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Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
Valuing Health at the End of Life: Defining Public Preferences
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Valuing Health at the End of Life: Defining Public Preferences

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What does the public think about assigning priority to end-of-life treatment? In this presentation, OHE's Koonal Shah describes the results of research intended to tease out both preferences and, …

What does the public think about assigning priority to end-of-life treatment? In this presentation, OHE's Koonal Shah describes the results of research intended to tease out both preferences and, where possible, the reasoning behind them. The findings may surprise some -- for example, that priority is not given to end-of-life treatments when the treatments they would supplant offer greater health gains.

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  • 1. Valuing Health at the End of Life A Stated Preference Discrete Choice Experiment Koonal Shah Heath Economics Research Centre Seminar Series University of Oxford • 21 January 2014
  • 2. Study team and note on funding • This study is a collaboration with Allan Wailoo and Aki Tsuchiya (both University of Sheffield) • The study was funded by the National Institute for Health and Care Excellence (NICE) through its Decision Support Unit • The views, and any errors or omissions, expressed in this presentation are those of the authors only Valuing health at the end of life (HERC seminar) 21/01/2013 2
  • 3. NICE end-of-life criteria Criteria that need to be satisfied for NICE’s supplementary end-of-life policy to apply are currently as follows: C1 The treatment is indicated for patients with a short life expectancy, normally less than 24 months C2 There is sufficient evidence to indicate that the treatment offers an extension to life, normally of at least an additional three months, compared to current NHS treatment C3 The treatment is licensed, or otherwise indicated for, small patient populations Valuing health at the end of life (HERC seminar) 21/01/2013 3
  • 4. NICE end-of-life criteria (2) • Placing additional weight on survival benefits in patients with short remaining life expectancy could be considered a valid representation of society's preferences • But the NICE consultation revealed concerns that little scientific evidence supports this premise Valuing health at the end of life (HERC seminar) 21/01/2013 4
  • 5. Overview of project Study 1: Exploratory study • • • Aim: to pilot an approach to eliciting priority-setting preferences Aim: to explore the rationales underpinning people’s stated preferences Small scale (n=21); convenience sample; face-to-face interviews Study 2: Preference study • • • Aim: to test whether there is public support for giving priority to end-of-life treatments Aim: to validate the approach and worth of conducting a large scale study Medium scale (n=50); representative sample; face-to-face interviews Study 3: Discrete choice experiment • • • Aim: to examine people’s preferences regarding end of life more robustly Aim: to examine the extent to which people are willing to sacrifice overall health in order to give priority to end-of-life treatments Large scale (n=3,969); representative sample; web-based survey Valuing health at the end of life (HERC seminar) 21/01/2013 5
  • 6. Scenario 1 Both patients are same age today (Time=0) Time (years) 0 1 2 3 4 5 6 7 8 9 10 11 Patient A Patient B denotes time in full quality of life denotes life extension (at full quality of life) achievable from treatment Valuing health at the end of life (HERC seminar) 21/01/2013 6
  • 7. Scenario 2 Patient B is 9 years older than patient A today Time (years) 0 1 2 3 4 5 6 7 8 9 10 11 Patient A Patient B Valuing health at the end of life (HERC seminar) 21/01/2013 7
  • 8. Scenario 3 Both patients are same age today Time (years) -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 A B Valuing health at the end of life (HERC seminar) 21/01/2013 8 2
  • 9. Scenario 4 Both patients are same age today (30 years old) Time (years) 0 1 2 A B denotes life extension (at 50% quality of life) achievable from treatment denotes improvement from 50% quality of life to full quality of life achievable from treatment Valuing health at the end of life (HERC seminar) 21/01/2013 9
  • 10. Scenario 5 Both patients are same age today (70 years old) Time (years) 0 1 2 A B Valuing health at the end of life (HERC seminar) 21/01/2013 10
