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EGEMS (Wash DC). 2019 Jul 23;7(1):31. doi: 10.5334/egems.292.

Denominators Matter: Understanding Medical Encounter Frequency and Its Impact on Surveillance Estimates Using EHR Data.

Author information

1
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, US.
2
Commonwealth Informatics, US.
3
Department of Medicine, Brigham and Women's Hospital, US.

Abstract

Background:

There is scant guidance for defining what denominator to use when estimating disease prevalence via electronic health record (EHR) data.

Objectives:

Describe the intervals between medical encounters to inform the selection of denominators for population-level disease rates, and evaluate the impact of different denominators on the prevalence of chronic conditions.

Methods:

We analyzed the EHRs of three practices in Massachusetts using the Electronic medical record Support for Public Health (ESP) system. We identified adult patients' first medical encounter per year (2011-2016) and counted days to next encounter. We estimated the prevalence of asthma, hypertension, obesity, and smoking using different denominators in 2016: ≥1 encounter in the past one year or two years and ≥2 encounters in the past one year or two years.

Results:

In 2011-2016, 1,824,011 patients had 28,181,334 medical encounters. The median interval between encounters was 46, 56, and 66 days, depending on practice. Among patients with one visit in 2014, 82-84 percent had their next encounter within 1 year; 87-91 percent had their next encounter within two years. Increasing the encounter interval from one to two years increased the denominator by 23 percent. The prevalence of asthma, hypertension, and obesity increased with successively stricter denominators - e.g., the prevalence of obesity was 24.1 percent among those with ≥1 encounter in the past two years, 26.3 percent among those with ≥1 encounter in the last one year, and 28.5 percent among those with ≥2 encounters in the past one year.

Conclusions:

Prevalence estimates for chronic conditions can vary by >20 percent depending upon denominator. Understanding such differences will inform which denominator definition is best to be used for the need at hand.

KEYWORDS:

chronic disease; electronic health records; epidemiologic monitoring; prevalence; public health surveillance

Conflict of interest statement

The authors have no competing interests to declare.

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