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Probability discounting of treatment decisions in multiple sclerosis: associations with disease knowledge, neuropsychiatric status, and adherence

Abstract

Rationale

Patients weigh risks and benefits when making treatment decisions. Despite this, relatively few studies examine the behavioral patterns underpinning these decisions. Moreover, individual differences in these patterns remain largely unexplored.

Objectives

The purpose of this study was to test a probability discounting model to explain the independent influences of risks and benefits when patients make hypothetical treatment decisions. Furthermore, we examine how individual differences in this probability discounting function are associated with patient demographics, clinical characteristics, disease knowledge, neuropsychiatric status, and adherence.

Methods

Two hundred eight participants with relapsing-remitting multiple sclerosis (MS) indicated their likelihood (0–100%) of taking a hypothetical medication as the probability of mild side effects (11 values from .1 to 99.9%) and reported medication efficacies (11 values from .1 to 99.9%) varied systematically. They also completed a series of questionnaires and cognitive tests.

Results

Individual components of medication treatment decision making were successfully described with a probability discounting model. High rates of discounting based on risks were associated with poor treatment adherence and less disease-specific knowledge. In contrast, high rates of discounting of benefits was associated with poorer cognitive functioning. Regression models indicated that risk discounting predicted unique variance in treatment adherence.

Conclusions

Insights gained from the present study represent an important early step in understanding individual differences associated with medical decision making in MS. Future research may wish to use this knowledge to inform the development of empirically supported adherence interventions.

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References

  • Berry MS, Johnson PS, Collado A, Loya JM, Yi R, Johnson MW (2018) Sexual probability discounting: a mechanism for sexually transmitted infection among undergraduate students. Arch Sex Behav

  • Bickel WK, Vuchinich RE (2000) Reframing health behavior change with behavioral economics. Lawrence Erlbaum Associates Publishers, Mahwah, NJ

    Book  Google Scholar 

  • Bickel WK, Jarmolowicz DP, Mueller ET, Gatchalian KM (2011) The behavioral economics and neuroeconomics of reinforcer pathologies: implications for etiology and treatment of addiction. Current psychiatry reports 13:406–415

    Article  Google Scholar 

  • Bickel WK, Jarmolowicz DP, Mueller ET, Koffarnus MN, Gatchalian KM (2012) Excessive discounting of delayed reinforcers as a trans-disease process contributing to addiction and other disease-related vulnerabilities: emerging evidence. Pharmacol Ther 134:287–297

    CAS  Article  Google Scholar 

  • Bickel WK, Mellis AM, Snider SE, Moody L, Stein JS, Quisenberry AJ (2016) Novel therapeutics for addiction: behavioral and neuroeconomic approaches. Curr Treat Options Psychiatry 3:277–292

    Article  Google Scholar 

  • Bloomgren G, Richman S, Hotermans C, Subramanyam M, Goelz S, Natarajan A, Lee S, Plavina T, Scanlon JV, Sandrock A, Bozic C (2012) Risk of natalizumab-associated progressive multifocal leukoencephalopathy. N Engl J Med 366:1870–1880

    CAS  Article  Google Scholar 

  • Bosworth H (2006) Medication treatment adherence. In: Bosworth H, Oddone E, Weinberger M (eds) Patient treatment adherence: concepts interventions, and measurement. Lawrence Erlbaum Associates, London

    Google Scholar 

  • Bruce J, Hancock L, Lynch S (2010a) Objective adherence monitoring in multiple sclerosis: initial validation and association with self-report. Mult Scler 16:112–120

    Article  Google Scholar 

  • Bruce JM, Hancock L, Lynch S (2010b) Treatment adherence in multiple sclerosis: association with emotional status, personality, and cognition. J Behav Med 33:219–227

    Article  Google Scholar 

  • Bruce J, Bruce A, Lynch S, Strober L, O’Bryan S, Sobotka D, Thelen J, Ness A, Glusman M, Goggin K, Bradley-Ewing A, Catley D (2016a) A pilot study to improve adherence among MS patients who discontinue treatment against medical advice. J Behav Med 39:276–287

    Article  Google Scholar 

  • Bruce J, Bruce AS, Catley D et al (2016b) Being kind to your future self: probability discounting of health decision-making. Ann Behav Med 50:297–309

