Evidence map›Paper›PMID 40672911›Full record

ArticleFrontiers in public health2025

Effects of vector control interventions on spatio-temporal changes of

Changkuoth Jock Chol, Denekew Bitew Belay, Haile Mekonnen Fenta, Ding-Geng Chen

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
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.

Changkuoth Jock CholDepartment of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Denekew Bitew BelayDepartment of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Haile Mekonnen FentaDepartment of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Ding-Geng ChenDepartment of Statistics, University of Pretoria, Pretoria, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sub-Saharan Africa (SSA) has a disproportionately high malaria fatality rate globally, with young children accounting for the majority of fatalities. The objective of this study is to investigate the spatiotemporal dynamics of malaria infection risk and assess the effect of vector control interventions on malaria infection rates in SSA nations. Methods: We utilized data from the Malaria Atlas Project regarding the prevalence of Results: Between 2011 and 2020, the average annual prevalence rates of malaria infection among children aged 2 to 10 in SSA diminished from 21.32% in 2011 to 16.75% in 2016, with a slight resurgence observed in 2017. Each unit increase in the number of individuals utilizing insecticide-treated nets (ITN) annually correlates with a 34.07% reduction in the risk of malaria infection. A rise in malaria cases has prompted SSA to undertake serious control measures. The auto-regressive process reveals a highly significant temporal correlation, while the global spatial dependency parameter indicates a modest spatial correlation. The highest risk of malaria infection prevalence among children aged 2 to 10 was indicated in states in the West-central, Central, and certain Eastern regions. Conclusion: Given that the West-central, Central, and select Eastern states exhibit the highest rates of malaria infection, the global end malaria councils and the malaria control and elimination program should prioritize interventions in these regions, enhancing vector control measures and providing comprehensive training on their effective utilization to mitigate malaria risk in these areas.

Indexed as

Insecticide-Treated BednetsMalaria, FalciparumMosquito ControlAfrica South of the SaharaBayes TheoremChildChild, PreschoolFemaleHumansPrevalenceSpatio-Temporal AnalysisautocorrelationBayesianMarkov-chain Monte Carloprevalencevector control interventions

Identifiers

PMID40672911
PMCPMC12263685

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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.