Advancing Health Economics, Services, Policy and Ethics

2015 CADTH Symposium

Saskatoon, Saskatchewan

Dean A Regier, PhD

Cancer Control Research, BC Cancer Agency

Assistant Professor, School of Population and

Public Health, University of British Columbia

Problem #1

• Input from the public is not routinely pursued in health-care decision-making

• Public values viewed as biased

Problem #2

• Public values are (probably) biased

• Leads to misallocation of scarce resources

Public Engagement & Value

2

Involving the public in policy-forming activities

• Public includes patient/lay public

Normative & pragmatic motivations

• Democratic ideals; economic theory

• Comparative-effectiveness

Why Public Engagement?

3

4*Regier DA, Bentley C, Mitton C, et al. Public Engagement in Priority-Setting: Results from a pan-Canadian Survey of Decision-Makers in Cancer

Control. Social Science & Medicine; 2014: 122:130-139.

Relative to clinical effectiveness and cost

• Input from the public is rarely pursued

Barriers

• Implies public input is biased

Stated preference elicitation of utility

• Non-market valuation of goods

Hypothetical bias

• Benefit over-valuation leads to investing in goods that cost too much in terms of available alternatives

Mitigating hypothetical bias

• Rationality tests; cheap-talk; oath

Public Engagement & Bias

5

Communication theory

The medium is the message - McLuhan, 1964

• The medium delivers change separate from content

6

Hypothesis: a video introduction to a stated preference study will differently engage respondents and mitigate hypothetical bias

Next generation genomic sequencing

• Predictive therapy, prognostic therapy, hereditary cause of disease

Potential of incidental findings

• Information on diseases not related to current diagnosis

Background

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Published list of incidental findings (Green et al, 2013)

• High-penetrance & clinical utility

• List of 56 genes, 24 disorders

• Labs look for mutations, IF’s returned to patient, through managing physician

Controversial

• Patients not offered a choice

• (Public not consulted)

ACMG Recommendations

8

Objective

• Personal utility for the return incidental findings

• Discrete choice experiment (two choice + opt-out)

Respondent Sample

• General public in Canada (N=1200)

• English and French language versions

Objective & Sample

9

Define Attributes/levels

• Cognitive interviews (n=6)/ 2 focus groups (n=12)

Experimental design

• D-efficient design with informative priors

Statistical Analysis

• Mixed Logit Model (preference heterogeneity)

Welfare analysis

• Willingness to pay (compensating variation)

Methods Approach

10

Evaluate difference

in welfare estimates

D1

D2

D3Text Introduction

Only

Study design

Video Introduction

& Text Intro

English-speaking

Respondents

D4

randomized randomized

11

Choice task example

12

Option A

Option B

No information

Diseases with a 80% lifetime risk or higher

Diseases with a 5% lifetime risk or higher

No information

Recommended effective medical treatment and lifestyle change

Recommended effective medical treatment

only

No information

Mild health consequences

Moderate health consequences

No information

Does not provide information on carrier status

Information on if your family members could

be affected

No information

$425

$1500

$ 0

Option A

Option B

No Information

Disease Risk More disease will be identified if the lifetime risk is lower

Disease Treatability

Disease Severity Health consequences of the diseases you may develop You m

Carrier Status Disease risk not affecting you but can affect your family Cost to you

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Video+TextVersion

Text Version

Scenario 1Medical treatment , 80% or greater risk, severe QOL

$42095% CI 191-528

$515 95% CI 417-778

Scenario 2 (vs Scenario 1)Medical & No treatment , 80% or greater risk, severe QOL

$235 95% CI 195-275

$32095% CI 225-371

*t-test (unequal variances)=-1.66, p-val=0.11

• Lower WTP values in video version

• Potential to mitigate hypothetical bias

Welfare Analysis

1. Is it necessary for decision-makers to consult the public for each health care investment/disinvestment decisions?

2. Willingness to pay (and utility) is often biased, is there a role for this metric in decision-making?

• Focus on naturalistic units?

3. Do researchers need do more with how the public is engaged?

Questions

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A d v a n c i n g H e a l t h E c o n o m i c s , S e r v i c e s , P o l i c y a n d E t h i c s

Thank-you

• Acknowledgements: Stuart Peacock, Reka Pataky, Kimberly van der Hoek, Gail Jarvik, Jeffrey Hoch, David Veenstra

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• Funding for this research obtained from the Canadian Centre for Applied Research in Cancer Control (ARCC); ARCC is funded by the Canadian Cancer Society Research Institute grant #019789, #703549