1. Life-extending technologies for terminally ill patients: views v policy choices Presentation 3 / Session Title: Extending life for people with a terminal illness: a moral right or an expensive death? ECHE Dublin 14 th – 16 th July Neil McHugh Yunus Centre for Social Business and Health Glasgow Caledonian University neil.mchugh@gcu.ac.uk

2. Overview Views versus policy choices Rationale Factor Membership results Policy Choice 1: ‘Decision Rule’ Design Results Convergence hypotheses & results Policy Choice 2: ‘Treatment Choice’ Design Results Convergence hypotheses & results Summing up... Further analysis

3. Views versus policy choices – why? Relative value of life extending treatments for people with a terminal illness Understanding views that exist in society Examine distribution of views in society Why introduce policy choices? What happens to an individual’s views? Specific questions of resource allocation Designed to reflect the views identified Form of predictive validity – empirical question

4. Policy Choice Version: Factor Membership Sample 1429 respondents quota sampled across UK All: both policy choices, Approach 1, socio-demographics One further approach from Approaches 2-5 selected at random Factor membership (FM) calculated for policy choice version FM: intensity score > median Focus on: F1, F3, F1/3 => F1&3s (629 respondents) and F2s (443 respondents) Factor Membership% 1534% 244331% 3957% 1/21229% 1/348134% 2/3896% 1/2/3977% null493% Total1429100%

5. Policy Choice 1: ‘Decision Rule’ High level rules to govern decisions of provision How should a health system assess drugs for terminally ill patients that do not pass a value for money test? Opportunity cost and value for money test (VFMT) 3 mutually exclusive policies: Policy A: standard VFMT (applied to all new treatments) Policy B: all new treatments for terminal illnesses should be given special consideration (apply a different VFMT) role of cost value of different types of health benefits Policy C: some new treatments for terminal illnesses should be given special consideration (apply a different VFMT) treatment benefits / type of patient role of cost

6. ‘ Decision Rule’ – Results Policy A (standard VFMT) Policy B (all given special consideration) Policy C (it depends.. some special consideration)Total Choice between A - C 50335%27819%64845%1429 LE more important than QoL 73% Extend Life 6610% QoL more important than LE 10738% Improve QoL 46872% LE and QoL equally important 10237% Depending on. 11418% L more important but QoL must be good 6222% Regardless of cost-- 16459%23937% Limit-- 11441%40963%

7. Convergence hypotheses: views v ‘decision rule’ Hypothesis: views would predict decision rule Predictions: strong (S) weak (W) Hypotheses: views v ‘decision rule’ ViewsPolicy A (standard VFMT) Policy B (all special consideration) Policy C (it depends.. some special consideration) F1&3s (health max. / QoL) S – YesS – NoS – Yes F2 (patient)S – NoS – YesW – Yes

8. Convergence Results: views v ‘decision rule’ F2 v Decision rule %Hypotheses Prediction accuracy F2 = PA5913.3%S - No F2 = PB15234.3%S - Yes - F2 = PC23252.4%W - Yes - Total443100% F1&3s v Decision rule %Hypotheses Prediction accuracy F1&3s = PA34454.7%S - Yes F1&3s = PB538.4%S - No F1&3s = PC23236.9%S - Yes Total629100%

9. Policy Choice 2:'Treatment Choice’ Lower level decisions between competing treatments Choice between 3 mutually exclusive new treatments a fixed, additional health budget Rank treatments and select a reason for preferred choice Two person trade-off (PTO) questions: most preferred treatment v second-best treatment most preferred treatment v least preferred treatment

10. ‘Treatment Choice’: Treatment A Patients are currently suffering from a non-life threatening illness that causes them discomfort and fatigue. The illness also reduces their mobility and ability to undertake their usual activities. This occurs a few times throughout every year for the rest of their life. Each episode lasts for up to 2 weeks. A new treatment is available that will reduce their symptoms and make patients feel better, improving their quality of life for the rest of their life. Funding will mean that 100 patients can be treated in the next year.

11. ‘Treatment Choice’: Treatment B Patients are currently suffering from a terminal illness that causes them discomfort and fatigue. The illness also reduces their mobility and ability to undertake their usual activities. A new treatment is available for terminally ill patients in the last year of their life. The treatment will extend patients’ lives by three months. It will not improve their quality of life. Funding will mean that 100 patients can be treated in the next year.

12. ‘Treatment Choice’: Treatment C Patients are currently suffering from a terminal illness that causes them discomfort and fatigue. The illness also reduces their mobility and ability to undertake their usual activities. A new treatment is available for terminally ill patients in the last year of their life. The treatment will reduce their symptoms and make patients feel better, improving their quality of life. It will not extend their life. Funding will mean that 100 patients can be treated in the next year.

13. ‘Treatment Choice’ – Results Treatment A and C are the most popular choices Treatment B is the least preferred option Rankings Treatment A (Count/%)Treatment B (Count/%)Treatment C (Count/%) 1 732 / 51%86 / 6%611 / 43% 2 500 / 35%240 / 17%689 / 48% 3 197 / 14%1103 / 77%129 / 9% Total 1429

14. Convergence hypotheses: views v ‘treatment choice’ Hypothesis: views would predict treatment (T) choice Predictions: strong (S) weak (W) Hypotheses: views v ‘treatment choice’ ViewsTA (non-life threatening illness) TB (terminal illness – life extension) TC (terminal illness – quality of life) F1&3s (health max. / QoL) S – YesS – NoW – Yes F2 (patient)W – Yes

15. Convergence results: views v ‘treatment choice’ F1&3s v ‘Treatment Choice’ %Hypotheses Prediction accuracy F1&3s = TA 43469%S – Yes F1&3s = TB 152.4%S – No F1&3s = TC 18028.6%W – Yes Total629100% F2 v ‘Treatment Choice’ %Hypotheses Prediction accuracy F2 = TA13630.7%W – Yes F2 = TB4710.6%W – Yes F2 = TC26058.7%W – Yes Total443100%

16. Summing up..... Further Analysis Results seem intuitively correct Further analysis: Explore PTOs Convergence between ‘decision rule’ and ‘treatment choice’ Link with socio-demographic information Regression analysis on links between views and policy choice questions