Evidence map›Paper›PMID 32354095›Full record

ArticleInternational journal of environmental research and public health2020

A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa.

Samuel Manda, Ndamonaonghenda Haushona, Robert Bergquist

Abstract readScoping Review
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  9. A Review of Spatial Analysis Application in Childhood Malnutrition Studies.The Malaysian journal of medical sciences : MJMS · 2022
    Review
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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

3 authors.

Samuel MandaBiostatistics Research Unit, South African Medical Research Council, Pretoria 0001, South Africa.ORCID 0000-0002-9672-3312
Ndamonaonghenda HaushonaBiostatistics Research Unit, South African Medical Research Council, Pretoria 0001, South Africa.ORCID 0000-0002-8994-4133
Robert BergquistIngerod, SE-454 94 Brastad, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels.

Indexed as

Health ServicesHealth SurveysAdolescentAdultAfrica South of the SaharaBayes TheoremChildData AnalysisFemaleHumansMaleSpatial Analysisdisease mappinghealth surveysspatial methodsSub-Saharan Africa

Identifiers

PMID32354095
PMCPMC7246597

What Socratic 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 Socratic graph.