Adjusting for Confounding by Neighborhood Using a Proportional Odds Model and Complex Survey Data

In social epidemiology, an individual's neighborhood is considered to be an important determinant of health behaviors, mediators, and outcomes. Consequently, when investigating health disparities, researchers may wish to adjust for confounding by unmeasured neighborhood factors, such as local availability of health facilities or cultural predispositions. With a simple random sample and a binary outcome, a conditional logistic regression analysis that treats individuals within a neighborhood as a matched set is a natural method to use. The authors present a generalization of this method for ordinal outcomes and complex sampling designs. The method is based on a proportional odds model and is very simple to program using standard software such as SAS PROC SURVEYLOGISTIC (SAS Institute Inc., Cary, North Carolina). The authors applied the method to analyze racial/ethnic differences in dental preventative care, using 2008 Florida Behavioral Risk Factor Surveillance System survey data. The ordinal outcome represented time since last dental cleaning, and the authors adjusted for individual-level confounding by gender, age, education, and health insurance coverage. The authors compared results with and without additional adjustment for confounding by neighborhood, operationalized as zip code. The authors found that adjustment for confounding by neighborhood greatly affected the results in this example.