Variations in risk attitude across race, gender, and education
Variations in risk attitude across race, gender, and education
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DOI：
10.1177/0272989x03258431
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发表时间：
2003-11-01
影响因子：
3.6
通讯作者：
Downs, SM
中科院分区：
文献类型：
--
作者：
Rosen, AB;Tsai, JS;Downs, SM
背景。在医疗保健利用方面存在着性别和种族的显著差异。对于可能导致这种差异的患者特异性因素知之甚少。这项研究调查了主要社会人口统计学群体的风险态度差异。方法。一项调查得出了风险不敏感和风险敏感条件下（分别为时间权衡和标准赌博方法）健康状态的效用度量，风险态度假设为恒定比例风险姿态，因此使用的效用函数是幂函数。采用多元线性回归模型检验风险态度与社会人口学因素之间的关系。结果。在62名研究对象中，平均年龄为47.6岁，47%为女性，33%为非洲裔美国人。总体而言，37%的受访者坚决厌恶风险，37%适度厌恶风险，15%适度寻求风险，11%坚决寻求风险。在多元模型中，白人（P < 0.01）和低学历（P < 0.05）是风险厌恶增加的显著预测因子。女性也更倾向于规避风险（P = 0.07）。结论。该研究发现，不同种族和教育程度的风险态度存在显著差异，性别差异较小。需要进一步的研究来验证这些发现，并澄清它们对保健利用方面的种族和性别差异的贡献，以及它们未来在决策和成本效益分析中的作用。
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.