Mortality

1. Multiple Indicator Cluster SurveysData Interpretation, Further Analysis and Dissemination Workshop Mortality

2. Background • Mortality during the first five years of life – broken down by age segments and calculated as probabilities… • but still called “rates” • Infant (first one year) & under-5 mortality rates (first 5 years) are the most commonly calculated probabilities – the two MDG indicators

3. Periods for Under-5 Mortality Measurement

4. Measurement of child mortality

5. Conventional Data Sources • Vital registration • Population censuses • Surveillance systems, sample registration systems • Household surveys: • Birth histories: Direct estimates (DHS and MICS surveys) • Indirect estimates also possible • Summary birth histories: Indirect estimates (MICS and other household surveys)

6. Direct estimation • Based on birth histories • Required data • Date of birth for all children born (month and year) • Survival status • Date or (more frequently) age at death for each child who has died • Birth cohorts are retrospectively reconstructed and a synthetic cohort life table approach used to estimate indicators

8. Methods: Direct method • Rely heavily on the quality of information collected – works best in populations where • dates and durations are well-known, • data is collected with well-trained interviewers and good field supervision • Sources of errors: • Omission of births and deaths • Misreporting of age at death (age heaping at 12 months is common) • Birth misplacement

9. Heaping: Age at death in months

10. Age shifting: common issue in DHS

11. Methods: Direct method Check DQ tables to identify data quality issues in birth histories

12. At time t’, cannot calculate the probability of dying for cohort born between t and t’ during ages a and b Lexis diagram of birth cohorts and exposure Possible to take into account exposure of those born between t-b and t-a, as well as truncated exposure of those born between t’-b and t’-a

13. SPSS Table

14. Check unweighted numbers: • Do not show indicator if less than 250 in any segment covered • Parenthesize if 250-499 Exposure at beginning of each interval

15. Neonatal

16. PostNeonatal

17. Infant

18. Child

19. Under-5

20. Example: Calculation of infant mortality rate Infant mortality is the probability of dying during the first year of life We need to calculate the probability of surviving until the end of the first year of life, and subtract this from 1.0

21. Example: Calculation of infant mortality rate • The probability of surviving until the end of the first year of life is (1 - 0.02515) * (1 – 0.00732) * (1 – 0.01127) * (1 – 0.01795) = 0.939633 And the probability of dying is (1 – 0.939633) = 0.06037

22. Final (published) table We can use the mid-points of each period and show these estimates on a graph to see trends

23. Plotting mortality rates Survey date

24. Standard tables

25. Standard tables

26. Indirect estimation: Age version • Required data: • Age of women • The total number of children ever born by women • The total number of children who have died (or, the number who are still alive) • Requires the same information as the direct method, with the exception of dates of birth and ages at death – only aggregate numbers

27. Indirect estimation: Age version • Distributes children ever born to women retrospectively over time using mathematical models • Assumptions: • Little or no change in fertility levels & 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

28. Indirect estimation: Age version

29. Distribution of children born to women in each age group by the number of years since birth

30. Indirect estimation: Age version 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)

31. Indirect estimation: Age version • 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

32. Select the right model life table

33. Indirect estimation: Age version Check unweighted denominators for each age group - numbers of children ever born

34. Indirect estimation: Age version

36. Standard tables

37. Plotting mortality rates Survey date As the “final” or “most recent” estimate, we use an average of estimates based on women age 25-29 and 30-34 These estimates are not used as they are based on predominantly first births and births to adolescents

38. “Final” estimates

39. Indirect estimation: Time since first birth version • Required data: • Date of first birth for each woman (or number of years since first birth) • The total number of children ever born by women in each time since first birth group • The total number of children who have died among these children (or, the number who are still alive) • “Age groups” are replaced by “Time since first birth groups”

40. Indirect estimation: Time since first birth version

41. Indirect estimation: Time since first birth version • Generates estimates that are closer to the survey date • “Selection bias” less pronounced • Uses data better (5 groups as opposed to 7) • Distribution of births by time since first birth occur within narrower time intervals

42. Indirect estimation: Time since first birth version

43. Plotting mortality rates Survey date Used as the “final”, “most recent” estimate – however, analysis ongoing on whether this number is also subject to selection biases

44. “Final” estimates

45. MICS5 and mortality estimation Majority of surveys now including birth histories, which allows direct estimation From these surveys, direct estimates are generated and presented, but indirect estimates should also be produced (for checking data quality) When direct estimates are available, does not make sense to publish indirect estimates as well

46. MICS5 and mortality estimation If birth histories are not included, generate both age and time since first birth (TSFB) versions Interagency group recommends the use of the TSFB version – a very recent development Produce and compare both versions, decide which one will be used based on objective technical evaluation (sample sizes, patterns, out of range values, fluctuations)

47. MICS5 and mortality estimation Mortality estimates (both direct and indirect) are subject to relatively wide confidence intervals Need to calculate sampling errors Recently developed program (CMRJack) used to calculate sampling errors – will be adopted to MICS programming

48. Data quality issues in indirect estimation • Main errors in data on children ever born and children dead/surviving • Omission of deaths • Misreporting of women’s age or TSFB • Other drawbacks • Many assumptions • Use of models and applicability • Can only provide “rough” estimates (level and timing) – not sensitive enough to showing changes over short periods of time

49. 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

50. The IGME Work