Abstract
Discounting is a description of the devaluing of an outcome based on increased delay or decreased probability of that outcome. This framework can be extended to virtually any choice situation, including preventive medical procedures. One third of Americans will be diagnosed with cancer in their lifetimes. Identifying how key factors affect decisions to engage in preventive care could inform interventions that are designed to promote equitable access to care. Therefore, the purpose of this study was to better understand how individuals would weigh key factors in the decision to have surgery to remove a premalignant tumor. Two experiments were conducted. In each experiment, participants (n = 50) were provided with hypothetical situations in which they were faced with the decision to undergo surgery to remove a premalignant tumor. Probability of cancer, delay to onset of cancer, frame, and cost of surgery were varied across questions. Surgical cost and probability of malignancy had the most effect on tumor removal. Delay of cancer onset and the question being framed as malignant or benign also had a small effect on tumor removal. Most participants also reported having delayed a real medical procedure due to cost, indicating that cost is a major factor in health-related decision making. Likelihood of cancer and cost of health care is a major determinant of decision making when removing a premalignant tumor. Changes in the U.S. health-care system that decrease direct costs to patients could encourage important preventative procedures, and lower potentially worse long-term cancer outcomes and increased costs due to treatment.
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Data Availability
The datasets generated and analysed during the current study are available in the OPENICPS repository, 10.3886/E138762V1.
References
Amlung, M., Vedelago, L., Acker, J., Balodis, I., & MacKillop, J. (2017). Steep delay discounting and addictive behavior: A meta-analysis of continuous associations. Addiction, 112(1), 51–62. https://doi.org/10.1111/add.13535
Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health & Social Behavior, 36(1), 1. https://doi.org/10.2307/2137284
Asgarova, R., Macaskill, A. C., Robinson, B. J., & Hunt, M. J. (2017). Probability discounting and cardiovascular risk: The effect of side-effect severity and framing. The Psychological Record, 67(2), 169–179. https://doi.org/10.1007/s40732-017-0243-2
Bestvina, C. M., Zullig, L. L., & Yousuf Zafar, S. (2014). The implications of out-of-pocket cost of cancer treatment in the USA: A critical appraisal of the literature. Future Oncology, 10(14), 2189–2199. https://doi.org/10.2217/fon.14.130
Bickel, W. K., & Mueller, E. T. (2009). Toward the study of trans-disease processes: A novel approach with special reference to the study of co-morbidity. Journal of Dual Diagnosis, 5(2), 131–138. https://doi.org/10.1080/15504260902869147
Bickel, W. K., Jarmolowicz, D. P., Mueller, E. T., Koffarnus, M. N., & Gatchalian, K. M. (2012). Excessive discounting of delayed reinforcers as a trans-disease process contributing to addiction and other disease-related vulnerabilities: Emerging evidence. Pharmacology & Therapeutics, 134(3), 287–297. https://doi.org/10.1016/j.pharmthera.2012.02.004
Blackburn, M., & El-Deredy, W. (2013). The future is risky: Discounting of delayed and uncertain outcomes. Behavioural Processes, 94, 9–18. https://doi.org/10.1016/j.beproc.2012.11.005
Bleicher, R. J., Ruth, K., Sigurdson, E. R., Beck, J. R., Ross, E., Wong, Y.-N., Patel, S. A., Boraas, M., Chang, E. I., Topham, N. S., & Egleston, B. L. (2016). Time to surgery and breast cancer survival in the United States. JAMA Oncology, 2(3), 330. https://doi.org/10.1001/jamaoncol.2015.4508
Borges, A. M., Kuang, J., Milhorn, H., & Yi, R. (2016). An alternative approach to calculating Area-Under-the-Curve (AUC) in delay discounting research. Journal of the Experimental Analysis of Behavior, 106(2), 145–155. https://doi.org/10.1002/jeab.219
Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielson, A., Skaug, H. J., Maechler, M., & Bolker, B. M. (2017). GlmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400.
