IDEAS home Printed from https://ideas.repec.org/a/spr/eujhec/v23y2022i3d10.1007_s10198-021-01356-3.html
   My bibliography  Save this article

Valuing informal carers’ quality of life using best-worst scaling—Finnish preference weights for the Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer)

Author

Listed:
  • Lien Nguyen

    (Finnish Institute for Health and Welfare (THL))

  • Hanna Jokimäki

    (Finnish Institute for Health and Welfare (THL))

  • Ismo Linnosmaa

    (Finnish Institute for Health and Welfare (THL)
    University of Eastern Finland)

  • Eirini-Christina Saloniki

    (University of Kent
    University of Kent)

  • Laurie Batchelder

    (University of Kent)

  • Juliette Malley

    (London School of Economics and Political Science)

  • Hui Lu

    (RAND Europe)

  • Peter Burge

    (RAND Europe)

  • Birgit Trukeschitz

    (WU Vienna University of Economics and Business)

  • Julien Forder

    (University of Kent)

Abstract

This study developed Finnish preference weights for the seven-attribute Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer) and investigated survey fatigue and learning in best-worst scaling (BWS) experiments. An online survey that included a BWS experiment using the ASCOT-Carer was completed by a sample from the general population in Finland. A block of eight BWS profiles describing different states from the ASCOT-Carer were randomly assigned to each respondent, who consecutively made four choices (best, worst, second best and second worst) per profile. The analysis panel data had 32,160 choices made by 1005 respondents. A scale multinomial logit (S-MNL) model was used to estimate preference weights for 28 ASCOT-Carer attribute levels. Fatigue and learning effects were examined as scale heterogeneity. Several specifications of the generalised MNL model were employed to ensure the stability of the preference estimates. The most and least-valued states were the top and bottom levels of the control over daily life attribute. The preference weights were not on a cardinal scale. We observed the position effect of the attributes on preferences associated with the best or second-best choices. A learning effect was found. The established preference weights can be used in evaluations of the effects of long-term care services and interventions on the quality of life of service users and caregivers. The learning effect implies a need to develop study designs that ensure equal consideration to all profiles (choice tasks) in a sequential choice experiment.

