Testing Explanations for Skepticism of Personalized Risk Information

Area of Science:

• Health communication
• Medical decision making
• Public health

Background:

• Precision medicine relies on patient acceptance of personalized risk information.
• Skepticism towards personalized health data can hinder its effective implementation.
• Understanding the drivers of risk skepticism is crucial for advancing precision medicine.

Purpose of the Study:

• To investigate explanations for skepticism regarding personalized diabetes risk information.
• To test the roles of information evaluation skills, motivated reasoning, Bayesian updating, and personal relevance.
• To identify factors influencing acceptance, overestimation, or underestimation of personalized health risks.

Main Methods:

• Recruited 356 community participants for a risk communication intervention.
• Assessed personalized diabetes and chronic disease risk information.
• Measured risk skepticism and potential explanatory factors including cognitive, affective, and demographic variables.
• Utilized multinomial logistic regression to analyze relationships between factors and risk skepticism.

Main Results:

• 42% accepted their personalized diabetes risk, 40% overestimated it, and 18% underestimated it.
• Information evaluation skills did not explain risk skepticism.
• Motivated reasoning (negative affect) and belonging to a marginalized racial/ethnic group were associated with underestimation.
• Surprise (Bayesian updating) was linked to risk overestimation.

Conclusions:

• Risk skepticism is multifactorial, involving cognitive, affective, and motivational elements.
• Addressing these diverse explanations is essential for improving precision medicine's effectiveness.
• Interventions targeting specific skepticism drivers can enhance patient engagement with personalized health information.