Uncovering patterns for adverse pregnancy outcomes with a Bayesian spatial model: Evidence from Philadelphia

We introduce a Bayesian conditional autoregressive model for analyzing patient-specific and neighborhood risks of stillbirth and preterm birth within a city. Our fully Bayesian approach automatically learns the amount of spatial heterogeneity and spatial dependence between neighborhoods. Our model provides meaningful inferences and uncertainty quantification for both covariate effects and neighborhood risk probabilities through their posterior distributions. We apply our methodology to data from the city of Philadelphia. Using electronic health records (45,919 deliveries at hospitals within the University of Pennsylvania Health System) and United States Census Bureau data from 363 census tracts in Philadelphia, we find that both patient-level characteristics (e.g. self-identified race/ethnicity) and neighborhood-level characteristics (e.g. violent crime) are highly associated with patients' odds of stillbirth or preterm birth. Our neighborhood risk analysis further reveals that census tracts in West Philadelphia and North Philadelphia are at highest risk of these outcomes. Specifically, neighborhoods with higher rates of women in poverty or on public assistance have greater neighborhood risk for these outcomes, while neighborhoods with higher rates of college-educated women or women in the labor force have lower risk. Our findings could be useful for targeted individual and neighborhood interventions.