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Research ArticleOriginal Research

Non-Visit-Based Cancer Screening Using a Novel Population Management System

Steven J. Atlas, Adrian H. Zai, Jeffrey M. Ashburner, Yuchiao Chang, Sanja Percac-Lima, Douglas E. Levy, Henry C. Chueh and Richard W. Grant
The Journal of the American Board of Family Medicine July 2014, 27 (4) 474-485; DOI: https://doi.org/10.3122/jabfm.2014.04.130319
Steven J. Atlas
From the General Medicine Division, Medical Services (SJA, JMA, YC, SP-L), the Laboratory of Computer Science (AHZ, HCC), and the Mongan Institute for Health Policy (DEL), Massachusetts General Hospital, Harvard Medical School, Boston, MA; and the Division of Research, Kaiser Permanente Northern California, Oakland (RWG).
MD, MPH
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Adrian H. Zai
From the General Medicine Division, Medical Services (SJA, JMA, YC, SP-L), the Laboratory of Computer Science (AHZ, HCC), and the Mongan Institute for Health Policy (DEL), Massachusetts General Hospital, Harvard Medical School, Boston, MA; and the Division of Research, Kaiser Permanente Northern California, Oakland (RWG).
MD, PhD, MPH
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Jeffrey M. Ashburner
From the General Medicine Division, Medical Services (SJA, JMA, YC, SP-L), the Laboratory of Computer Science (AHZ, HCC), and the Mongan Institute for Health Policy (DEL), Massachusetts General Hospital, Harvard Medical School, Boston, MA; and the Division of Research, Kaiser Permanente Northern California, Oakland (RWG).
MPH
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Yuchiao Chang
From the General Medicine Division, Medical Services (SJA, JMA, YC, SP-L), the Laboratory of Computer Science (AHZ, HCC), and the Mongan Institute for Health Policy (DEL), Massachusetts General Hospital, Harvard Medical School, Boston, MA; and the Division of Research, Kaiser Permanente Northern California, Oakland (RWG).
PhD
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Sanja Percac-Lima
From the General Medicine Division, Medical Services (SJA, JMA, YC, SP-L), the Laboratory of Computer Science (AHZ, HCC), and the Mongan Institute for Health Policy (DEL), Massachusetts General Hospital, Harvard Medical School, Boston, MA; and the Division of Research, Kaiser Permanente Northern California, Oakland (RWG).
MD, PhD
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Douglas E. Levy
From the General Medicine Division, Medical Services (SJA, JMA, YC, SP-L), the Laboratory of Computer Science (AHZ, HCC), and the Mongan Institute for Health Policy (DEL), Massachusetts General Hospital, Harvard Medical School, Boston, MA; and the Division of Research, Kaiser Permanente Northern California, Oakland (RWG).
PhD
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Henry C. Chueh
From the General Medicine Division, Medical Services (SJA, JMA, YC, SP-L), the Laboratory of Computer Science (AHZ, HCC), and the Mongan Institute for Health Policy (DEL), Massachusetts General Hospital, Harvard Medical School, Boston, MA; and the Division of Research, Kaiser Permanente Northern California, Oakland (RWG).
MD, MS
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Richard W. Grant
From the General Medicine Division, Medical Services (SJA, JMA, YC, SP-L), the Laboratory of Computer Science (AHZ, HCC), and the Mongan Institute for Health Policy (DEL), Massachusetts General Hospital, Harvard Medical School, Boston, MA; and the Division of Research, Kaiser Permanente Northern California, Oakland (RWG).
MD, MPH
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  • Article
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Article Figures & Data

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    Figure 1.

    CONSORT diagram depicting the flow of study practice clusters and patients through randomization, intervention, and outcome analysis.

  • Figure 2.
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    Figure 2.

    Diagram depicting the workflow of intervention and comparison groups. Augmented usual care with primary care provider (PCP) input (intervention) is indicated by solid lines; augmented usual care without PCP input (comparison) is indicated by dotted lines.

  • Figure 3.
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    Figure 3.

    Adjusted rate differences and 95% confidence intervals for all cancer screenings combined in intervention and comparison groups in patient and practice subgroups. Rate differences compare patients in the intervention and comparison groups, controlling for age, ethnicity, insurance status, primary language, time since last visit to practice, patient–physician linkage, and sex while accounting for clustering by primary care physician or practice in a mixed effects model. For each subgroup analysis, the analogous variable was removed from the model if necessary. CRC, colorectal cancer.

