IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v41y2021i4p408-418.html
   My bibliography  Save this article

Who Gets the Last Bed? A Discrete-Choice Experiment Examining General Population Preferences for Intensive Care Bed Prioritization in a Pandemic

Author

Listed:
  • Amelia E. Street

    (Intensive Care Unit, Prince of Wales Hospital, Randwick, New South Wales, Australia)

  • Deborah J. Street

    (Centre for Health Economics Research and Evaluation (CHERE), University of Technology Sydney, Haymarket, New South Wales, Australia)

  • Gordon M. Flynn

    (Intensive Care Unit, Prince of Wales Hospital)

Abstract

Objective To explore the key patient attributes important to members of the Australian general population when prioritizing patients for the final intensive care unit (ICU) bed in a pandemic over-capacity scenario. Methods A discrete-choice experiment administered online asked respondents ( N = 306) to imagine the COVID-19 caseload had surged and that they were lay members of a panel tasked to allocate the final ICU bed. They had to decide which patient was more deserving for each of 14 patient pairs. Patients were characterized by 5 attributes: age, occupation, caregiver status, health prior to being infected, and prognosis. Respondents were randomly allocated to one of 7 sets of 14 pairs. Multinomial, mixed logit, and latent class models were used to model the observed choice behavior. Results A latent class model with 3 classes was found to be the most informative. Two classes valued active decision making and were slightly more likely to choose patients with caregiving responsibilities over those without. One of these classes valued prognosis most strongly, with a decreasing probability of bed allocation for those 65 y and older. The other valued both prognosis and age highly, with decreasing probability of bed allocation for those 45 y and older and a slight preference in favor of frontline health care workers. The third class preferred more random decision-making strategies. Conclusions For two-thirds of those sampled, prognosis, age, and caregiving responsibilities were the important features when making allocation decisions, although the emphasis varies. The remainder appeared to choose randomly.

Suggested Citation

  • Amelia E. Street & Deborah J. Street & Gordon M. Flynn, 2021. "Who Gets the Last Bed? A Discrete-Choice Experiment Examining General Population Preferences for Intensive Care Bed Prioritization in a Pandemic," Medical Decision Making, , vol. 41(4), pages 408-418, May.
  • Handle: RePEc:sae:medema:v:41:y:2021:i:4:p:408-418
    DOI: 10.1177/0272989X21996615
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X21996615
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X21996615?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
    ---><---

