Multiple Indicator Cluster Surveys Data Interpretation, Further Analysis and Dissemination Workshop Mortality

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

Periods for Under-5 Mortality Measurement

Measurement of child mortality

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)

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

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

Heaping: Age at death in months

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 2001. Some births and deaths in 2001 were shifted to 2000. Therefore, deficits of births and deaths in 2001 are found.  

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

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

SPSS Table

Exposure at beginning of each interval 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

Neonatal

Post Neonatal

Infant

Child

Under-5

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

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

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

Plotting mortality rates Survey date

Standard tables

Standard tables

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

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

Indirect estimation: Age version

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

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)

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

Select the right model life table

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

Indirect estimation: Age version

Standard tables

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

“Final” estimates

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”

Indirect estimation: Time since first birth version

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

Indirect estimation: Time since first birth version

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

“Final” estimates

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

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)

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

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

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

The IGME Work

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

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

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

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

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

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.

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

CMEInfo The IGME’s Child Mortality Database: www.childmortality.org

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