National and regional under-5 mortality rate by economic status for low-income and middle-income countries: a systematic assessment

Abstract

SummaryBackgroundThe progress to achieve the fourth Millennium Development Goal in reducing mortality rate in children younger than 5 years since 1990 has been remarkable. However, work remains to be done in the Sustainable Development Goal era. Estimates of under-5 mortality rates at the national level can hide disparities within countries. We assessed disparities in under-5 mortality rates by household economic status in low-income and middle-income countries (LMICs).MethodWe estimated country-year-specific under-5 mortality rates by wealth quintile on the basis of household wealth indices for 137 LMICs from 1990 to 2016, using a Bayesian statistical model. We estimated the association between quintile-specific and national-level under-5 mortality rates. We assessed the levels and trends of absolute and relative disparity in under-5 mortality rate between the poorest and richest quintiles, and among all quintiles.FindingsIn 2016, for all LMICs (excluding China), the aggregated under-5 mortality rate was 64·6 (90% uncertainty interval [UI] 61·1–70·1) deaths per 1000 livebirths in the poorest households (first quintile), 31·3 (29·5–34·2) deaths per 1000 livebirths in the richest households (fifth quintile), and in between those outcomes for the middle quintiles. Between 1990 and 2016, the largest absolute decline in under-5 mortality rate occurred in the two poorest quintiles: 77·6 (90% UI 71·2–82·6) deaths per 1000 livebirths in the poorest quintile and 77·9 (72·0–82·2) deaths per 1000 livebirths in the second poorest quintile. The difference in under-5 mortality rate between the poorest and richest quintiles decreased significantly by 38·8 (90% UI 32·9–43·8) deaths per 1000 livebirths between 1990 and 2016. The poorest to richest under-5 mortality rate ratio, however, remained similar (2·03 [90% UI 1·94–2·11] in 1990, 1·99 [1·91–2·08] in 2000, and 2·06 [1·92–2·20] in 2016). During 1990–2016, around half of the total under-5 deaths occurred in the poorest two quintiles (48·5% in 1990 and 2000, 49·5% in 2016) and less than a third were in the richest two quintiles (30·4% in 1990, 30·5% in 2000, 29·9% in 2016). For all regions, differences in the under-5 mortality rate between the first and fifth quintiles decreased significantly, ranging from 20·6 (90% UI 15·9–25·1) deaths per 1000 livebirths in eastern Europe and central Asia to 59·5 (48·5–70·4) deaths per 1000 livebirths in south Asia. In 2016, the ratios of under-5 mortality rate in the first quintile to under-5 mortality rate in the fifth quintile were significantly above 2·00 in two regions, with 2·49 (90% UI 2·15–2·87) in east Asia and Pacific (excluding China) and 2·41 (2·05–2·80) in south Asia. Eastern and southern Africa had the smallest ratio in 2016 at 1·62 (90% UI 1·48–1·76). Our model suggested that the expected ratio of under-5 mortality rate in the first quintile to under-5 mortality rate in the fifth quintile increases as national-level under-5 mortality rate decreases.InterpretationFor all LMICs (excluding China) combined, the absolute disparitie...

Socioeconomic factors contributing to under-five mortality in sub-Saharan Africa: a decomposition analysis

“…However, the gender of the child may represent both gender discrimination and a biological disadvantage [14]. These four factors are the main socioeconomic factors used to describe U5MR by population subgroups at the international level [6,10,14,15].…”

“…The magnitude of socioeconomic inequalities in U5MR may be assessed by studying each socioeconomic factor separately, using the concentration index, and income or wealth are among the most widely studied [8][9][10]. However, the concentration index measuring wealth-related inequalities in U5MR was not significant in several sub-Saharan countries [8,11], an observation calling for further research on other dimensions of socioeconomic inequalities in U5MR.…”

Pursuing the limits of child survival in the most and least developed countries

“…The age-distribution of young deaths has not received much attention in the extensive literature on under-5 mortality trends in SSA and other low-and middle-income countries (Bendavid 2014;Mercer et al 2015;Burke, Heft-Neal, and Bendavid 2016;Wang et al 2016;Golding et al 2017Golding et al , 2017Abajobir et al 2017;Fadel et al 2017;Chao et al 2018), but just recently new methodologies have been developed that approximate child mortality patterns by age based on summary (Burstein et al 2018) or full (Mejía-Guevara et al 2019) birth history data. The importance of the age distribution of under-5 deaths has been underscored by methodological work (Guillot et al 2012) demonstrating that traditional life table models do not adequately approximate child mortality patterns in SSA, and by the use of age profiles of under-5 mortality to forecast mortality rates (Mejía-Guevara et al 2019) for the assessment of Sustainable Development Goals (SDGs) in SSA.…”

“…;Adda, Chandola, and Marmot 2003;Deaton 2003;Marmot 2005;Mackenbach et al 2005;Cesare et al 2013;Abajobir et al 2017;Marmot 2018). Further, although previous studies have examined the association between infant mortality and various social determinants of health-including maternal education, place of residence, socioeconomic status(Bendavid 2014;Moucheraud et al 2016;Burke, Heft-Neal, and Bendavid 2016;Beatriz et al 2018;Chao et al 2018)-future research should assess the role of underlying and social determinants of mortality change in a country-basis(Marmot 2005;Accorsi et al 2010;Amouzou, Habi, and Bensaïd 2012;Afnan-Holmes et al 2015;Musafili et al 2015;Chao et al 2018) that interact with the age-at-death distribution/inequality that could provide further light on the state of mortality of the most vulnerable groups in each specific context and aiming to identify true epidemiological patterns(Guillot et al 2012).…”

Age Distribution, Trends, and Forecasts of Under-5 Mortality in Sub-Saharan Africa

“…Recent evidence reveals uneven trends in the reduction of child mortality rates in low-and middleincome countries (LMICs), particularly for specific population subgroups: by sex (7,8); by wealth status -with absolute disparities in mortality declining between the poorest and richest households but with persistent relative differences (9,10); over space, with substantial spatial heterogeneity within countries (11) but some convergence at subnational levels (12); and for some causes of death (6,13). Methodological work has addressed the inadequacy of traditional life table models applied to child mortality in SSA (14), and small area smoothing with data from sample surveys and demographic surveillance systems (15).…”