Bayesian spatio-temporal evaluations of spotted fever group rickettsioses with socio-economic and environmental factors: 2013 - 2018

Abstract

Recent advances in disease mapping allow for the simultaneous evaluation of space-time dynamics of diseases and the drivers of such dynamics, which are useful for designing public health campaigns and surveillance systems. This study determined the space-time patterns of spotted fever group rickettsioses (SFR), a group of tick-borne Rickettsial diseases widely prevalent in the U.S, and further evaluated the associations of socio-economic and environmental (land cover, climate) factors with SFR. County-level SFR cases reported to the Kansas Department of Health and Environment between years 2013 -- 2018 and publicly available covariate data were used in a Bayesian hierarchical modeling framework to quantify trends and associations. The results show a steady increase in space-time trend for SFR in Kansas, the spread of SFR to newer counties over the study period, and two clusters of high-risk areas in the southeast and northeastern parts of Kansas. The space-time pattern of SFR is influenced by poverty status, the number of older homes in a county, and higher relative humidity conditions. The relevance of these findings is discussed in the context of public health and climate change implications on health.