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Abstract:
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Bronchiolitis is the single most common cause of infant hospitalization in the US, with approximately 1,915 per 100,000 infants hospitalized per year during winter virus season. To improve prevention strategies, the impact of environmental variables, such as pollution and temperature, on bronchiolitis prevalence at the local level is required. The US Military Health System (MHS) has compiled data on about 140,000 cases of infant bronchiolitis between the years 2003-2013 and nearly 900,000 controls. However, for privacy reasons, patient home address and date of diagnosis have been randomized to within 400 meters and 3 days, respectively, of the original location and date. This randomization, or "jittering," creates spatial uncertainty and can lead to incorrect inferences on bronchiolitis abundances. In this talk, we propose a method to account for such spatial uncertainty within a spatial Bayesian probit regression model. Using this model and the MHS data, we provide inferences on the environmental impact of various pollution and weather variables on bronchiolitis rates at four unique cities across the US.
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