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dc.contributor.authorLowe, Rachel
dc.contributor.authorLee, Sophie A
dc.contributor.authorO'Reilly, Kathleen M
dc.contributor.authorBrady, Oliver J
dc.contributor.authorBastos, Leonardo
dc.contributor.authorCarrasco-Escobar, Gabriel
dc.contributor.authorde Castro Catão, Rafael
dc.contributor.authorColón-González, Felipe J
dc.contributor.authorBarcellos, Christovam
dc.contributor.authorCarvalho, Marilia Sá
dc.contributor.authorBlangiardo, Marta
dc.contributor.authorRue, Haavard
dc.contributor.authorGasparrini, Antonio
dc.date.accessioned2021-04-13T11:30:34Z
dc.date.available2021-04-13T11:30:34Z
dc.date.issued2021-04-11
dc.identifier.citationLowe, R., Lee, S. A., O’Reilly, K. M., Brady, O. J., Bastos, L., Carrasco-Escobar, G., … Gasparrini, A. (2021). Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study. The Lancet Planetary Health, 5(4), e209–e219. doi:10.1016/s2542-5196(20)30292-8
dc.identifier.issn2542-5196
dc.identifier.pmid33838736
dc.identifier.doi10.1016/s2542-5196(20)30292-8
dc.identifier.urihttp://hdl.handle.net/10754/668727
dc.description.abstractTemperature 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. 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. 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. 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. 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. For the Portuguese translation of the abstract see Supplementary Materials section.
dc.description.sponsorshipRL was supported by a Royal Society Dorothy Hodgkin Fellowship. SAL was supported by a Royal Society Research Grant for Research Fellows associated with RL's Dorothy Hodgkin Fellowship. OJB was funded by a Sir Henry Wellcome Fellowship from the Wellcome Trust (206471/Z/17/Z). GC-E was supported by National Institutes of Health/Fogarty International Center Global Infectious Diseases Training Program (D43 TW007120). MSC received grants from Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (E_26/201.356/2014) and support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (304101/2017-6). CB was supported by the Brazilian Climate and Health Observatory, financed by Rede Clima, National Council for Scientific and Technological Development, and the Brazilian Ministry of Health. AG was supported by the Medical Research Council UK (Grant ID: MR/R013349/1) and the Natural Environment Research Council (Grant ID: NE/R009384/1). We are grateful to Ian Harris from the National Centre for Atmospheric Science at the Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UK, for providing the self-calibrated PDSI data for the Climatic Research Unit gridded Time Series version 4.04 ahead of public release for the purpose of this study. We also acknowledge useful discussions of this work with members of the Planetary Health Infectious Disease Lab at the London School of Hygiene & Tropical Medicine, London, UK. We thank Rochelle Schneider dos Santos from the London School of Hygiene & Tropical Medicine, London, UK, for reviewing the Portuguese abstract.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S2542519620302928
dc.rightsThis is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleCombined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study.
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalThe Lancet. Planetary health
dc.eprint.versionPublisher's Version/PDF
dc.identifier.volume5
dc.identifier.issue4
dc.identifier.pagese209-e219
kaust.personRue, Haavard
refterms.dateFOA2021-04-13T11:31:49Z


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This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.