1. Advancing Health Economics, Services, Policy and Ethics Regier DA, Bentley C, McTaggart-Cowan H, Burgess M, Peacock S 2015 CADTH Symposium Saskatoon, Saskatchewan Identifying a “Representative Public”: Recruiting for Demographic and Values Diversity for a Public Engagement Event on Priority Setting and Cancer Drug Funding in Vancouver

2. Public engagement event Recruitment (background) Diversity of experience Diversity of utility Recruitment Algorithm Outline 2

3. Objective of event: Solicit the public’s values on setting priorities, cancer drug funding, and the need to make trade-offs using deliberative public engagement Deliberative methods (Burgess et al, 2008; 2014) Collective solutions to challenging issues in the form of recommendations; not consensus oriented Mini public; include marginal groups; non experts Free, equal, and respectful exchange of views and reasons for them Presence of decision makers, end users (to observe event) “Making Decisions about Funding for Cancer Drugs: A Deliberative Public Engagement” 3

4. Understanding different views Listen and consider all points of view Respectful engagement Participants are equals, avoid reactive positions Informed deliberation on trade-offs Present participants with a variety of information and information types Include diverse experiences Wide range of views Key aspects for deliberative event 4

5. Sampling for representativeness Stratified random sampling SRS + civic lottery (Bombard et al 2011/Dowlen 2008) Do randomly sampled respondents encompass full range of interests and perspectives? Recruit for diversity of interests Wide range of participants drawing from distinct life experiences, values, and styles of reasoning (Longstaff and Burgess, 2010) Interests are situational and may not be stable Background - recruitment 5

6. Recruitment strategies (Longstaff and Burgess, 2010) 1. Recruit participants from each of the geographic BC health regions 2. Random digit dial to recruit a small sample with only basic filters for gender, age. This allows all citizens to have an equal opportunity of being selected. 3. Recruit a sample that accurately represents the population of BC by allowing all citizens to have an equal opportunity of being selected 4. Recruit those who are typically absent from deliberative events (e.g., ethno-cultural groups, youth) 5. Recruit a demographically stratified sample of 25 from a registry and minority groups (e.g., voters list or health care system subscribers) 6. Recruit by advertising for interested participants and with identifiable groups (e.g., recruit for enclave representation) 7. Screen to avoid like-minded stakeholders or those with extreme views 8. Recruiting a mix of citizens, politicians, industry representatives Guidance (Diversity of Interests) 6

7. Objective: to recruit members from the BC public who represent a diversity of interests Diversity of interests = diversity of experiences and utility weightings Recruitment objective 7

8. Proxy for diversity of experience Demographic characteristics of the BC population, incl. oversample of minority and younger age groups Proxy for diversity of utility weightings Discrete choice experiment method to elicit utility Proxy for experience and utility? 8

9. Online questionnaire: Pre-screens: tobacco, policy maker, availability, experience with chronic disease Stratified by age, sex, geography (urban/rural; health authority), parenthood, ethnicity, income, education, experience with chronic diseases) Recruit 80 people (from 35,000) –Representative of BC population –Also completed a discrete choice experiment 9 Diversity of Experience

10. Snapshot of BC demographic data Based on BC Ministry of Health data 9 demographic categories in total –(not pictured, education, sex, children) 10 n=30n=80 Urban 2669 Rural 411 AGE 18-24 38 25-34 513 35-49 821 50-64 821 65+ 616 HEALTH AUTHORITY Fraser Health 1129 Interior Health 513 Island Health 513 Northern Health 25 Vancouver Coastal Health 719 ETHNICITY Aboriginal 25 Caucasian 2053 Chinese 38 South Asian 38 Other 25 CHRONIC DISEASE Confirmed (65+) 1129 INCOME <$19,999 25 $20,000-$34,999 411 $35,000-$49,999 $50,000-$79,999 4848 11 21 $80,000+ 1232