  • 11. Scenario 6 Patient B is 9 years older than patient A today Time (years) 0 1 2 3 4 5 6 7 8 9 10 A B Valuing health at the end of life (HERC seminar) 21/01/2013 11
  • 12. Findings from preliminary studies • Elicitation approach found to be feasible • No consensus set of preferences • Majority wished to give priority to the end-of-life patient, but a sizeable minority expressed the opposite preference • ‘No preference’ rarely expressed • Preference for treatments that improve quality of life • Preferences appear to be driven by how long patients have known about their illness (i.e. how long they have to ‘prepare for death’) • People are happy to prioritise based on characteristics of patients/disease/treatment when gains to all patients are equal in size … next step is to understand the extent to which they would sacrifice health gain to pursue equity objectives Valuing health at the end of life (HERC seminar) 21/01/2013 12
  • 13. DCE study • DCEs elicit people’s preferences based on their stated preferences given hypothetical choices • Surveys comprise several ‘choice sets’, each containing competing alternative ‘profiles’ described using defined ‘attributes’ and a range of attribute ‘levels’ • Respondents’ choices between these profiles are analysed to estimate the contribution of the attributes to overall utility Valuing health at the end of life (HERC seminar) 21/01/2013 13
  • 14. Attributes and levels Attribute Unit Levels Life expectancy without treatment months 3, 12, 24, 36, 60 Quality of life without treatment % 50, 100 Life expectancy gain from treatment months 0, 1, 2, 3, 6, 12 Quality of life gain from treatment % 0, 25, 50 • Concept of ‘50% health’ was explained as follows: ‘Suppose there is a health state which involves some health problems. If patients tell us that being in this health state for two years is equally desirable as being in full health for one year, then we would describe someone in this health state as being in 50% health’. Valuing health at the end of life (HERC seminar) 21/01/2013 14
  • 15. Study design • Forced choices (no ‘neither A nor B’ option) • Generic descriptions of patients, illnesses and treatments • Steps taken to avoid bias due to task order or possibility of respondents reverting to default choices • 10 standard DCE tasks, followed by two ‘extension tasks’ designed specifically to explore whether respondents’ choices are influenced by information about how long the patients have known about their illness Valuing health at the end of life (HERC seminar) 21/01/2013 15
  • 16. Valuing health at the end of life (HERC seminar) 21/01/2013 16
  • 17. Valuing health at the end of life (HERC seminar) 21/01/2013 17
  • 18. Web-based surveys Pros • • • • • Can recruit a vey large sample quickly and cheaply Avoids interviewer bias Survey highly customisable – e.g. randomisation procedures Quality control procedures can be put into place Any less likely to be representative than other modes of administration? Cons • • • • • No guarantee that respondents have read or understood instructions Concerns about effort and engagement High level of drop out Limited debriefing opportunity Concerns about representativeness of sample Valuing health at the end of life (HERC seminar) 21/01/2013 18
  • 19. Background characteristics # Total Gender Male Female Age 18-24 25-44 45-64 65+ Social grade A B C1 C2 D E % 3,969 100 gen pop % 100 1,942 2,027 49 51 49 51 404 1,413 1,228 924 10 36 31 23 11 38 31 21 221 1,114 1,150 645 357 482 6 28 29 16 9 12 4 22 29 21 15 8 Valuing health at the end of life (HERC seminar) 21/01/2013 19
  • 20. Background characteristics (2) # 963 3,006 Household composition With children Without children Education No education beyond minimum school leaving age Education beyond minimum school leaving age; no degree Education beyond minimum school leaving age; degree Self-reported general health level Very good Good Fair Poor Very poor Experience of close friends or family with terminal illness Yes No Question skipped by respondent % 24 76 889 1,244 1,836 22 31 46 1,008 1,958 770 210 23 25 49 19 5 1 2,689 1,197 83 68 30 2 Valuing health at the end of life (HERC seminar) 21/01/2013 20
