Evidence map›Paper›PMID 38594716›Full record

ArticleMalaria journal2024

Modelling spatiotemporal variation in under-five malaria risk in Ghana in 2016-2021.

Justice Moses K Aheto, Lynette J Menezes, Wisdom Takramah, Liwang Cui

Open access · goldAbstract read
In one paragraph

Article in Malaria journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
6.0field-weighted citation impact, top 3% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

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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

4 authors at 2 institutions in 2 countries.

Justice Moses K AhetoDepartment of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Accra, Ghana. justiceaheto@yahoo.com.
Lynette J MenezesDepartment of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Wisdom TakramahDepartment of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Accra, Ghana.
Liwang CuiDepartment of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
University of Ghana · GHUniversity of South Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGhana is among the top 10 highest malaria burden countries, with about 20,000 children dying annually, 25% of which were under five years. This study aimed to produce interactive web-based disease spatial maps and identify the high-burden malaria districts in Ghana.

methodsThe study used 2016-2021 data extracted from the routine health service nationally representative and comprehensive District Health Information Management System II (DHIMS2) implemented by the Ghana Health Service. Bayesian geospatial modelling and interactive web-based spatial disease mapping methods were employed to quantify spatial variations and clustering in malaria risk across 260 districts. For each district, the study simultaneously mapped the observed malaria counts, district name, standardized incidence rate, and predicted relative risk and their associated standard errors using interactive web-based visualization methods.

resultsA total of 32,659,240 malaria cases were reported among children < 5 years from 2016 to 2021. For every 10% increase in the number of children, malaria risk increased by 0.039 (log-mean 0.95, 95% credible interval = - 13.82-15.73) and for every 10% increase in the number of males, malaria risk decreased by 0.075, albeit not statistically significant (log-mean - 1.82, 95% credible interval = - 16.59-12.95). The study found substantial spatial and temporal differences in malaria risk across the 260 districts. The predicted national relative risk was 1.25 (95% credible interval = 1.23, 1.27). The malaria risk is relatively the same over the entire year. However, a slightly higher relative risk was recorded in 2019 while in 2021, residing in Keta, Abuakwa South, Jomoro, Ahafo Ano South East, Tain, Nanumba North, and Tatale Sanguli districts was associated with the highest malaria risk ranging from a relative risk of 3.00 to 4.83. The district-level spatial patterns of malaria risks changed over time.

conclusionThis study identified high malaria risk districts in Ghana where urgent and targeted control efforts are required. Noticeable changes were also observed in malaria risk for certain districts over some periods in the study. The findings provide an effective, actionable tool to arm policymakers and programme managers in their efforts to reduce malaria risk and its associated morbidity and mortality in line with the Sustainable Development Goals (SDG) 3.2 for limited public health resource settings, where universal intervention across all districts is practically impossible.

Indexed as

MalariaBayes TheoremChildGhanaHealth ServicesHumansMaleRiskBayesian methodsGeospatial methodsINLAIntegrated Nested Laplace ApproximationInteractive web-based mappingMalariaMapping malaria riskSpatio-temporal methodsSub-Saharan AfricaUnder-five malaria

Identifiers

PMID38594716
PMCPMC11005246
OpenAlexW4394594490

What OpenQuestion 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 OpenQuestion graph.