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Babies born with low and very low birthweights -- i.e., birthweights below2,500 and 1,500 grams, respectively -- have an increased risk of complicationscompared to other babies, and the proportion of babies with a low birthweightis a common metric used when evaluating public health in a population. Whilemany factors increase the risk of a baby having a low birthweight, many can belinked to the mother's socioeconomic status, which in turn contributes to largeracial disparities in the incidence of low weight births. Here, we employBayesian statistical models to analyze the proportion of babies with lowbirthweight in Pennsylvania counties by race/ethnicity. Due to the small numberof births -- and low weight births -- in many Pennsylvania counties whenstratified by race/ethnicity, our methods must walk a fine line. On one hand,leveraging spatial structure can help improve the precision of our estimates.On the other hand, we must be cautious to avoid letting the model overwhelm theinformation in the data and produce spurious conclusions. As such, we firstdevelop a framework by which we can measure (and control) the informativenessof our spatial model. After demonstrating the properties of our framework viasimulation, we analyze the low birthweight data from Pennsylvania and examinethe extent to which the commonly used conditional autoregressive model can leadto oversmoothing. We then reanalyze the data using our proposed framework andhighlight its ability to detect (or not detect) evidence of racial disparitiesin the incidence of low birthweight.

Geographic and Racial Disparities in the Incidence of Low Birthweight in Pennsylvania