Climate blind spots in malaria control: Frontline perspectives on health system readiness in Zambia
Bibliographic record
Abstract
Abstract Background Climate change is increasingly recognised as a significant barrier to malaria elimination, especially in low-and middle-income countries (LMICs), where vulnerability to vector-and waterborne diseases is heightened. Climate variability increasingly influences malaria transmission dynamics, yet its impact on malaria control efforts remains underexplored. This study explored healthcare workers’ and community-based volunteers’ (CBVs) perspectives on climate change and the perceived contribution of climate variability to malaria transmission in Zambia. Methods A cross-sectional qualitative study was conducted between August and October 2023 across twenty purposefully selected districts representing high-and low-burden malaria settings. Nine key informant interviews and fourteen focus group discussions were conducted with malaria program officers, clinicians, environmental health officers and CBVs. Data were transcribed verbatim, imported into ATLAS.ti version 23, and analysed thematically. Results Participants consistently reported that flooding, drought, deforestation, and shifting rainfall patterns were increasing mosquito breeding sites and altering malaria transmission seasons. Climate-related disruptions, poor road access during floods and competing health priorities, including cholera outbreaks and COVID-19, were perceived to hinder effective malaria prevention and case management. While participants acknowledged the need for a more integrated response, they largely emphasised reinforcing existing malaria control strategies, such as indoor residual spraying (IRS) and insecticide-treated nets (ITNs), with limited reference to broader climate adaptation measures or national climate policies, highlighting gaps in policy dissemination and implementation. Participants also noted contextual barriers, including vector resistance and diagnostic inaccuracies. Notably, the emerging role of malaria vaccination was not mentioned, indicating a potential knowledge gap in climate-adaptive malaria strategies. Conclusions Frontline perspectives highlight substantial climate-related challenges to sustaining malaria control in Zambia and gaps in climate-health knowledge among HCWs and CBVs. Strengthening climate-resilient systems, improving policy dissemination and integrating climate adaptation into malaria programming and training are critical to sustaining progress towards elimination. Author Summary Despite clear evidence that climate change is reshaping malaria transmission in sub-Saharan Africa, little is known about how frontline health workers perceive and respond to these shifts. This study provides the first multi-district qualitative examination of healthcare worker and community volunteer perspectives on climate–malaria interactions in Zambia. Our findings reveal critical knowledge gaps, limited awareness of existing climate–health policies, and an over-reliance on traditional malaria interventions that fail to integrate climate-resilient strategies. These insights underscore a pressing need for targeted training, strengthened policy dissemination, and multisectoral collaboration to build climate-ready malaria programmes. By illuminating the disconnect between climate science and frontline practice, this study highlights a fundamental barrier to sustaining malaria elimination in a rapidly changing climate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".