ArticleEGEMS (Washington, DC)2019
Denominators Matter: Understanding Medical Encounter Frequency and Its Impact on Surveillance Estimates Using EHR Data.
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.
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Who cites it
8 citing papers in PubMed.
- "International journal of qualitative studies on health and well-being · 2025Article
- Development of a Hypertension Electronic Phenotype for Chronic Disease Surveillance in Electronic Health Records: Key Analytic Decisions and Their Effects.Preventing chronic disease · 2023Article
- Diagnostic rate estimation from Medicare records: Dependence on claim numbers and latent clinical features.Journal of biomedical informatics · 2023Article
- Epidemiology of Asbestosis between 2010-2014 and 2015-2019 Periods in Colombia: Descriptive Study.Annals of global health · 2023Article
- Small-area estimation for public health surveillance using electronic health record data: reducing the impact of underrepresentation.BMC public health · 2022Article
- RiskScape: A Data Visualization and Aggregation Platform for Public Health Surveillance Using Routine Electronic Health Record Data.American journal of public health · 2021Article
- Glossary for public health surveillance in the age of data science.Journal of epidemiology and community health · 2020Article
- The COVID-19 Pandemic and Access to Selected Ambulatory Care Services Among Populations With Severely Uncontrolled Diabetes and Hypertension in Massachusetts.Public health reports (Washington, D.C. : 1974)Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
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.
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