Setting the Stage for Equity-Sensitive Monitoring of the Health Millennium Development Goals

1. Setting the Stage for Equity-Sensitive Monitoring of the Health Millennium Development Goals Deborah Balk CUNY Institute for Demographic Research & School of Public Affairs, Baruch College, CUNY

2. A Collaboration with • Enrique Delamonica, UNICEF • Alberto Minujin, UNICEF • Emma Sacks, CIESIN, Columbia University • Adam Storeygard, CIESIN, Columbia University • Meg Wirth, Consultant to Millennium Project Task Force 4 • With Funding from the UN Millennium Project

3. Agenda • Background • Why Equity? Why Maternal Health? Why Africa? • Findings from DHS equity analysis done for the UN Millennium Project • Implications for programming, monitoring and policy

4. Background • Part of larger project for UN Millennium Project Task Force on Child health and Maternal health • Original study examined • data on 20 maternal and child health indicators across 6 social strata • Using both the DHS and MICS data sets • General findings published in the July 2006 Bulletin of WHO— but dataset is large & maternal health warranted specific study • Full tables available online: www.ciesin.columbia.edu/Health_Equity_Tables.pdf

5. Why equity? Isn’t meeting the MDGs sufficient? • Health equity is based on simple notions of fairness, human rights principles, and distributive justice • An equity focus is necessaryto ensure that marginalized groups see the benefit of aggregate gains in healthand feasible using current data Assumption: Differences in health (or in important influences on health) that are systematically associated with being socially disadvantaged (poor, racial/ethnic minority, female, etc.) and which put those in disadvantaged groups at further disadvantage for poor health (Braveman 2006)

6. Disparities do not correspond to political boundaries

7. Assembling the PiecesMultiple classes of social strata Move beyond quintiles of a wealth index to: • ethnicity • sex • region • residence • maternal education • and combinations thereof

8. Most of the statistically significant reductions in U5MR take place among the richest

9. Why Maternal Health? • Maternal mortality has increased in some countries in the past decade: • health system collapse, • increasing poverty among women, • lack of access to skilled care for delivery, • weak national human resource management and lack of resources • A full analysis of inequities across multiple dimensions of maternal health is rarely taken • The nuances of marginalization within poor countries must be highlighted and quantified • The MDG (Goal 4)—the maternal health MDG—does not include an equity dimension

10. What does an equity lens mean for the MDGs? • In practice, an equity focus means that monitoring of the MDGs and designing policies to meet them should ensure that the worst-off groups benefit at the same or greater rate as the better-off groups • Current MDG Goal #4: • By 2015, reduce maternal mortality ratio by ¾. • Equity-sensitive (‘localized’) MDG Goal #4: • By 2015 reduce by ¾ the maternal mortality ratio, ensuring that marginalized groups progress at the same or better rate as the population as a whole • By 2015 improve access to a Skilled Birth Attendants for rural dwellers and for women with no education at the same or better rate than the national average (or than the rest of the population)

11. Why Africa? • Most of sub-Saharan Africa is lagging in maternal health indicators • Yet within country inequity exists and must be tracked

12. Methodology for Equity Analysis • Data source: Demographic Health Survey Data • Three countries: Ghana, Kenya and Ethiopia • Four indicators: • Skilled birth attendant; • Contraceptive prevalence rate; • AIDS knowledge; and • Access to a health facility. • Six social strata: • Poverty status; • Education; • Region; • Ethnicity; and • wealth quintile.

13. Analysis set up • Simple and easily implementable • Data is stratified singly (e.g. by region) • Then stratified simultaneously (e.g. by region and by education) • in order to examine the compounded affect of dual forms of vulnerability. • Run tests of significance • only run on 2-ways; • p values reported in tables • Visualization • Charts • Maps • Further analyses could include multivariate models.

14. Single Stratification CPR-MM by Quintile - Kenya Utilization of modern contraception is the same for women in all but the richest quintile

15. Single Stratification Birth Assistant by Quintile - Kenya The presence of an assistant at birth is different for each level of wealth (steep gradient)

16. Single Stratification:Comparison Across Indicators Red marks indicate mean value, blue marks represent the values of specific groups.

