Age patterns of under-5 mortality in sub-Saharan Africa during 1990‒2018: A comparison of estimates from demographic surveillance with full birth histories and the historic record

Abstract

BACKGROUND Estimates of under-5 mortality (U5M) for sub-Saharan African populations often rely heavily on full birth histories (FBHs) collected in surveys and model age patterns of mortality calibrated against vital statistics from other populations. Health and Demographic Surveillance Systems (HDSSs) are alternate sources of population-based data in much of sub-Saharan Africa, which are less formally utilized in estimation. OBJECTIVE In this study we compare the age pattern of U5M in different African data sources (HDSSs, Demographic and Health Surveys (DHS), and Multiple Indicator Cluster Surveys (MICS)), and contrast these with the historical record as summarized in the Human Mortality Database and model age patterns. METHODS We examined the relative levels of neonatal, postneonatal, infant, and child mortality across data sources. We directly compared estimates for DHS and MICS subnational regions with HDSS, and used linear regression to identify data and contextual attributes that correlated with the disparity between estimates. RESULTS HDSS and FBH data suggests that African populations have higher levels of child mortality and lower infant mortality than the historic record. This age pattern is most explicit for Western African populations, but also characterizes data for other subregions.

Quantifying within-city inequalities in child mortality across neighbourhoods in Accra, Ghana: a Bayesian spatial analysis

“…Investment in national civil registration systems across SSA, including in Ghana, could provide these data in real time and enable governments to monitor changes and progress in lowering child mortality and reducing inequalities. 72 73 Finally, we highlight that although we report U5M at a fine-spatial scale, populations within neighbourhoods may still be heterogeneous in their child mortality risk and its determinants, particularly within the more populous inner-city neighbourhoods. This is often discussed in the context of the Modifiable Areal Unit Problem, a statistical bias that can arise when area-level measurements are sensitive to the scale or zoning scheme used.…”

“…In our study, we were unable to separately estimate neonatal, infant or cause-specific mortality rates with the available methods for mortality estimation from summary birth history data and are unaware of any other data that are representative at the neighbourhood level and would enable this analysis. Investment in national civil registration systems across SSA, including in Ghana, could provide these data in real time and enable governments to monitor changes and progress in lowering child mortality and reducing inequalities 72 73. Finally, we highlight that although we report U5M at a fine-spatial scale, populations within neighbourhoods may still be heterogeneous in their child mortality risk and its determinants, particularly within the more populous inner-city neighbourhoods.…”

Pregnancy Surveillance Methods within Health and Demographic Surveillance Systems

“…The pregnancy surveillance methods in HDSS are diverse and often lack detailed documentation. There are also questions surrounding the quality of HDSS estimates of neonatal mortality, which are subject to huge variability and are on average lower than corresponding estimates from demographic health surveys 7 . Unreliable data on the vital events of newborns has been described as one of the most challenging issues facing HDSS 60 .…”

“…The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) stillbirth and child mortality estimates are derived from vital registration systems, censuses, and various demographic health survey data 6 . Poor data quality in many developing nations makes accurate estimation more challenging 7 . Furthermore, the use of models to correct such estimates is complicated by lingering questions regarding their applicability to African settings 8 .…”

Assessment of the consistency of health and demographic surveillance and household survey data: A demonstration at two HDSS sites in The Gambia

“…Also, mis-statement of dates of birth and death of children by mothers can cause displacement within the respective age categories considered in the analysis, as well as across calendar periods [ 25 ]. For the prospective demographic surveillance systems, pregnancies and their outcomes, in particular pregnancies that end in early neonatal deaths, can be missed between rounds of data collection [ 26 ]. The result in Farafenni, therefore, that consistently higher neonatal mortality rates were obtained from the household survey than were derived from the HDSS data for the 15-year period considered in the analysis is not particularly surprising.…”