Department of Psychological Science, University of California, Irvine, USA.
Community Health Sciences, Fielding School of Public Health, University of California, Los Angeles, USA.
Ann Behav Med. 2024 Mar 12;58(4):242-252. doi: 10.1093/abm/kaad055.
Individuals confronting health threats may display an optimistic bias such that judgments of their risk for illness or death are unrealistically positive given their objective circumstances.
We explored optimistic bias for health risks using k-means clustering in the context of COVID-19. We identified risk profiles using subjective and objective indicators of severity and susceptibility risk for COVID-19.
Between 3/18/2020-4/18/2020, a national probability sample of 6,514 U.S. residents reported both their subjective risk perceptions (e.g., perceived likelihood of illness or death) and objective risk indices (e.g., age, weight, pre-existing conditions) of COVID-19-related susceptibility and severity, alongside other pandemic-related experiences. Six months later, a subsample (N = 5,661) completed a follow-up survey with questions about their frequency of engagement in recommended health protective behaviors (social distancing, mask wearing, risk behaviors, vaccination intentions).
The k-means clustering procedure identified five risk profiles in the Wave 1 sample; two of these demonstrated aspects of optimistic bias, representing almost 44% of the sample. In OLS regression models predicting health protective behavior adoption at Wave 2, clusters representing individuals with high perceived severity risk were most likely to report engagement in social distancing, but many individuals who were objectively at high risk for illness and death did not report engaging in self-protective behaviors.
Objective risk of disease severity only inconsistently predicted health protective behavior. Risk profiles may help identify groups that need more targeted interventions to increase their support for public health policy and health enhancing recommendations more broadly.
面对健康威胁的个体可能会表现出乐观偏见，即他们对疾病或死亡风险的判断在客观情况下过于乐观。
我们使用 COVID-19 背景下的 k-均值聚类方法来探讨健康风险的乐观偏见。我们使用 COVID-19 相关严重程度和易感性的主观和客观指标来确定风险特征。
在 2020 年 3 月 18 日至 4 月 18 日期间，一项针对美国居民的全国概率抽样调查要求 6514 名美国居民报告他们对 COVID-19 的主观风险感知（例如，患病或死亡的可能性）和客观风险指数（例如，年龄、体重、预先存在的疾病），以及其他与大流行相关的经历。六个月后，一个子样本（N=5661）完成了一项后续调查，其中包含有关他们参与推荐的健康保护行为（社交距离、戴口罩、风险行为、疫苗接种意愿）的频率的问题。
k-均值聚类程序在第 1 波样本中确定了 5 种风险特征；其中两种表现出乐观偏见的方面，占样本的近 44%。在预测第 2 波健康保护行为采用的 OLS 回归模型中，代表高感知严重程度风险的聚类最有可能报告参与社交距离，但许多客观上患病和死亡风险高的个体并没有报告参与自我保护行为。
疾病严重程度的客观风险仅不一致地预测健康保护行为。风险特征可能有助于识别需要更有针对性干预的群体，以更广泛地增加他们对公共卫生政策和促进健康建议的支持。
