A Bayesian zero-inflated spatially varying coefficients model for overdispersed binomial data

Music City Center 

Cardiometabolic risk factors (CRFs) during pregnancy are early indicators of maternal diseases, such as stroke and type 2 diabetes. The total number of CRFs typically takes the form of binomial counts that exhibit overdispersion and zero inflation due to correlations among the underlying CRFs. Motivated by an examination of spatiotemporal trends in five CRFs among pregnant women in the US state of South Carolina during the COVID-19 pandemic, we develop a Bayesian zero-inflated beta-binomial model within a spatiotemporal framework. This model combines a point mass at zero to account for zero inflation and a beta-binomial distribution to model the remaining CRF counts. Given the notable racial disparities in CRFs that vary across the state over time, we incorporate a spatially varying coefficients model to explore the complex relationships between CRFs and geographic and temporal disparities among non-Hispanic White and non-Hispanic Black women. For posterior inference, we develop an efficient hybrid Markov Chain Monte Carlo algorithm that relies on easily sampled Gibbs and Metropolis-Hastings steps. In simulation studies, the model effectively captures trends between the two racial groups and accurately predicts mean CRF scores in each region over time. Our analysis of CRFs in South Carolina reveals that certain counties, such as Chesterfield and Clarendon, exhibit particularly wide gaps in racial health disparities, making them prime candidates for community-level interventions aimed at reducing these disparities.

Cardiometabolic risk

Gaussian Markov random field

Health disparity

Spatiotemporal model

Pólya-Gamma data augmentation

Zero-inflated beta-binomial distribution