Data and Methodology to Estimate Child Mortality Danzhen You UNICEF Dec 8, 2009 Prepared for the ESCWA Workshop in Beirut, Lebanon

MDG 4  MDG4 – reduce under-five mortality rate (U5MR) by two thirds from 1990 to 2015

Child Mortality Indicators: Definition Mortality among young children can be subdivided by age group CategoryIncludes deaths that occur: Neonatal mortalityDuring the first 28 days of life Post-neonatal mortalityAt ages 1 to 11 months Infant mortalityBetween birth and exact age 1 Child mortalityAt ages 1 to 4 years Under-five mortalityBetween birth and exact age 5

Child Mortality Indicators: Definition (cont’d) Mortality rates such as U5MR and IMR are not strictly rates but are the probability of dying within a specific period IndicatorDefinition Under-five mortality rate Probability of dying between birth and exact age 5 years expressed as per 1000 live births. Infant mortality rate Probability of dying between birth and exact age 1 year expressed as per 1000 live births.

Sources of Data There are a variety of sources used to estimate child mortality  Vital registration  Sample registration  Demographic surveillance sites  Population censuses  Household surveys The first three are prospective – collect data as deaths occur. The last two are generally retrospective – interview people about events in the past

Vital Registration  The preferred source of data for child mortality if system is good.  Records all births and deaths that occur in a country. Individual events are reported shortly after they occur.  Produces estimates annually and for sub-national areas.  Deaths are often less registered than births  Poor and rural families are less likely to register births and deaths  Good systems are not common in developing countries as they require an extensive infrastructure that is consistent and accurate. Currently around 50 countries have vital registration data that are considered good enough to be the sole source of data for child mortality.

Sample Registration  Sample registration systems are designed to collect information from a representative sample of the population. This allows both national and sub-national estimates of births and deaths to be produced provided the sample is large enough.  They generally provide data on causes of deaths, which are valuable for planning and evaluating programmes to reduce child mortality.  However, such systems are complex and expensive. While they are rare, they function in the two largest countries in the world.

Demographic Surveillance  Demographic surveillance sites have similarities with sample registration systems, but their coverage and purpose are quite different. Such sites are limited to small geographically defined populations, typically less than 200,000 people.  Their primary aim is not to represent the national population, but rather to provide a base for specific studies and intervention trials.  While easier to operate than sample registration systems, they still require substantial ongoing resources and continuity.

Population Census  Population censuses are carried out in most countries. They provide a unique source of demographic data since they aim to collect data on every member of the national population. This provides not only national data on basic household and person characteristics, but also provides such data for the smallest administrative units.  However, because of their very large scale and high cost, national censuses are typically conducted at ten-year intervals. Such resource and logistical constraints also limit the content of census questionnaires. Nevertheless they usually collect data on children ever born and those still living, thus enabling indirect estimates of child mortality to be calculated. Although sampling errors are absent, non-sampling errors are present.

Household Survey  Given the lack of good vital registration systems and the infrequency of population censuses, household surveys have become the primary source of data on child mortality.  While challenging to successfully carry out, well-designed and well- implemented household surveys can produce high-quality data on child mortality levels and trends.  Such surveys often collect a range of other data on health, education and other socio-economic indicators, which provide essential information for guiding and assessing programmes to reduce child mortality.  These surveys are constrained by their sample size, and increasingly by the amount of data collected. Survey sample sizes are increasing, as is content, and while this improves the range of disaggregation that can be reported, they also pose logistical and quality challenges.

Data Problems  Sampling errors (Surveys only)  Omission of Deaths  Misreporting of child’s age at death or date of birth (direct only)  Selection bias  Violation of assumptions (indirect only)

Examples: U5MR, Syria

Examples: U5MR, Egypt

Examples: IMR, Tunisia

Examples: U5MR, Nigeria

Work of IGME  Inter-agency Group for Child Mortality Estimation (IGME), formed in 2004, including UNICEF, WHO, UN Population Division, World Bank  Technical Advisory Group of the IGME  IGME aims to develop methods to better estimate child mortality, share data, harmonize estimates between partners, and increase the transparency of the estimates  Other activities: Child mortality workshops, Country Support  CME Info database

Data Collection  Collect all available data – census, household surveys (DHS, MICS, etc.), vital registration, and so on  UNICEF’s main data collection process – Country Report on Indicators for the Goals (CRING)  WHO routine data collection process - vital registration data

Data Evaluation  Response rate  Age misreporting, age heaping  Birth transference (DHS only)  Omission of deaths  Others

Estimation Methods  Estimates Fitting a regression curve to the data points which are believed having good quality Spline: Weighted Least Square with Various Slope LOESS: Locally weighted Lease square Different methods for countries with high HIV/AIDS prevalence

Estimation Methods: Spline  The model used is:  Date is calendar year  Postkj = (date - dateknot j ) if (date-dateknot j ) is positive  = 0 if (date-dateknot j ) is negative  The knots are defined backward into the past and each time the sum of the weights reaches a multiple of 5  Thus number and location of knots is data-driven

Estimation Methods: LOESS  Function estimated is log(y) = β 0 + β 1 (x) + β 2 (z) + ε Where y is U5MR, x is date and z is a dummy variable indicating whether the observation is from civil registration  Selection of α: Range from 0.05 (or smallest value that captures at least 3 points) to 2.0 (or largest value that allows some variability)  Uncertainty: 1,000 draws per value of α

Examples: U5MR, Syria

Examples: U5MR, Egypt

Examples: IMR, Tunisia

Examples: U5MR, Nigeria

CME Info (

Structure of the CME Info System

Series Window

Data points Window

Estimates Window

U5MR in West Asia a

Progress towards MDG4 in the World, 2008 On track: under-five mortality rate (U5MR) is less than 40, or U5MR is 40 or more and the average annual rate of reduction (AARR) in the U5MR observed for is 4.0 percent or more No Progress: U5MR is 40 or more and AARR is less than 1.0 per cent Insufficient Progress: U5MR is 40 or more and AARR is less than 4.0 percent but equal to or greater than 1.0 percent Data not available