  • 21. Results Best fitting model included main effects plus three interactions: • LE without treatment against LE gain – Rationale: small gains in life expectancy may be increasingly important when life expectancy without treatment is short • LE without treatment against QOL gain – Rationale: whether a quality of life improvement or a gain in life expectancy is preferred may depend on life expectancy without treatment • LE gain against QOL gain – Rationale: the important of a gain in life expectancy may depend on whether it is accompanied by a quality of life improvement Valuing health at the end of life (HERC seminar) 21/01/2013 21
  • 22. Attribute / level LE without treatment 3 months [baseline] 12 months 24 months 36 months 60 months QOL without treatment 50% [baseline] 100% LE gain 0 months [baseline] 1 month 2 months 3 months 6 months 12 months QOL gain 0% [baseline] 25% 50% Coefficien p-value t 0.1755 0.9307 0.7841 1.2625 0.12 0.00 0.00 0.00 0.6730 0.00 0.1855 0.8517 1.0855 2.0433 2.9381 0.08 0.00 0.00 0.00 0.00 0.0632 1.0212 0.47 0.00 Attribute / level Coefficient p-value Interaction: LE without treatment # LE gain 0.15 -0.1715 12 months # 1 months 0.00 -0.4220 12 months # 2 months 0.18 -0.1633 12 months # 3 months 0.00 -0.7294 12 months # 6 months 0.00 -0.6039 12 months # 12 months 0.00 -1.1308 24 months # 1 months 0.00 -1.0782 24 months # 2 months 0.00 -0.8614 24 months # 3 months 0.00 -1.2413 24 months # 6 months 0.00 -1.2601 24 months # 12 months 0.00 -0.7280 36 months # 1 months 0.00 -1.0428 36 months # 2 months 0.00 -1.2252 36 months # 3 months 0.00 -1.6695 36 months # 6 months 0.00 -1.3963 36 months # 12 months 0.00 -1.3159 60 months # 1 months 0.00 -1.4933 60 months # 2 months 0.00 -1.2558 60 months # 3 months 0.00 -2.0434 60 months # 6 months 0.00 -1.7114 60 months # 12 months Interaction: LE without treatment # QOL gain 0.00 0.4562 12 months # 25% 0.00 0.2139 12 months # 50% 0.00 0.2734 24 months # 25% 0.00 0.4123 24 months # 50% 0.00 0.8457 36 months # 25% 0.00 0.7374 36 months # 50% 0.00 0.5379 60 months # 25% 0.00 0.6676 60 months # 50% Interaction: LE gain # QOL gain 0.00 0.7649 1 months # 25% 0.00 0.5254 1 months # 50% 0.00 0.3197 2 months # 25% 0.00 0.3543 2 months # 50% 0.00 0.6321 3 months # 25% 0.00 0.3163 3 months # 50% 0.00 0.6661 6 months # 25% Valuing health at the end of life (HERC seminar) 0.00 0.2744 6 months # 50% 21/01/2013 22 0.00 0.3466 12 months # 25% 12 months # 50% [baseline]
  • 23. Transforming into predicted probabilities • Following the approach used by Green and Gerard* we calculated the relative predicted probabilities for all of the 110 profiles • This allows us to compare the profiles that are likely to be most preferred overall with those that are likely to be least preferred overall • The predicted probability of alternative i being chosen from the complete set of alternatives (j=1,…,J) is given by: 𝑃𝑃𝑛𝑛𝑛𝑛 = 𝑒𝑒 𝑉𝑉 𝑛𝑛𝑛𝑛 𝑉𝑉 ∑J 𝑒𝑒 𝑛𝑛𝑛𝑛 𝑗𝑗=1 𝑗𝑗 = 1, … , J * Green, C. and Gerard, K., 2009. Exploring the social value of health care interventions: a stated preference discrete choice experiment. Health Economics, 18(8), pp. 951-976. Valuing health at the end of life (HERC seminar) 21/01/2013 23
  • 24. Estimated utility score and predicted probability of choice for all profiles Rank - best fitting model Rank – main effects model LE without treatment (mths) QOL without treatment (%) LE gain (mths) QOL gain (%) Utility Prob. Cumul. Prob. - - 60 36 24 3 12 3 - 50 50 50 50 50 100 - 12 12 12 12 12 12 - 50 50 50 50 50 0 - 4.17809 4.08461 4.04235 3.95938 3.74493 3.61116 0.0155 0.0154 0.0153 0.0152 0.0148 0.0145 0.0155 0.0309 0.0462 0.0614 0.0762 0.0908 0.0029 0.0028 0.0028 0.0026 0.0025 0.9870 0.9898 0.9926 0.9952 0.9977 110 108 24 50 1 0 0.0023 1.0000 1 2 3 4 5 6 105 106 107 108 109 1 2 3 5 4 20 107 109 110 104 94 36 12 3 60 3 50 50 50 50 50 1 1 1 1 0 0 0 0 0 25 - 0.24171 0.18955 0.18553 0.13213 0.06320 0.01452 - - Valuing health at the end of life (HERC seminar) 21/01/2013 24
  • 25. Levels of QALYs without treatment / gains associated with all 110 profiles 6 5 QALYs 4 3 2 1 0 0.0023 0.0040 0.0055 0.0062 0.0072 0.0085 0.0100 0.0112 0.0120 0.0130 0.0140 -1 Standardised predicted probability of being chosen QALY without QALY gain Linear (QALY without) Linear (QALY gain) Valuing health at the end of life (HERC seminar) 21/01/2013 25
  • 26. Most and least preferred profiles LE without treatment (mths) QOL LE gain without (mths) treatment (%) QOL gain (%) QALYs QALYs without gained treatment 27 55 11 38 1.14 1.76 55 most preferred 27 57 7 31 1.27 1.22 55 least preferred 27 65 2 10 1.49 0.29 10 least preferred 28 50 1 3 1.18 0.06 10 most preferred Valuing health at the end of life (HERC seminar) 21/01/2013 26