    References listed on IDEAS

    as
    1. Sarrias, Mauricio & Daziano, Ricardo, 2017. "Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i02).
    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. Akinwehinmi, Oluwagbenga & Ogundari, Kolawole & Amos, Taiwo, 2021. "Consumers' Food Control Risk Perception and Preference for Government-Controlled Safety Certification in Emerging Food Markets," 2021 Conference, August 17-31, 2021, Virtual 315312, International Association of Agricultural Economists.
    2. Jeff Luckstead & Rodolfo M. Nayga & Heather A. Snell, 2023. "US domestic workers' willingness to accept agricultural field jobs," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(3), pages 1693-1715, September.
    3. McQueen, Michael & Clifton, Kelly J., 2022. "Assessing the perception of E-scooters as a practical and equitable first-mile/last-mile solution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 395-418.
    4. Liu, Zhaoyang & Hanley, Nick & Campbell, Danny, 2020. "Linking urban air pollution with residents’ willingness to pay for greenspace: A choice experiment study in Beijing," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    5. Meressa, Abrha Megos & Navrud, Stale, 2020. "Not my cup of coffee: Farmers’ preferences for coffee variety traits – Lessons for crop breeding in the age of climate change," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 9(3), December.
    6. Stefania Troiano & Daniel Vecchiato & Francesco Marangon & Tiziano Tempesta & Federico Nassivera, 2019. "Households’ Preferences for a New ‘Climate-Friendly’ Heating System: Does Contribution to Reducing Greenhouse Gases Matter?," Energies, MDPI, vol. 12(13), pages 1-19, July.
    7. Solomon Zena Walelign & Martin Reinhardt Nielsen & Jette Bredahl Jacobsen, 2019. "Roads and livelihood activity choices in the Greater Serengeti Ecosystem, Tanzania," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-21, March.
    8. Wang, Chen & Sun, Jiayi & Russell, Roddy & Daziano, Ricardo A., 2018. "Analyzing willingness to improve the resilience of New York City's transportation system," Transport Policy, Elsevier, vol. 69(C), pages 10-19.
    9. Chakraborty, Rahul & Chakravarty, Sujoy, 2023. "Factors affecting acceptance of electric two-wheelers in India: A discrete choice survey," Transport Policy, Elsevier, vol. 132(C), pages 27-41.
    10. Wendong Zhang & Brent Sohngen, 2018. "Do U.S. Anglers Care about Harmful Algal Blooms? A Discrete Choice Experiment of Lake Erie Recreational Anglers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(3), pages 868-888.
    11. Aravindakshan, Sreejith & Krupnik, Timothy J. & Amjath-Babu, T.S. & Speelman, Stijn & Tur-Cardona, Juan & Tittonell, Pablo & Groot, Jeroen C.J., 2021. "Quantifying farmers' preferences for cropping systems intensification: A choice experiment approach applied in coastal Bangladesh's risk prone farming systems," Agricultural Systems, Elsevier, vol. 189(C).
    12. Uddin, Md Azhar & Gao, Zhifeng & Farnsworth, Derek & Borisova, Tatiana & Bolques, Alejandro, 2022. "Mitigation of Hypothetical Bias in Estimating Consumers' Willingness to Pay for Best Management Practice Labels," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322431, Agricultural and Applied Economics Association.
    13. Prateek Bansal & Roselinde Kessels & Rico Krueger & Daniel J Graham, 2021. "Face masks, vaccination rates and low crowding drive the demand for the London Underground during the COVID-19 pandemic," Papers 2107.02394, arXiv.org.
    14. Bansal, Prateek & Daziano, Ricardo A. & Achtnicht, Martin, 2018. "Comparison of parametric and semiparametric representations of unobserved preference heterogeneity in logit models," Journal of choice modelling, Elsevier, vol. 27(C), pages 97-113.
    15. Gabriel Rodríguez-Puello & Ariel Arcos & Benjamin Jara, 2022. "Would you Value a few More Hours of work? Underemployment and Subjective Well-Being Across Chilean Workers," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(2), pages 885-912, April.
    16. Van Asselt, Joanna & Nian, Yefan & Soh, Moonwon & Morgan, Stephen & Gao, Zhifeng, 2022. "Do plastic warning labels reduce consumers' willingness to pay for plastic egg packaging? – Evidence from a choice experiment," Ecological Economics, Elsevier, vol. 198(C).
    17. Anders Dugstad & Kristine Grimsrud & Gorm Kipperberg & Henrik Lindhjem & Ståle Navrud, 2020. "Acceptance of national wind power development and exposure. A case-control choice experiment approach," Discussion Papers 933, Statistics Norway, Research Department.
    18. Muhammad Safdar & Arshad Jamal & Hassan M. Al-Ahmadi & Muhammad Tauhidur Rahman & Meshal Almoshaogeh, 2022. "Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country," Sustainability, MDPI, vol. 14(5), pages 1-25, February.
    19. Bansal, Prateek & Kessels, Roselinde & Krueger, Rico & Graham, Daniel J., 2022. "Preferences for using the London Underground during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 45-60.
    20. Danaf, Mazen & Atasoy, Bilge & Ben-Akiva, Moshe, 2020. "Logit mixture with inter and intra-consumer heterogeneity and flexible mixing distributions," Journal of choice modelling, Elsevier, vol. 35(C).

    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:sae:medema:v:41:y:2021:i:4:p:408-418. 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: SAGE Publications (email available below). General contact details of provider: .

    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.