James Chirombo|Pietro Ceccato|Rachel Lowe|Dianne J. Terlouw|Madeleine C. Thomson|Austin Gumbo|Peter J. Diggle|Jonathan M. Read
Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors
Lancaster University|University of Malawi|Malawi-Liverpool-Wellcome Trust Clinical Research Programme|University of Liverpool|London School of Hygiene & Tropical Medicine|Barcelona Institute for Global Health|Liverpool School of Tropical Medicine|Malawi-Liverpool-Wellcome Trust Clinical Research Programme|University of Liverpool|Ministry of Health|Lancaster University|Lancaster University
abstract background malaria transmission influenced complex interplay factors including climate socioeconomic environmental factors interventions malaria control efforts across africa shown mixed impact climate driven factors may play increasing role climate change efforts strengthen routine facilitybased monthly malaria data collection across africa create increasingly valuable data source interpret burden trends monitor control programme progress better understanding association climatic nonclimatic drivers malaria incidence time space may help guide interpret impact interventions methods routine monthly paediatric outpatient clinical malaria case data compiled 27 districts malawi 2004 2017 analysed combination data climatic environmental socioeconomic interventional factors district level population estimates spatiotemporal generalized linear mixed model fitted using bayesian inference order quantify strength association various risk factors districtlevel variation clinical malaria rates malawi visualized using maps results 2004 2017 reported childhood clinical malaria case rates showed slight increase 50 53 cases per 1000 population considerable variation across country climatic zones climatic environmental factors including average monthly air temperature rainfall anomalies normalized difference vegetative index ndvi rdt use diagnosis showed significant relationship malaria incidence temperature current month 3 months prior showed significant relationship disease incidence unlike rainfall anomaly associated malaria incidence three months prior estimated risk maps show relatively high risk along lake shire valley regions malawi conclusion modelling approach identify locations likely unusually high low risk malaria incidence across malawi distinguishes contributions risk explained measured riskfactors unexplained residual spatial variation also spatial statistical methods applied readily available routine data provides alternative information source supplement survey data policy development implementation direct surveillance intervention efforts