Research Article: Age distribution, trends, and forecasts of under-5 mortality in 31 sub-Saharan African countries: A modeling study

Date Published: March 12, 2019

Publisher: Public Library of Science

Author(s): Iván Mejía-Guevara, Wenyun Zuo, Eran Bendavid, Nan Li, Shripad Tuljapurkar, Lars Åke Persson

Abstract: BackgroundDespite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.Methods and findingsData came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee–Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.ConclusionsTo our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050.

Partial Text: Under-5 mortality analysis has been critical in evaluating progress toward the Millennium Development Goal 4 (MDG-4) that called for a reduction of under-5 mortality rates (U5MRs) by two-thirds between 1990 and 2015 [1] and more recently toward the Sustainable Development Goal 3 (SDG-3), which aims to reduce neonatal mortality rates (NMRs) to fewer than 12 per 1,000 live births and U5MRs to at least as low 25 per 1,000 births by 2030 [2]. The monitoring of child survival is conducted by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) [1], which has adopted a methodology for child mortality estimation [3,4] and regularly updates the resulting mortality levels and trends around the world [4].

This study follows the guidelines in STrenthening the Reporting of OBservational studies in Epidemiology (STROBE) for reporting observational cross-sectional studies as well as the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) [31,32]. The analysis is based on information collected from unidentified individuals who provided informed consent prior to the survey interview. Ethical approval for Demographic and Health Surveys (DHSs) was obtained by the ORC Macro Institutional Review Board and by individual review boards within each participating country.

This paper describes 3 novel findings. First, we advanced the modeling of age patterns of under-5 mortality for detailed age groups using FBH data from SSA, providing important information on under-5 mortality patterns and representing a step forward in the analysis of changes in age patterns of mortality across time and by country. We used the latest refinements in the estimation of child mortality based on full birth histories from survey data and adjusted and validated our rates using official estimates of U5MRs that are derived from a robust model [1,3]. Second, we made probabilistic projections of age patterns of mortality by 2030 (and where possible to 2050) in order to assess progress toward the SDGs of child mortality reduction. In making that assessment, our use of probabilistic methods allowed us to account for different degrees of uncertainty. Our predictions are consistent with estimates from UN IGME, as we found discrepancies for only four countries in the timing where SDG-3 would be achieved. Third, we predict that Kenya, Rwanda, Senegal, Tanzania, and Uganda are on track to achieve SDG-3 for under-5 mortality reduction, but only Rwanda and Tanzania would meet the neonatal target as well, and 13 countries would achieve both targets only after 2050.

This study contributes to the development of detailed age patterns of mortality for under-5 children and stresses their importance in the monitoring of child survival of specific age groups to identify distinct patterns of mortality decline at early ages in most countries of SSA. Our estimates and forecasts relied on a robust LLT model that was suitable for our data with year gaps, providing different degrees of uncertainty and capturing most of the variation of under-5 mortality in the SSA region. Its accuracy could be refined if further reliable sources of information become available, such as the development of new vital registration systems. It should also be considered in the design and scale-up of targeted interventions intended to accelerate progress toward achieving the SDG-3 targets for child mortality reduction. Future research should explore a detailed assessment of age inequality in early mortality, compression, and convergence, as well as the true relationships between age patterns of mortality and epidemiological trajectories.



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