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新冠疫情的时空动态:利用生态和基于推特的评估方法,在时间和社区背景下考察其影响。

The where and when of COVID-19: Using ecological and Twitter-based assessments to examine impacts in a temporal and community context.

机构信息

Department of Psychology, Stony Brook University, Stony Brook, New York, United States of America.

Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America.

出版信息

PLoS One. 2022 Feb 23;17(2):e0264280. doi: 10.1371/journal.pone.0264280. eCollection 2022.

DOI:10.1371/journal.pone.0264280
PMID:35196353
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8865674/
Abstract

In March 2020, residents of the Bronx, New York experienced one of the first significant community COVID-19 outbreaks in the United States. Focusing on intensive longitudinal data from 78 Bronx-based older adults, we used a multi-method approach to (1) examine 2019 to early pandemic (February-June 2020) changes in momentary psychological well-being of Einstein Aging Study (EAS) participants and (2) to contextualize these changes with community distress scores collected from public Twitter posts posted in Bronx County. We found increases in mean loneliness from 2019 to 2020; and participants that were higher in neuroticism had greater increases in thought unpleasantness and feeling depressed. Twitter-based Bronx community scores of anxiety, depressivity, and negatively-valenced affect showed elevated levels in 2020 weeks relative to 2019. Integration of EAS participant data and community data showed week-to-week fluctuations across 2019 and 2020. Results highlight how community-level data can characterize a rapidly changing environment to supplement individual-level data at no additional burden to individual participants.

摘要

2020 年 3 月,纽约州布朗克斯区的居民经历了美国首批重大社区 COVID-19 疫情之一。我们专注于来自 78 名布朗克斯区老年人的密集纵向数据,使用多方法方法来:(1)检查爱因斯坦老龄化研究(EAS)参与者在 2019 年至大流行早期(2020 年 2 月至 6 月)期间的瞬间心理健康变化;(2)将这些变化与从布朗克斯县发布的公共 Twitter 帖子中收集到的社区困扰分数联系起来。我们发现孤独感从 2019 年到 2020 年有所增加;神经质程度较高的参与者在思维不愉快和抑郁感方面的增加更大。基于 Twitter 的布朗克斯社区的焦虑、抑郁和负性情绪评分显示,2020 年的评分相对于 2019 年有所升高。EAS 参与者数据和社区数据的整合显示,2019 年和 2020 年每周都有波动。研究结果突出表明,社区层面的数据如何能够描述快速变化的环境,从而在不增加个体参与者负担的情况下补充个体层面的数据。

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