Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
J Community Psychol. 2022 Jul;50(5):2431-2442. doi: 10.1002/jcop.22785. Epub 2021 Dec 30.
The objective of this study is to determine county-level factors associated with anxiety, depression, and isolation during the coronavirus disease 2019 (COVID-19) pandemic. This study used daily data from 23,592,355 respondents of a nationwide Facebook-based survey from April 2020 to July 2021, aggregated to the week-county level to yield 212,581 observations. Mental distress prevalences were modeled using weighted linear mixed-effects models with a county random effect. These models revealed that weekly percentages of mental distress were higher in counties with higher unemployment rates, populations, and education levels; higher percentages of females, young adults, individuals with a medical condition, and individuals very worried about their finances and COVID-19; and lower percentages of individuals who were working outside the home, living with children, without health insurance, and Black. Anxiety peaked in April 2020, depression in October 2020, and isolation in December 2020. Therefore, United States counties experienced the mental health effects of the pandemic differently dependent upon their characteristics, and mental distress prevalence varied across time.
本研究旨在确定与 2019 年冠状病毒病（COVID-19）大流行期间焦虑、抑郁和孤独相关的县级因素。本研究使用了 2020 年 4 月至 2021 年 7 月期间来自全国范围内基于 Facebook 的调查的 23,592,355 名受访者的每日数据，将其汇总到周-县级别，得到 212,581 个观测值。使用带有县随机效应的加权线性混合效应模型对精神困扰的流行率进行建模。这些模型表明，失业率、人口和教育水平较高的县每周精神困扰的百分比更高；女性、年轻人、有医疗状况的个体、非常担心自己财务状况和 COVID-19 的个体的百分比更高；而外出工作、与孩子同住、没有医疗保险和黑人的个体的百分比更低。焦虑在 2020 年 4 月达到峰值，抑郁在 2020 年 10 月达到峰值，孤独在 2020 年 12 月达到峰值。因此，美国各县因其特征的不同而经历了大流行的心理健康影响，精神困扰的流行率也随时间而变化。
