Integrating population- and patient-level data for secondary use of electronic health records to study overweight and obesity.

We combined patient-level clinical data derived from the Electronic Health Record (EHR) with area-level environmental and socioeconomic data to study factors independently associated with overweight and obesity. Our multinomial logistic regression model showed that area-level factors such as farmers' markets, grocery stores and percent college-educated at the zip code level were significantly associated with the outcomes. However, mismatch in the granularity of community and clinical data limited us in creating a discriminatory model. While these results are promising, they reveal challenges that must be overcome in order to maximize secondary use of EHR data to further explore population health status.