Subtractive Risk Judgments in Health and Medical Care Decisions

Mehdi Mourali, University of Calgary
Zhiyong Yang, Miami University

Shared Decision-Making in Medicine

Multiple Risks

How do consumers combine information about the probabilities of multiple adverse effects when assessing overall risk?

The Present Research

Consumers judge the medication with one high-probability and one low-probability side effect to be less risky than the medication with only one high-probability side effect.

Theoretical Framework

  • Fuzzy Trace Theory (Reyna and Brainerd 1991)
    • People encode both the verbatim representation of numbers and their gist representations.
    • They prefer to rely on the least precise representation that allows task completion.
  • Information Integration Theory (Anderson 1965)
    • People use simple computational rules to integrate information from multiple cues.
    • Consumers tend to use an averaging rule when integrating categorical judgments to form overall impressions (e.g., Brough and Chernev 2011).

Predictions

  • When combining items with categorical judgments of “high risk” and “low risk,” consumers will perceive lower overall risk than when judging the “high risk” item alone.
  • Subtractive risk judgments are less likely to occur if the averaging rule is not used to integrate information about multiple risks:
  1. When one side effect is perceived to be too risky (Study 2).
  1. When using graphs to highlight the additive property of multiple risks (Study 3).

Study 1 - Single versus Combined Risks

  • 330 US residents on Prolific started the study. 295 passed the attention check (149 men, 142 women, 1 preferred not to say, and 3 identified as other; Mage = 33.5, SDage = 12.8).
  • Participants were randomly assigned to one of 3 conditions (single vs. high-low vs. high-high).
    • Single: medication comes with a 30% chance of experiencing abdominal cramps and pain.
    • High-low: in addition to the 30% chance of abdominal cramps and pain, there was a 1% chance of experiencing blurry vision and increased light sensitivity.
    • High-high: in addition to the 30% chance of abdominal cramps and pain, there was a 35% chance of experiencing blurry vision and increased light sensitivity.
  • DVs: Perceived risk (0 = very low risk, 100 = very high risk), and likelihood to start the medication (0 = not likely at all, 100 = very likely).

Study 1 - Results: Perceived Risk

Study 2 - When The Risk is Too High

  • 1600 US residents on Prolific started the study. 1551 passed the attention check (772 men, 726 women, 9 preferred not to say, and 44 identified as other; Mage = 35.1, SDage = 13.0).

  • They were randomly assigned to one of 14 conditions in a 2 (first side effect (FSE): 45% vs. 75%) × 7 (second side effect (SSE): none vs. 1% vs. 6% vs. 17% vs. 22% vs. 30% vs. 35%) between-subjects design.

  • DVs: Perceived risk (0 = very low risk, 10 = very high risk), and likelihood to start the medication (0 = not likely at all, 10 = very likely).

Study 2 - Results: Perceived Risk

Study 3: Using Bar Graphs

  • 750 US residents started the study. 664 passed the attention check (387 men, 267 women, 3 preferred not to say, and 7 identified as other; Mage = 31.4, SDage = 11.1).

  • They were randomly assigned to one of five conditions (single side effect/numerical vs. single side effect/graph vs. combined side effects/numerical vs. combined side effects/graph emphasizing additive risks vs. combined side effects/graph not emphasizing additive risks).

Study 3: Using Bar Graphs

Study 3 - Results: Perceived Risk

Discussion

  • Consumers tend to judge combined items with high- and low-risk levels to be less risky overall than the high-risk item alone.

  • Categorical averaging of numerical risk is a key mechanism through which consumers assess multiple risks.

  • Communicating risk information using formats that visualize the additive property of multiple risks (e.g., stacked bars) helps correct the misperception.



Thank you!

References

Anderson, Norman H. (1965), “Averaging versus Adding as a Stimulus-Combination Rule in Impression Formation,” Journal of Experimental Psychology, 70 (4), 394–400.

Brough, Aaron R., and Alexander Chernev (2011), “When Opposites Detract: Categorical Reasoning and Subtractive Valuations of Product Combinations,” Journal of Consumer Research, 39(August), 399–414.

Reyna, Valerie F., and Charles J. Brainerd (1991), “Fuzzy Trace Theory and Framing Effects in Choice: Gist Extraction, Truncation, and Conversion,” Journal of Behavioral Decision Making, 4(4), 249-262.

ACR 2022 DENVER