IDEAS home Printed from https://ideas.repec.org/p/cns/cnscwp/201901.html
   My bibliography  Save this paper

Mixture models for consumers' preferences in healthcare

Author

Listed:
  • M. Meleddu
  • M. Pulina
  • G. Solinas
  • S. Capecchi

Abstract

This paper aims to explain preferences behaviour by a sample of Sardinia residents with respect to combined choice of attributes related to cardiology services. The rating of proposed cards, containing a combination of several attributes to qualify the services, are examined in terms of intrinsic components and main drivers to determine the ordinal choice - location, screening mode, cost, waiting time for the visit and subjects' covariates. The topic is relevant in telemedicine as experienced in Sardinia, a region with a mobility and a socio-economic disadvantage. The innovative approach allows for effective visual support to interpret and compare results and it is useful also to predict the respondents' profile with respect to their individual characteristics. Empirical evidence supports policy interventions and suggests the usefulness of the implemented statistical procedure.

Suggested Citation

  • M. Meleddu & M. Pulina & G. Solinas & S. Capecchi, 2019. "Mixture models for consumers' preferences in healthcare," Working Paper CRENoS 201901, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    • Handle: RePEc:cns:cnscwp:201901
    as

    Download full text from publisher

    File URL: https://crenos.unica.it/crenos/node/7206
    Download Restriction: no
    File URL: https://crenos.unica.it/crenos/sites/default/files/wp-19-01.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stefania Capecchi & Maria Iannario & Rosaria Simone, 2018. "Well-Being and Relational Goods: A Model-Based Approach to Detect Significant Relationships," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 729-750, January.
    2. Maria Iannario & Domenico Piccolo, 2016. "A comprehensive framework of regression models for ordinal data," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 233-252, August.
    3. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    4. Charles Chen & Shih-Wei Chou, 2010. "Measuring Patients’ Perceptions and Social Influence on Home Telecare Management System Acceptance," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 5(3), pages 44-68, July.
    5. Green, Paul E, 1974. "On the Design of Choice Experiments Involving Multifactor Alternatives," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 1(2), pages 61-68, Se.
    6. Milena Pavlova & Wim Groot & Godefridus Merode, 2005. "An Application of Rating Conjoint Analysis to Study the Importance of Quality-, Access- and Price-attributes to Health Care Consumers," Economic Change and Restructuring, Springer, vol. 37(3), pages 267-286, September.
    7. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D., 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, September.
    8. Catherine Henderson & Martin Knapp & José-Luis Fernández & Jennifer Beecham & Shashivadan P Hirani & Martin Cartwright & Lorna Rixon & Michelle Beynon & Anne Rogers & Peter Bower & Helen Doll & Ray Fi, 2013. "Cost effectiveness of telehealth for patients with long term conditions (Whole Systems Demonstrator telehealth questionnaire study): nested economic evaluation in a pragmatic, cluster randomised contr," LSE Research Online Documents on Economics 56772, London School of Economics and Political Science, LSE Library.
    9. Stefania Capecchi & Marta Meleddu & Manuela Pulina, 2019. "Quality evaluation and preferences of healthcare services: the case of telemedicine in Sardinia," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2339-2351, September.
    10. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    11. Stefania Capecchi & Domenico Piccolo, 2016. "Investigating the determinants of job satisfaction of Italian graduates: a model-based approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 169-179, January.
    12. Ryan, Mandy, 1999. "Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation," Social Science & Medicine, Elsevier, vol. 48(4), pages 535-546, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Milena Pavlova & Wim Groot & Godefridus Merode, 2005. "An Application of Rating Conjoint Analysis to Study the Importance of Quality-, Access- and Price-attributes to Health Care Consumers," Economic Change and Restructuring, Springer, vol. 37(3), pages 267-286, September.
    2. Biesma, R.G. & Pavlova, M. & van Merode, G.G. & Groot, W., 2007. "Using conjoint analysis to estimate employers preferences for key competencies of master level Dutch graduates entering the public health field," Economics of Education Review, Elsevier, vol. 26(3), pages 375-386, June.
