Multiple Indicator Cluster Surveys Data Dissemination - Further Analysis Workshop

1. Multiple Indicator Cluster SurveysData Dissemination - Further Analysis Workshop Mortality MICS4 Data Dissemination and Further Analysis Workshop

2. Background • Child mortality: Probabilities of dying during the first 5 years of life, usually broken down by conventional age segments • Infant (first one year) and under-5 mortality rates (first 5 years) are the most commonly calculated probabilities

3. Periods for Under-5 Mortality Measurement Under-5 Mortality (0-4 Years) Birth 5 1 Neonatal Mortality (First month) Post-Neonatal Mortality (1-11 months) Child Mortality (1 to 4 years) Infant Mortality (Birth to One Year)

4. Background • MDG 4: reduce under-5 mortality by two-thirds, between 1990 and 2015 • Indicator 1.3 – Under-5 Mortality Rate • Indicator 1.4 – Infant Mortality Rate • Both indicators are measured in MICS surveys • Child mortality indicators are broad indicators of social development/health status • Used to evaluate impact of interventions based on trends

5. Measurement of child mortality

6. Data Sources • Vital registration • Population censuses • Surveillance systems, sample registration systems • Household surveys • Direct: Data from full birth histories, as in DHS and some MICS surveys • Indirect: Data from summary birth histories, to use “Brass methods” • Note that surveys that include birth histories can be used both for direct and indirect estimation

7. Methods: Direct method • Based on birth histories • Required data: • Data of birth for each child (month and year) • Survival status • Date or age at death for each child who has died • Typically, synthetic cohort life table approach used to estimate rates

9. Methods: Direct method • Rely heavily on the quality of information collected – work best in populations where dates and durations are well-known • Sources of errors: • Omission of births and deaths • Misreporting of age at death (age heaping at 12 months is common) • Birth misplacement

10. Age heaping: child’s death at 12 months

11. Age shifting: common issue in DHS

12. Check denominators for: Less than 250 cases * 250-499 cases ( )

13. Neonatal mortality

14. Post-neonatal mortality Infant mortality

15. Child mortality

16. Under-5 mortality

17. Estimates from direct method

18. C1 MICS Survey

19. Methods: Indirect method • Required data • Age of women • The total number of children she has ever borne, and • The number of those children who have died (or, the number who are still alive) • Require relatively fewer information than direct method

20. Indirect method

21. Methods: Indirect method • Distributes children ever born to women retrospectively over time using models • Assumes • Little or no change in fertility levels and age patterns • No change or a linear decline in mortality • A pattern of mortality by age that conforms to known model life table “families” which basically derived from European experience

22. Methods: Indirect method (3) Converts proportion dead of children ever born (D(i)) reported by women in age groups 15-19, 20-24, etc. into estimates of probability of dying before attaining certain exact childhood ages, q(x): q(x) = K(i)*D(i) where the multiplier K(i) is meant to adjust for non mortality factors determining the value of D(i) MICS4 Workshop

23. Methods: Indirect method • The age pattern of child mortality --- select the right model life table • Coale-Demeny patterns by region: • East, North, South, and West • United Nations patterns by region: • Latin America, Chilean, South Asian, Far Eastern, and General

24. Select the right model life table: India

25. Indirect method Check denominators

26. Indirect method

27. Indirect method

28. MICS Survey

29. Final estimates • As the “final” or “most recent” estimate, we use an average of estimates based on women age 25-29 and 30-34 • Ignore estimates based on women age 15-19 and 20-24: selection bias

30. C1: “Final” estimates

31. Data quality issues • Main errors in data on children ever born and children dead/surviving • Omission of deaths • Misreporting of women’s age • Other drawbacks • Violation of assumptions • Use model life tables to adjust the data for the age pattern of mortality in the general population --- Inappropriate model life table may results in mis-estimation of trends.

32. Checking quality of mortality estimates • Compare child mortality across sub-groups • Expected patterns by sex, background characteristics

33. Check estimates from successive data sources

34. Compare CEB, CS, CD data

35. Quality check: sample size • Sample size needs to be sufficiently large to produce statistically reliable estimates of infant and under-five mortality • Mortality data may carry wide confidence intervals • Number of births and deaths for children of women aged 15-19 is often very small, thus have effects on the parity ratio and on the regression used to derive estimation equations, therefore may bias the indirect estimates

36. For further analysis • Compare estimates from different sources • Analyze mortality by coverage indicators • Check age patterns of mortality (from direct method), compare with model patterns • Multivariate analyses

37. The IGME Work

38. Members of the IGME • UN Inter-agency Group for Child Mortality Estimation (IGME) was formed in 2004, led by UNICEF, WHO, and includes members of UN Population Division and The World Bank • Technical Advisory Group (TAG) of the IGME • Independent • Composed of leading experts in demography and biostatistics • Provide technical guidance on estimation methods, technical issues and strategies for data analysis and data quality assessment

39. Objectives of the IGME • Objectives of the IGME • Harmonize estimates within the UN system • Improve methods for child mortality estimation • Produce consistent estimates of child mortality worldwide for reporting on progress towards MDG 4 • Enhance the capacity of countries to produce timely estimates of child mortality: regional workshops and country visits

40. The IGME method to estimate child mortality • Update estimates annually • Compile all nationally representative data for each country • Check data quality • Fit a regression line to all data points that meet data quality standards established by the IGME and extrapolate to a common reference year • Additional adjustment applied to countries with high HIV/AIDS prevalence • The IGME Estimates are based on national data from surveys, census, vital registrations, etc, but may differ from these data

41. Why is it necessary to produce interagency child mortality estimates • No single, high quality source in most countries • Multiple data sources often inconsistent • Project estimates • Important to estimate since 1990 • Consistent methodology

42. Example: Data rich and consistency countries Mali The available data sources cluster over a narrow band and show considerable consistency The estimate line is fitted to all the data

43. Example: Data rich countries with wide variations in mortality level from different sources Nigeria Has one of the widest spreads of source data, with a range from 120 to 240 deaths per 1,000 live birth In driving the estimate line, all sources with dotted lines are rated of lower quality and are not used.

44. Discrepancies between national and interagency estimates • National estimates: often use data directly from censuses, surveys, or vital registration systems • IGME estimates: use national data from censuses, surveys, or vital registration systems as underlying data to generate estimates by fitting a tend line to these data • For countries with consistent data, national estimates and interagency estimates are similar. • For countries with inconsistent or messy data, differences might be large

45. Direct and indirect estimates

46. CMEInfoThe IGME’s Child Mortality Database: www.childmortality.org