Evidence map›Paper›PMID 41214664›Full record

ArticleMalaria journal2025

Effect of exposure to malaria messages on insecticide-treated net use among women and under-five children in Uganda: a propensity score matched analysis.

Edson Mwebesa, Delight Mawufemor Agbi, Daniel Thoya Iha, Lameck Ondieki Agasa, Ann Mwangi

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Article in Malaria journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Edson MwebesaSchool of Science and Aerospace Studies, Moi University, Eldoret, Kenya. mwebesa.edson@gmail.com.ORCID http://orcid.org/0000-0003-4421-5356
Delight Mawufemor AgbiSchool of Science and Aerospace Studies, Moi University, Eldoret, Kenya.
Daniel Thoya IhaSchool of Science and Aerospace Studies, Moi University, Eldoret, Kenya.
Lameck Ondieki AgasaSchool of Science and Aerospace Studies, Moi University, Eldoret, Kenya.
Ann MwangiSchool of Science and Aerospace Studies, Moi University, Eldoret, Kenya.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundMalaria remains a global public health issue, with almost 2.5 million cases and more than 0.6 million deaths each year, of which women and children remain disproportionately affected. Insecticide-treated nets (ITNs) are a cornerstone of malaria prevention in endemic malaria regions. Malaria messages (MM) are intended for positive and measurable social, attitude, and behavioural change towards malaria prevention uptake. However, their impact on insecticide-treated nets (ITNs) use has not been sufficiently investigated through rigorous statistical approaches. This study investigated the effect of MM on ITN use among women and children under five years in Uganda using Propensity Score Matched Analysis (PSMA).

methodsThe study utilized the Uganda Malaria Indicator Survey (UMIS) 2018 -19 data. Women (n = 8868) and children (n = 6915) datasets were extracted from Individual Women's and Children's Data, respectively. The children's dataset included data on interviewed women (caregivers), household indicators and under-five children's data, while the women's dataset contains records of every eligible woman in selected households. Propensity score-matched analysis (PSMA) was utilized to identify the effect of the MM (intervention) on ITN use (outcome) among women of reproductive age and children under five in Uganda.

resultsThe results revealed that 69.3 [95% CI: 67.2, 71.2] of the women and 71.8 [95% CI: 69.1, 74.4] of the children under five years slept under a mosquito bed net the night preceding the survey. The results further revealed a significant and positive effect of exposure to malaria messages on ITN use among exposed women [Average Treatment Effect of the Treated (ATT) = 5.1%, 95% CI: 1.5-8.9%] and under-five children of exposed women [ATT = 4.3%, 95% CI: 1.0-7.6%].

conclusionEffective and relevant behavioural change communications, such as malaria messages, are an effective way of influencing the use of ITN. Such an intervention may reduce malaria endemicity, morbidity, and mortality in Uganda.

Indexed as

Endemic DiseasesHealth EducationHealth Knowledge, Attitudes, PracticeInsecticide-Treated BednetsMalariaAdolescentAdultChild, PreschoolFemaleHumansInfantInfant, NewbornMaleMiddle AgedPropensity ScoreSurveys and QuestionnairesChildren under-fiveInsecticide-treated netsMalariaPropensity score-matched analysisWomen

Identifiers

PMID41214664
PMCPMC12604199

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LicenceCC BY-NC-ND
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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.