Linking national primary care electronic health records to individual records from the U.S. Census Bureau's American Community Survey: evaluating the likelihood of linkage based on patient health.

Objectives: To evaluate the likelihood of linking electronic health records (EHRs) to restricted individual-level American Community Survey (ACS) data based on patient health condition.

Materials and methods: Electronic health records (2019-2021) are derived from a primary care registry collected by the American Board of Family Medicine. These data were assigned anonymized person-level identifiers (Protected Identification Keys [PIKs]) at the U.S. Census Bureau. These records were then linked to restricted individual-level data from the ACS (2005-2022). We used logistic regressions to evaluate match rates for patients with health conditions across a range of severity: hypertension, diabetes, and chronic kidney disease.

Results: Among more than 2.8 million patients, 99.2% were assigned person-level identifiers (PIKs). There were some differences in the odds of receiving an identifier in adjusted models for patients with hypertension (OR = 1.70, 95% CI: 1.63, 1.77) and diabetes (OR = 1.17, 95% CI: 1.13, 1.22), relative to those without. There were only small differences in the odds of matching to ACS in adjusted models for patients with hypertension (OR = 1.03, 95% CI: 1.03, 1.04), diabetes (OR = 1.02, 95% CI: 1.01, 1.03), and chronic kidney disease (OR = 1.05, 95% CI: 1.03, 1.06), relative to those without.

Discussion and conclusion: Our work supports evidence-building across government consistent with the Foundations for Evidence-Based Policymaking Act of 2018 and the goal of leveraging data as a strategic asset. Given the high PIK and ACS match rates, with small differences based on health condition, our findings suggest the feasibility of enhancing the utility of EHR data for research focused on health.