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Electronic health records (EHRs) offer a cost-effective alternative for population health surveillance. While not perfectly representative, EHR data show small, predictable selection biases, supporting their cautious use in public health research.

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Area of Science:

  • Public Health Surveillance
  • Health Informatics
  • Biostatistics

Background:

  • Electronic health records (EHRs) are increasingly considered for population health surveillance due to cost-effectiveness and rich data.
  • Concerns persist regarding the representativeness of EHR data for public health monitoring and social determinants of health.
  • Validating EHR data against population benchmarks is crucial for reliable public health insights.

Purpose of the Study:

  • To evaluate the population representativeness of EHR data from a large health system (UNC Health) in North Carolina.
  • To assess how demographic, socioeconomic, and insurance factors influence patient inclusion in EHRs.
  • To inform the appropriate use and weighting of EHR data for public health surveillance.

Main Methods:

  • Linked individual-level EHR data (2018-2022, 2.12 million patients) with the nationally representative American Community Survey (ACS) microdata (2018-2022).
  • Utilized linear probability models to analyze selection biases based on age, sex, race/ethnicity, education, employment, poverty, food stamps, public assistance, and health insurance.
  • Compared characteristics of UNC Health patients with the general North Carolina population represented in the ACS.

Main Results:

  • UNC Health EHR data are not fully representative of the North Carolina population, but selection biases are generally small.
  • Biases align with known healthcare utilization patterns, showing overrepresentation of females, older adults, and insured individuals.
  • Moderate selection was observed by race/ethnicity and socioeconomic status, with overrepresentation at both high and low socioeconomic levels.

Conclusions:

  • EHR data can be cautiously used for population health monitoring when appropriately weighted.
  • Selection biases in EHR data are often predictable and manageable.
  • Linking EHRs with nationally representative surveys is valuable for assessing and enhancing EHR utility in public health research.