Skip to main content

Main menu

  • HOME
  • ARTICLES
    • Current Issue
    • Ahead of Print
    • Archives
    • Abstracts In Press
    • Special Issue Archive
    • Subject Collections
  • INFO FOR
    • Authors
    • Reviewers
    • Call For Papers
    • Subscribers
    • Advertisers
  • SUBMIT
    • Manuscript
    • Peer Review
  • ABOUT
    • The JABFM
    • The Editing Fellowship
    • Editorial Board
    • Indexing
    • Editors' Blog
  • CLASSIFIEDS
  • Other Publications
    • abfm

User menu

Search

  • Advanced search
American Board of Family Medicine
  • Other Publications
    • abfm
American Board of Family Medicine

American Board of Family Medicine

Advanced Search

  • HOME
  • ARTICLES
    • Current Issue
    • Ahead of Print
    • Archives
    • Abstracts In Press
    • Special Issue Archive
    • Subject Collections
  • INFO FOR
    • Authors
    • Reviewers
    • Call For Papers
    • Subscribers
    • Advertisers
  • SUBMIT
    • Manuscript
    • Peer Review
  • ABOUT
    • The JABFM
    • The Editing Fellowship
    • Editorial Board
    • Indexing
    • Editors' Blog
  • CLASSIFIEDS
  • JABFM on Bluesky
  • JABFM On Facebook
  • JABFM On Twitter
  • JABFM On YouTube
Research ArticleOriginal Research

Estimating the Cardiovascular Disease Risk Reduction of a Quality Improvement Initiative in Primary Care: Findings from EvidenceNOW

Stephan R. Lindner, Bijal Balasubramanian, Miguel Marino, K. John McConnell, Thomas E. Kottke, Samuel T. Edwards, Sam Cykert and Deborah J. Cohen
The Journal of the American Board of Family Medicine May 2023, 36 (3) 462-476; DOI: https://doi.org/10.3122/jabfm.2022.220331R1
Stephan R. Lindner
From the Center for Health Systems Effectiveness, Oregon Health & Science University (SRL, KJM); OHSU-PSU School of Public Health (SRL, MM, KJM); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas (BB); Department of Family Medicine, Oregon Health & Science University (MM, STE, DJC); HealthPartners Institute, Minneapolis, Minnesota (TEK); Section of General Internal Medicine, Veterans Affairs Portland Health Care System (STE); The Cecil G. Sheps Center for Health Services Research and Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill (DJC); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (DJC).
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bijal Balasubramanian
From the Center for Health Systems Effectiveness, Oregon Health & Science University (SRL, KJM); OHSU-PSU School of Public Health (SRL, MM, KJM); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas (BB); Department of Family Medicine, Oregon Health & Science University (MM, STE, DJC); HealthPartners Institute, Minneapolis, Minnesota (TEK); Section of General Internal Medicine, Veterans Affairs Portland Health Care System (STE); The Cecil G. Sheps Center for Health Services Research and Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill (DJC); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (DJC).
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Miguel Marino
From the Center for Health Systems Effectiveness, Oregon Health & Science University (SRL, KJM); OHSU-PSU School of Public Health (SRL, MM, KJM); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas (BB); Department of Family Medicine, Oregon Health & Science University (MM, STE, DJC); HealthPartners Institute, Minneapolis, Minnesota (TEK); Section of General Internal Medicine, Veterans Affairs Portland Health Care System (STE); The Cecil G. Sheps Center for Health Services Research and Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill (DJC); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (DJC).
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
K. John McConnell
From the Center for Health Systems Effectiveness, Oregon Health & Science University (SRL, KJM); OHSU-PSU School of Public Health (SRL, MM, KJM); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas (BB); Department of Family Medicine, Oregon Health & Science University (MM, STE, DJC); HealthPartners Institute, Minneapolis, Minnesota (TEK); Section of General Internal Medicine, Veterans Affairs Portland Health Care System (STE); The Cecil G. Sheps Center for Health Services Research and Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill (DJC); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (DJC).
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Thomas E. Kottke
From the Center for Health Systems Effectiveness, Oregon Health & Science University (SRL, KJM); OHSU-PSU School of Public Health (SRL, MM, KJM); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas (BB); Department of Family Medicine, Oregon Health & Science University (MM, STE, DJC); HealthPartners Institute, Minneapolis, Minnesota (TEK); Section of General Internal Medicine, Veterans Affairs Portland Health Care System (STE); The Cecil G. Sheps Center for Health Services Research and Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill (DJC); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (DJC).
MD, MSPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Samuel T. Edwards
From the Center for Health Systems Effectiveness, Oregon Health & Science University (SRL, KJM); OHSU-PSU School of Public Health (SRL, MM, KJM); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas (BB); Department of Family Medicine, Oregon Health & Science University (MM, STE, DJC); HealthPartners Institute, Minneapolis, Minnesota (TEK); Section of General Internal Medicine, Veterans Affairs Portland Health Care System (STE); The Cecil G. Sheps Center for Health Services Research and Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill (DJC); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (DJC).
MD, MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sam Cykert
From the Center for Health Systems Effectiveness, Oregon Health & Science University (SRL, KJM); OHSU-PSU School of Public Health (SRL, MM, KJM); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas (BB); Department of Family Medicine, Oregon Health & Science University (MM, STE, DJC); HealthPartners Institute, Minneapolis, Minnesota (TEK); Section of General Internal Medicine, Veterans Affairs Portland Health Care System (STE); The Cecil G. Sheps Center for Health Services Research and Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill (DJC); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (DJC).
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Deborah J. Cohen
From the Center for Health Systems Effectiveness, Oregon Health & Science University (SRL, KJM); OHSU-PSU School of Public Health (SRL, MM, KJM); Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas (BB); Department of Family Medicine, Oregon Health & Science University (MM, STE, DJC); HealthPartners Institute, Minneapolis, Minnesota (TEK); Section of General Internal Medicine, Veterans Affairs Portland Health Care System (STE); The Cecil G. Sheps Center for Health Services Research and Division of General Internal Medicine and Clinical Epidemiology, The University of North Carolina School of Medicine at Chapel Hill, Chapel Hill (DJC); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University (DJC).
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • References
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Tables

