Bayesian Point Process Modeling to Quantify Geographic Regions of Excess Stillbirth Risk

Motivated by the paucity of high-quality stillbirth surveillance data and the spatial analysis of such data, we describe the pattern of stillbirth events as a step toward increased understanding of risk factors which can better guide future measures to mitigate these events. A challenge in such an analysis is that some mothers experience stillbirth events from independent pregnancies within a defined study period. To account for these dependencies, we parameterize our model to include a maternal contextual effect and broaden the appeal of Bayesian Poisson point process modeling to quantify excess stillbirth risk while considering a form of a log-Gaussian Cox process. In the presence of extra unobserved spatial variation in risk, we demonstrate a pragmatic methodologic strategy to model the risk surface in relation to covariates and that there is a variance-bias trade-off associated with the use of a maternal contextual effect. We applied our strategy to the spatial distribution of stillbirth in Iowa during the years 2005–2011 using data obtained from an active, statewide public health surveillance program. We identified areas of excess risk for further investigation based on model components that captured important features of the data.