Intrinsically Autoregressive Spatiotemporal Models With Application to Aggregated Birth Outcomes

原文摘要

A class of hierarchical Bayesian models is proposed for adverse birth outcomes such as preterm birth, which are assumed to follow a conditional binomial distribution. The log-odds of an adverse outcome in a particular county, logit(pi), follow a linear model that includes observed covariates and normally-distributed random effects. Spatial dependence between neighboring regions is allowed for by including an intrinsically autoregressive (IAR) prior or an IAR convolution prior in the linear predictor. Temporal dependence is incorporated by including a temporal IAR term also. It is shown that the variance parameters underlying these random effects (IAR, convolution, convolution plus temporal IAR) are identifiable. The Deviance Information Criterion (DIC) is considered as a way to compare spatial hierarchical models. Simulations are performed to test whether the DIC can identify whether binomial outcomes come from a hierarchical model that includes different combinations of random and fixed effects. Having validated the DIC as a means of comparing models, we examine preterm birth and low birth weight counts in the state of Arkansas from 1994–2005. We find that preterm birth and low birth weight have different spatial patterns of risk, and that rates of low birth weight can be fit with a relatively simple model that includes a constant spatial effect for all periods, a linear trend, and three covariates (multiple birth, black mother, smoking). It is also found that the risks of each outcome are increasing over time, even with adjustment for covariates.

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