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The medium is the message: public engagement, value and bias

The medium is the message: public engagement, value and bias. Dean A Regier, PhD Cancer Control Research, BC Cancer Agency Assistant Professor, School of Population and Public Health, University of British Columbia. 2015 CADTH Symposium Saskatoon, Saskatchewan. Public Engagement & Value.

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The medium is the message: public engagement, value and bias

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  1. The medium is the message: public engagement, value and bias Dean A Regier, PhD Cancer Control Research, BC Cancer Agency Assistant Professor, School of Population and Public Health, University of British Columbia 2015 CADTH Symposium Saskatoon, Saskatchewan

  2. Public Engagement & Value 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

  3. Why Public Engagement? Involving the public in policy-forming activities • Public includes patient/lay public Normative & pragmatic motivations • Democratic ideals; economic theory • Comparative-effectiveness

  4. Relative to clinical effectiveness and cost • Input from the public is rarely pursued • Barriers • Implies public input is biased *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.

  5. Public Engagement & Bias 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

  6. Communication theory The medium is the message - McLuhan, 1964 • The medium delivers change separate from content • Hypothesis: a video introduction to a stated preference study will differently engage respondents and mitigate hypothetical bias

  7. Background Next generation genomic sequencing • Predictive therapy, prognostic therapy, hereditary cause of disease Potential of incidental findings • Information on diseases not related to current diagnosis

  8. ACMG Recommendations 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)

  9. Objective & Sample 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

  10. Methods Approach 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)

  11. Study design D1 Video Introduction & Text Intro D2 Evaluate difference in welfare estimates English-speaking Respondents randomized randomized D3 Text Introduction Only D4

  12. Choice task example

  13. Welfare Analysis • Lower WTP values in video version • Potential to mitigate hypothetical bias

  14. Questions • Is it necessary for decision-makers to consult the public for each health care investment/disinvestment decisions? • Willingness to pay (and utility) is often biased, is there a role for this metric in decision-making? • Focus on naturalistic units? • Do researchers need do more with how the public is engaged?

  15. Thank-you • 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 • Acknowledgements: Stuart Peacock, Reka Pataky, Kimberly van der Hoek, Gail Jarvik, Jeffrey Hoch, David Veenstra

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