Integrating geo-linked electronic health records with neighborhood data to identify place-based barriers for diabetes management in a public healthcare system

Background Despite the body of research on place-based disparities in diabetes, there is room for advancing methods in this space by leveraging multiple datasets, including population census and clinical datasets. Specifically, healthcare systems can leverage geo-linked electronic health records (EHRs) to investigate geospatial clustering and identify social determinants of health. Methods We examined geographic clustering of poor diabetes control and corresponding structural and social determinants of health within San Francisco’s safety-net healthcare system. Specifically, we used EHR data to identify individual patient-level addresses and clinical outcomes for diabetes, using hot spot analysis to determine significant hot and cold clusters of diabetes control throughout San Francisco. In addition, by linking patient addresses to public datasets, we described the neighborhood-level conditions associated with hot spots. Results Our hot spot analysis showed 18.8% of patients with poor glycemic control (n = 2,126) clustered in hot spots, and 12.8% of patients with controlled diabetes clustered in cold spots. Neighborhoods of patients in hot spots had more Black, Latinx, Native American, and Native Hawaiian or Pacific Islander residents and more residents receiving SNAP benefits, experiencing unemployment, food insecurity, greater rent burden, and low grocery store access. Conclusions We present methodological advantages of hotspot analyses utilizing EHR data, as well as the value of combining these approaches with additional public population census data about neighborhood conditions. Moving forward, healthcare systems must partner with public health agencies to utilize EHR data as a means to evaluate structural interventions and eventually target investment in place-based health-enabling resources.