Testing Explanations for Skepticism of Personalized Risk Information

Erika A. Waters, Jennifer M. Taber, Nicole Ackermann, Julia Maki, Amy M. McQueen and Laura D. Scherer
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Erika A. Waters: Washington University in St. Louis, Saint Louis, Missouri, USA
Jennifer M. Taber: Kent State University, Kent, Ohio, USA
Nicole Ackermann: Washington University in St. Louis, Saint Louis, Missouri, USA
Julia Maki: Washington University in St. Louis, Saint Louis, Missouri, USA
Amy M. McQueen: Washington University in St. Louis, Saint Louis, Missouri, USA
Laura D. Scherer: University of Colorado, Aurora, Colorado, USA

Medical Decision Making, 2023, vol. 43, issue 4, 430-444

Abstract: Background The promise of precision medicine could be stymied if people do not accept the legitimacy of personalized risk information. We tested 4 explanations for skepticism of personalized diabetes risk information. Method We recruited participants ( N = 356; M age = 48.6 [ s = 9.8], 85.1% women, 59.0% non-Hispanic white) from community locations (e.g., barbershops, churches) for a risk communication intervention. Participants received personalized information about their risk of developing diabetes and heart disease, stroke, colon cancer, and/or breast cancer (women). Then they completed survey items. We combined 2 items (recalled risk, perceived risk) to create a trichotomous risk skepticism variable (acceptance, overestimation, underestimation). Additional items assessed possible explanations for risk skepticism: 1) information evaluation skills (education, graph literacy, numeracy), 2 ) motivated reasoning (negative affect toward the information, spontaneous self-affirmation, information avoidance); 3) Bayesian updating (surprise), and 4) personal relevance (racial/ethnic identity). We used multinomial logistic regression for data analysis. Results Of the participants, 18% believed that their diabetes risk was lower than the information provided, 40% believed their risk was higher, and 42% accepted the information. Information evaluation skills were not supported as a risk skepticism explanation. Motivated reasoning received some support; higher diabetes risk and more negative affect toward the information were associated with risk underestimation, but spontaneous self-affirmation and information avoidance were not moderators. For Bayesian updating, more surprise was associated with overestimation. For personal relevance, belonging to a marginalized racial/ethnic group was associated with underestimation. Conclusion There are likely multiple cognitive, affective, and motivational explanations for risk skepticism. Understanding these explanations and developing interventions that address them will increase the effectiveness of precision medicine and facilitate its widespread implementation.

Keywords: decision making; health communication; motivated reasoning; precision medicine; risk perception (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:43:y:2023:i:4:p:430-444

DOI: 10.1177/0272989X231162824

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