Understanding the influence of climate and other environmental factors on blastomycosis incidence and seasonality in northern Great Lakes states - PROJECT SUMMARY Blastomycosis is a disease caused by inhalation of fungal spores of Blastomyces spp. that grow in soil or decaying wood. Infection typically manifests as a respiratory infection that can disseminate to other parts of the body. In the U.S., 57% of diagnosed cases are hospitalized and 8% die. Awareness of blastomycosis is low, leading to diagnostic delays and antibiotic overuse. The Great Lakes region has among the highest incidence rates of blastomycosis in the U.S., and annual case counts have risen over 6-fold since 2000 within this region. Further, diagnoses of blastomycosis in areas not previously considered endemic suggests the range of the fungus is expanding. Climatic changes, including wetter winters and drier summers, more frequent flooding, and transitions from snow to rainfall during winter months, may be associated with increases in incidence. However, little is known about the relationship between climate and blastomycosis, due in part to limited case data availability. These gaps hinder the public health response, including understanding of specific weather conditions associated with higher risk of transmission. To address these gaps, we have compiled the largest time series of blastomycosis cases in humans and dogs to date at a sub-county spatial resolution in the general population. We draw from over two decades of reportable surveillance data from Minnesota, Wisconsin, and Michigan, which together account for roughly 85% of all reported blastomycosis cases. The research focuses on three aims: 1) understand how variation in precipitation and temperature influences blastomycosis incidence within the Great Lakes region and examine heterogeneity in this relationship by land cover and social vulnerability; 2) estimate the effect of extreme precipitation and flooding on blastomycosis incidence; and 3) determine the seasonality of blastomycosis and investigate how variation in precipitation and temperature explain seasonal timing and strength over space and time. To achieve these aims, we will link georeferenced case data across Great Lakes states since 2000 with fine-scale data on flooding and precipitation, partitioned into both liquid and solid forms. We will use novel time series approaches to understand the delayed and non-linear associations between precipitation patterns, extreme weather events, and disease incidence over time. We will evaluate heterogeneity in the dose-response relationships by land cover and axes of neighborhood social vulnerability. We will apply wavelet analyses to elucidate seasonality of disease across space and time to understand how climate variation explains heterogeneity in seasonal dynamics. Understanding associations between climate variation and blastomycosis incidence will help public health officials anticipate seasonal surges as well as future incidence in the region, which can in turn guide mitigation measures and awareness campaigns. The results of this work may also inform environmental policy to mitigate exposure to environmental hazards, such as floods. Altogether the results of this work will aid in anticipating and planning for future fungal disease risks in the context of global climate change.