Bruce, J. M., Bruce, A. S., Lynch, S., Thelen, J., Lim, S.-L., Smith, J., Catley, D., Reed, D. D., & Jarmolowicz, D. P. (2018a). Probability discounting of treatment decisions in multiple sclerosis: Associations with disease knowledge, neuropsychiatric status, and adherence. Psychopharmacology, 235(11), 3303–3313. https://doi.org/10.1007/s00213-018-5037-y
Bruce, J. M., Jarmolowicz, D. P., Lynch, S., Thelen, J., Lim, S.-L., Smith, J., Catley, D., & Bruce, A. S. (2018b). How patients with multiple sclerosis weigh treatment risks and benefits. Health Psychology, 37(7), 680–690. https://doi.org/10.1037/hea0000626
Chapman, G. B. (1996). Temporal discounting and utility for health and money. Journal of Experimental Psychology: Learning, Memory, & Cognition, 22(3), 771–791. https://doi.org/10.1037/0278-7393.22.3.771
Chapman, G. B., & Elstein, A. S. (1995). Valuing the future: Temporal discounting of health and money. Medical Decision Making, 15(4), 373–386. https://doi.org/10.1177/0272989X9501500408
Chapman, G. B., Brewer, N. T., Coups, E. J., Brownlee, S., Leventhal, H., & Levanthal, E. A. (2001). Value for the future and preventive health behavior. Journal of Experimental Psychology: Applied, 7(3), 235–250. https://doi.org/10.1037/1076-898X.7.3.235
Choosing Wisely. (2014, October 24). https://www.choosingwisely.org/
Clifford, S., Jewell, R. M., & Waggoner, P. D. (2015). Are samples drawn from Mechanical Turk valid for research on political ideology? Research & Politics, 2(4), 205316801562207. https://doi.org/10.1177/2053168015622072
Cox, D. J., & Dallery, J. (2016). Effects of delay and probability combinations on discounting in humans. Behavioural Processes, 131, 15–23. https://doi.org/10.1016/j.beproc.2016.08.002
Doty, M. M., Collins, S. R., Rustgi, S. D., & Kriss, J. L. (2008). Seeing red: The growing burden of medical bills and debt faced by U.S. families. Issue Brief (Commonwealth Fund), 42, 1–12.
Dowle, M., & Srinivasan, A. (2020). data.table: Extension of “data.frame” (1.13.0) [Computer software]. https://CRAN.R-project.org/package=data.table
Edwards, A., Elwyn, G., Matthews, E., & Pill, R. (2001). Presenting risk information A review of the effects of framing and other manipulations on patient outcomes. Journal of Health Communication, 6(1), 61–82. https://doi.org/10.1080/10810730150501413
Estle, S. J., Green, L., Myerson, J., & Holt, D. D. (2006). Differential effects of amount on temporal and probability discounting of gains and losses. Memory & Cognition, 34(4), 914–928. https://doi.org/10.3758/BF03193437
Fox, J., & Weisberg, S. (2019). An R companion to applied regression (3rd ed.). Sage https://socialsciences.mcmaster.ca/jfox/Books/Companion/
Friedel, J. E., DeHart, W. B., Frye, C. C. J., Rung, J. M., & Odum, A. L. (2016). Discounting of qualitatively different delayed health outcomes in current and never smokers. Experimental & Clinical Psychopharmacology, 24(1), 18–29. https://doi.org/10.1037/pha0000062
Friedel, J. E., DeHart, W. B., Foreman, A. M., & Andrew, M. E. (2019). A Monte Carlo method for comparing generalized estimating equations to conventional statistical techniques for discounting data. Journal of the Experimental Analysis of Behavior, 111(2), 207–224. https://doi.org/10.1002/jeab.497
Gong, J., Zhang, Y., Yang, Z., Huang, Y., Feng, J., & Zhang, W. (2013). The framing effect in medical decision-making: A review of the literature. Psychology, Health & Medicine, 18(6), 645–653. https://doi.org/10.1080/13548506.2013.766352
Green, L., Myerson, J., Oliveira, L., & Chang, S. E. (2014). Discounting of delayed and probabilistic losses over a wide range of amounts. Journal of the Experimental Analysis of Behavior, 101(2), 186–200. https://doi.org/10.1002/jeab.56
Hagell, P., Westergren, A., & Årestedt, K. (2017). Beware of the origin of numbers: Standard scoring of the SF-12 and SF-36 summary measures distorts measurement and score interpretations. Research in Nursing & Health, 40(4), 378–386. https://doi.org/10.1002/nur.21806
Hartig, F. (2020). DHARMa: Residual diagnostics for hierarchical (multi-level / mixed) regression models. (0.3.3.0) [Computer software]. https://CRAN.R-project.org/package=DHARMa
Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & Fagerstrom, K.-O. (1991). The Fagerstrom Test for Nicotine Dependence: A revision of the Fagerstrom Tolerance Questionnaire. Addiction, 86(9), 1119–1127. https://doi.org/10.1111/j.1360-0443.1991.tb01879.x
Himmelstein, D. U., Thorne, D., Warren, E., & Woolhandler, S. (2009). Medical bankruptcy in the United States, 2007: Results of a national study. American Journal of Medicine, 122(8), 741–746. https://doi.org/10.1016/j.amjmed.2009.04.012
Himmelstein, D. U., Lawless, R. M., Thorne, D., Foohey, P., & Woolhandler, S. (2019). Medical Bankruptcy: Still Common Despite the Affordable Care Act. American Journal of Public Health, 109(3), 431–433. https://doi.org/10.2105/AJPH.2018.304901
Huff, C., & Tingley, D. (2015). “Who are these people?” Evaluating the demographic characteristics and political preferences of MTurk survey respondents. Research & Politics, 2(3), 205316801560464. https://doi.org/10.1177/2053168015604648
Islami, F., Miller, K. D., Siegel, R. L., Zheng, Z., Zhao, J., Han, X., Ma, J., Jemal, A., & Yabroff, K. R. (2019). National and state estimates of lost earnings from cancer deaths in the United States. JAMA Oncology, 5(9), e191460. https://doi.org/10.1001/jamaoncol.2019.1460
Jarmolowicz, D. P., Bruce, A. S., Glusman, M., Lim, S.-L., Lynch, S., Thelen, J., Catley, D., Zieber, N., Reed, D. D., & Bruce, J. M. (2017). On how patients with multiple sclerosis weigh side effect severity and treatment efficacy when making treatment decisions. Experimental & Clinical Psychopharmacology, 25(6), 479–484. https://doi.org/10.1037/pha0000152
Jarmolowicz, D. P., Reed, D. D., Francisco, A. J., Bruce, J. M., Lemley, S. M., & Bruce, A. S. (2018). Modeling effects of risk and social distance on vaccination choice. Journal of the Experimental Analysis of Behavior, 110(1), 39–53. https://doi.org/10.1002/jeab.438
Johnson, M. W., & Bickel, W. K. (2002). Within-subject comparison of real and hypothetical money rewards in delay discounting. Journal of the Experimental Analysis of Behavior, 77(2), 129–146. https://doi.org/10.1901/jeab.2002.77-129
Johnson, M. W., & Bickel, W. K. (2008). An algorithm for identifying nonsystematic delay-discounting data. Experimental & Clinical Psychopharmacology, 16(3), 264–274. https://doi.org/10.1037/1064-1297.16.3.264
Kees, J., Berry, C., Burton, S., & Sheehan, K. (2017). An analysis of data quality: Professional panels, student subject pools, and Amazon’s Mechanical Turk. Journal of Advertising, 46(1), 141–155. https://doi.org/10.1080/00913367.2016.1269304
Lejeune, C., Sassi, F., Ellis, L., Godward, S., Mak, V., Day, M., & Rachet, B. (2010). Socio-economic disparities in access to treatment and their impact on colorectal cancer survival. International Journal of Epidemiology, 39(3), 710–717. https://doi.org/10.1093/ije/dyq048
Lenth, R. (2020). emmeans: Estimated marginal means, aka least-squares means (1.5.2-1) [Computer software]. https://CRAN.R-project.org/package=emmeans
Lin, C., Clark, R., Tu, P., Bosworth, H. B., & Zullig, L. L. (2017). Breast cancer oral anti-cancer medication adherence: A systematic review of psychosocial motivators and barriers. Breast Cancer Research & Treatment, 165(2), 247–260. https://doi.org/10.1007/s10549-017-4317-2
Madden, G. J., Begotka, A. M., Raiff, B. R., & Kastern, L. L. (2003). Delay discounting of real and hypothetical rewards. Experimental & Clinical Psychopharmacology, 11(2), 139–145. https://doi.org/10.1037/1064-1297.11.2.139
Madden, G. J., Raiff, B. R., Lagorio, C. H., Begotka, A. M., Mueller, A. M., Hehli, D. J., & Wegener, A. A. (2004). Delay discounting of potentially real and hypothetical rewards: II. Between- and within-subject comparisons. Experimental & Clinical Psychopharmacology, 12(4), 251–261. https://doi.org/10.1037/1064-1297.12.4.251
Mazur, J. E. (1987). An adjusting procedure for studying delayed reinforcement. In: M. L. Commons, J. E. Mazur, J. A. Nevin, & H. Rachlin (Eds.), The effect of delay and of intervening events on reinforcement value (pp. 55–73). Lawrence Erlbaum Associates.
