Department of Sociology, University of South Florida, Tampa, FL, USA.
Department of Geography, University of Alabama, Tuscaloosa, AL, USA.
Am J Health Promot. 2021 Nov;35(8):1078-1083. doi: 10.1177/08901171211017286. Epub 2021 May 13.
To examine multilevel predictors on American public response to COVID-19.
Multilevel study.
A national survey was conducted by Qualtrics from August 24 to September 11, 2020. The state-level variables were constructed on data from multiple sources.
2,440 respondents 18 years and older from all 50 states and D.C.
The outcome variable is the public response to COVID-19 measured by threat perception, behavioral adjustment, and policy support. The predictors include individual-level sociodemographic factors and state-level indicators about public health conditions, political context, and economic recovery.
Multilevel structural equation modeling is used for statistical estimation.
People from states with more COVID-19 cases (β = 0.020, p < 0.1), mandatory face mask policies (β = 0.069, p < 0.05), and liberal governments (β = 0.002, p < 0.05) are more likely to respond while people from states whose economies have recovered closer to the pre-pandemic level are less likely to do so (β = -0.005, p < 0.05). Regarding individual-level predictors, older people (β = 0.005, p < 0.001) and people with better education (β = 0.029, p < 0.01), leaning toward the Democrat Party (β = 0.066, p < 0.001) and liberal political ideology (β = 0.094, p < 0.001), and have stronger generalized trust (β = 0.033, p < 0.001) are more likely to respond than their counterparts.
Differences in the public response to the pandemic stem from variations in individual characteristics and contextual factors of states where people live. These findings contribute to the rapidly growing literature and have implications for public health policies.
研究美国公众对 COVID-19 反应的多层次预测因素。
多层次研究。
2020 年 8 月 24 日至 9 月 11 日,Qualtrics 进行了一项全国性调查。州级变量是根据来自多个来源的数据构建的。
来自美国 50 个州和哥伦比亚特区的 2440 名 18 岁及以上的受访者。
因变量是公众对 COVID-19 的反应,通过威胁感知、行为调整和政策支持来衡量。预测因素包括个人层面的社会人口因素和州级指标,包括公共卫生状况、政治背景和经济复苏。
采用多层次结构方程模型进行统计估计。
来自 COVID-19 病例较多的州(β=0.020,p<0.1)、强制戴口罩政策(β=0.069,p<0.05)和自由政府(β=0.002,p<0.05)的人更有可能做出反应,而经济恢复更接近大流行前水平的州的人则不太可能做出反应(β=-0.005,p<0.05)。就个人层面的预测因素而言,年龄较大的人(β=0.005,p<0.001)和受教育程度较高的人(β=0.029,p<0.01)、倾向于民主党的人(β=0.066,p<0.001)和自由政治意识形态的人(β=0.094,p<0.001)以及具有更强的普遍信任的人(β=0.033,p<0.001)比他们的同龄人更有可能做出反应。
公众对大流行的反应差异源于人们居住的州的个人特征和背景因素的差异。这些发现有助于丰富不断增长的文献,并对公共卫生政策具有启示意义。