diet Sexual practices Household-level behavior (cooking, sanititation, etc.)

4. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Data requirements: Living standards or socioeconomic status Living standards: –Direct approaches e.g., income, expenditure Cardinal – can compare magnitudes of differences –Proxy measures e.g., assets index Ordinal – provide rankings Socioeconomic status: –Education (level or years) –Occupational class

5. Data requirements for health equity analysis Health UtilizationLiving standards (ordinal) Living standards (cardinal) Unit subsidies User payments Back- ground vbls Health inequality Equity in utilization Multivariate analysis Or Benefit-incidence analysis ( ) Health financing Progressivity Catastrophic payments Poverty impact

6. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Data sources Household surveys and non-routine data –Large-scale, multi-purpose surveys e.g., LSMS (World Bank), MICS (UNICEF) –Health / demographic surveys e.g., DHS (ORC Macro), WHS (WHO) –Household budget surveys –Facility-based surveys (exit polls) Routine data –Administrative data from HIS, vital registration, etc. –Census data

7. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Pros and cons of household survey data ExamplesAdvantagesDisadvantages Living Standards Measurement Study (LSMS), Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), World Health Surveys (WHS)  Data are representative for a specific population (often nationally), as well as for subpopulations  Many surveys have rich data on health, living standards, and other complementary variables  Surveys are often conducted on a regular basis, sometimes following households over time  Sampling and non- sampling errors can be important  Survey may not be representative of small subpopulations of interest

8. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Pros and cons of user exit poll data ExamplesAdvantagesDisadvantages Ad hoc surveys, often linked to facility surveys  Cost of implementation is relatively low  Detailed information that can be related to provider characteristics is provided about users of health services  Data on payments and other characteristics of visit are more likely to be accurate  Exit polls provide no information about nonusers  Data often contain limited information about household and socioeconomic characteristics  Survey responses may be biased from “courtesy” to providers or fear of repercussions

9. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Pros and cons of administrative data ExamplesAdvantagesDisadvantages HIS, vital registration, national surveillance system, sentinel site surveillance  Data are readily available  Data may be of poor quality  Data may not be representative for the population as a whole  Data contain limited complementary information, e.g., about living standards

10. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Pros and cons of census data ExamplesAdvantagesDisadvantages Implemented on a national scale in many countries  Data cover the entire target population (or nearly so)  Data contain only limited data on health  Data collection is irregular  Data contain limited complementary information, e.g., about living standards

11. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Sample design and the analysis of survey data Multi-purpose and health surveys often have a complex design Stratification – separate sampling from population sub-groups e.g., urban / rural Cluster sampling – clusters of observations not sampled independently e.g., villages Unequal selection probabilities – e.g. oversampling of the poor, uninsured

12. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Standard stratified sampling Population categorised by relatively few strata e.g. urban/rural, regions Separate random sample of pre-defined size selected from each strata Sample strata proportions need not correspond to population proportions  sample weights (separate issue)

13. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Stratification and descriptive analysis If pop. mean differs by strata, stratification reduces sample variance of its estimator Standard errors for means and other descriptive stats. should be adjusted down If regression used to estimate conditional means, then adjust the standard errors

14. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Cluster sampling Two (or more) stage sampling process 1.Clusters sampled from pop./strata 2.Households sampled from clusters Observations are not independent within clusters and likely correlated through unobservables Standard errors of parameter estimates should be adjusted to take account of the within cluster correlation

15. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Sample weights Stratification, over-sampling, non-response and attrition can all lead to a sample that is not representative of the population Sample weights are the inverse of the probability that an observation is a sample member Sample weights must be applied to get unbiased estimates of population means, etc. and correct standard errors Should also be applied in “descriptive regressions”

16. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Stata computation Set the sample design parameters svyset locality [pw=wgt], strata(strata) Estimate the mean and get the correct SE svy: mean vacc, over(quint)

17. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Child Immunization Rates by Household Consumption Quintile, Mozambique 1997 QuintileMeans.e.Deff poorest0.5450.0141.000 20.6590.0141.000 30.7080.0131.000 40.8050.0111.000 richest0.8920.0081.000 Total0.7280.0061.000 n6447 No. strata1 No. PSUs6447 QuintileMeans.e.Deff poorest0.5310.0171.694 20.6290.0192.196 30.6210.0192.117 40.7080.0243.416 richest0.8430.0141.488 Total0.6540.0092.138 n 6447 No. strata 1 No. PSUs 6447 No allowance for sample design With sample weights

18. “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity Child Immunization Rates by Household Consumption Quintile, Mozambique 1997 QuintileMeans.e.Deff poorest0.5310.0171.630 20.6290.0192.164 30.6210.0192.075 40.7080.0243.366 richest0.8430.0141.456 Total0.6540.0081.942 n 6447 No. strata 21 No. PSUs 6447 QuintileMeans.e.Deff poorest0.5310.0284.469 20.6290.0336.577 30.6210.0264.014 40.7080.0295.092 richest0.8430.0182.485 Total0.6540.0178.313 n 6447 No. strata 21 No. PSUs 273 With stratification With stratification and clustering