McKerchar, T. L., & Renda, C. R. (2012). Delay and probability discounting in humans: An overview. The Psychological Record, 62(4), 817–834. https://doi.org/10.1007/BF03395837
Merz, Z. C., Lace, J. W., & Eisenstein, A. M. (2020). Examining broad intellectual abilities obtained within an mTurk internet sample. Current Psychology. https://doi.org/10.1007/s12144-020-00741-0
Mitchell, S. H., & Wilson, V. B. (2010). The subjective value of delayed and probabilistic outcomes: Outcome size matters for gains but not for losses. Behavioural Processes, 83(1), 36–40. https://doi.org/10.1016/j.beproc.2009.09.003
Murphy, C. T., Galloway, T. J., Handorf, E. A., Egleston, B. L., Wang, L. S., Mehra, R., Flieder, D. B., & Ridge, J. A. (2016). Survival impact of increasing time to treatment initiation for patients with head and neck cancer in the United States. Journal of Clinical Oncology, 34(2), 169–178. https://doi.org/10.1200/JCO.2015.61.5906
Myerson, J., Green, L., & Warusawitharana, M. (2001). Area under the curve as a measure of discounting. Journal of the Experimental Analysis of Behavior, 76(2), 235–243. https://doi.org/10.1901/jeab.2001.76-235
Myerson, J., Baumann, A. A., & Green, L. (2017). Individual differences in delay discounting: Differences are quantitative with gains, but qualitative with losses. Journal of Behavioral Decision Making, 30(2), 359–372. https://doi.org/10.1002/bdm.1947
National Cancer Institute. (2015, April 29). Risk factors: Age (nciglobal,ncienterprise) [CgvArticle]. Retrieved April 23, 2021. https://www.cancer.gov/about-cancer/causes-prevention/risk/age
National Cancer Institute. (2020, September 25). Cancer Statistics—National Cancer Institute (nciglobal,ncienterprise) [CgvArticle]. Retrieved April 23, 2021. https://www.cancer.gov/about-cancer/understanding/statistics
Odum, A. L. (2011). Delay discounting: I’m a k, you’re a k. Journal of the Experimental Analysis of Behavior, 96(3), 427–439. https://doi.org/10.1901/jeab.2011.96-423
Olsson, J. K., Schultz, E. M., & Gould, M. K. (2009). Timeliness of care in patients with lung cancer: A systematic review. Thorax, 64(9), 749–756. https://doi.org/10.1136/thx.2008.109330
Patel, D. C., He, H., Berry, M. F., Yang, C.-F. J., Trope, W., Lui, N., Liou, D. Z., Backhus, L. M., & Shrager, J. B. (2020). Cancer diagnoses and survival rise as 65-year-olds become Medicare eligible. Journal of Clinical Oncology, 38(15_suppl), 2015–2015. https://doi.org/10.1200/JCO.2020.38.15_suppl.2015
Peer, E., Vosgerau, J., & Acquisti, A. (2014). Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behavior Research Methods, 46(4), 1023–1031. https://doi.org/10.3758/s13428-013-0434-y
Petry, N. M. (2003). Discounting of money, health, and freedom in substance abusers and controls. Drug & Alcohol Dependence, 71(2), 133–141. https://doi.org/10.1016/S0376-8716(03)00090-5
R Core Team. (2020). R: A language for statistical computing (4.03) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/
Rachlin, H., Raineri, A., & Cross, D. (1991). Subjective probability and delay. Journal of the Experimental Analysis of Behavior, 55(2), 233–244. https://doi.org/10.1901/jeab.1991.55-233
Revelle, W. (2020). psych: Procedures for psychological, psychometric, and personality research (2.0.8) [Computer software]. https://CRAN.R-project.org/package=psychVersion=2.0.8
Saunders, J. B., Aasland, O. G., Babor, T. F., De La Fuente, J. R., & Grant, M. (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of Persons with harmful alcohol consumption-II. Addiction, 88(6), 791–804. https://doi.org/10.1111/j.1360-0443.1993.tb02093.x
Shead, N. W., & Hodgins, D. C. (2009). Probability discounting of gains and losses: Implications for risk attitudes and impulsivity. Journal of the Experimental Analysis of Behavior, 92(1), 1–16. https://doi.org/10.1901/jeab.2009.92-1
