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Sadoine ML, Zinszer K, Liu Y, Gachon P, Fournier M, Dueymes G, Dorsey G, Llerena A, Namuganga JF, Nasri B, Smargiassi A. Predicting malaria risk considering vector control interventions under climate change scenarios. Sci Rep 2024; 14:2430. [PMID: 38286803 PMCID: PMC10824718 DOI: 10.1038/s41598-024-52724-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 01/23/2024] [Indexed: 01/31/2024] Open
Abstract
Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010-2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min-Max]: 16,785 [9,902-74,382] vs 21,289 [11,796-70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.
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Affiliation(s)
- Margaux L Sadoine
- School of Public Health, Université de Montréal, Montreal, Quebec, Canada.
- Center for Public Health Research, Université de Montréal, Montreal, Quebec, Canada.
| | - Kate Zinszer
- School of Public Health, Université de Montréal, Montreal, Quebec, Canada
- Center for Public Health Research, Université de Montréal, Montreal, Quebec, Canada
| | - Ying Liu
- School of Public Health, Université de Montréal, Montreal, Quebec, Canada
- Center for Public Health Research, Université de Montréal, Montreal, Quebec, Canada
| | - Philippe Gachon
- ESCER (Étude et Simulation du Climat à l'Échelle Régionale) Centre, Université du Québec à Montréal, Montreal, Quebec, Canada
| | - Michel Fournier
- Department of Public Health, Montreal Regional, Montreal, Quebec, Canada
| | - Guillaume Dueymes
- ESCER (Étude et Simulation du Climat à l'Échelle Régionale) Centre, Université du Québec à Montréal, Montreal, Quebec, Canada
| | - Grant Dorsey
- University of California San Francisco, San Francisco, USA
| | - Ana Llerena
- Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montreal, Quebec, Canada
| | | | - Bouchra Nasri
- School of Public Health, Université de Montréal, Montreal, Quebec, Canada
- Center for Public Health Research, Université de Montréal, Montreal, Quebec, Canada
| | - Audrey Smargiassi
- School of Public Health, Université de Montréal, Montreal, Quebec, Canada
- Center for Public Health Research, Université de Montréal, Montreal, Quebec, Canada
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