    Article  Google Scholar 

  • Bruce JM, Jarmolowicz DP, Lynch S, Thelen J, Lim SL, Smith J, Catley D, Bruce AS (2018) How patients with multiple sclerosis weigh treatment risks and benefits. Health Psychol 37:680–690

    Article  Google Scholar 

  • Burks J, Marshall TS, Ye X (2017) Adherence to disease-modifying therapies and its impact on relapse, health resource utilization, and costs among patients with multiple sclerosis. Clinicoecon Outcomes Res 9:251–260

    Article  Google Scholar 

  • Carr KA, Daniel TO, Lin H et al (2011) Reinforcement pathology and obesity. Curr Drug Abuse Rev 4:190–196

    Article  Google Scholar 

  • Charvet LE, Yang J, Shaw MT, Sherman K, Haider L, Xu J, Krupp LB (2017) Cognitive function in multiple sclerosis improves with telerehabilitation: results from a randomized controlled trial. PLoS One 12:e0177177

    Article  Google Scholar 

  • Collado A, Johnson PS, Loya JM, Johnson MW, Yi R (2017) Discounting of condom-protected sex as a measure of high risk for sexually transmitted infection among college students. Arch Sex Behav 46:2187–2195

    Article  Google Scholar 

  • Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372:793–795

    CAS  Article  Google Scholar 

  • Cox DJ, Dallery J (2016) Effects of delay and probability combinations on discounting in humans. Behav Process 131:15–23

    Article  Google Scholar 

  • DiMatteo M, Hays, RD, Gritz, ER, Bastani, R, Crane, L, Elashoff, R, & Marcus, A. (1993) Patient Adherence to Cancer Control Regimens: Scale Development and Initial Validation: Psychological Assessment

  • Epstein LH, Salvy SJ, Carr KA, Dearing KK, Bickel WK (2010) Food reinforcement, delay discounting and obesity. Physiol Behav 100:438–445

    CAS  Article  Google Scholar 

  • Giordano A, Uccelli MM, Pucci E et al (2009) The multiple sclerosis knowledge questionnaire: a self-administered instrument for recently diagnosed patients. Mult Scler 16:100–111

    Article  Google Scholar 

  • Grytten N, Aarseth J, Espeset K et al (2012) Health-related quality of life and disease-modifying treatment behaviour in relapsing-remitting multiple sclerosis—a multicentre cohort study. Acta Neurol Scand 195:51–57

    Article  Google Scholar 

  • Guarnera C, Bramanti P, Mazzon E (2017) Comparison of efficacy and safety of oral agents for the treatment of relapsing-remitting multiple sclerosis. Drug Des Devel Ther 11:2193–2207

    Article  Google Scholar 

  • Heesen C, Kopke S, Richter T et al (2007) Shared decision making and self-management in multiple sclerosis—a consequence of evidence. J Neurol 254(Suppl 2):II116–II121

    PubMed  Google Scholar 

  • Heesen C, Kopke S, Solari A et al (2013) Patient autonomy in multiple sclerosis—possible goals and assessment strategies. J Neurol Sci 331:2–9

    CAS  Article  Google Scholar 

  • Heesen C, Bruce J, Feys P, Sastre-Garriga J, Solari A, Eliasson L, Matthews V, Hausmann B, Ross AP, Asano M, Imonen-Charalambous K, Köpke S, Clyne W, Bissell P (2014) Adherence in multiple sclerosis (ADAMS): classification, relevance, and research needs. A meeting report. In: Multiple sclerosis, vol 20, pp 1795–1798

    Google Scholar 

  • Higuera L, Carlin CS, Anderson S (2016) Adherence to disease-modifying therapies for multiple sclerosis. J Manag Care Spec Pharm 22:1394–1401

    PubMed  Google Scholar 

  • Hilyard KM, Quinn SC, Kim KH, Musa D, Freimuth VS (2014) Determinants of parental acceptance of the H1N1 vaccine. Health Educ Behav 41:307–314

    Article  Google Scholar 

  • Hohol MJ, Orav EJ, Weiner HL (1995) Disease steps in multiple sclerosis: a simple approach to evaluate disease progression. Neurology 45:251–255

    CAS  Article  Google Scholar 

  • Jarmolowicz DP, Cherry JB, Reed DD et al (2014) Robust relation between temporal discounting rates and body mass. Appetite 78:63–67