Suggested Citation

  • Lien Nguyen & Hanna Jokimäki & Ismo Linnosmaa & Eirini-Christina Saloniki & Laurie Batchelder & Juliette Malley & Hui Lu & Peter Burge & Birgit Trukeschitz & Julien Forder, 2022. "Valuing informal carers’ quality of life using best-worst scaling—Finnish preference weights for the Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer)," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(3), pages 357-374, April.
    • Handle: RePEc:spr:eujhec:v:23:y:2022:i:3:d:10.1007_s10198-021-01356-3
    DOI: 10.1007/s10198-021-01356-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10198-021-01356-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10198-021-01356-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Flynn, Terry Nicholas & Louviere, Jordan J. & Peters, Tim J. & Coast, Joanna, 2010. "Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters," Social Science & Medicine, Elsevier, vol. 70(12), pages 1957-1965, June.
    2. Hess, Stephane & Train, Kenneth, 2017. "Correlation and scale in mixed logit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 1-8.
    3. Scott J. Savage & Donald M. Waldman, 2008. "Learning and fatigue during choice experiments: a comparison of online and mail survey modes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 351-371.
    4. Mikolaj Czajkowski & Marek Giergiczny & William H. Greene, 2014. "Learning and Fatigue Effects Revisited: Investigating the Effects of Accounting for Unobservable Preference and Scale Heterogeneity," Land Economics, University of Wisconsin Press, vol. 90(2), pages 324-351.
    5. Potoglou, Dimitris & Burge, Peter & Flynn, Terry & Netten, Ann & Malley, Juliette & Forder, Julien & Brazier, John E., 2011. "Best-worst scaling vs. discrete choice experiments: An empirical comparison using social care data," Social Science & Medicine, Elsevier, vol. 72(10), pages 1717-1727, May.
    6. Xavier Badia & Montserrat Roset & Michael Herdman & Paul Kind, 2001. "A Comparison of United Kingdom and Spanish General Population Time Trade-off Values for EQ-5D Health States," Medical Decision Making, , vol. 21(1), pages 7-16, February.
    7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    8. Hunt, Sonja M. & Alonso, Jordi & Bucquet, Denis & Niero, Mauro & Wiklund, Ingela & McKenna, Stephen, 1991. "Cross-cultural adaptation of health measures," Health Policy, Elsevier, vol. 19(1), pages 33-44, September.
    9. Nguyen, Lien & Jokimäki, Hanna & Linnosmaa, Ismo & Saloniki, Eirini Christina & Batchelder, Laurie & Malley, Juliette & Lu, Hui & Burge, Peter & Trukeschitz, Birgit & Forder, Julien, 2021. "Do you prefer safety to social participation? Finnish population-based preference weights for the Adult Social Care Outcomes Toolkit (ASCOT) for service users," LSE Research Online Documents on Economics 110757, London School of Economics and Political Science, LSE Library.
    10. van den Berg, Bernard & Fiebig, Denzil G. & Hall, Jane, 2014. "Well-being losses due to care-giving," Journal of Health Economics, Elsevier, vol. 35(C), pages 123-131.
    11. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884.
    12. Danny Campbell & Seda Erdem, 2015. "Position Bias in Best-worst Scaling Surveys: A Case Study on Trust in Institutions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(2), pages 526-545.
    13. Coast, Joanna & Flynn, Terry N. & Natarajan, Lucy & Sproston, Kerry & Lewis, Jane & Louviere, Jordan J. & Peters, Tim J., 2008. "Valuing the ICECAP capability index for older people," Social Science & Medicine, Elsevier, vol. 67(5), pages 874-882, September.
    14. Bauer, Jan Michael & Sousa-Poza, Alfonso, 2015. "Impacts of Informal Caregiving on Caregiver Employment, Health, and Family," IZA Discussion Papers 8851, Institute of Labor Economics (IZA).
    15. Carlsson, Fredrik & Raun Mørkbak, Morten & Bøye Olsen, Søren, 2010. "The first time is the hardest: A test of ordering effects in choice experiments," Working Papers in Economics 470, University of Gothenburg, Department of Economics.
    16. Terry N. Flynn & Elisabeth Huynh & Tim J. Peters & Hareth Al‐Janabi & Sam Clemens & Alison Moody & Joanna Coast, 2015. "Scoring the Icecap‐a Capability Instrument. Estimation of a UK General Population Tariff," Health Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 258-269, March.
    17. Narimasa Kumagai, 2017. "Distinct impacts of high intensity caregiving on caregivers’ mental health and continuation of caregiving," Health Economics Review, Springer, vol. 7(1), pages 1-14, December.
    18. Campbell, Danny & Boeri, Marco & Doherty, Edel & George Hutchinson, W., 2015. "Learning, fatigue and preference formation in discrete choice experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 345-363.
    19. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    20. Hess, Stephane & Hensher, David A. & Daly, Andrew, 2012. "Not bored yet – Revisiting respondent fatigue in stated choice experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 626-644.
    21. Day, Brett & Bateman, Ian J. & Carson, Richard T. & Dupont, Diane & Louviere, Jordan J. & Morimoto, Sanae & Scarpa, Riccardo & Wang, Paul, 2012. "Ordering effects and choice set awareness in repeat-response stated preference studies," Journal of Environmental Economics and Management, Elsevier, vol. 63(1), pages 73-91.
    22. Huynh, Elisabeth & Coast, Joanna & Rose, John & Kinghorn, Philip & Flynn, Terry, 2017. "Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life," Social Science & Medicine, Elsevier, vol. 189(C), pages 114-128.
    23. Trine Kjær & Mickael Bech & Dorte Gyrd‐Hansen & Kristian Hart‐Hansen, 2006. "Ordering effect and price sensitivity in discrete choice experiments: need we worry?," Health Economics, John Wiley & Sons, Ltd., vol. 15(11), pages 1217-1228, November.
    24. Hajji, Assma & Trukeschitz, Birgit & Malley, Juliette & Batchelder, Laurie & Saloniki, Eirini & Linnosmaa, Ismo & Lu, Hui, 2020. "Population-based preference weights for the Adult Social Care Outcomes Toolkit (ASCOT) for service users for Austria: Findings from a best-worst experiment," Social Science & Medicine, Elsevier, vol. 250(C).