    • View popup
    Table 1.

    Sample Characteristics

    Patient characteristics (percent)
    Female56.5
    White59.4
    Black15.5
    Hispanic19.5
    Age 40 to 5957.3
    Age 60 to 7539.0
    Age 76 to 793.6
    Patient population groups (percent)
    Embedded Image 49.8
    Embedded Image 33.9
    Embedded Image 1.6
    Embedded Image 2.8
    Embedded Image 4.2
    Embedded Image 7.8
    Number of patients
    NHANES sample1295
    EN patient population3,961,384
    • Notes: The table shows characteristics of the NHANES sample. Weights are used for all patient characteristics and patient population group values. The number of EvidenceNOW patients corresponding to the NHANES sample is based on calculations using number of clinicians and number of patients per clinicans (see Appendix for details).

      Abbreviations: EN = EvidenceNOW; Embedded Image = people in the denominator only requiring smoking intervention; Embedded Image = people in the denominator for cholesterol management and smoking intervention; Embedded Image = people in the denominator for blood pressure control and smoking intervention; Embedded Image = people in the denominator for aspirin prescription, cholesterol management and smoking intervention; Embedded Image = people in the denominator for blood pressure control, cholesterol management and smoking intervention; GABCS = people in the denominator for aspirin prescription, blood pressure control, cholesterol management and smoking intervention.

    • View popup
    Table 2.

    Average ASCVD Risk and ASCVD Risk Reductions Due to Improvements in the ABCS

    AverageImprovement in clinical outcomes
    ASCVDAll ABCSAspirin OnlyBlood Pressure OnlyCholesterol OnlySmoking Only
    Baseline10.1110.1110.1110.1110.11
    All practices
    Post-intervention10.0310.1010.0810.0810.10
    Absolute change (p-value)−0.08 (P < .001)−0.01 (P > .05)−0.03 (P > .05)−0.03 (P > .05)−0.01 (P > .05)
    Relative change−0.79−0.14−0.30−0.28−0.07
    Practices with median or higher improvement
    Post-intervention9.7910.069.9310.0410.09
    Absolute change−0.32 (P < .001)−0.05 (P > .05)−0.18 (P > .05)−0.08 (P > .05)−0.02 (P > .05)
    Relative change−3.28−0.53−1.79−0.75−0.20
    • Notes: The table shows estimated average ASCVD risk in the EvidenceNOW patient population at baseline and post-intervention as well as the absolute and relative change in ASCVD risk for five scenarios: improvement in all ABCS; improvement only in aspirin prescribing; improvement only in blood pressure control; improvement only in cholesterol monitoring; and improvement only in smoking intervention. Results for absolute changes also include bootstrapped p-values in parenthesis. Baseline ASCVD risks are identical for all interventions displayed in the table because they are all based on the full study sample. Baseline levels of ABCS were as follows: 61.9 (aspirin prescribing); 63.3 (blood pressure control); 60.2 (cholesterol management); 58.4 (smoking intervention). Changes in ABCS (if assumed for a scenario) for all practices were: 3.4 (aspirin prescribing); 1.6 (blood pressure control); 4.4 (cholesterol management); 7.4 (smoking intervention). Changes in ABCS (if assumed for a scenario) for practices with median or higher improvements were: 12.9 (aspirin prescribing); 9.4 (blood pressure control); 12.0 (cholesterol management); 20.1 (smoking intervention).