Shead, N. W., Callan, M. J., & Hodgins, D. C. (2008). Probability discounting among gamblers: Differences across problem gambling severity and affect-regulation expectancies. Personality & Individual Differences, 45(6), 536–541. https://doi.org/10.1016/j.paid.2008.06.008
Skinner, H. A. (1982). The drug abuse screening test. Addictive Behaviors, 7(4), 363–371. https://doi.org/10.1016/0306-4603(82)90005-3
Smith, K. R., Lawyer, S. R., & Swift, J. K. (2018). A meta-analysis of nonsystematic responding in delay and probability reward discounting. Experimental & Clinical Psychopharmacology, 26(1), 94–107. https://doi.org/10.1037/pha0000167
Smithson, M., & Verkuilen, J. (2006). A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods, 11(1), 54–71. https://doi.org/10.1037/1082-989X.11.1.54
Vanderveldt, A., Green, L., & Myerson, J. (2015). Discounting of monetary rewards that are both delayed and probabilistic: Delay and probability combine multiplicatively, not additively. Journal of Experimental Psychology: Learning, Memory, & Cognition, 41(1), 148–162. https://doi.org/10.1037/xlm0000029
Ware, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-Item Short-Form Health Survey: Construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220–233.
Warren, J. L., Yabroff, K. R., Meekins, A., Topor, M., Lamont, E. B., & Brown, M. L. (2008). Evaluation of trends in the cost of initial cancer treatment. JNCI Journal of the National Cancer Institute, 100(12), 888–897. https://doi.org/10.1093/jnci/djn175
Weatherly, J. N. (2014). On several factors that control rates of discounting. Behavioural Processes, 104, 84–90. https://doi.org/10.1016/j.beproc.2014.01.020
Weatherly, J. N., & Derenne, A. (2013). Probability and delay discounting of gains and losses using the multiple-choice method. The Psychological Record, 63(3), 563–582. https://doi.org/10.11133/j.tpr.2013.63.3.011
Yeh, Y.-H., Myerson, J., Strube, M. J., & Green, L. (2020). Choice patterns reveal qualitative individual differences among discounting of delayed gains, delayed losses, and probabilistic losses. Journal of the Experimental Analysis of Behavior, 113(3), 609–625. https://doi.org/10.1002/jeab.597
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Appendices
Appendix 1
Percentage Comprehension Test
[] indicates correct position for slider.
For the test and training questions, participants could only select options of 0 (100% A), 25 (75% A), 50 (50% A, 50% B), 75 (75% B), and 100 (100% B)
Appendix 2
Open-Ended Questions
For avoiding/delaying a medical procedure due to cost.
If yes to any of:
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Have you ever delayed a medical procedure due to cost?
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Have you ever refused a medical procedure due to cost?
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Have you ever avoided going to a doctor for an illness due to cost?
“Please describe in more detail your experience of delaying/refusing/avoiding some medical procedure/visit due to cost.”
Open-ended optional question regarding how participants responded to the monetary or medical decision-making questions:
“Please comment on your decision-making processes during the previous tasks.”
Appendix 3
Example Questions.
[] indicates default position of slider
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Rzeszutek, M.J., DeFulio, A. & Brown, H.D. Risk of Cancer and Cost of Surgery Outweigh Urgency and Messaging in Hypothetical Decisions to Remove Tumors. Psychol Rec 72, 331–352 (2022). https://doi.org/10.1007/s40732-021-00489-4
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DOI: https://doi.org/10.1007/s40732-021-00489-4