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We introduce a Bayesian conditional autoregressive model for analyzingpatient-specific and neighborhood risks of stillbirth and preterm birth withina city. Our fully Bayesian approach automatically learns the amount of spatialheterogeneity and spatial dependence between neighborhoods. Our model providesmeaningful inferences and uncertainty quantification for both covariate effectsand neighborhood risk probabilities through their posterior distributions. Weapply our methodology to data from the city of Philadelphia. Using electronichealth records (45,919 deliveries at hospitals within the University ofPennsylvania Health System) and United States Census Bureau data from 363census 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 orpreterm birth. Our neighborhood risk analysis further reveals that censustracts in West Philadelphia and North Philadelphia are at highest risk of theseoutcomes. Specifically, neighborhoods with higher rates of women in poverty oron public assistance have greater neighborhood risk for these outcomes, whileneighborhoods with higher rates of college-educated women or women in the laborforce have lower risk. Our findings could be useful for targeted individual andneighborhood interventions.

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