1. Investigating the prevalence and distribution of views across the UK population Helen Mason ECHE Dublin 14 th July 2014 Extending life for people with a terminal illness: a moral right or an expensive death? Empirical and Methodological issues

2. Objectives I.To identify and describe societal perspectives on the (relative) value of end of life technologies by eliciting the views of both members of the public and experts in relevant fields; II.To develop methods to investigate the distribution of those views, including their association with other characteristics, in a nationally representative sample of the UK general public.

3. From Q methodology to Q survey “Q2S” From ‘describing’ to ‘measuring’ using Q techniques What is the purpose of Q survey methods –To understand how many people within a larger population hold these views, to what extent, and do they relate to any other characteristics –Could not (would not) complete Q sorting with a larger number of people

4. Connecting Q factors and surveys What are Q survey methods? –Derived from the factor solution of an existing Q study –Number of potential survey approaches e.g. small number of carefully selected statements short descriptions of factors –Respondents indicate strength of agreement using ranking or Likert scale methods

5. Survey Design “Approach 1 – Individual Item Likert Scale” Original Q study = 49 statements, 3 factors How can we best represent our 3 factors from a smaller number of statements Selection of Statements which are distinguishing and salient for each factor 18 statements selected from the original 49 – 6 per factor Rating each statement between 1 (completely disagree) and 7(completely agree) on a Likert Scale

6. Survey Design – Statements Factor 1 38. The health system should be about getting the greatest benefit overall for the population. 5. At the end of their life, patients should be cared for at home with a better quality of life rather than have aggressive and expensive treatments that will only extend life for a short period of time. 26. It is wrong to raise hopes and expectations by making a special case for treatments that will only extend life by a short time. 3. Treatments should be directed towards people who have a greater chance of survival. 2. We should support an individual patient's choice for treatments that give short life extensions 13. I would place more value on end-of-life treatments than many medical treatments for non-terminal conditions.

7. Factor 2 “20. We all have the right to life” Factor 3 “41. I wouldn’t want my life to be extended just for the sake of it - just keeping breathing is not life” Survey Design – Statements

8. Survey Administration Online survey conducted in the UK (May 2014) 3 versions –Introductory Video (all) –(Up to) 3 Q survey approaches –Policy Choice and Social Value Orientation questions (2 versions) –Demographic questions (all)

9. Sample Quota sampled to be nationally representative of the UK population based on: –Age –Gender –Socioeconomic status –Ethnicity N = 4412 (all 3 versions)

10. Analysis Reliability analysis of 6 statements representing each factor –Remove 1 statement representing F3 from further analysis Sum Likert scores for each block of statements Rescale scores on 0-10 scale (intensity score) to account for different possible max and min score on each Factor

11. Summary Data

12. Assigning Factor Membership Each respondent will be associated with each factor to some extent (will score something between 0-10 on each factor) In the first instance we set up rules to assign people to the factors ‘Generous’ rule – assignment to factor if respondents intensity score for an individual factor is greater than the median intensity score for that factor

13. First stage –F1 – intensity score > 6.39 –F2 - intensity score > 6.67 –F3 - intensity score > 5.00 Not mutually exclusive categories – can have a score higher than on the median on each factor Can we tease out where people are “pure” factor members? Assigning Factor Membership FactorNumber of respondents 12048 22043 31990

14. FactorNumber of respondents% Null (scored <median on all 3 factors) 3868.75 13026.84 2146333.16 33387.66 1/2 (mixed F1 and F2) 2716.14 1/3 (mixed F1 and F3) 134330.44 2/3 (mixed F2 and F3) 1774.01 1/2/3 (mixed F1 and F2 and F3) 1322.99 Assigning Factor Membership Mixed factors – intensity score is higher than the median on more than one factor

15. Discussion of factor membership High number of respondents mixed Factor 1/Factor 3 –Not unexpected given correlations between F1 and F3 in original Q sort More than one viewpoint exists with the UK population –More than just a ‘patient viewpoint’ –Utilitarian view is a common view (approx 45% if including F1, F3, F1/3)

16. Next steps Other ‘rules’ to assign respondents to factors How to make best use of the intensity scores Examining the data from the other 4 approaches (ranking/rating/choice tasks) Assessing the Feasibility, Reliability and Validity of each of the 5 approaches Association with demographics, attitudinal questions and Policy Choice questions