11. Discrete choice experiment method Attribute-based survey measure of utility –Any good can be described by its attributes Creates a market using experimental design –In healthcare it is difficult to observe real-world choices people make between health technologies Individuals choose between alternative goods –Opportunity cost and trade-offs (in-line with objective of event) Diversity of Utility Weightings 11

12. 12 Choice example 16 choice questions posed to 80 respondents Latent class analysis used to analyse limited dependent data Each individual N=80 was assigned a probability of belonging to a latent class

13. What did your choices tell us? CategoryClass A (14 people) Class B (16 people) Class C (50 people) Perfect health Reference category Some problems with usual activity, no pain -0.32-0.25-2.21 Some problems with usual activity, moderate pain -0.78-0.69-2.54 Some problems with usual activity, extreme pain -3.78-1.23-7.59 Duration of life 0.140.4800.64 Extra tax payment -0.0002-0.0001-0.00026 Utility of moderate pain to no pain 0.46 gain in QOL, willingness to pay $2,271 0.44 gain in QOL, Willingness to pay $4,400 0.32 gain in QOL, willingness to pay $1,215 13 Latent class analysis

14. Sample recruitment list 14

15. The sample of 30 –Randomly select 30 from population of N=80 – record their demographic and “latent class” characteristics –Using I-statistic, determine how close the hypothetical sample of 30 is to the target demographics and latent classes –Repeat many, many times to determine the sample of 30 closest to specified criteria Determining the sample of 30 15

16. 16 n=30n=24 Actual (24) Sex (male)151211 Urban262126 Rural434 AGE 18-24333 25-34545 35-49868 50-64868 65+656 HEALTH AUTHORITY Fraser Health1199 Interior Health544 Island Health543 Northern Health222 Vancouver Coastal Health 756 ETHNICITY Aboriginal222 Caucasian2016 Chinese323 South Asian321 Other222 Target of 30 (Random sample 99% close to stratified sample) Target number if 24 Actual characteristics of 24 participants attending the event

17. 17 n=30n=24 Actual (24) Children (yes)1714 Education High School1196 College9710 Some University322 University or above766 Latent Class Class 1543 Class 2655 Class 3191413 INCOME <$19,999223 $20,000-$34,999433 $35,000-$49,999 $50,000-$79,999 4848 3636 3737 $80,000+12108 CHRONIC DISEASE Confirmed (65+)1198 Target of 30 (Random sample 99% close to stratified sample) Target number if 24 Actual characteristics of 24 subjects attending the event

18. We recruited on life experience & utility weight Demographic proxy for life experience Discrete choice experiment – utility Created a sample of 30 (from 80) using novel sampling strategy Future work to incorporate ‘reasoning’ Overview 18

19. “Making Decision about Funding for Cancer Drugs: A Deliberative Public Engagement” Research Team Stuart Peacock, PI – BC Cancer Agency (BCCA), Canadian Centre for Applied Research in Cancer Control (ARCC), U of British Columbia (UBC) Mike Burgess, Co-I – UBC Dean Regier, Co-I – BCCA, ARCC, UBC – dregier@bccrc.cadregier@bccrc.ca Colene Bentley – BCCA, ARCC Helen McTaggart-Cowan – BCCA, ARCC Event Team Liz Wilcox, Sarah Costa, Reka Pataky, Sonya Cressman, Emily McPherson, Lisa Scott, Kim van der Hoek, Holly Longstaff Funders 19

20. Burgess, M., O'Doherty, K., & Secko, D. (2008). Biobanking in British Columbia: discussions of the future of personalized medicine through deliberative public engagement. Personalized Medicine, 5, 285-296 Longstaff H, Burgess M. (2010) Recruitment for representation in public deliberation on the ethics of biobanks. Public Understanding of Science, 19(2), 212-224. Bombard Y, Abelson J, Simeonov D, Gauvin FP. (2011). Eliciting ethical and social values in health technology assessment: a participatory approach. Social Science & Medicine. 73 135-144. Burgess, M. (2014). From 'trust us' to participatory governance: Deliberative publics and science policy. Public Understanding of Science, 23, 48-52. References 20