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

• Public engagement event

• Recruitment (background)

• Diversity of experience

• Diversity of utility

• Recruitment Algorithm

Outline

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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”

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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

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

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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)

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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

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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?

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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

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Diversity of Experience

Snapshot of BC demographic data

Based on BC Ministry of Health data

9 demographic categories in total

– (not pictured, education, sex, children)

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n=30 n=80

Urban 26 69

Rural 4 11

AGE

18-24 3 8

25-34 5 13

35-49 8 21

50-64 8 21

65+ 6 16

HEALTH AUTHORITY

Fraser Health 11 29

Interior Health 5 13

Island Health 5 13

Northern Health 2 5

Vancouver Coastal Health 7 19

ETHNICITY

Aboriginal 2 5

Caucasian 20 53

Chinese 3 8

South Asian 3 8

Other 2 5

CHRONIC DISEASE

Confirmed (65+) 11 29

INCOME

<$19,999 2 5

$20,000-$34,999 4 11

$35,000-$49,999

$50,000-$79,999

4

8

11

21

$80,000+ 12 32

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

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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

What did your choices tell us?Category Class A

(14 people)Class B(16 people)

Class C(50 people)

Perfect health Reference category Reference category 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 0.14 0.480 0.64

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

$1,21513

Latent class analysis

Sample recruitment list

Respondent# Class# Sex Age

Category

Location/HA Rural Ethnic

origin

1 2 Male 18-34 Vancouver No Canadian

2 3 Male 35-50 Vancouver No Chinese

3 3 Female 51-64 Prince

George

No First

Nations

4 2 Female 35-50 Pemberton Yes Scottish

5 1 Female 35-50 Vernon No Korean

6 1 Female 51-64 Surrey No Indo-

Canadian

...

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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

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n=30 n=24 Actual

(24)

Sex (male) 15 12 11

Urban 26 21 26

Rural 4 3 4

AGE

18-24 3 3 3

25-34 5 4 5

35-49 8 6 8

50-64 8 6 8

65+ 6 5 6

HEALTH AUTHORITY

Fraser Health 11 9 9

Interior Health 5 4 4

Island Health 5 4 3

Northern Health 2 2 2

Vancouver Coastal

Health

7 5 6

ETHNICITY

Aboriginal 2 2 2

Caucasian 20 16 16

Chinese 3 2 3

South Asian 3 2 1

Other 2 2 2

Target of 30

(Random sample 99%

close to stratified sample)

Target number if 24

Actual characteristics of

24 participants attending

the event

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n=30 n=24 Actual

(24)

Children (yes) 17 14 14

Education

High School 11 9 6

College 9 7 10

Some University 3 2 2

University or above 7 6 6

Latent Class

Class 1 5 4 3

Class 2 6 5 5

Class 3 19 14 13

INCOME

<$19,999 2 2 3

$20,000-$34,999 4 3 3

$35,000-$49,999

$50,000-$79,999

4

8

3

6

3

7

$80,000+ 12 10 8

CHRONIC DISEASE

Confirmed (65+) 11 9 8

Target of 30

(Random sample 99%

close to stratified sample)

Target number if 24

Actual characteristics of

24 subjects attending the

event

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

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“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.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

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• 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

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