Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever

Abstract

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed.

Fig 1. Plot of reported number of cases submitted to different state health departments in the study region.

Fig 2. The posterior median and 95% CrI for the overall time trend in the covariate model.

Fig 3. County–level crude rate estimates of Rocky Mountain spotted fever prevalence reported to the state health departments for the study period, 2005–2014.

Fig 4. County–specific Bayesian smoothed estimates (posterior median) of Rocky Mountain spotted fever prevalence for the study period between years 2005–2014.

Fig 5. Posterior median of county–specific differential trends. Counties with values closer to 0 indicate a higher risk for RMSF.