Summary
The authors' generalized linear mixed model predicts health outcomes using both individual-level and neighborhood-level predictors. The model’s feature of nonparametric smoothing function on neighborhood-level variables better captures the association between neighborhood environment and the outcome. Using 2011 to 2012 data from the California Health Interview Survey, authors demonstrate an empirical application of this method to estimate the fraction of residents without health insurance for ZIP Code Tabulation Areas (ZCTAs). The method generated stable estimates of uninsurance for 1519 of 1765 ZCTAs (86%) in California.
The proposed method can increase the value of health surveys by providing modeled estimates for health data at a granular geographic level. It can account for variations in health outcomes at the neighborhood level as a result of both socioeconomic characteristics and geographic locations.
