Data needs for analyses of inequalities: WHAT WE LEARNED FROM THE COUNTDOWN TO 2015 By Cesar G Victora

Transcription

1 ESA/STAT/AC.320/1 EXPERT GROUP MEETING ON DATA DISAGGREGATION JUNE 2016 NEW YORK Data needs for analyses of inequalities: WHAT WE LEARNED FROM THE COUNTDOWN TO 2015 By Cesar G Victora

2 DATA NEEDS FOR ANALYSES OF INEQUALITIES: WHAT WE LEARNED FROM THE COUNTDOWN TO 2015 United Nations and UNICEF Expert Group Meeting on Data Disaggregation 27 June 2016 Cesar G Victora

3 Outline Where we came from What we do What lies ahead

4 Millennium Development Goals MDG1: reduce the prevalence of underweight in children by 1/2 MDG4: reduce under-five mortality by 2/3 MDG5: reduce maternal mortality by 3/4 MDG6: control HIV/AIDS, TB, malaria, other infections Equity completely overlooked!

5 Lancet Child Survival Series 2003 Regular monitoring of inequities and use of the resulting information for education, advocacy, and increased accountability is urgently needed (Lancet 2003)

6 Lancet Child Survival Series 2003 We commit ourselves to convening a series of meetings, every 2 years, to take stock of progress in preventing child deaths, and to hold countries and their partners accountable.

7 Countdown to 2015 Multi-stake holder initiative to monitor coverage in RMNCH g

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9 Outline Where we came from What we do What lies ahead

10 Countdown Equity WG Univ. of Pelotas team

11 Disaggregated analyses Stratifiers Sex of child Wealth quintiles Woman s age Woman s education Urban/rural residence Region of the country Outcomes Intervention coverage RMNC(A)H continuum Child mortality Child nutrition

12 Data management National RMNCH surveys 100+ countries 300+ surveys >3.5 million women >7 million children Semi-processed databases Standard definitions Equity stratification Summary equity indices Standard errors Double-checked against original survey reports Analyses of multiple surveys in batch mode Surveys

13 Data structure Country/ Year Province/ State Household Mother Child

14 Current status of the database 198 DHS, 94 MICS, 2 other surveys incorporated 107 countries Other surveys (under way) 27 Reproductive Health Surveys (up to 2008) 10 PMA 2020 surveys (data collection under way) Non-standard surveys Brazil, Mexico, etc. Recently released DHS/MICS

15 The equiplot: country level analyses Composite coverage index by wealth in selected countries

16 The equiplot: global level analyses Mean coverage by quintile for selected interventions 16

17 The equiplot: national trends Institutional deliveries in Brazil

18 Global trends for rich and poor

19 Dissemination: WHO/GHO

20 Outline Where we came from What we do What lies ahead

21 SDGs and Countdown to

22 The last SDG Data, monitoring and accountability to increase significantly the availability of highquality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.

23 Challenges - Which data sources - Primary reliance on surveys - Which stratifiers? - Which outcomes? - Which analyses? - Sample sizes - Summary measures of inequality - Absolute vs relative inequalities - Standalone versus aggregate coverage indicators

24 SDG 17.18: Nigeria ethnic groups 2008 stratifiers Easier to measure: income, gender, age, geographic location Harder to measure: race, ethnicity, migratory status, disability Total: 310 groups!

25 Wealth quintiles or deciles?

26 Double stratification: wealth and residence SBA coverage is higher in urban than rural areas in all wealth quinales (average of CD countries)

27 Challenges: outcomes - Mortality - Difficult (impossible?) for maternal mortality - Straightforward for child mortality levels - But not for causes of death - Nutrition (under and over) - Easy - Coverage - Separate indicators (over 70 in the Countdown) - Summary indicators (averages or sums)

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29 Composite coverage index Need a proxy for UHC in the RMNCH context CCI = weighted average of 8 interventions equal weights to 4 stages in the continuum of care family planning maternal and newborn care immunization case management of sick children!!" = 1 4 &'( + (*+ + +,!( '/3 + 1(2 + *! / +!',1 2.!

30 CCI with a priori weights correlates well with PCA-derived indices

31 CCI by wealth in different countries Q1 Q2 Q3 Q4 Q5 Kyrgyzstan Timor.Leste Ethiopia Guatemala Morocco Nepal Chad CCIGcoverage

32 Global CCI trends by wealth quintile CCI coverage CCI inequality CCI coverage richest poorest Inequality level absolute relative Year Year Predicted CCI for Q1 Predicted CCI for Q5 SII for CCI (pct. points) CIX for CCI

33 Co-coverage: sum of eight preventive interventions needed by every child

34 Challenges: analyses - Sample sizes - Stratified analyses - How to express inequalities - Extreme group comparisons - Whole distribution measures

35 Sample sizes in 300+ surveys Stratifiers Stratifier Level Median 10th centile 90th centile % total Wealth quintile Q % Place of residence Maternal education Maternal age Sex of child All Q % Q % Q % Q % Rural % Urban % None % Primary % Secondary % yrs % yrs % yrs % Male % Female % All %

36 Sample sizes in 300+ surveys Selected outcomes Stratifier Wealth quintile Place of residence Maternal education Level FPS SBA DPT3 ORT CPNM Q Q Q Q Q Rural Urban None Primary Secondary Maternal age yrs yrs yrs Sex of child Male All Female All

37 Extreme group comparisons

38 Summary measures of inequality: absolute or relative? Summary measures take the full distribution into account Less affected by sample size than comparisons between extreme groups Covage in each quintile 100% 80% 60% 40% 20% Slope index of inequality 0% 0% 20% 40% 60% 80% 100% Cummulative fraction of the population ranked by wealth Cumulative fraction of coverage outcome 100% 80% 60% 40% 20% 0% Concentration index 0% 20% 40% 60% 80% 100% Cummulative fraction of the population ranked by wealth

39 Absolute vs relative inequality: does it really matter? Less absolute inequality Less relative inequality

40 The last SDG Data, monitoring and accountability to increase significantly the availability of highquality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.

41 Cesar Victora