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Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study.
Li, Minghui; Hua, Yining; Liao, Yanhui; Zhou, Li; Li, Xue; Wang, Ling; Yang, Jie.
  • Li M; Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Hua Y; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China.
  • Liao Y; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Zhou L; Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States.
  • Li X; Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Wang L; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Yang J; Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States.
J Med Internet Res ; 24(10): e39676, 2022 10 13.
Article in English | MEDLINE | ID: covidwho-2109563
ABSTRACT

BACKGROUND:

The COVID-19 pandemic and its corresponding preventive and control measures have increased the mental burden on the public. Understanding and tracking changes in public mental status can facilitate optimizing public mental health intervention and control strategies.

OBJECTIVE:

This study aimed to build a social media-based pipeline that tracks public mental changes and use it to understand public mental health status regarding the pandemic.

METHODS:

This study used COVID-19-related tweets posted from February 2020 to April 2022. The tweets were downloaded using unique identifiers through the Twitter application programming interface. We created a lexicon of 4 mental health problems (depression, anxiety, insomnia, and addiction) to identify mental health-related tweets and developed a dictionary for identifying health care workers. We analyzed temporal and geographic distributions of public mental health status during the pandemic and further compared distributions among health care workers versus the general public, supplemented by topic modeling on their underlying foci. Finally, we used interrupted time series analysis to examine the statewide impact of a lockdown policy on public mental health in 12 states.

RESULTS:

We extracted 4,213,005 tweets related to mental health and COVID-19 from 2,316,817 users. Of these tweets, 2,161,357 (51.3%) were related to "depression," whereas 1,923,635 (45.66%), 225,205 (5.35%), and 150,006 (3.56%) were related to "anxiety," "insomnia," and "addiction," respectively. Compared to the general public, health care workers had higher risks of all 4 types of problems (all P<.001), and they were more concerned about clinical topics than everyday issues (eg, "students' pressure," "panic buying," and "fuel problems") than the general public. Finally, the lockdown policy had significant associations with public mental health in 4 out of the 12 states we studied, among which Pennsylvania showed a positive association, whereas Michigan, North Carolina, and Ohio showed the opposite (all P<.05).

CONCLUSIONS:

The impact of COVID-19 and the corresponding control measures on the public's mental status is dynamic and shows variability among different cohorts regarding disease types, occupations, and regional groups. Health agencies and policy makers should primarily focus on depression (reported by 51.3% of the tweets) and insomnia (which has had an ever-increasing trend since the beginning of the pandemic), especially among health care workers. Our pipeline timely tracks and analyzes public mental health changes, especially when primary studies and large-scale surveys are difficult to conduct.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 / Sleep Initiation and Maintenance Disorders Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 39676

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 / Sleep Initiation and Maintenance Disorders Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 39676