Literature DB >> 25290223

Exploring electronic health records as a population health surveillance tool of cardiovascular disease risk factors.

Abbey C Sidebottom1, Pamela Jo Johnson, Jeffrey J VanWormer, Arthur Sillah, Tamara J Winden, Jackie L Boucher.   

Abstract

The objective of this study was to examine the utility of using electronic health record (EHR) data for periodic community health surveillance of cardiovascular disease (CVD) risk factors through 2 research questions. First, how many years of EHR data are needed to produce reliable estimates of key population-level CVD health indicators for a community? Second, how comparable are the EHR estimates relative to those from community screenings? The study takes place in the context of the Heart of New Ulm Project, a 10-year population health initiative designed to reduce myocardial infarctions and CVD risk factor burden in a rural community. The community is served by 1 medical center that includes a clinic and hospital. The project screened adult residents of New Ulm for CVD risk factors in 2009. EHR data for 3 years prior to the heart health screenings were extracted for patients from the community. Single- and multiple-year EHR prevalence estimates were compared for individuals ages 40-79 years (N=5918). EHR estimates also were compared to screening estimates (N=3123). Single-year compared with multiyear EHR data prevalence estimates were sufficiently precise for this rural community. EHR and screening prevalence estimates differed significantly-systolic blood pressure (BP) (124.0 vs. 128.9), diastolic BP (73.3 vs. 79.2), total cholesterol (186.0 vs. 201.0), body mass index (30.2 vs. 29.5), and smoking (16.6% vs. 8.2%)-suggesting some selection bias depending on the method used. Despite differences between data sources, EHR data may be a useful source of population health surveillance to inform and evaluate local population health initiatives.

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Year:  2014        PMID: 25290223     DOI: 10.1089/pop.2014.0058

Source DB:  PubMed          Journal:  Popul Health Manag        ISSN: 1942-7891            Impact factor:   2.459


  9 in total

1.  Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records.

Authors:  Kathleen S Tatem; Matthew L Romo; Katharine H McVeigh; Pui Ying Chan; Elizabeth Lurie-Moroni; Lorna E Thorpe; Sharon E Perlman
Journal:  Prev Chronic Dis       Date:  2017-06-08       Impact factor: 2.830

2.  Monitoring Prevalence, Treatment, and Control of Metabolic Conditions in New York City Adults Using 2013 Primary Care Electronic Health Records: A Surveillance Validation Study.

Authors:  Lorna E Thorpe; Katharine H McVeigh; Sharon Perlman; Pui Ying Chan; Katherine Bartley; Lauren Schreibstein; Jesica Rodriguez-Lopez; Remle Newton-Dame
Journal:  EGEMS (Wash DC)       Date:  2016-12-15

3.  Adult weight management across the community: population-level impact of the LOSE IT to WIN IT challenge.

Authors:  J J VanWormer; R F Pereira; A Sillah; A C Sidebottom; G A Benson; R Lindberg; C Winters; J L Boucher
Journal:  Obes Sci Pract       Date:  2018-03-14

4.  Generalizability of Indicators from the New York City Macroscope Electronic Health Record Surveillance System to Systems Based on Other EHR Platforms.

Authors:  Katharine H McVeigh; Elizabeth Lurie-Moroni; Pui Ying Chan; Remle Newton-Dame; Lauren Schreibstein; Kathleen S Tatem; Matthew L Romo; Lorna E Thorpe; Sharon E Perlman
Journal:  EGEMS (Wash DC)       Date:  2017-12-07

5.  Prevalence of Multiple Chronic Conditions Among Older Adults in Florida and the United States: Comparative Analysis of the OneFlorida Data Trust and National Inpatient Sample.

Authors:  Zhe He; Jiang Bian; Henry J Carretta; Jiwon Lee; William R Hogan; Elizabeth Shenkman; Neil Charness
Journal:  J Med Internet Res       Date:  2018-04-12       Impact factor: 5.428

6.  Cardiovascular Health Trends in Electronic Health Record Data (2012-2015): A Cross-Sectional Analysis of The Guideline Advantage™.

Authors:  Joyce E Rudy; Yosef Khan; Julie K Bower; Sejal Patel; Randi E Foraker
Journal:  EGEMS (Wash DC)       Date:  2019-07-18

7.  Rapid identification of familial hypercholesterolemia from electronic health records: The SEARCH study.

Authors:  Maya S Safarova; Hongfang Liu; Iftikhar J Kullo
Journal:  J Clin Lipidol       Date:  2016-08-06       Impact factor: 5.365

8.  Population-Level Reach of Cardiovascular Disease Prevention Interventions in a Rural Community: Findings from the Heart of New Ulm Project.

Authors:  Abbey C Sidebottom; Gretchen Benson; Marc Vacquier; Raquel Pereira; Joy Hayes; Peter Boersma; Jackie L Boucher; Rebecca Lindberg; Barbara Pribyl; Jeffrey J VanWormer
Journal:  Popul Health Manag       Date:  2020-01-22       Impact factor: 2.459

9.  Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data.

Authors:  Jackie Szymonifka; Sarah Conderino; Christine Cigolle; Jinkyung Ha; Mohammed Kabeto; Jaehong Yu; John A Dodson; Lorna Thorpe; Caroline Blaum; Judy Zhong
Journal:  JAMIA Open       Date:  2020-12-05
  9 in total

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