Evidence map›Paper›PMID 39830277›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Climate Change and Malaria: A Call for Robust Analytics.

Daniel J Laydon, David L Smith, Kaustubh Chakradeo, Mark P Khurana, Jaffer Okiring, David A Duchene, Samir Bhatt

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Daniel J LaydonMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.ORCID 0000-0003-4270-3321
David L SmithDepartment of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.ORCID 0000-0003-4367-3849
Kaustubh ChakradeoSection of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0003-0946-0524
Mark P KhuranaSection of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-1123-7674
Jaffer OkiringClinical Epidemiology Unit, Makerere University College of Health Sciences, Kampala, Uganda.ORCID 0000-0003-3668-8997
David A DucheneSection of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-5479-1974
Samir BhattMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.ORCID 0000-0002-0891-4611

Funding

Spatial Targeting and Adaptive Vector Control for Residual Transmission and Malaria Elimination in Urban African SettingsR01AI163398 · NIAID · UNIVERSITY OF WASHINGTON · PI SMITH, DAVID L. · 2021 to 2025
$3.2M
Gates Foundation INV-030600NIAID NIH HHS R01 AI163398Wellcome Trust
6 · The paper itself

Abstract

Mosquito ecology and behavior and malaria parasite development display marked sensitivity to weather, in particular to temperature and precipitation. Therefore, climate change is expected to profoundly affect malaria epidemiology in its transmission, spatiotemporal distribution and consequent disease burden. However, malaria transmission is also complicated by other factors (e.g. urbanization, socioeconomic development, genetics, drug resistance) which together constitute a highly complex, dynamical system, where the influence of any single factor can be masked by others. In this study, we therefore aim to re-evaluate the evidence underlying the widespread belief that climate change will increase worldwide malaria transmission. We review two broad types of study that have contributed to this evidence-base: i) studies that project changes in transmission due to inferred relationships between environmental and mosquito entomology, and ii) regression-based studies that look for associations between environmental variables and malaria prevalence. We then employ a simple statistical model to show that environmental variables alone do not account for the observed spatiotemporal variation in malaria prevalence. Our review raises several concerns about the robustness of the analyses used for advocacy around climate change and malaria. We find that, while climate change's effect on malaria is highly plausible, empirical evidence is much less certain. Future research on climate change and malaria must become integrated into malaria control programs, and understood in context as one factor among many. Our work outlines gaps in modelling that we believe are priorities for future research.

Identifiers

PMID39830277
PMCPMC11741450

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.