Literature DB >> 12546292

Measuring preferences for health care interventions using conjoint analysis: an application to HIV testing.

Kathryn A Phillips1, Tara Maddala, F Reed Johnson.   

Abstract

OBJECTIVE: To examine preferences for HIV test methods using conjoint analysis, a method used to measure economic preferences (utilities). DATA SOURCES: Self-administered surveys at four publicly funded HIV testing locations in San Francisco, California, between November 1999 and February 2000 (n = 365, 96 percent response rate). STUDY
DESIGN: We defined six important attributes of HIV tests and their levels (location, price, ease of collection, timeliness/accuracy, privacy/anonymity, and counseling). A fractional factorial design was used to develop scenarios that consisted of combinations of attribute levels. Respondents were asked 11 questions about whether they would choose "Test A or B" based on these scenarios. DATA ANALYSIS: We used random effects probit models to estimate utilities for testing attributes. Since price was included as an attribute, we were able to estimate willingness to pay, which provides a standardized measure for use in economic evaluations. We used extensive analyses to examine the reliability and validity of the results, including analyses of: (1) preference consistency, (2) willingness to trade among attributes, and (3) consistency with theoretical predictions. PRINCIPAL
FINDINGS: Respondents most preferred tests that were accurate/timely and private/anonymous, whereas they had relatively lower preferences for in-person counseling. Respondents were willing to pay an additional $35 for immediate, highly accurate results; however, they had a strong disutility for receiving immediate but less accurate results. By using conjoint analysis to analyze new combinations of attributes, we found that respondents would most prefer instant, highly accurate home tests, even though they are not currently available in the U.S. Respondents were willing to pay $39 for a highly accurate, instant home test.
CONCLUSIONS: The method of conjoint analysis enabled us to estimate utilities for specific attributes of HIV tests as well as the overall utility obtained from various HIV tests, including tests that are under consideration but not yet available. Conjoint analysis offers an approach that can be useful for measuring and understanding the value of other health care goods, services, and interventions.

Entities:  

Mesh:

Year:  2002        PMID: 12546292      PMCID: PMC1464051          DOI: 10.1111/1475-6773.01115

Source DB:  PubMed          Journal:  Health Serv Res        ISSN: 0017-9124            Impact factor:   3.402


  27 in total

1.  Using conjoint analysis to elicit preferences for health care.

Authors:  M Ryan; S Farrar
Journal:  BMJ       Date:  2000-06-03

2.  Home sample collection for HIV testing.

Authors:  K A Phillips; S Morin; T Coates; S Fernyak; A Marsh; L Ramos-Irizarry
Journal:  JAMA       Date:  2000-01-12       Impact factor: 56.272

3.  A role for conjoint analysis in technology assessment in health care?

Authors:  M Ryan
Journal:  Int J Technol Assess Health Care       Date:  1999       Impact factor: 2.188

4.  Preference measurement using conjoint methods: an empirical investigation of reliability.

Authors:  S Bryan; L Gold; R Sheldon; M Buxton
Journal:  Health Econ       Date:  2000-07       Impact factor: 3.046

5.  Deciding where and how to be tested for HIV: what matters most?

Authors:  H S Skolnik; K A Phillips; D Binson; J W Dilley
Journal:  J Acquir Immune Defic Syndr       Date:  2001-07-01       Impact factor: 3.731

6.  Willingness to use instant home HIV tests: data from the California Behavioral Risk Factor Surveillance Survey.

Authors:  Kathryn A Phillips; James L Chen
Journal:  Am J Prev Med       Date:  2003-05       Impact factor: 5.043

7.  Measuring what people value: a comparison of "attitude" and "preference" surveys.

Authors:  Kathryn A Phillips; F Reed Johnson; Tara Maddala
Journal:  Health Serv Res       Date:  2002-12       Impact factor: 3.402

8.  Patients' preferences regarding the process and outcomes of life-saving technology. An application of conjoint analysis to liver transplantation.

Authors:  J Ratcliffe; M Buxton
Journal:  Int J Technol Assess Health Care       Date:  1999       Impact factor: 2.188

9.  Subjective knowledge of AIDS and use of HIV testing.

Authors:  K A Phillips
Journal:  Am J Public Health       Date:  1993-10       Impact factor: 9.308

10.  Potential use of home HIV testing.

Authors:  K A Phillips; S J Flatt; K R Morrison; T J Coates
Journal:  N Engl J Med       Date:  1995-05-11       Impact factor: 91.245

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  66 in total

1.  Measuring what people value: a comparison of "attitude" and "preference" surveys.

Authors:  Kathryn A Phillips; F Reed Johnson; Tara Maddala
Journal:  Health Serv Res       Date:  2002-12       Impact factor: 3.402

Review 2.  Improving topical microbicide applicators for use in resource-poor settings.

Authors:  Janet G Vail; Jessica A Cohen; Kimberly L Kelly
Journal:  Am J Public Health       Date:  2004-07       Impact factor: 9.308

3.  Conjoint analysis: a 'new' way to evaluate patients' preferences.

Authors:  Sarah T Hawley
Journal:  Patient       Date:  2008-12-01       Impact factor: 3.883

4.  A Conjoint Analysis Framework for Evaluating User Preferences in Machine Translation.

Authors:  Katrin Kirchhoff; Daniel Capurro; Anne M Turner
Journal:  Mach Transl       Date:  2014-03-01

5.  Things are Looking up Since We Started Listening to Patients: Trends in the Application of Conjoint Analysis in Health 1982-2007.

Authors:  John F P Bridges; Elizabeth T Kinter; Lillian Kidane; Rebekah R Heinzen; Colleen McCormick
Journal:  Patient       Date:  2008-12-01       Impact factor: 3.883

Review 6.  A descriptive review on methods to prioritize outcomes in a health care context.

Authors:  Inger M Janssen; Ansgar Gerhardus; Milly A Schröer-Günther; Fülöp Scheibler
Journal:  Health Expect       Date:  2014-08-25       Impact factor: 3.377

7.  Comparison of two multi-criteria decision techniques for eliciting treatment preferences in people with neurological disorders.

Authors:  Maarten J Ijzerman; Janine A van Til; Govert J Snoek
Journal:  Patient       Date:  2008-12-01       Impact factor: 3.883

8.  Which preferred providers are really preferred? Effectiveness of insurers' channeling incentives on pharmacy choice.

Authors:  Lieke H H M Boonen; Frederik T Schut; Bas Donkers; Xander Koolman
Journal:  Int J Health Care Finance Econ       Date:  2009-02-26

9.  Marketing the HIV test to MSM: ethnic differences in preferred venues and sources.

Authors:  Julia Lechuga; Jill T Owczarzak; Andrew E Petroll
Journal:  Health Promot Pract       Date:  2012-10-22

10.  Using discrete choice modeling to evaluate the preferences and willingness to pay for leptospirosis vaccine.

Authors:  Joseph Arbiol; Mitsuyasu Yabe; Hisako Nomura; Maridel Borja; Nina Gloriani; Shin-ichi Yoshida
Journal:  Hum Vaccin Immunother       Date:  2015       Impact factor: 3.452

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