A hierarchical mixed effect hurdle model for spatiotemporal count data and its application to identifying factors impacting health professional shortages

Count data are common in many fields such as public health. Hurdle models have been developed to model count data when the zero count could be either inflated or deflated. However, when data are repeatedly collected over time and spatially correlated, it is very challenging to model the data appropriately. For example, to study health professional shortage areas, the number of primary care physicians along with other demographic characteristics are collected at the county level in the USA and over different years. Since the data are repeatedly collected over time, counties are nested within the state, and adjacent counties are geographically correlated, the dependence structure of the data is very complex. We develop a Bayesian hurdle model with multilayered random effects to incorporate this complex structure. We use a time‐varying random effect for each state to capture the time effect at the state level, and a temporal thin plate spline to capture the spatiotemporal correlation across different counties. We use STAN to obtain samples for inference from the posterior distribution. By using the model proposed, we can identify the important factors which impact health professional shortage areas. Simulation studies also confirm the effectiveness of the model.