  • 27. Subgroup analysis • We defined a selection of respondent subgroups whose choices may be expected to differ from those of the rest of the sample • Respondents with experience of close friends or family with terminal illness • Respondents with responsibility for children • Respondents who voluntarily left open-ended comments • Respondents who completed the survey unusually quickly • We found no substantial differences between the results for any of these subgroups and those for the full sample Valuing health at the end of life (HERC seminar) 21/01/2013 27
  • 28. Categorising according to ‘choice strategy’ Number (%) of respondents who… % choices made according to this strategy never followed this strategy sometimes followed this strategy always followed this strategy Choose patient with larger QALY gain 0.75 1 (0.0%) 3,530 (88.9%) 438 (11.0%) Choose patient with larger LE gain 0.69 20 (0.5%) 3,405 (85.8%) 544 (13.7%) Choose patient with fewer QALYs without treatment 0.47 182 (4.6%) 3,701 (93.2%) 86 (2.2%) Choose patient with less LE without treatment 0.45 355 (8.9%) 3,434 (86.5%) 180 (4.5%) Choice strategy • • Multinomial logit regressions used to identify driving factor(s) behind respondents’ membership in the subgroup ‘always / never choose patient with fewer QALYs without treatment’ Marginal effects of age and health satisfaction were found to be statistically significant, but both are small in practical terms • As age increases, the probability of always choosing the patient with fewer QALYs without treatment decreases, but even a 30-year increase in age would not be sufficient for a 1% decrease in this probability Valuing health at the end of life (HERC seminar) 21/01/2013 28
  • 29. Extension tasks • Extension tasks showed that including information about the amount of time that patients have known about their prognosis has a clear impact on preferences • Holding everything else constant, respondents are less likely to choose to treat a patient who has know about their illness for two years than if the patient has only just found out about their illness • Caveat: focusing effect may exaggerate importance Valuing health at the end of life (HERC seminar) 21/01/2013 29
  • 30. Extension tasks (2) • The above figure shows the impact on choices of providing information on how long patients have known about their illness, summed across all 16 extension tasks Valuing health at the end of life (HERC seminar) 21/01/2013 30
  • 31. Summary of findings • Choices driven by size of health gain • Concern about the extent to which the patient is at the end of life appears to have a negligible effect • Overall view seems to be that giving higher priority to those who are worse off is desirable only if the gains from treatment are substantial • No evidence of public support for giving higher priority to end of life treatments than to other types of treatments if the health gains offered by the treatments being ‘deprioritised’ are larger than those offered by the end of life treatments Valuing health at the end of life (HERC seminar) 21/01/2013 31
  • 32. Caveats and limitations • Small range of scenarios covered – all involve poor prognoses (some people might consider 5 years to be ‘end of life’) • Does not necessarily refute evidence elsewhere in the literature that people wish to pursue equity concerns • Great deal of preference heterogeneity • Limited opportunities for feedback and debriefing – cannot know for certain the extent to which the choice data truly reflect respondents’ beliefs and preferences (or whether there were adopting heuristics) • Framing effects clearly exist in stated preference studies Valuing health at the end of life (HERC seminar) 21/01/2013 32
  • 33. About OHE To enquire about additional information, please contact Koonal Shah at kshah@ohe.org To keep up with the latest news and research, subscribe to our blog, OHE News Follow us on Twitter @OHENews, LinkedIn and SlideShare The Office of Health Economics is a research and consulting organisation that has been providing specialised research, analysis and expertise on a range of health care and life sciences issues and topics for more than 50 years. OHE’s publications may be downloaded free of charge for registered users of its website. Office of Health Economics Southside, 7th Floor 105 Victoria Street London SW1E 6QT United Kingdom +44 20 7747 8850 www.ohe.org ©2013 OHE Valuing health at the end of life (HERC seminar) 21/01/2013 33

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