Evidence map›Paper›PMID 31367648›Full record

ArticleEGEMS (Washington, DC)2019

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

Noelle M Cocoros, Aileen Ochoa, Karen Eberhardt, Bob Zambarano, Michael Klompas

Abstract read
In one paragraph

Article in EGEMS (Washington, DC), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. "International journal of qualitative studies on health and well-being · 2025
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  7. Glossary for public health surveillance in the age of data science.Journal of epidemiology and community health · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Noelle M CocorosDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, US.
Aileen OchoaDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, US.
Karen EberhardtCommonwealth Informatics, US.
Bob ZambaranoCommonwealth Informatics, US.
Michael KlompasDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, US.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

objectivesDescribe 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.

methodsWe 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.

resultsIn 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.

conclusionsPrevalence 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.

Indexed as

chronic diseaseelectronic health recordsepidemiologic monitoringprevalencepublic health surveillance

Identifiers

PMID31367648
PMCPMC6659575

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.