Evidence map›Paper›PMID 33858833›Full record

ArticleBMJ global health2021

Subnational estimates of factors associated with under-five mortality in Kenya: a spatio-temporal analysis, 1993-2014.

Peter M Macharia, Noel K Joseph, Benn Sartorius, Robert W Snow, Emelda A Okiro

Abstract read
In one paragraph

Article in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

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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

5 authors.

Peter M MachariaPopulation Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya pmacharia@kemri-wellcome.org.ORCID 0000-0003-3410-1881
Noel K JosephPopulation Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.ORCID 0000-0002-0509-1373
Benn SartoriusCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID 0000-0001-6761-2325
Robert W SnowPopulation Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.ORCID 0000-0003-3725-6088
Emelda A OkiroPopulation Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.ORCID 0000-0001-9543-8360

Funding

EPA EP-C-15-003Wellcome Trust 103602Wellcome Trust 107769/Z/10/ZWellcome Trust 201866Wellcome Trust 201866/Z/16/ZWellcome Trust 203077Wellcome Trust 212176
6 · The paper itself

Abstract

backgroundTo improve child survival, it is necessary to describe and understand the spatial and temporal variation of factors associated with child survival beyond national aggregates, anchored at decentralised health planning units. Therefore, we aimed to provide subnational estimates of factors associated with child survival while elucidating areas of progress, stagnation and decline in Kenya.

methodsTwenty household surveys and three population censuses conducted since 1989 were assembled and spatially aligned to 47 subnational Kenyan county boundaries. Bayesian spatio-temporal Gaussian process regression models accounting for inadequate sample size and spatio-temporal relatedness were fitted for 43 factors at county level between 1993 and 2014.

resultsNationally, the coverage and prevalence were highly variable with 38 factors recording an improvement. The absolute percentage change (1993-2014) was heterogeneous ranging between 1% and 898%. At the county level, the estimates varied across space and over time with a majority showing improvements after 2008 which was preceded by a period of deterioration (late-1990 to early-2000). Counties in Northern Kenya were consistently observed to have lower coverage of interventions and remained disadvantaged in 2014 while areas around Central Kenya had and historically have had higher coverage across all intervention domains. Most factors in Western and South-East Kenya recorded moderate intervention coverage although having a high infection prevalence of both HIV and malaria.

conclusionThe heterogeneous estimates necessitates prioritisation of the marginalised counties to achieve health equity and improve child survival uniformly across the country. Efforts are required to narrow the gap between counties across all the drivers of child survival. The generated estimates will facilitate improved benchmarking and establish a baseline for monitoring child development goals at subnational level.

Indexed as

BenchmarkingVulnerable PopulationsBayes TheoremChildHumansKenyaSpatio-Temporal AnalysisUnited Stateschild healthepidemiologygeographic information systemshealth services researchindices of health and disease and standardisation of rates

Identifiers

PMID33858833
PMCPMC8054106

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.