A spatial bivariate probit model for correlated binary data with application to adverse birth outcomes

Abstract

Motivated by a study examining geographic variation in birth outcomes, we develop a spatial bivariate probit model for the joint analysis of preterm birth and low birth weight. The model uses a hierarchical structure to incorporate individual and areal-level information, as well as spatially dependent random effects for each spatial unit. Because rates of preterm birth and low birth weight are likely to be correlated within geographic regions, we model the spatial random effects via a bivariate conditionally autoregressive prior, which induces regional dependence between the outcomes and provides spatial smoothing and sharing of information across neighboring areas. Under this general framework, one can obtain region-specific joint, conditional, and marginal inferences of interest. We adopt a Bayesian modeling approach and develop a practical Markov chain Monte Carlo computational algorithm that relies primarily on easily sampled Gibbs steps. We illustrate the model using data from the 2007–2008 North Carolina Detailed Birth Record.