      Abbreviation: ASCVD = atherosclerotic cardiovascular disease.

    • View popup
    Table 3.

    Relative Risk Factors and Risk Reduction Due to Improvements in ABCS

    GroupsRelative Population Size (%)ASCVD Risk at baseline (%)Contribution to ASCVD reduction (%)
    Embedded Image 49.87.73.6
    Embedded Image 33.914.931.1
    Embedded Image 1.617.74.1
    Embedded Image 2.813.17.8
    Embedded Image 4.217.212.2
    Embedded Image 7.824.641.2
    Total10010.1100.0
    • Notes: The table shows the relative population size, average ASCVD risk at baseline, and contribution to ASCVD reduction. The contribution to ASCVD reduction for each group is calculated as the change in ASCVD risk relative to the overall change in ASCVD risk.

      Abbreviations: Embedded Image = people in the denominator for cholesterol management and smoking intervention; Embedded Image = people in the denominator for blood pressure control and smoking intervention; Embedded Image = people in the denominator for aspirin prescription, cholesterol management and smoking intervention; Embedded Image = people in the denominator for blood pressure control, cholesterol management and smoking intervention; Embedded Image = people in the denominator for aspirin prescription, blood pressure control, cholesterol management and smoking intervention. ASCVD = atherosclerotic cardiovascular disease.

PreviousNext
Back to top

In this issue

The Journal of the American Board of Family     Medicine: 36 (3)
The Journal of the American Board of Family Medicine
Vol. 36, Issue 3
May-June 2023
  • Table of Contents
  • Table of Contents (PDF)
  • Cover (PDF)
  • Index by author
  • Back Matter (PDF)
  • Front Matter (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on American Board of Family Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Estimating the Cardiovascular Disease Risk Reduction of a Quality Improvement Initiative in Primary Care: Findings from EvidenceNOW
(Your Name) has sent you a message from American Board of Family Medicine
(Your Name) thought you would like to see the American Board of Family Medicine web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
4 + 5 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
Estimating the Cardiovascular Disease Risk Reduction of a Quality Improvement Initiative in Primary Care: Findings from EvidenceNOW
Stephan R. Lindner, Bijal Balasubramanian, Miguel Marino, K. John McConnell, Thomas E. Kottke, Samuel T. Edwards, Sam Cykert, Deborah J. Cohen
The Journal of the American Board of Family Medicine May 2023, 36 (3) 462-476; DOI: 10.3122/jabfm.2022.220331R1

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Estimating the Cardiovascular Disease Risk Reduction of a Quality Improvement Initiative in Primary Care: Findings from EvidenceNOW
Stephan R. Lindner, Bijal Balasubramanian, Miguel Marino, K. John McConnell, Thomas E. Kottke, Samuel T. Edwards, Sam Cykert, Deborah J. Cohen
The Journal of the American Board of Family Medicine May 2023, 36 (3) 462-476; DOI: 10.3122/jabfm.2022.220331R1
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Methods
    • Results
    • Discussion
    • Conclusion
    • Acknowledgments
    • Appendix
    • Notes
    • References
  • Figures & Data
  • References
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • What AHRQ Learned While Working to Transform Primary Care
  • Response: Re: Estimating the Cardiovascular Disease Risk Reduction of a Quality Improvement Initiative in Primary Care: Findings from EvidenceNOW
  • Re: Estimating the Cardiovascular Disease Risk Reduction of a Quality Improvement Initiative in Primary Care: Findings from EvidenceNOW
  • Google Scholar

More in this TOC Section

  • Evaluating Pragmatism of Lung Cancer Screening Randomized Trials with the PRECIS-2 Tool
  • Perceptions and Preferences for Defining Biosimilar Products in Prescription Drug Promotion
  • Successful Implementation of Integrated Behavioral Health
Show more Original Research

Similar Articles

Keywords

  • Cardiology
  • Cardiovascular Diseases
  • Nutrition Surveys
  • Preventive Health Care
  • Primary Health Care
  • Quality Improvement

Navigate

  • Home
  • Current Issue
  • Past Issues

Authors & Reviewers

  • Info For Authors
  • Info For Reviewers
  • Submit A Manuscript/Review

Other Services

  • Get Email Alerts
  • Classifieds
  • Reprints and Permissions

Other Resources

  • Forms
  • Contact Us
  • ABFM News

© 2025 American Board of Family Medicine

Powered by HighWire