    3. Haghani, Milad & Bliemer, Michiel C.J. & Rose, John M. & Oppewal, Harmen & Lancsar, Emily, 2021. "Hypothetical bias in stated choice experiments: Part I. Macro-scale analysis of literature and integrative synthesis of empirical evidence from applied economics, experimental psychology and neuroimag," Journal of choice modelling, Elsevier, vol. 41(C).
    4. Stefania Capecchi & Rosaria Simone, 2019. "A Proposal for a Model-Based Composite Indicator: Experience on Perceived Discrimination in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 95-110, January.
    5. Ryan H. L. Ip & K. Y. K. Wu, 2024. "A mixture distribution for modelling bivariate ordinal data," Statistical Papers, Springer, vol. 65(7), pages 4453-4488, September.
    6. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods," Papers 2102.02945, arXiv.org.
    7. Robert J. Johnston & Kevin J. Boyle & Wiktor (Vic) Adamowicz & Jeff Bennett & Roy Brouwer & Trudy Ann Cameron & W. Michael Hanemann & Nick Hanley & Mandy Ryan & Riccardo Scarpa & Roger Tourangeau & Ch, 2017. "Contemporary Guidance for Stated Preference Studies," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(2), pages 319-405.
    8. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    9. Tappenden, P & Brazier, J & Ratcliffe, J, 2006. "Does the National Institute for Health and Clinical Excellence take account of factors such as uncertainty and equity as well as incremental cost-effectiveness in commissioning health care services? A," MPRA Paper 29772, University Library of Munich, Germany.
    10. Gennaro Punzo & Rosalia Castellano & Mirko Buonocore, 2018. "Job Satisfaction in the “Big Four” of Europe: Reasoning Between Feeling and Uncertainty Through CUB Models," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(1), pages 205-236, August.
    11. Lancsar, Emily & Louviere, Jordan & Flynn, Terry, 2007. "Several methods to investigate relative attribute impact in stated preference experiments," Social Science & Medicine, Elsevier, vol. 64(8), pages 1738-1753, April.
    12. Manalo, Alberto B., 1989. "Benefits Sought by Apple Consumers," Working Papers 115908, Regional Research Project NE-165 Private Strategies, Public Policies, and Food System Performance.
    13. Vetschera, Rudolf & Weitzl, Wolfgang & Wolfsteiner, Elisabeth, 2014. "Implausible alternatives in eliciting multi-attribute value functions," European Journal of Operational Research, Elsevier, vol. 234(1), pages 221-230.
    14. Menictas, Con & Wang, Paul Z. & Louviere, Jordan J., 2012. "Assessing the validity of brand equity constructs," Australasian marketing journal, Elsevier, vol. 20(1), pages 3-8.
    15. Álvarez-Farizo, Begoña & Gil, José M. & Howard, B.J., 2009. "Impacts from restoration strategies: Assessment through valuation workshops," Ecological Economics, Elsevier, vol. 68(3), pages 787-797, January.
    16. Chan-Halbrendt, Catherine & Yu, Jin & Keung, Helen & Lin, Tun & Ferguson, Carol, 2006. "Guangzhou Buyers Preference for Premium Hawaiian Grown Product Gift Baskets," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 9(4), pages 1-15.
    17. Ferrer-i-Carbonell, Ada & van Praag, Bernard M. S. & Theodossiou, Ioannis, 2011. "Vignette Equivalence and Response Consistency: The Case of Job Satisfaction," IZA Discussion Papers 6174, Institute of Labor Economics (IZA).
    18. Poulos, Christine & Yang, Jui-Chen & Levin, Carol & Van Minh, Hoang & Giang, Kim Bao & Nguyen, Diep, 2011. "Mothers' preferences and willingness to pay for HPV vaccines in Vinh Long Province, Vietnam," Social Science & Medicine, Elsevier, vol. 73(2), pages 226-234, July.
    19. Lim, Sesil & Huh, Sung-Yoon & Shin, Jungwoo & Lee, Jongsu & Lee, Yong-Gil, 2019. "Enhancing public acceptance of renewable heat obligation policies in South Korea: Consumer preferences and policy implications," Energy Economics, Elsevier, vol. 81(C), pages 1167-1177.
    20. Alessandro Mengoni & Chiara Seghieri & Sabina Nuti, 2013. "The application of discrete choice experiments in health economics: a systematic review of the literature," Working Papers 201301, Scuola Superiore Sant'Anna of Pisa, Istituto di Management.

    More about this item

    Keywords

    CUB models; E-health Preferences; Discrete modelling; Decision drivers;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cns:cnscwp:201901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CRENoS (email available below). General contact details of provider: https://edirc.repec.org/data/crenoit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.