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Population-based Time Preferences for Future Health Outcomes

Author

Listed:
  • Theodore G. Ganiats
  • Richard T. Carson
  • Robert M. Hamm
  • Scott B. Cantor
  • Walton Sumner
  • Stephen J. Spann
  • Michael D. Hagen
  • Christopher Miller

Abstract

Context. Time preference (how preference for an outcome changes depending on when the outcome occurs) affects clinical decisions, but little is known about determinants of time preferences in clinical settings. Objectives. To determine whether information about mean population time preferences for specific health states can be easily assessed, whether mean time preferences are constant across different diseases, and whether under certain circumstances substantial fractions of the patient population make choices that are consistent with a negative time preference. Design . Self-administered survey. Setting. Family physician waiting rooms in four states. Pa tients. A convenience sample of 169 adults. Intervention. Subjects were presented five clinical vignettes. For each vignette the subject chose between interventions maximizing a present and a future health outcome. The options for individual vignettes varied among the patients so that a distribution of responses was obtained across the population of patients. Main outcome measure. Logistic regression was used to estimate the mean preference for each vignette, which was translated into an implicit discount rate for this group of patients. Results. There were marked differences in time preferences for future health outcomes based on the five vignettes, ranging from a negative to a high positive (116%) discount rate. Conclusions. The study provides empirical evidence that time preferences for future health outcomes may vary substantially among disease conditions. This is likely because the vignettes evoked different rationales for time preferences. Time preference is a critical element in patient decision making and cost-effectiveness research, and more work is necessary to improve our understanding of patient preferences for future health outcomes. Key words: time factors ; models, psychological; attitude to health; outcome assessment (health care). (Med Decis Making 2000;20:263-270)

Suggested Citation

  • Theodore G. Ganiats & Richard T. Carson & Robert M. Hamm & Scott B. Cantor & Walton Sumner & Stephen J. Spann & Michael D. Hagen & Christopher Miller, 2000. "Population-based Time Preferences for Future Health Outcomes," Medical Decision Making, , vol. 20(3), pages 263-270, July.
    • Handle: RePEc:sae:medema:v:20:y:2000:i:3:p:263-270
    DOI: 10.1177/0272989X0002000302
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    References listed on IDEAS

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    1. Cropper, Maureen L & Portney, Paul R, 1990. "Discounting and the Evaluation of Lifesaving Programs," Journal of Risk and Uncertainty, Springer, vol. 3(4), pages 369-379, December.
    2. Theodore G. Ganiats & Jonathan B.C. Humphrey & Howard L. Taras & Robert M. Kaplan, 1991. "Routine Neonatal Circumcision," Medical Decision Making, , vol. 11(4), pages 282-292, December.
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    Citations

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    Cited by:

    1. Mohammed Abdellaoui & Cédric Gutierrez & Emmanuel Kemel, 2018. "Temporal discounting of gains and losses of time: An experimental investigation," Journal of Risk and Uncertainty, Springer, vol. 57(1), pages 1-28, August.
    2. Richard Norman & Rosalie Viney, 2008. "The effect of discounting on quality of life valuation using the Time Trade-Off, CHERE Working Paper 2008/3," Working Papers 2008/3, CHERE, University of Technology, Sydney.
    3. Philip Streich & Jack S. Levy, 2007. "Time Horizons, Discounting, and Intertemporal Choice," Journal of Conflict Resolution, Peace Science Society (International), vol. 51(2), pages 199-226, April.
    4. David Bradford & Charles Courtemanche & Garth Heutel & Patrick McAlvanah & Christopher Ruhm, 2017. "Time preferences and consumer behavior," Journal of Risk and Uncertainty, Springer, vol. 55(2), pages 119-145, December.
    5. Stefan A. Lipman & Arthur E. Attema, 2024. "A systematic review of unique methods for measuring discount rates," Journal of Risk and Uncertainty, Springer, vol. 69(2), pages 145-189, October.
    6. Shotaro Shiba & Kazumi Shimizu, 2020. "Does time inconsistency differ between gain and loss? An intra-personal comparison using a non-parametric elicitation method," Theory and Decision, Springer, vol. 88(3), pages 431-452, April.
    7. Joshua A. Salomon & Christopher J.L. Murray, 2004. "A multi‐method approach to measuring health‐state valuations," Health Economics, John Wiley & Sons, Ltd., vol. 13(3), pages 281-290, March.
    8. Alireza Mahboub-Ahari & Abolghasem Pourreza & Ali Akbari Sari & Trevor A Sheldon & Maryam Moeeni, 2019. "Private and social time preference for health outcomes: A general population survey in Iran," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-13, February.
    9. Shotaro Shiba & Kazumi Shimizu, 2017. "Does Time Inconsistency Differ between Gain and Loss? An Intra-Personal Comparison Using a Non-Parametric Designed Experimen," Working Papers 1714, Waseda University, Faculty of Political Science and Economics.
    10. Julia Fimpel & Michael Stolpe, 2010. "The welfare costs of HIV/AIDS in Eastern Europe: an empirical assessment using the economic value-of-life approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(3), pages 305-322, June.
    11. Arthur E. Attema & Matthijs M. Versteegh, 2013. "Would You Rather Be Ill Now, Or Later?," Health Economics, John Wiley & Sons, Ltd., vol. 22(12), pages 1496-1506, December.
    12. Marjon Pol & Larissa Roux, 2005. "Time preference bias in time trade-off," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 6(2), pages 107-111, June.
    13. Vasquez-Lavín, Felipe & Ponce Oliva, Roberto D. & Hernández, José Ignacio & Gelcich, Stefan & Carrasco, Moisés & Quiroga, Miguel, 2019. "Exploring dual discount rates for ecosystem services: Evidence from a marine protected area network," Resource and Energy Economics, Elsevier, vol. 55(C), pages 63-80.

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