Climate blind spots in malaria control: Frontline perspectives on health system readiness in Zambia
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».