Emotional Adaptation and the End of Economics

1. Emotional Adaptation and the End of Economics Peter A. Ubel, M.D. Center for Behavioral and Decision Sciences in Medicine Ann Arbor VAMC University of Michigan Health System

3. The answer depends on who you ask • TTO utility of ESRD (0-1 scale) • Patients = .56 • Community = .39 • Moods (-2 to +2 scale) • Patients = .66 • Community prediction of patients = -.17

4. Economic Importance of This Discrepancy • Whose utilities should we include in cost effectiveness analyses?

5. As if the “who” question wasn’t enough! • We need to figure out what question to ask • Specifically: should policy decisions be based on • decision utility or • experience utility

6. A Quick and Inaccurate History of Economics • Economics = Science of utility maximization • Original notion of utility • Jeremy Bentham • Balance of pleasure & pain = Experience utility • More recent view of utility • Revealed preferences • Rational people’s free choices lead to utility maximization = Decision utility

7. Decision Utility and Revealed Preferences

8. High Pain Pain 60 60 90 Time Time Illustration of distinction between Experience and Decision Utility

9. What happened in this study? • Experience utility – • The 60 second bucket was better than the 90 • Decision utility – • The 90 second bucket was better • People misremembered their 2 experiences, causing them to make a bad decision

10. Goals of Talk • Potential flaw of decision utility • Based on mispredictions and misrememberings of experience utility • Potential flaw of experience utility • Goals of healthcare go beyond mood maximization • Point to future research directions • Empirical • Normative

11. Global versus Momentary QoL: source of patient overestimation?

12. Global versus Momentary Reports of Well-being (SWB) • People have difficulty describing average emotions over time • There may be a discrepancy between • moment to moment SWB • general evaluation of SWB

13. High Pain Pain 60 60 90 Time Time No pain, no gain High Pain

14. It’s the little things in life

15. Imagine a Dialysis Patient’s Quality of Life • How happy are you right now (0 - 10)? • 6 • 5 • 5 • 6 • 8 • How happy do you feel generally? • 7

17. +2 Very pleasant +1 Slightly pleasant 0 Neutral -1 Slightly unpleasant -2 Very unpleasant 100 Entry: Percentage of time, during a typical week, that you are in each of the following moods?

18. Palm Week

19. Predicted Mood Patients .78 Controls .61 Predicted Moods

20. Actual Mood Patients .78 .66 Controls .61 .80 Predicted vs. Actual Mood Predicted Mood

21. 4 positive measures 5 negative measures Patients 3.21 1.00 Controls 3.23 .99 Specific Moods (0-6 scale)- Palm Data -

22. Imagining life on Dialysis P< .001

23. Imagining perfect Health(Never had kidney problems. . .) P< .028

24. A Whole Lot of Mispredicting Going On • Patients • Mispredict life without kidney disease • General public • Mispredict life with kidney disease • These mispredictions of experience utility could influence decision utility

25. Theory Driven Recall Bias: an alternative to scale recalibration

26. Hope I die before I get old! • Common belief (among rock stars, at least) • Getting old stinks • But happiness data suggest that • Happiness relatively stable across adult years • Not much change between 30 and 70 • If anything, less negative affect with age • What do real people believe? • < 40 year olds • > 60 year olds • And how do beliefs compare to “reality”?

27. Study Design • Internet study of people • 21 – 40 (n = 264; median age = 31) • 60 and up (n = 255; median age = 68) • Asked to: • Report current happiness (0 – 10 scale) • Estimate happiness of typical • 30 year old • 70 year old

28. Results: Self reported happiness 10 0 7.3 Happiness 6.6 <40 > 60

29. Results: Predictions of 21–40 year olds 10 0 7.3 7.0 Happiness 6.6 6.2 <40 > 60

30. Results: Predictions of old people 7.6 10 0 7.3 7.0 Happiness 6.6 6.3 6.2 <40 > 60

31. A whole lot of mispredicting and misremembering going on • Young people mispredict happiness as they age • Old people misremember happiness of their youth • People are out of touch with their own experience

32. Looking forward to a kidney transplant

33. Misestimating the benefits of kidney transplantation • Surveyed patients waiting for kidney transplant at University of Pennsylvania • Measured QoL • Asked them to predict QoL 1 year after successful transplant • Resurveyed them after transplant • Measured QoL • Asked them to remember pre-transplant QoL

34. Mispredictions Prediction for Post-tx Actual Post-tx Domain Pre-tx QoL (0-100) 66 91 83 Travel (days/yr) 9 20 12 Work (hrs/wk) 12 32 15 Energy (1-5) 3.2 5.1 4.3

35. More mispredictions Prediction for Post-tx Actual Post-tx Domain Pre-tx Happy w/ life 2.8 3.9 3.5 …health 2.4 3.9 3.6 …std. of lvg. 2.6 3.7 3.1 …work 2.5 3.6 3.1 …love life 3.5 3.8 3.5 …family life 3.8 3.8 3.8 …social life 3.5 3.8 3.6 …spiritual life 3.4 3.7 3.6 …leisure life 3.0 3.8 3.3

36. Misremembering ESRD Time Pre-tx QoL Post-tx QoL Pre-tx 65 91 Post-tx: Immediate 57 78 6 months 55 80 12 months 48 83

37. What’s it like to have a colostomy?

38. Colostomy Patient Survey • Surveyed people who have received colostomies at UM within last 5 years • some had permanent and some had reversed colostomies • 94 permanent • 100 reversed • What do these two groups think of life with a colostomy?

39. Adapting to life with a Colostomy • Overall quality of life (0-100) • permanent = 67 • reversed = 71 • Overall positive affect positive mood (0-4) • permanent = 3.1 • reversed = 3.1 • Overall negative mood (0-4) • permanent = 1.8 • reversed = 1.9 So ... little to no difference in mood or quality of life

40. How Happy: Now & 5 yrs. ago 7 6.8 6.4 6.1 6 6.0 5 Now 5 yrs. ago

41. How bad do these groups think it is to have a colostomy? • Time tradeoff (TTO) utility question • Imagine you will live 10 years with a colostomy then die. • How many months (0-120) would you give up to get rid of the colostomy? • permanent = 18 months • reversed = 44 months

44. Decision Utility and Revealed Preferences

45. Rational decision making and revealed preferences • Ua = P1U1 + P2U2 + … • Ub = P7U7 + P8U8 + … • If I chose A over B • Then Ua > Ub • Similarly, if I’m willing to pay • $10 for X • $15 for Y • Then Uy > Ux

46. Flaws with revealed preference assumptions • People mispredict utilities • As I’ve shown already • Even given utilities • People don’t always integrate p’s and u’s in rational manner • Here’s an example

47. Imagine you have Colon Cancer Surgery A • 80% cure without complications • 16% die of disease • 1% colostomy • 1% intermittent bowel obstruction • 1% wound infection • 1% diarrhea • Surgery B • 80% cure without complications • 20% die • Which surgery would you choose?

49. Give me colostomy or give me death! • When we ask people to choose between being dead or having one of these four complications • >90% prefer each of the four complications to death • To be consistent with these preferences • <10% should choose the uncomplicated surgery

50. Our Survey says . . . • 50-60% of people choose surgery B, the uncomplicated surgery