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COMBINED EFFECTS OF HYDROMETEOROLOGICAL HAZARDS AND URBANISATION ON DENGUE RISK IN BRAZIL: A SPATIOTEMPORAL MODELLING STUDY
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Affilliation
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK / Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
University of California San Diego. Scripps Institution of Oceanography. San Diego, CA, USA / Universidad Peruana Cayetano Heredia. Institute of Tropical Medicine Alexander von Humboldt. Health Innovation Laboratory. Lima, Peru.
Federal University of Espírito Santo. Department of Geography. Vitória, ES, Brazil.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
Imperial College London. Department of Epidemiology and Biostatistics. MRC Centre for Environment and Health. London, UK.
King Abdullah University of Science and Technology. Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division. Thuwal, Saudi Arabia.
London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Public Health Environments and Society. London, UK / London School of Hygiene & Tropical Medicine. Centre for Statistical Modelling. Centre for Statistical Modelling. London, UK.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK / Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
University of California San Diego. Scripps Institution of Oceanography. San Diego, CA, USA / Universidad Peruana Cayetano Heredia. Institute of Tropical Medicine Alexander von Humboldt. Health Innovation Laboratory. Lima, Peru.
Federal University of Espírito Santo. Department of Geography. Vitória, ES, Brazil.
London School of Hygiene & Tropical Medicine. Centre for Mathematical Modelling of Infectious Diseases. London, UK / London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Infectious Disease Epidemiology. London, UK.
Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
Imperial College London. Department of Epidemiology and Biostatistics. MRC Centre for Environment and Health. London, UK.
King Abdullah University of Science and Technology. Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division. Thuwal, Saudi Arabia.
London School of Hygiene & Tropical Medicine. Centre on Climate Change and Planetary Health. London, UK / London School of Hygiene & Tropical Medicine. Department of Public Health Environments and Society. London, UK / London School of Hygiene & Tropical Medicine. Centre for Statistical Modelling. Centre for Statistical Modelling. London, UK.
Abstract in Portuguese
Contexto: Os padrões de temperatura e precipitação são reconhecidos por influenciar a sazonalidade da transmissão da dengue. No entanto, o impacto de eventos climáticos extremos (seca ou chuvas intensas) ainda é pouco compreendido em relação ao momento e a intensidade das epidemias de dengue. Neste estudo, buscamos quantificar os efeitos não lineares e tardios de eventos hidrometeorológicos extremos sobre o risco de dengue ao longo de um gradiente urbano no Brasil utilizando um modelo espaço-temporal. Métodos: Combinamos modelos não lineares de defasagem distribuída, com modelos espaçotemporais bayesianos para determinar a associação de exposição-defasagem-desfecho entre o risco relativo (RR) de dengue e o índice de intensidade da seca. Ajustamos o modelo a dados mensais de casos de dengue para as 558 microrregiões do Brasil entre janeiro de 2001 e dezembro de 2019, levando em consideração fatores de confusão não observados, autocorrelação espacial, sazonalidade e variabilidade interanual. Avaliamos a variação do RR ao longo de um gradiente urbano por meio de uma interação entre o índice de intensidade da seca e o grau de urbanização. Também examinamos o impacto de extremos hidrometeorológicos no risco de dengue em áreas com alta frequência de interrupção do abastecimento de água. Resultados: O banco de dados contava com 12 895 293 casos de dengue notificados no Brasil entre 2001 e 2019. O RR de dengue aumentou entre 0-3 meses após condições extremamente úmidas (máximo de RR em um mês com defasagem 1·56 [95% IC 1·41–1·73]), enquanto as condições de seca aumentaram o risco três a cinco meses depois (RR máximo em quatro meses de defasagem 1·43 [1·22–1·67]). Houve um ganho no ajuste do modelo ao incluir a interação linear entre o índice de intensidade da seca e o grau de urbanização, o que mostrou um risco mais alto de dengue em áreas rurais que em áreas altamente urbanizadas durante condições extremamente úmidas (máximo de RR 1·77 [1·32–2·37] sem defasagem vs RR máximo 1·58 [1·39–1·81] com dois meses de defasagem). No entanto, o risco de dengue foi maior em áreas altamente urbanizadas após seca extrema que em áreas rurais (RR máximo 1∙60 [1∙33–1∙92] vs 1∙15 [1∙08–1∙22], ambos com quatro meses de defasagem). Também verificamos que o risco de dengue após uma seca extrema foi maior em áreas com maior frequência de interrupção do abastecimento de água. Interpretação: Nosso estudo mostra que condições extremamente úmidas e secas podem aumentar o risco relativo de dengue com tempos de retardo diferentes. O risco associado a condições extremamente úmidas foi maior em áreas mais rurais, enquanto o risco associado a secas extremas é exacerbado em áreas altamente urbanizadas, que sofrem com a escassez de água e abastecimento intermitente de água durante as secas. Esses resultados permitem o direcionamento de atividades de controle de vetores em áreas com problemas de infraestrutura urbana, não apenas durante a estação chuvosa e quente, mas também durante os períodos de seca.
Abstract
Background: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model.
Methods: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages.
Findings: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0-3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41-1·73]) and 3-5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22-1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32-2·37] at 0 months lag vs maximum RR 1·58 [1·39-1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33-1·92] vs 1·15 [1·08-1·22], both at 4 months lag). We also found the dengue risk following extreme drought was higher in areas that had a higher frequency of water supply shortages.
Interpretation: Wet conditions and extreme drought can increase the risk of dengue with different delays. The risk associated with extremely wet conditions was higher in more rural areas and the risk associated with extreme drought was exacerbated in highly urbanised areas, which have water shortages and intermittent water supply during droughts. These findings have implications for targeting mosquito control activities in poorly serviced urban areas, not only during the wet and warm season, but also during drought periods.
Funding: Royal Society, Medical Research Council, Wellcome Trust, National Institutes of Health, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, and Conselho Nacional de Desenvolvimento Científico e Tecnológico.
Translation: For the Portuguese translation of the abstract see Supplementary Materials section.
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