Accumulating Birth Histories Across Surveys for Improved Estimates of Child Mortality

Abstract

Producing reliable estimates for childhood mortality rates is essential to monitor progress towards the United Nations Sustainable Development Goals (UN SDGs) and correctly evaluate policies designed to reduce childhood mortality rates. Different model-based approaches have been proposed to assess levels and trends in childhood mortality indicators. In this paper, we propose a design-based complement that accumulates birth histories across different household surveys to increase the precision of childhood mortality rates estimates. We accumulate birth histories across different cross-sectional Demographic Health Surveys/Multiple Cluster Indicator Surveys collected in Senegal and Malawi and estimate pooled childhood mortality rates based on calendar years. We show that accumulating birth histories smoothens fluctuations in time series for national and sub-national mortality rates, establishes more stable and reliable time trends, and results in estimated standard errors of the cumulated rates that are about 50–60% lower than their counterparts from separate surveys.

Antenatal care coverage and early childhood mortality in Zimbabwe: new interpretations from nationally representative household surveys

“…Despite improvements over time, it is difficult to say with confidence what the extent of ANC coverage and early childhood mortality are due to seemingly inconsistent DHS and MICS estimates in Zimbabwe and SSA ( Ngandu et al , 2016 ; Schmidt and Elkasabi, 2022 ). Different point estimates can lead to divergent responses that can negatively impact mothers and children ( Lindenmayer and Likens, 2009 ).…”

“…MICS estimates of ANC coverage are often regarded as more accurate because they are based on a shorter and more recent recall period (2 years) compared to DHS’ longer recall of 5 years ( Cutts et al , 2013 ; Schmidt and Elkasabi, 2022 ). The length of the recall period influences the amount of recall bias, which can affect the accuracy of estimates and correct targeting of policies and practices ( Ngandu et al , 2016 ; Zimmerman et al , 2019 ).…”