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.
Information about how the risk of death varies with age within the 0–5 age range represents critical evidence for guiding health policy. This study proposes a new model for summarizing regularities about how under-5 mortality is distributed by detailed age. The model is based on a newly compiled database that contains under-5 mortality information by detailed age in countries with high-quality vital registration systems, covering a wide array of mortality levels and patterns. It uses a log-quadratic approach in predicting a full mortality schedule between ages 0 and 5 on the basis of only one or two parameters. With its larger number of age-groups, the proposed model offers greater flexibility than existing models in terms of both entry parameters and model outcomes. We present applications of this model for evaluating and correcting under-5 mortality information by detailed age in countries with problematic mortality data.
The lifetime risk of maternal death is the risk that a 15-year-old girl will die of a maternal cause in her reproductive lifetime. Its intuitive appeal means it is a widely used summary measure for advocacy and international comparisons of maternal health. But relative to mortality, women are at a higher risk of experiencing life-threatening maternal morbidity called 'maternal near miss events' - complications so severe that women almost died. As maternal mortality continues to decline, stronger health indicators that include information on both fatal and non-fatal maternal outcomes are required. Thus, we propose a novel measure - the lifetime risk of maternal near miss - to estimate the risk a girl will experience at least one maternal near miss in her reproductive lifetime, accounting for survival from ages 15-49. This new indicator is urgently needed because existing measures of maternal morbidity prevalence (near miss ratio or rate) do not capture the cumulative risk over the reproductive life course. We use estimates of fertility and survival from the World Population Prospects for Kenya in 2021 along with simulated data on the maternal near miss ratio to demonstrate the calculation of the lifetime risk of maternal near miss. We estimate that the lifetime risk of maternal near miss in Kenya is 1 in 37, compared to a lifetime risk of maternal death of 1 in 59.
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