19. Simultaneous Stratification • Why simultaneously stratify? • Build upon the simple stratification by adding a second social stratifier • This reflects the reality that multiple forms of marginality interact • e.g.: comparison between health outcomes for women of different wealth level and ethnicity • Ask: “Which stratifiers, together, seem to confer the greatest risk… across all health indicators?”

20. Simultaneous StratificationConstructing Tables that measure combined effect of two stratifiers • Cleaning the data • Similar to single stratification • Small sample size is more likely to be a problem • Especially for groups with many categories (ethnicity, region) • Can be rectified with regrouping • Sample size is a serious issue • Statistical analysis requires sufficient observations in all categories of interest to have confidence in the conclusions (e.g. ethnic groups may be too small)

21. Simultaneous Stratification with p-values Skilled Birth Attendant in Kenya

22. Meaning of Significance Test • These are probabilities that, given the random selection of households in the survey, the values of an indicator across levels of stratifier are not significantly different from each other.

23. How is access to a Skilled Birth attendant (SBA) affected by poverty status and educational level in Kenya?

24. Examine multiple variables at once: Ethiopia, 2000

25. Examine multiple variables at once: Kenya, 2000

26. Examine multiple countries at once: CPR – MM, 1998-2000

27. General Findings (1) • National averages mask huge disparities between groups within a country • Marginalization in health is multi-dimensional • Stratifiers vary dramatically by indicator and country • Almost all disparities were found to be significant • The stratifier with the strongest effect on health outcomes varied by indicator and by country • In some cases, urban-dwelling is a more significant advantage than wealth and in others, educational status trumps poverty status. • Multiple forms of disadvantage are pervasive and measurable

28. General findings (2) • The nuances of this analysis are important for policymaking processes aimed at reaching the MDGs and incorporating maternal health. • Disaggregated and stratified data gives a better picture of who is more vulnerable (beyond income poverty) • Mapping inequities can better focus interventions and services to ensure universal access • The spirit of the MDGs requires that poor, marginalized and vulnerable groups be prioritized.

29. General Findings (3) • Rigorous Statistics • Sampling and selection of stratifiers: Think about groups of interest • Statistical analysis requires sufficient observations, in all categories of interests, to have confidence in the conclusions • Even simple statistics can go a long way to assisting in achieving program goals • Ways to generate and evaluate results can be done with moderate levels of training, and not-too-demanding software

30. Take-home messages • Measuring and monitoring inequity in access to maternal health is possible even in low resource settings—using current data • Statistically significant health gaps exist not just between rich and poor, but across other population groups as well, and multiple forms of disadvantage confer greater risk • Go beyond wealth or “pro-poor” policies to look at the many levels of disadvantage • Policies must be aligned with reducing health gaps in access to key maternal health services.

31. Implications for Programming: Linking policy choices and action • Contribute to national level policy processes to ensure health disparities are addressed in targets, in policies • Monitor and track disparities (first do no harm—be sure programs are not exacerbating inequalities) • Quantify program impact in terms of how well interventions reach different groups of the population • Contribute to regional & international discourse on what works to reach the poor, vulnerable and marginalized • Monitor progress

32. USING AN EQUITY LENS SET PROGRAM OR POLICY OBJECTIVE (in equity terms)

33. Thank you! • Full tables of data for Ghana, Kenya, Ethiopia, Cambodia, Dominican Republic, Tajikistan, are available at: http://www.ciesin.columbia.edu/Health_Equity_Tables.pdf • Websites: • UNMP: www.unmillenniumproject.org • CIESIN: www.ciesin.columbia.edu • Acknowledgements • Paula Braveman, Cesar Victora, Mushtaque Chowdhury, Lynn Freedman, James Connolly, Davidson Gwatkin, and Jeanette Vega • The World Bank’s Japan Policy andHuman Resource Development (PHRD) Fund Poverty Mapping Project at CIESIN, Columbia University‘s Earth Institute