Electronic Health Records Versus Survey Small Area Estimates for Public Health Surveillance

Victoria M Nielsen; Glory Song; Catherine Rocchio; Bob Zambarano; Michael Klompas; Tom Chen

doi:10.1016/j.amepre.2024.02.018

Electronic Health Records Versus Survey Small Area Estimates for Public Health Surveillance

Am J Prev Med. 2024 Mar 4:S0749-3797(24)00074-6. doi: 10.1016/j.amepre.2024.02.018. Online ahead of print.

Authors

Victoria M Nielsen¹, Glory Song², Catherine Rocchio³, Bob Zambarano³, Michael Klompas⁴, Tom Chen⁵

Affiliations

¹ Massachusetts Department of Public Health, Office of Population Health, Boston, Massachusetts. Electronic address: victoria.m.nielsen@mass.gov.
² Massachusetts Department of Public Health, Bureau of Community Health and Prevention, Boston, Massachusetts.
³ Commonwealth Informatics, Waltham, Massachusetts.
⁴ Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
⁵ Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.

PMID: 38447855
DOI: 10.1016/j.amepre.2024.02.018

Abstract

Introduction: Electronic health records (EHRs) are increasingly being leveraged for public health surveillance. EHR-based small area estimates (SAEs) are often validated by comparison to survey data such as the Behavioral Risk Factor Surveillance System (BRFSS). However, survey and EHR-based SAEs are expected to differ. In this cross-sectional study, SAEs were generated using MDPHnet, a distributed EHR-based surveillance network, for all Massachusetts municipalities and zip code tabulation areas (ZCTAs), compared to BRFSS PLACES SAEs, and reasons for differences explored.

Methods: This study delineated reasons a priori for how SAEs derived using EHRs may differ from surveys by comparing each strategy's case classification criteria and reviewing the literature. Hypertension, diabetes, obesity, asthma, and smoking EHR-based SAEs for 2021 in all ZCTAs and municipalities in Massachusetts were estimated with Bayesian mixed effects modeling and poststratification in the summer/fall of 2023. These SAEs were compared to BRFSS PLACES SAEs published by the U.S. Centers for Disease Control and Prevention.

Results: Mean prevalence was higher in EHR data versus BRFSS in both municipalities and ZCTAs for all outcomes except asthma. ZCTA and municipal symmetric mean absolute percentages ranged from 12.0 to 38.2% and 13.1 to 39.8%, respectively. There was greater variability in EHR-based SAEs versus BRFSS PLACES in both municipalities and ZCTAs.

Conclusions: EHR-based SAEs tended to be higher than BRFSS and more variable. Possible explanations include detection of undiagnosed cases and over-classification using EHR data, and under-reporting within BRFSS. Both EHR and survey-based surveillance have strengths and limitations that should inform their preferred uses in public health surveillance.