A local join counts methodology for spatial clustering in disease from relative risk models

This paper considers adaptation of hierarchical models for small area disease counts to detect disease clustering. A high risk area may be an outlier (in local terms) if surrounded by low risk areas, whereas a high risk cluster requires that both the focus area and surrounding areas demonstrate common elevated risk. A local join count method is suggested to detect local clustering of high disease risk in a single health outcome, and extends to assessing bivariate spatial clustering in relative risk. Applications include assessing spatial heterogeneity in effects of area predictors according to local clustering configuration, and gauging sensitivity of bivariate clustering to random effect assumptions.