1. Multiple Indicator Cluster Surveys Data 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
Age shifting or birth transference refers to a systematic error whereby the dates of birth of children born about 5 years before a DHS survey are recorded as occurring earlier than was actually the case. In DHS surveys, this birth transference tends to be more pronounced for deceased than for surviving children. When this occurs it results in the under-five mortality rate being under-estimated for the most recent period (0 – 4 years before the survey) Birth transference is partly caused by the design of the questionnaire of a survey. DHS questionnaires include a lengthy series of questions which are asked to mothers concerning maternal and child health. This series of questions must be asked for all births for which the date of birth is subsequent to a specified date—usually set as January of the fifth or sixth calendar year proceeding the year of the survey. It appears that interviewers learn that they can reduce their workload by incorrectly recording some births that actually occurred after the cutoff date as occurring prior to that date. In DHS surveys, interviewers appear to be particularly anxious to avoid asking the health questions about deceased children.
In this example, detailed questions must be asked for children born after Some births and deaths in 2001 were shifted to Therefore, deficits of births and deaths in 2001 are found.

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

12. Lexis diagram of birth cohorts and exposure
At time t’, cannot calculate the probability of dying for cohort born between t and t’ during ages a and b
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. Exposure at beginning of each interval
Check unweighted numbers:
Do not show indicator if less than 250 in any segment covered
Parenthesize if
Exposure at beginning of each interval

15. Neonatal

16. Post Neonatal

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 – ) * (1 – ) * (1 – ) =
And the probability of dying is (1 – ) =

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 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

51. 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

52. 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

53. 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

54. 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

55. 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

56. 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.

57. 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 trend 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

58. CMEInfo The IGME’s Child Mortality Database:

59. Thank You