Variations in risk attitude across race, gender, and education

Med Decis Making. 2003 Nov-Dec;23(6):511-7. doi: 10.1177/0272989X03258431.


Background: Significant disparities in health care utilization exist across gender and race. Little is known about the patient-specific factors that may contribute to this variation. This study examined variations in risk attitude across major sociodemographic groups.

Methods: A survey elicited utility measures for health states under risk-insensitive and risk-sensitive conditions (time tradeoff and standard gamble methods, respectively). Risk attitude was modeled assuming constant proportional risk posture, thus the utility function used was a power function. A multivariable linear regression model was used to examine the relationship between risk attitude and sociodemographic factors.

Results: Of the 62 study subjects, the mean age was 47.6 years, 47% were female, and 33% were African American. Overall, 37% of respondents were decidedly risk averse, 37% moderately risk averse, 15% moderately risk seeking, and 11% decidedly risk seeking. Significant predictors of increasing risk aversion in multivariate modeling were white race (P < 0.01) and lower education (P < 0.05). Women also tended to be more risk averse (P = 0.07).

Conclusions: This study found significant differences in risk attitude across race and educational status, with a smaller difference across gender. Further research is needed to validate these findings and clarify their contribution to racial and gender variations in health care utilization and their future role in decision and cost-effectiveness analyses.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Black or African American / psychology
  • Decision Making
  • Educational Status
  • Female
  • Health Services / statistics & numerical data*
  • Health Status Indicators*
  • Humans
  • Interviews as Topic
  • Male
  • Middle Aged
  • North Carolina
  • Patient Acceptance of Health Care / ethnology
  • Patient Acceptance of Health Care / psychology*
  • Quality of Life
  • Quality-Adjusted Life Years
  • Risk-Taking*
  • Sex Factors
  • White People / psychology