    Article  Google Scholar 

  • Jarmolowicz DP, Reed D, DiGennaro Reed F et al (2015) The behavioral and neuroeconomics of reinforcer pathologies: implications for managerial and health decision making. Manag Decis Econ 37:274–293

    Article  Google Scholar 

  • Jarmolowicz DP, Reed DD, Bruce AS et al (in press) Modeling effects of side-effect probability, side effect severity, and medication efficacy on patients with multiple sclerosis medication choice. Exp Clin Psychopharmacol

  • Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33:1444–1452

    CAS  Article  Google Scholar 

  • Learmonth YC, Motl RW, Sandroff BM, Pula JH, Cadavid D (2013) Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurol 13:37

    Article  Google Scholar 

  • Lezak M (1995) Neuropsychological assessment, third edn. Oxford University Press, New York

  • Loewenstein G, Brennan T, Volpp KG (2007) Asymmetric paternalism to improve health behaviors. JAMA 298:2415–2417

    CAS  Article  Google Scholar 

  • MacKillop J, Amlung MT, Few LR, Ray LA, Sweet LH, Munafò MR (2011) Delayed reward discounting and addictive behavior: a meta-analysis. Psychopharmacology 216:305–321

    CAS  Article  Google Scholar 

  • Margolis JM, Fowler R, Johnson BH, Kassed CA, Kahler K (2011) Disease-modifying drug initiation patterns in commercially insured multiple sclerosis patients: a retrospective cohort study. BMC Neurol 11:122

    Article  Google Scholar 

  • Meredith SE, Petry NM (2017) Improving medication adherence with behavioral economics. In: Hanoch Y, Barnes A, Rice T (eds) Behavioral economics and healthy behaviors: Key concepts and current research. Routledge, NY, pp 109–126

    Google Scholar 

  • Milo R (2015) Effectiveness of multiple sclerosis treatment with current immunomodulatory drugs. Expert Opin Pharmacother 16:659–673

    CAS  Article  Google Scholar 

  • National Institute of Neurological Disorders and Stroke (2012) Multiple sclerosis: hope through research. Available at: www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Hope-Through-Research/Multiple-Sclerosis-Hope-Through-Research (accessed 31 August 2018)

  • Odum AL, Madden GJ, Badger GJ, Bickel WK (2000) Needle sharing in opioid-dependent outpatients: psychological processes underlying risk. Drug Alcohol Depend 60:259–266

    CAS  Article  Google Scholar 

  • Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O'Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69:292–302

    Article  Google Scholar 

  • Pozzilli C, Schweikert B, Ecari U, Oentrich W, BetaPlus Study group (2011) Supportive strategies to improve adherence to IFN Beta-1b in multiple sclerosis—results of the BPlus observational cohort study. J Neurol Sci 307:120–126

    Article  Google Scholar 

  • Rachlin H (2006) Notes on discounting. J Exp Anal Behav 85:425–435

    Article  Google Scholar 

  • Rachlin H, Raineri A, Cross D (1991) Subjective probability and delay. J Exp Anal Behav 55:233–244

    CAS  Article  Google Scholar 

  • Reach G (2010) Is there an impatience genotype leading to non-adherence to long-term therapies? Diabetogia 53:1562–1567

    CAS  Article  Google Scholar 

  • Remington G, Rodriguez Y, Logan D, Williamson C, Treadaway K (2013) Facilitating medication adherence in patients with multiple sclerosis. International journal of MS care 15:36–45

    Article  Google Scholar 

  • Reynolds MW, Stephen R, Seaman C, Rajagopalan K (2010) Persistence and adherence to disease modifying drugs among patients with multiple sclerosis. Curr Med Res Opin 26:663–674

    CAS  Article  Google Scholar 

  • Rizzo MA, Hadjimichael OC, Preiningerova J, Vollmer TL (2004) Prevalence and treatment of spasticity reported by multiple sclerosis patients. Mult Scler 10:589–595

    CAS  Article  Google Scholar 

  • Shead NW, Hodgins DC (2009) Probability discounting of gains and losses: implications for risk attitudes and impulsivity. J Exp Anal Behav 92:1–16

