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Research ArticleMethodology

Using Best-Worst Scaling to Understand Patient Priorities: A Case Example of Papanicolaou Tests for Homeless Women

Eve Wittenberg, Monica Bharel, John F. P. Bridges, Zachary Ward and Linda Weinreb
The Annals of Family Medicine July 2016, 14 (4) 359-364; DOI: https://doi.org/10.1370/afm.1937
Eve Wittenberg
1Center for Health Decision Science, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
MPP, PhD
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  • For correspondence: ewittenb@hsph.harvard.edu
Monica Bharel
2The Boston Health Care for the Homeless Program, and Department of Medicine, Massachusetts General Hospital and Boston Medical Center, Boston, Massachusetts; currently: Department of Public Health, Commonwealth of Massachusetts, Boston, Massachusetts
MD, MPH
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John F. P. Bridges
3Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
PhD
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Zachary Ward
1Center for Health Decision Science, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
MPH
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Linda Weinreb
4Department of Family Medicine and Community Health, University of Massachusetts Medical School, Worcester, Massachusetts
MD
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    Figure 1

    Sample screen shot of a question from the survey.

    Pap = Papanicolaou.

    Note: Respondents were shown 11 questions similar to this, each with a different set of 5 attributes (ie, “objects,” predefined per the experimental design, so that each was seen 5 times and in combination with each other attribute twice during the course of the survey). They were instructed to choose which attribute would have the biggest influence on women’s decision to get tested, and which would have the smallest. A button appeared highlighted with color when it was “clicked.” After choosing biggest and smallest, respondents proceeded to the next question until completion of the 11 total sets.

  • Figure 2
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    Figure 2

    Standardized scores for the 11 hypothetical screening attributes.

    Note: Standardized “biggest – smallest” scores and 95% confidence intervals for the 11 hypothetical screening attributes influencing homeless women’s decision to be tested. A total of 165 women participated, each of whom chose biggest and smallest attributes from 11 sets of 5 attributes each (3,630 total choices).

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    Table 1

    Sample Characteristics and Questionnaire Completion (N = 165)

    CharacteristicMean (SD) or No. (%)
    Age, y43.1 (13.1)
    Racea
     White69 (41.8)
     African American or black57 (34.5)
     Native Hawaiian or Pacific Islander1 (0.6)
     Other20 (12.1)
     Multiple4 (2.4)
     Hispanic onlyb14 (8.5)
    Ethnicity: Latina/Hispanic45 (27.3)
    Education
     <12 years36 (21.8)
     High school diploma/GED57 (34.5)
     Some college or college diploma68 (41.2)
     Other type of degree/not sure4 (2.4)
    Has childrenc119 (72.6)
    Reported ≥1 medical condition133 (80.6)
    Reported condition(s)d
     Depression/mental health condition76 (46.1)
     Addiction54 (32.7)
     High blood pressure49 (29.7)
     Asthma/lung disease46 (27.9)
     Liver disease/hepatitis C30 (18.2)
     Diabetes28 (17.0)
     Cancer15 (9.1)
     Heart disease8 (4.8)
     Another condition/disease60 (36.4)
    Time since last Pap smear
     ≤12 months103 (62.4)
     >12 months to ≤3 years46 (27.9)
     >3 years7 (4.2)
     Never had one4 (2.4)
     Don’t know5 (3.0)
    Questionnaire completion
     Spanish version19 (11.5)
     Self-completed or some interviewer assistancee91 (55.8)
     Time taken, min13.2 (4.4)
    • GED = General Educational Development Test; Pap = Papanicolaou.

      Note: Percentages may not sum to 100 because of rounding.

    • ↵a None reported Asian race.

    • ↵b Indicated only ethnicity.

    • ↵c Including children living elsewhere—data missing for 1 respondent.

    • ↵d Of women reporting at least 1 condition.

    • ↵e Data missing for 2 respondents.

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    Table 2

    Subjective Priority of Attributes Influencing Acceptance of Pap Smears Among Homeless Women: Frequency Counts and Standardized Score

    AttributeNo. of Times Chosen Standardized Scorea
    Biggest InfluenceSmallest Influence
    Support is available for all issues the woman is facing370510.39
    Testing is done at no cost277960.22
    Testing is not contingent on substance use189208−0.02
    Counseling is available to discuss results182720.13
    Testing is done at convenient time1741190.07
    Choice of provider sex169178−0.01
    Time during procedure for questions/explanations105133−0.03
    Setting is accepting of homeless104231−0.15
    Provider is kind88156−0.08
    Personal hygiene accommodations83309−0.27
    Provider is familiar to woman74262−0.23
    • Pap = Papanicolaou.

    • ↵a Difference between count of chosen as biggest and count of chosen as smallest, divided by the number of times attribute was available to be selected per experimental design (for this design, 5 × number of respondents). Standardized scores indicate the salience of an attribute on a scale from −1.0 to +1.0. Scores toward +1.0 indicate salience as a biggest influence on testing, scores toward −1.0 indicate salience as a smallest influence on testing, and a score of 0 indicates no salience to the decision to undergo testing.

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  • The Article in Brief

    Using Best-Worst Scaling to Understand Patient Priorities: A Case Example of Papanicolaou Tests for Homeless Women

    Eve Wittenberg , and colleagues

    Background Best-Worst Scaling (BWS) is a survey method for assessing individuals' priorities: what is best and worst among a set of items. This report applies the BWS method to cervical cancer screening priorities for homeless women.

    What This Study Found Best-Worst Scaling quantifies patients' priorities in a way that is transparent and accessible. To demonstrate the use of BWS in primary care, participants were asked to evaluate attributes of Pap services. The biggest influence on participants' decision about whether to have a Pap test was the availability of support for issues beyond health, followed by no-cost testing. Least important was the availability of accommodations for hygiene and participants' familiarity with the clinician.

    Implications

    • The authors conclude that BWS can be easily understood by patients and is relatively easy to administer. This approach can be applied to other areas of health care where prioritization is helpful in guiding decisions.
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The Annals of Family Medicine: 14 (4)
The Annals of Family Medicine: 14 (4)
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July/August 2016
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Using Best-Worst Scaling to Understand Patient Priorities: A Case Example of Papanicolaou Tests for Homeless Women
Eve Wittenberg, Monica Bharel, John F. P. Bridges, Zachary Ward, Linda Weinreb
The Annals of Family Medicine Jul 2016, 14 (4) 359-364; DOI: 10.1370/afm.1937

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Using Best-Worst Scaling to Understand Patient Priorities: A Case Example of Papanicolaou Tests for Homeless Women
Eve Wittenberg, Monica Bharel, John F. P. Bridges, Zachary Ward, Linda Weinreb
The Annals of Family Medicine Jul 2016, 14 (4) 359-364; DOI: 10.1370/afm.1937
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