Changes in the spatial distribution of the under-five mortality rate: Small-area analysis of 122 DHS surveys in 262 subregions of 35 countries in Africa

Abstract

The under-five mortality rate (U5MR) is a critical and widely available population health indicator. Both the MDGs and SDGs define targets for improvement in the U5MR, and the SDGs require spatial disaggregation of indicators. We estimate trends in the U5MR for Admin-1 subnational areas using 122 DHS surveys in 35 countries in Africa and assess progress toward the MDG target reductions for each subnational region and each country as a whole. In each country, direct weighted estimates of the U5MR from each survey are calculated and combined into a single estimate for each Admin-1 region across five-year periods. Our method fully accounts for the sample design of each survey. The region-time-specific estimates are smoothed using a Bayesian, space-time model that produces more precise estimates (when compared to the direct estimates) at a one-year scale that are consistent with each other in both space and time. The resulting estimated distributions of the U5MR are summarized and used to assess subnational progress toward the MDG 4 target of two-thirds reduction in the U5MR during 1990–2015. Our space-time modeling approach is tractable and can be readily applied to a large collection of sample survey data. Subnational, regional spatial heterogeneity in the levels and trends in the U5MR vary considerably across Africa. There is no generalizable pattern between spatial heterogeneity and levels or trends in the U5MR. Subnational, small-area estimates of the U5MR: (i) identify subnational regions where interventions are still necessary and those where improvement is well under way; and (ii) countries where there is very little spatial variation and others where there are important differences between subregions in both levels and trends. More work is necessary to improve both the data sources and methods necessary to adequately measure subnational progress toward the SDG child survival targets.

Mapping changes in the spatiotemporal distribution of lumpy skin disease virus

“…(Table ). Previous studies have neglected the inclusion of space‐time interactions (Allepuz et al, ), which is known to improve accuracy of this kind of models and their ability to reveal spatial and temporal risk patterns trends (Zehang et al, ). The identified hot areas with increased infection risk after controlling for the contribution of maximum average temperature, monthly precipitation and average wind speed were mapped (Figure ).…”

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

“…Country-level estimates facilitate international comparisons but mask important geographical heterogeneity. Previous assessments of mortality of children under 5 have noted significant within-country heterogeneity, particularly in sub-Saharan Africa 5,7,[10][11][12][13][14] , as well as in Brazil 15 , Iran 16 and China 17 . Understanding public health risks at more granular subpopulation levels is central to the emerging concept of precision public health 18 , which uses "the best available data to target more effectively and efficiently interventions…to those most in need" 18 .…”

“…Over the 2000-2017 period, this number is 71.8 (68.5-74.9) million deaths, or 58% (55-61) of the total number of deaths of children under 5. Total deaths attributable to inequality in this scenario ranged from 13 (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)…”

From Start to Finish: A Framework for the Production of Small Area Official Statistics

“…We can validate against reliable direct estimates because of the choice of the spatial and temporal resolution of analysis. Full details are in Li et al (2018).…”