    Article  Google Scholar 

  • Sheffer C, Mackillop J, McGeary J et al (2012) Delay discounting, locus of control, and cognitive impulsiveness independently predict tobacco dependence treatment outcomes in a highly dependent, lower socioeconomic group of smokers. Am J Addict 21:221–232

    Article  Google Scholar 

  • Strough J, Karns TE, Schlosnagle L (2011) Decision-making heuristics and biases across the life span. Ann N Y Acad Sci 1235:57–74

    Article  Google Scholar 

  • Sundgren M, Piehl F, Wahlin A, Brismar T (2016) Cognitive function did not improve after initiation of natalizumab treatment in relapsing-remitting multiple sclerosis. A prospective one-year dual control group study. Mult Scler Relat Disord 10:36–43

    CAS  Article  Google Scholar 

  • Tan H, Cai Q, Agarwal S, Stephenson JJ, Kamat S (2011) Impact of adherence to disease-modifying therapies on clinical and economic outcomes among patients with multiple sclerosis. Adv Ther 28:51–61

    Article  Google Scholar 

  • Vanderveldt A, Green L, Myerson J (2015) Discounting of monetary rewards that are both delayed and probabilistic: delay and probability combine multiplicatively, not additively. J Exp Psychol Learn Mem Cogn 41:148–162

    Article  Google Scholar 

  • Wallin MT, Culpepper WJ, Coffman P, Pulaski S, Maloni H, Mahan CM, Haselkorn JK, Kurtzke JF, for the Veterans Affairs Multiple Sclerosis Centres of Excellence Epidemiology Group (2012) The Gulf War era multiple sclerosis cohort: age and incidence rates by race, sex and service. Brain 135:1778–1785

    Article  Google Scholar 

  • Washio Y, Higgins ST, Heil SH, McKerchar TL, Badger GJ, Skelly JM, Dantona RL (2011) Delay discounting is associated with treatment response among cocaine-dependent outpatients. Exp Clin Psychopharmacol 19:243–248

    Article  Google Scholar 

  • Watson TM, Ford E, Worthington E, Lincoln NB (2014) Validation of mood measures for people with multiple sclerosis. International journal of MS care 16:105–109

    Article  Google Scholar 

  • Wechsler D (1997) Wechsler adult intelligence scale—third edition. The Psychological Corporation, San Antonio

    Google Scholar 

  • Wong J, Gomes T, Mamdani M, Manno M, O'Connor PW (2011) Adherence to multiple sclerosis disease-modifying therapies in Ontario is low. Can J Neurol Sci 38:429–433

    Article  Google Scholar 

  • World Health Organization (2003) Adherence to long-term tehrapies: evidence for action. Available at: www.who.int/chp/knowledge/publications/adherence_report/en/ (accessed 31 August 2018)

  • World Health Organization (2005) World alliance for patient safety : WHO draft guidelines for adverse event reporting and learning systems : from information to action. World Health Organization, Geneva http://www.who.int/iris/handle/10665/69797 (accessed 31 August 2018)

    Google Scholar 

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Funding

This research was funded by a grant from the National Multiple Sclerosis Society to the first author (HC-1411-01993).

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Corresponding author

Correspondence to Jared M. Bruce.

Ethics declarations

This study was approved by the University of Missouri-Kansas City Institutional Review Board and the University of Kansas Medical Center Human Subjects Committee.

Competing interests

Dr. J. Bruce provides non-branded talks for Novartis, is a part-time employee of the National Hockey League, does consulting for Princeton University’s Department of Athletic Medicine, and is a consultant to Major League Soccer’s Sporting KC.

Dr. Sharon Lynch has participated in multi-center drug trials through Novartis, Teva, Biogen, Sanofi, Genzyme, Genentech, Roche, NMSS and NIH, Alexion, Opexa, Sun Pharma, Vaccinex, Actelion, and Mallinkrodt.

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Bruce, J.M., Bruce, A.S., Lynch, S. et al. Probability discounting of treatment decisions in multiple sclerosis: associations with disease knowledge, neuropsychiatric status, and adherence. Psychopharmacology 235, 3303–3313 (2018). https://doi.org/10.1007/s00213-018-5037-y

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  • DOI: https://doi.org/10.1007/s00213-018-5037-y

Keywords

  • Multiple sclerosis
  • Adherence
  • Probability discounting
  • Disease-modifying therapy
  • Medical decision making