Tables

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    Table 1. Practice, Physician, and Patient Characteristics among Intervention and Comparison Practices
    CharacteristicsIntervention (n = 51,071)Comparison (n = 52,799)P Value
    Practice/physician characteristics
        Practice sites (n)99
            Community health center33
        Physicians per practice, n (median)92 (9)77 (9)
        Mean age, years (SD)49.7 (10.1)47.5 (10.0).14
        Sex50 (54.4)45 (58.4).59
        Years since medical school graduation, mean (SD)21.8 (10.4)19.6 (10.1).15
        Years in primary care network, mean (SD)15.8 (10.9)13.1 (10.4).09
    Patient characteristics
        Mean age, years (SD)51.5 (14.3)48.5 (14.8)<.001
        Female sex37,906 (74.2)40,568 (76.8)<.001
        Ethnicity<.001
            African-American2920 (5.7)3319 (6.3)
            Asian2724 (5.3)3473 (6.6)
            Hispanic3865 (7.6)5964 (11.3)
            Other/unknown1256 (2.5)1384 (2.6)
            Non-Hispanic white40,306 (78.9)38,659 (73.2)
        Insurance status<.001
            Commercial35,665 (69.8)37,895 (71.8)
            Medicaid4602 (9.0)5486 (10.4)
            Medicare9058 (17.7)7437 (14.1)
            No insurance, self-pay/free1746 (3.4)1981 (3.8)
        Primary language spoken, English46,560 (91.2)46,478 (88.0)<.001
    Patient-physician connectedness status<.001
        Physician-connected42,449 (83.1)42,132 (79.8)
        Practice-connected8622 (16.9)10,667 (20.2)
    Time since last practice visit (months)<.001
        <631,439 (61.6)30,658 (58.1)
        >6–1210,206 (20.0)10,473 (19.8)
        >126668 (13.1)8663 (16.4)
        New patient2758 (5.4)3005 (5.7)
    Community health center practice type7008 (13.7)8935 (16.9)<.001
    Baseline screening rates, n/N (%)
        Breast cancer18,389/22,425 (82.0)16,556/20,439 (81.0).01
        Cervical cancer23,031/27,748 (83.0)26,847/31,961 (84.0).001
        Colorectal cancer20,642/26,843 (76.9)17,431/23,556 (74.0)<.001
    • Data are n (%) unless otherwise indicated. SD, standard deviation.

    • View popup
    Table 2. Cancer Screening Rates Among Intervention and Comparison Group Patients Eligible for at Least 1 Cancer Screening Test During the Study Period
    Cancer ScreeningAverage Cancer Screening Test Completion Rates
    UnadjustedAdjusted*
    InterventionComparisonP Value
    No.Rate (%)No.Rate (%)Intervention (%)Comparison (%)P Value
    All eligible cancers51,07181.652,79981.4.9081.681.4.84
    Breast24,60282.822,35182.7.9382.782.7.96
    Cervical32,12184.235,88984.7.7284.184.7.60
    Colorectal30,35377.926,75676.2.3377.876.2.33
    • ↵* Adjusted rates and P values obtained from mixed effects models comparing intervention and control groups controlling for patient age, ethnicity, insurance status, primary language, time since last visit to practice, patient–physician linkage, and sex (for colorectal cancer and all screenings combined) while accounting for clustering by primary care physician or practice in a mixed effects model.

    • View popup
    Table 3. Cancer Screening Rates among Eligible Intervention and Comparison Group Patients Overdue for at Least 1 Cancer Screening Test During the Study Period
    Average Cancer Screening Test Completion Rates
    UnadjustedAdjusted*
    InterventionComparisonP Value
    No.Rate (%)No.Rate (%)Intervention (%)Comparison (%)P Value
    All eligible cancers18,87318.319,20117.8.5918.8%17.8%.28
    Breast692723.2648624.0.5223.7%24.0%.80
    Cervical891923.3964021.1.2323.4%21.1%.14
    Colorectal81358.777409.3.299.0%9.3%.53
    Practices in top tertile of TopCare practice delegate use
    All eligible cancers627622.3767816.9.00320.816.9<.001
    Breast250326.5281123.1.0626.423.1.06
    Cervical316628.4359918.7.00228.218.7<.001
    Colorectal22799.332629.4.929.79.4.79
    • ↵* Adjusted rates and P values obtained from mixed effects models comparing intervention and control groups controlling for patient age, ethnicity, insurance status, primary language, time since last visit to practice, patient–physician linkage, and sex (for colorectal cancer and all screenings combined) while accounting for clustering by primary care physician or practice in a mixed effects model.

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The Journal of the American Board of Family     Medicine: 27 (4)
The Journal of the American Board of Family Medicine
Vol. 27, Issue 4
July-August 2014
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Non-Visit-Based Cancer Screening Using a Novel Population Management System
Steven J. Atlas, Adrian H. Zai, Jeffrey M. Ashburner, Yuchiao Chang, Sanja Percac-Lima, Douglas E. Levy, Henry C. Chueh, Richard W. Grant
The Journal of the American Board of Family Medicine Jul 2014, 27 (4) 474-485; DOI: 10.3122/jabfm.2014.04.130319

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Non-Visit-Based Cancer Screening Using a Novel Population Management System
Steven J. Atlas, Adrian H. Zai, Jeffrey M. Ashburner, Yuchiao Chang, Sanja Percac-Lima, Douglas E. Levy, Henry C. Chueh, Richard W. Grant
The Journal of the American Board of Family Medicine Jul 2014, 27 (4) 474-485; DOI: 10.3122/jabfm.2014.04.130319
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