Evidence map›Paper›PMID 41654939›Full record

ArticleMalaria journal2026

Climate variability and malaria incidence trends in Yumbe District, West Nile Sub-region of Uganda (2017-2021).

Lesley Rose Ninsiima, Rogers Musiitwa, Zaitune Nanyunja, James Muleme, Chris Maasaba, Twahiri Anule, David Musoke

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Article in Malaria journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lesley Rose NinsiimaDepartment of Disease Control and Environmental Health, Makerere University School of Public Health, Kampala, Uganda. lninsiima04@gmail.com.
Rogers MusiitwaDepartment of Disease Control and Environmental Health, Makerere University School of Public Health, Kampala, Uganda.
Zaitune NanyunjaDepartment of Biological Sciences, Kyambogo University, Kampala, Uganda.
James MulemeDepartment of Disease Control and Environmental Health, Makerere University School of Public Health, Kampala, Uganda.
Chris MaasabaDepartment of Disease Control and Environmental Health, Makerere University School of Public Health, Kampala, Uganda.
Twahiri AnuleHealth Department, Yumbe District Local Government, Yumbe, Uganda.
David MusokeDepartment of Disease Control and Environmental Health, Makerere University School of Public Health, Kampala, Uganda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMalaria remains a major global public health concern with the greatest burden in tropical and subtropical regions, particularly sub-Saharan Africa. Uganda ranks among the world's highest burden countries, with its warm temperatures, abundant rainfall and diverse mosquito breeding habitats sustaining year-round malaria transmission in malaria endemic areas. This study assessed malaria incidence trends and their association with climate variables in Yumbe district, Uganda.

methodsA retrospective ecological time-series study analysed malaria incidence (2017-2021) in Yumbe district, Uganda, using District Health Information System reports and Uganda National Meteorological Authority climate data (daily temperature and rainfall). Data were cleaned in Excel and analysed in R software V4.5.1. Monthly/annual summaries, seasonal pattern graphs, Kendall's tau correlations for non-linear associations, and Multiple Linear and Poisson regressions with lag effects were done. Time series analysis involved seasonal decomposition, cross-correlation, and ARIMAX modelling. A multivariable OLS regression on log(1 + cases) with best-lagged rainfall and minimum temperature further assessed climate influence.

resultsBetween 2017 and 2021, a total of 2,066,711 malaria cases were reported in Yumbe district. Malaria trends closely followed rainfall patterns, peaking during the period of high precipitation. Time-series analysis showed that rainfall was positively associated with malaria incidence at one-month lag (β = 0.38, p < 0.05), while minimum temperature was inversely associated (β = - 0.29, p < 0.05). Statistical analysis revealed rainfall (mm) strongly led malaria cases by 1 month (r = 0.759, p < 0.001). Maximum temperature showed no significant effect on malaria incidence.

conclusionMalaria incidence in Yumbe district is strongly influenced by rainfall and minimum temperature. This study highlights the role of climate variability in malaria transmission in malaria endemic areas. Integrating climate data into surveillance and early warning systems could enhance timely interventions in malaria endemic areas like Yumbe district.

Indexed as

ClimateMalariaHumansIncidenceRainRetrospective StudiesSeasonsTemperatureUgandaClimate variablesMalaria incidence trendsRainfallTemperature

Identifiers

PMID41654939
PMCPMC12922325

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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.