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Research ArticleOriginal Research

Improving Delivery of Cardiovascular Disease Preventive Services in Small-to-Medium Primary Care Practices

Bijal A. Balasubramanian, Stephan Lindner, Miguel Marino, Rachel Springer, Samuel T. Edwards, K. John McConnell and Deborah J. Cohen
The Journal of the American Board of Family Medicine October 2022, 35 (5) 968-978; DOI: https://doi.org/10.3122/jabfm.2022.05.220038
Bijal A. Balasubramanian
From Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health, Dallas, TX (BAB; Center for Health Systems Effectiveness and Department of Emergency Medicine, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Oregon Health & Science University, Portland, OR (MM, RS, STE, DJC); School of Public Health, Oregon Health & Science University, Portland, OR (MM); Section of General Internal Medicine, Veterans Affairs (VA) Portland Health Care System, Portland OR (STE); Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR (STE); Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland OR (STE); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC).
MBBS, PhD
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Stephan Lindner
From Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health, Dallas, TX (BAB; Center for Health Systems Effectiveness and Department of Emergency Medicine, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Oregon Health & Science University, Portland, OR (MM, RS, STE, DJC); School of Public Health, Oregon Health & Science University, Portland, OR (MM); Section of General Internal Medicine, Veterans Affairs (VA) Portland Health Care System, Portland OR (STE); Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR (STE); Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland OR (STE); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC).
PhD
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Miguel Marino
From Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health, Dallas, TX (BAB; Center for Health Systems Effectiveness and Department of Emergency Medicine, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Oregon Health & Science University, Portland, OR (MM, RS, STE, DJC); School of Public Health, Oregon Health & Science University, Portland, OR (MM); Section of General Internal Medicine, Veterans Affairs (VA) Portland Health Care System, Portland OR (STE); Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR (STE); Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland OR (STE); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC).
PhD
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Rachel Springer
From Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health, Dallas, TX (BAB; Center for Health Systems Effectiveness and Department of Emergency Medicine, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Oregon Health & Science University, Portland, OR (MM, RS, STE, DJC); School of Public Health, Oregon Health & Science University, Portland, OR (MM); Section of General Internal Medicine, Veterans Affairs (VA) Portland Health Care System, Portland OR (STE); Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR (STE); Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland OR (STE); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC).
MS
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Samuel T. Edwards
From Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health, Dallas, TX (BAB; Center for Health Systems Effectiveness and Department of Emergency Medicine, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Oregon Health & Science University, Portland, OR (MM, RS, STE, DJC); School of Public Health, Oregon Health & Science University, Portland, OR (MM); Section of General Internal Medicine, Veterans Affairs (VA) Portland Health Care System, Portland OR (STE); Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR (STE); Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland OR (STE); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC).
MD, MPH
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K. John McConnell
From Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health, Dallas, TX (BAB; Center for Health Systems Effectiveness and Department of Emergency Medicine, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Oregon Health & Science University, Portland, OR (MM, RS, STE, DJC); School of Public Health, Oregon Health & Science University, Portland, OR (MM); Section of General Internal Medicine, Veterans Affairs (VA) Portland Health Care System, Portland OR (STE); Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR (STE); Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland OR (STE); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC).
PhD
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Deborah J. Cohen
From Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health, Dallas, TX (BAB; Center for Health Systems Effectiveness and Department of Emergency Medicine, Oregon Health & Science University, Portland, OR (SL, KJM); Department of Family Medicine, Oregon Health & Science University, Portland, OR (MM, RS, STE, DJC); School of Public Health, Oregon Health & Science University, Portland, OR (MM); Section of General Internal Medicine, Veterans Affairs (VA) Portland Health Care System, Portland OR (STE); Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR (STE); Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland OR (STE); Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR (DJC).
PhD
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Article Figures & Data

Figures

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    Figure 1.

    EvidenceNOW consort diagram.

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    Figure 2.

    Event study estimates.

Tables

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    Table 1.

    Specification of ABCS Clinical Quality Outcome Measures

    AspirinBlood PressureCholesterolSmoking
    Denominator
    Patients 18 years and older with at least one face to face visit who (i) had an active diagnosis of ischemic vascular disease at any time during the current measurement period; (ii) were discharged alive for acute myocardial infarction, coronary artery bypass graft or percutaneous coronary interventions in the 12 months before the measurement periodPatients 18 years and older and 85 years or younger with at least one face to face visit and active diagnosis of essential hypertension at any time before the first date of month 7 of the measurement period and who did not (i) have an active diagnosis of pregnancy at any time during the measurement period; or (ii) have evidence of end stage renal disease, dialysis, or renal transplant before or during the measurement periodPatients 21 and older with at least one face to face visit who have (i) an active diagnosis of clinical atherosclerotic cardiovascular disease during the current measurement period or any time period; (ii) LDL-C result >= 190 mg/dL at any time during or before the measurement period; (iii) aged 40 to 75 years at the beginning of the measurement period with an active diagnosis of diabetes with the highest LDL-C result of 70 to 189 mg/dL during the current measurement period or two years before the beginning of the measurement period; and who (i) did not have adverse effect, allergy or intolerance to statin medication therapy; (ii) did not have an active diagnosis of pregnancy or breastfeeding; (iii) did not receive palliative care; (iv) did not have an active liver disease or hepatic disease of insufficiency; (v) did not have end stage renal disease; or (vi) did not have a most recent LDL-C results < 70 mg/dL for patients with a diabetes diagnosis who are not currently receiving statin medication therapyPatients 18 years and older as of the first day of the measurement period with at least two visits during the measurement period
    Numerator
    Number of patients who have documentation of use of aspirin or another antithrombotic during the measurement periodNumber of patients whose blood pressure at the most recent visit is adequately controlled (systolic blood pressure < 140 mmgHg and diastolic blood pressure < 90 mm Hg) during the measurement periodNumber of patients with a statin medication current on the medication list or prescribed a statin medication during the measurement periodNumber of patients who were screened for tobacco use at least once within 24 months and who received tobacco cessation intervention if identified as a tobacco user.
    Measurement period (EvidenceNOW practices)
    Data quarter and preceding three quartersData quarter and preceding three quartersData quarter and preceding three quartersData quarter and preceding three quarters
    • Notes: Measure specifications are based on CMS164v4 (aspirin prescription when appropriate), CMS165v4 (blood pressure control), CMS347v1 (cholesterol management) and CMS138v4 (smoking cessation support counseling).

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    Table 2.

    Characteristics of the EvidenceNOW and DARTNet Practice Sample

    Practice and patient characteristicEvidenceNOW PracticesDARTNet Practices
    Practice size
     Solo22.7
     2 to 5 clinicians46.7
     6 to 10 clinicians14.0
     11 to 15 clinicians10.6
     Missing5.9
    Practice ownership
     Clinician42.146.0
     Hospital/health system/HMO22.746.0
     FQHCs19.63.0
     Other11.44.9
     Missing4.20.0
    Practice location
     Rural area12.8
     Large town10.8
     Suburban6.3
     Core urban59.5
     Missing10.6
    Insurance status: Fraction of patients
     Uninsured9.9
     Medicaid23.0
     Medicare22.9
     Dually eligible7.1
     Commercially insured34.5
     Other insurance2.7
    Race/ethnicity: Fraction of patients classified as
     White59.849.1
     Black15.66.0
     Unknown race7.80.0
     Hispanic19.16.7
     Unknown ethnicity9.30.0
    Practice participated in demonstration program
     No53.7
     Yes19.7
     Missing26.6
    Practice has MUA HER
     No44.5
     Yes18.4
     Missing37.1
    • Notes: Numbers in the table are percentage values for practice characteristics and mean percentage values for patient characteristics. The sample includes all practices with at least one valid ABCS outcome measure during the study period (n = 1278 for EvidenceNOW practices; n = 613 for DARTNet practices). Practice location is based on the rural-urban commuting areas using 2010 Census data. Numbers for insurance status and race/ethnicity are average percentage values. They do not sum to 100 percent because practices were not required to report estimates that did so. Dual eligible insurance status includes patients receiving both Medicaid and Medicare. Demonstration programs include State Innovation Models Initiative, Comprehensive Primary Care Initiative, Transforming Clinical Practice Initiative–Support and Alignment Network, Community Health Worker training program, Blue Cross/Blue Shield patient-centered medical home program, Association of State and Territorial Health Officials’ Million Hearts State Learning Collaborative, Million Hearts: Cardiovascular Disease Risk Reduction Model, and any other program identified by the practice. HMO: Health Management Organization; FQHC: Federally Qualified Health Center; EHR: electronic health records; MUA: Meaningful Use. Sources: EvidenceNOW practice survey; DARTNet practice data.

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    Table 3.

    Baseline ABCS Levels and Estimates of Effectiveness of EvidenceNOW

    ApproachAspirinBlood PressureCholesterolSmoking
    Event study
    Baseline level, % (SD)64.9 (23.5)63.6 (15.0)61.5 (19.4)62.0 (30.7)
    Change estimate3.391.594.437.33
    95% CI0.61, 6.170.12, 3.060.33, 8.534.70, 9.96
    P value0.01670.03370.03420.0000
    Pre-intervention trend test estimate (P value)0.58 (0.37)0.41 (0.19)1.46 (0.27)2.03 (0.002)
    Difference-in-differences, DARTNet comparison (all states)
    Baseline level, % (SD) (EvidenceNOW)65.1 (23.2)63.1 (15.2)62.5 (19.2)64.9 (30.2)
    Baseline level, % (SD) (DARTNet)29.6 (20.3)64.7 (20.4)39.4 (19.2)12.6 (9.1)
    Change estimate3.752.763.878.32
    95% CI−0.56, 8.06−0.06, 5.58−3.44, 11.182.89, 13.75
    P value0.08750.05560.29900.0027
    Parallel trend test estimate (P value)0.65 (0.50)0.49 (0.46)1.49 (0.26)1.49 (0.35)
    Difference-in-differences, DARTNet comparison (EvidenceNOW states)
    Baseline level, % (SD) (EvidenceNOW)64.9 (23.6)62.9 (15.3)62.0 (19.0)61.4 (30.5)
    Baseline level, % (SD) (DARTNet)31.0 (19.6)63.6 (16.9)40.9 (21.4)15.9 (10.7)
    Change estimate4.882.873.8111.54
    95% CI−3.33, 13.090.07, 5.67−6.46, 14.083.56, 19.52
    P value0.24440.04470.46800.0046
    Parallel-trends test estimate (P value)−0.54 (0.46)−0.55 (0.74)0.53 (0.72)2.67 (0.28)
    • Abbreviations: SD, standard deviation; CI, confidence interval.

    • Notes: For the event study, baseline levels correspond to the last quarter before intervention begin (fourth quarter of 2015 for the first cohort to fourth quarter of 2016 for the fifth cohort). Estimates of the effect of interventions correspond to average estimates of the fifth to eighth post-intervention quarters. Pre-intervention trend estimates are based on the fourth to second pre-intervention period. For the difference-in-difference analysis, baseline levels correspond to the fourth quarter of 2015. Estimates of the effect of the intervention correspond to average estimates of the interaction term between an indicator for EvidenceNOW practices and 2017 quarters. Parallel-trends tests are based on the first and second quarter of 2016.

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The Journal of the American Board of Family     Medicine: 35 (5)
The Journal of the American Board of Family Medicine
Vol. 35, Issue 5
September/October 2022
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Improving Delivery of Cardiovascular Disease Preventive Services in Small-to-Medium Primary Care Practices
Bijal A. Balasubramanian, Stephan Lindner, Miguel Marino, Rachel Springer, Samuel T. Edwards, K. John McConnell, Deborah J. Cohen
The Journal of the American Board of Family Medicine Oct 2022, 35 (5) 968-978; DOI: 10.3122/jabfm.2022.05.220038

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Improving Delivery of Cardiovascular Disease Preventive Services in Small-to-Medium Primary Care Practices
Bijal A. Balasubramanian, Stephan Lindner, Miguel Marino, Rachel Springer, Samuel T. Edwards, K. John McConnell, Deborah J. Cohen
The Journal of the American Board of Family Medicine Oct 2022, 35 (5) 968-978; DOI: 10.3122/jabfm.2022.05.220038
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