Multiple Indicator Cluster Surveys Data Dissemination - Further Analysis Workshop Mortality MICS4 Data Dissemination and Further Analysis Workshop

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

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

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

Measurement of child mortality

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

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

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

Age heaping: child’s death at 12 months

Age shifting: common issue in DHS

Check denominators for: Less than 250 cases * cases( )

Neonatal mortality

Post-neonatal mortality Infant mortality

Child mortality

Under-5 mortality

Estimates from direct method Table CM.1: Early childhood mortality rates Neonatal, post-neonatal, Infant, child and under-five mortality rates for five year periods preceding the survey, (Total) Neonatal mortality rate [1] Post- neonatal mortality rate [2] Infant mortality rate [3] Child mortality rate [4] Under five mortality rate [5] Periods of analysis of 5 years

C1 MICS Survey

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

Indirect method Table CM.1: Children ever born, children surviving and proportion dead Mean and total numbers of children ever born, children surviving and proportion dead by age of women, Country, 2010 Mean number of children ever born Total number of children ever born Mean number children surviving Total number of children surviving Proportion dead Number of women Age Total

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

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

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

Select the right model life table: India

Indirect method Check denominators Table CM.1: Children ever born, children surviving and proportion dead Mean and total numbers of children ever born, children surviving and proportion dead by age of women, Country, 2010 Mean number of children ever born Total number of children ever born Mean number children surviving Total number of children surviving Proportion dead Number of women Age Total

Indirect method Coale-Demeny Models (Trussel equations) Mean children ever born Proportion children dead of bornAge i Q(i) Northt(i) North Q(i) Southt(i) South Age group

Indirect method Under-five Mortality Rate (Male) Reference date North Under-five Mortality Rate North Reference date South Under-five Mortality Rate South Reference date East Under-five Mortality Rate East Reference date West Age group

MICS Survey

Final estimates As the “final” or “most recent” estimate, we use an average of estimates based on women age and Ignore estimates based on women age and 20-24: selection bias

C1: “Final” estimates Infant Mortality Rate [1] Under-five Mortality Rate [2] SexoMasculino Feminino98146 RegionSAB87119 Leste Northe Sul83112 Area de residência Urbano93131 Rural Quintil de riqueza Mais rico Segundo Meio Quarto Mais pobre 6989 Total

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.

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

Check estimates from successive data sources

Compare CEB, CS, CD data Age in 2000 Mean Number of Children Ever Born Mean Number of Children Surviving Mean Number of Children Deceased

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 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

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 –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 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

Direct and indirect estimates

CMEInfo The IGME’s Child Mortality Database: