Child physical abuse is a major public health issue in the United States. Environmental child welfare research has focused on neighborhood characteristics and the influence of alcohol and marijuana establishments. To our knowledge, child welfare studies have singularly examined the outcome in terms of victims, that is, at the level of child population, and have not considered the parent population. Thus, in this exploratory study, we use spatial scan statistics to analyze patterns of child physical abuse at the child and household level, and we use Bayesian hierarchical spatial conditional autoregressive models to determine the relative influence of alcohol availability and other environmental factors. We find that household clusters are nested in child clusters and that controlling for alcohol establishments reduces cluster size. In the Bayesian regression models, alcohol availability increased risk slightly, while neighborhood diversity (measured using Blau's Index) elevated risk considerably. Immediate implications for child welfare agencies are discussed.
Keywords: Bayesian hierarchical spatial regression with INLA; alcohol outlets; child physical abuse; geographic clusters; spatial scan statistics.