Abstract
The disease surveillance software SaTScan™ is used to identify spatial and space-time clusters of counties with unusually high counts or rates of SD bridges. Initially, a descriptive data analysis of over 600,000 bridges, on which data were available for 2017, identified the kind of material and design of all bridges. This was followed by analyzing data on SD bridges for the 3108 counties. The clusters were tested for significance with Monte Carlo study to designate significant SD clusters. While the purely spatial analysis was based on data for 2017, the space-time analysis used data for the years 2006–2017. A Negative Binomial regression model was used in addition to a cluster analysis. Regression analysis was performed to adjust SD counts for several covariates or risk factors. This study identified counties with high rates of SD bridges as rural counties with old bridges where there is cold weather and low daily traffic.
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