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State and Local Chronic Disease Surveillance Using Electronic Health Record Systems.

Author information

Affiliations

  • 1. Michael Klompas, Noelle M. Cocoros, John T. Menchaca, and Jessica Malenfant are with the Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA. Diana Erani, Ellen Hafer, and Mark Josephson are with the Massachusetts League of Community Health Centers, Boston. Brian Herrick and Michelle D. Payne Weiss are with Cambridge Health Alliance, Cambridge, MA. Michael Lee is with Atrius Health, Boston. Bob Zambarano and Karen R. Eberhardt are with Commonwealth Informatics Inc, Waltham, MA. Laura Nasuti and Thomas Land are with the Office of Data Management and Outcomes Assessment, Massachusetts Department of Public Health, Boston.
      Authors
    • Klompas M 1
    • Cocoros NM 1
    • Menchaca JT 1
    • Erani D 1
    • Hafer E 1
    • Herrick B 1
    • Josephson M 1
    • Lee M 1
    • Payne Weiss MD 1
    • Zambarano B 1
    • Eberhardt KR 1
    • Malenfant J 1
    • Nasuti L 1
    • Land T 1
    (14 authors)

ORCIDs linked to this article

American Journal of Public Health, 20 Jul 2017, 107(9):1406-1412
https://doi.org/10.2105/ajph.2017.303874 PMID: 28727539 

A comment on this article appears in "Successes and Continued Challenges of Electronic Health Records for Chronic Disease Surveillance." Am J Public Health. 2017 Sep;107(9):1365-1367.

Abstract 

Objectives

To assess the feasibility of chronic disease surveillance using distributed analysis of electronic health records and to compare results with Behavioral Risk Factor Surveillance System (BRFSS) state and small-area estimates.

Methods

We queried the electronic health records of 3 independent Massachusetts-based practice groups using a distributed analysis tool called MDPHnet to measure the prevalence of diabetes, asthma, smoking, hypertension, and obesity in adults for the state and 13 cities. We adjusted observed rates for age, gender, and race/ethnicity relative to census data and compared them with BRFSS state and small-area estimates.

Results

The MDPHnet population under surveillance included 1 073 545 adults (21.8% of the state adult population). MDPHnet and BRFSS state-level estimates were similar: 9.4% versus 9.7% for diabetes, 10.0% versus 12.0% for asthma, 13.5% versus 14.7% for smoking, 26.3% versus 29.6% for hypertension, and 22.8% versus 23.8% for obesity. Correlation coefficients for MDPHnet versus BRFSS small-area estimates ranged from 0.890 for diabetes to 0.646 for obesity.

Conclusions

Chronic disease surveillance using electronic health record data is feasible and generates estimates comparable with BRFSS state and small-area estimates.

Full text links 

Read article at publisher's site: https://doi.org/10.2105/ajph.2017.303874

Read article for free, from open access legal sources, via Unpaywall: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5551591Unpaywall

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