    25. Makai, Peter & Brouwer, Werner B.F. & Koopmanschap, Marc A. & Stolk, Elly A. & Nieboer, Anna P., 2014. "Quality of life instruments for economic evaluations in health and social care for older people: A systematic review," Social Science & Medicine, Elsevier, vol. 102(C), pages 83-93.
    26. Axel C. Mühlbacher & Anika Kaczynski & Peter Zweifel & F. Reed Johnson, 2016. "Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview," Health Economics Review, Springer, vol. 6(1), pages 1-14, December.
    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. Hajji, Assma & Trukeschitz, Birgit & Malley, Juliette & Batchelder, Laurie & Saloniki, Eirini & Linnosmaa, Ismo & Lu, Hui, 2020. "Population-based preference weights for the Adult Social Care Outcomes Toolkit (ASCOT) for service users for Austria: Findings from a best-worst experiment," Social Science & Medicine, Elsevier, vol. 250(C).
    2. Aizaki, Hideo & Fogarty, James, 2019. "An R package and tutorial for case 2 best–worst scaling," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    3. Maaya, Leonard & Meulders, Michel & Vandebroek, Martina, 2021. "Joint analysis of preferences and drop out data in discrete choice experiments," Journal of choice modelling, Elsevier, vol. 41(C).
    4. Oehlmann, Malte & Meyerhoff, Jürgen & Mariel, Petr & Weller, Priska, 2017. "Uncovering context-induced status quo effects in choice experiments," Journal of Environmental Economics and Management, Elsevier, vol. 81(C), pages 59-73.
    5. Campbell, Danny & Boeri, Marco & Doherty, Edel & George Hutchinson, W., 2015. "Learning, fatigue and preference formation in discrete choice experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 345-363.
    6. Jennifer A. Whitty & Julie Ratcliffe & Gang Chen & Paul A. Scuffham, 2014. "Australian Public Preferences for the Funding of New Health Technologies," Medical Decision Making, , vol. 34(5), pages 638-654, July.
    7. Nicolas Krucien & Verity Watson & Mandy Ryan, 2017. "Is Best–Worst Scaling Suitable for Health State Valuation? A Comparison with Discrete Choice Experiments," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 1-16, December.
    8. Tomasz Gajderowicz & Gabriela Grotkowska, 2019. "Polarization of Tastes: Stated Preference Stability in Sequential Discrete Choices," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 70-87.
    9. Mikołaj Czajkowski & Marek Giergiczny & William H. Greene, 2012. "Learning and Fatigue Effects Revisited. The Impact of Accounting for Unobservable Preference and Scale Heterogeneity on Perceived Ordering Effects in Multiple Choice Task Discrete Choice Experiments," Working Papers 2012-08, Faculty of Economic Sciences, University of Warsaw.
    10. Penn, Jerrod & Hu, Wuyang, 2018. "Quantifying the Effect of Choice Set Interdependence in the Presence of Hypothetical Bias and Choice Order Effect," 2018 Annual Meeting, August 5-7, Washington, D.C. 274014, Agricultural and Applied Economics Association.
    11. Logar, Ivana & Brouwer, Roy & Campbell, Danny, 2020. "Does attribute order influence attribute-information processing in discrete choice experiments?," Resource and Energy Economics, Elsevier, vol. 60(C).
    12. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "A multinomial and rank-ordered logit model with inter- and intra-individual heteroscedasticity," Tinbergen Institute Discussion Papers 20-069/III, Tinbergen Institute.
    13. 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.
    14. Nguyen, Thanh Cong & Le, Hoa Thu & Nguyen, Hang Dieu & Le, Thanh Ha & Nguyen, Hong Quang, 2021. "Estimating economic benefits associated with air quality improvements in Hanoi City: An application of a choice experiment," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 420-433.
    15. Kaambwa, Billingsley & Lancsar, Emily & McCaffrey, Nicola & Chen, Gang & Gill, Liz & Cameron, Ian D. & Crotty, Maria & Ratcliffe, Julie, 2015. "Investigating consumers' and informal carers' views and preferences for consumer directed care: A discrete choice experiment," Social Science & Medicine, Elsevier, vol. 140(C), pages 81-94.
    16. West, Grant H. & Snell, Heather & Kovacs, Kent & Nayga, Rodolfo M., 2020. "Estimation of the preferences for the intertemporal services from groundwater," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304220, Agricultural and Applied Economics Association.
    17. Lancsar, Emily & Louviere, Jordan & Donaldson, Cam & Currie, Gillian & Burgess, Leonie, 2013. "Best worst discrete choice experiments in health: Methods and an application," Social Science & Medicine, Elsevier, vol. 76(C), pages 74-82.
    18. Aravena, Claudia & Hutchinson, W. George & Carlsson, Fredrik & Matthews, David I, 2015. "Testing preference formation in learning design contingent valuation (LDCV) using advanced information and repetitivetreatments," Working Papers in Economics 619, University of Gothenburg, Department of Economics.
    19. Gabriela D. Oliveira & Luis C. Dias, 2020. "The potential learning effect of a MCDA approach on consumer preferences for alternative fuel vehicles," Annals of Operations Research, Springer, vol. 293(2), pages 767-787, October.
    20. Emily Lancsar & Peter Burge, 2014. "Choice modelling research in health economics," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 28, pages 675-687, Edward Elgar Publishing.

    More about this item

    Keywords

    Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer); Informal care; Outcome measurement; Quality of life; Evaluation; Best-worst scaling (BWS); Scale multinomial logit; Learning and fatigue effects;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I39 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Other

    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:spr:eujhec:v:23:y:2022:i:3:d:10.1007_s10198-021-01356-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.