Evidence map›Paper›PMID 28958464›Full record

ReviewLancet (London, England)2017

Mapping under-5 and neonatal mortality in Africa, 2000-15: a baseline analysis for the Sustainable Development Goals.

Nick Golding, Roy Burstein, Joshua Longbottom, Annie J Browne, Nancy Fullman, Aaron Osgood-Zimmerman, Lucas Earl, Samir Bhatt, Ewan Cameron, Daniel C Casey and 20 more

Abstract readComparative StudyReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Lancet (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 175 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
175citing papers in PubMed, 7 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

175 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  15. Anthropometry and Malaria among Children in Niger: A Cross-Sectional Study.The American journal of tropical medicine and hygiene · 2018
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115 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

30 authors.

Nick GoldingSchool of BioSciences, University of Melbourne, Parkville, VIC, Australia.
Roy BursteinInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Joshua LongbottomBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Annie J BrowneBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Nancy FullmanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Aaron Osgood-ZimmermanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Lucas EarlInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Samir BhattBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Ewan CameronBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Daniel C CaseyInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Laura Dwyer-LindgrenInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Tamer H FaragInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Abraham D FlaxmanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Maya S FraserInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Peter W GethingBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Harry S GibsonBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Nicholas GraetzInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
L Kendall KrauseBill & Melinda Gates Foundation, Seattle, WA, USA.
Xie Rachel KulikoffInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Stephen S LimInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Bonnie MappinBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Chloe MorozoffInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Robert C ReinerInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Amber SligarInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
David L SmithInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Haidong WangInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Daniel J WeissBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Christopher J L MurrayInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Catherine L MoyesBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Simon I HayBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. Electronic address: sihay@uw.edu.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundDuring the Millennium Development Goal (MDG) era, many countries in Africa achieved marked reductions in under-5 and neonatal mortality. Yet the pace of progress toward these goals substantially varied at the national level, demonstrating an essential need for tracking even more local trends in child mortality. With the adoption of the Sustainable Development Goals (SDGs) in 2015, which established ambitious targets for improving child survival by 2030, optimal intervention planning and targeting will require understanding of trends and rates of progress at a higher spatial resolution. In this study, we aimed to generate high-resolution estimates of under-5 and neonatal all-cause mortality across 46 countries in Africa.

methodsWe assembled 235 geographically resolved household survey and census data sources on child deaths to produce estimates of under-5 and neonatal mortality at a resolution of 5 × 5 km grid cells across 46 African countries for 2000, 2005, 2010, and 2015. We used a Bayesian geostatistical analytical framework to generate these estimates, and implemented predictive validity tests. In addition to reporting 5 × 5 km estimates, we also aggregated results obtained from these estimates into three different levels-national, and subnational administrative levels 1 and 2-to provide the full range of geospatial resolution that local, national, and global decision makers might require.

findingsAmid improving child survival in Africa, there was substantial heterogeneity in absolute levels of under-5 and neonatal mortality in 2015, as well as the annualised rates of decline achieved from 2000 to 2015. Subnational areas in countries such as Botswana, Rwanda, and Ethiopia recorded some of the largest decreases in child mortality rates since 2000, positioning them well to achieve SDG targets by 2030 or earlier. Yet these places were the exception for Africa, since many areas, particularly in central and western Africa, must reduce under-5 mortality rates by at least 8·8% per year, between 2015 and 2030, to achieve the SDG 3.2 target for under-5 mortality by 2030.

interpretationIn the absence of unprecedented political commitment, financial support, and medical advances, the viability of SDG 3.2 achievement in Africa is precarious at best. By producing under-5 and neonatal mortality rates at multiple levels of geospatial resolution over time, this study provides key information for decision makers to target interventions at populations in the greatest need. In an era when precision public health increasingly has the potential to transform the design, implementation, and impact of health programmes, our 5 × 5 km estimates of child mortality in Africa provide a baseline against which local, national, and global stakeholders can map the pathways for ending preventable child deaths by 2030.

fundingBill & Melinda Gates Foundation.

Indexed as

Cause of DeathConservation of Natural ResourcesAfrica, WesternAge FactorsBayes TheoremChild MortalityChild, PreschoolDeveloping CountriesFemaleGoalsHumansInfantInfant MortalityInfant, NewbornMalePopulation Surveillance

Identifiers

PMID28958464
PMCPMC5687451

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.