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      Declining well-being during the COVID-19 pandemic reveals US social inequities

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          Abstract

          Background

          The COVID-19 pandemic led to mental health fallout in the US; yet research about mental health and COVID-19 primarily rely on samples that may overlook variance in regional mental health. Indeed, between-city comparisons of mental health decline in the US may provide further insight into how the pandemic is disproportionately affecting at-risk groups.

          Purpose

          This study leverages social media and COVID-19-city infection data to measure the longitudinal (January 22- July 31, 2020) mental health effects of the COVID-19 pandemic in 20 metropolitan areas.

          Methods

          We used longitudinal VADER sentiment analysis of Twitter timelines (January-July 2020) for cohorts in 20 metropolitan areas to examine mood changes over time. We then conducted simple and multivariate Ordinary Least Squares (OLS) regressions to examine the relationship between COVID-19 infection city data, population, population density, and city demographics on sentiment across those 20 cities.

          Results

          Longitudinal sentiment tracking showed mood declines over time. The univariate OLS regression highlighted a negative linear relationship between COVID-19 city data and online sentiment (β = -.017). Residing in predominantly white cities had a protective effect against COVID-19 driven negative mood (β = .0629, p < .001).

          Discussion

          Our results reveal that metropolitan areas with larger communities of color experienced a greater subjective well-being decline than predominantly white cities, which we attribute to clinical and socioeconomic correlates that place communities of color at greater risk of COVID-19.

          Conclusion

          The COVID-19 pandemic is a driver of declining US mood in 20 metropolitan cities. Other factors, including social unrest and local demographics, may compound and exacerbate mental health outlook in racially diverse cities.

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          Most cited references45

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          Mental Health and the Covid-19 Pandemic

          Betty Pfefferbaum, Carol North (2020)
          Article 0 comments Cited 1786 times – based on 0 reviews     Review now
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            Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population

            Matthias Pierce, Holly F Hope, Tamsin Ford … (2020)
            Summary Background The potential impact of the COVID-19 pandemic on population mental health is of increasing global concern. We examine changes in adult mental health in the UK population before and during the lockdown. Methods In this secondary analysis of a national, longitudinal cohort study, households that took part in Waves 8 or 9 of the UK Household Longitudinal Study (UKHLS) panel, including all members aged 16 or older in April, 2020, were invited to complete the COVID-19 web survey on April 23–30, 2020. Participants who were unable to make an informed decision as a result of incapacity, or who had unknown postal addresses or addresses abroad were excluded. Mental health was assessed using the 12-item General Health Questionnaire (GHQ-12). Repeated cross-sectional analyses were done to examine temporal trends. Fixed-effects regression models were fitted to identify within-person change compared with preceding trends. Findings Waves 6–9 of the UKHLS had 53 351 participants. Eligible participants for the COVID-19 web survey were from households that took part in Waves 8 or 9, and 17 452 (41·2%) of 42 330 eligible people participated in the web survey. Population prevalence of clinically significant levels of mental distress rose from 18·9% (95% CI 17·8–20·0) in 2018–19 to 27·3% (26·3–28·2) in April, 2020, one month into UK lockdown. Mean GHQ-12 score also increased over this time, from 11·5 (95% CI 11·3–11·6) in 2018–19, to 12·6 (12·5–12·8) in April, 2020. This was 0·48 (95% CI 0·07–0·90) points higher than expected when accounting for previous upward trends between 2014 and 2018. Comparing GHQ-12 scores within individuals, adjusting for time trends and significant predictors of change, increases were greatest in 18–24-year-olds (2·69 points, 95% CI 1·89–3·48), 25–34-year-olds (1·57, 0·96–2·18), women (0·92, 0·50–1·35), and people living with young children (1·45, 0·79–2·12). People employed before the pandemic also averaged a notable increase in GHQ-12 score (0·63, 95% CI 0·20–1·06). Interpretation By late April, 2020, mental health in the UK had deteriorated compared with pre-COVID-19 trends. Policies emphasising the needs of women, young people, and those with preschool aged children are likely to play an important part in preventing future mental illness. Funding None.
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              A Longitudinal Study on the Mental Health of General Population during the COVID-19 Epidemic in China

              Cuiyan Wang, Riyu Pan, Xiaoyang Wan … (2020)
              Highlights • A significant reduction in psychological impact 4 weeks after COVID outbreak. • The mean scores of respondents in both surveys were above PTSD cut-offs. • Female gender, physical symptoms associated with a higher psychological impact. • Hand hygiene, mask-wearing & confidence in doctors reduced psychological impact. • Online trauma-focused psychotherapy may be helpful to public during COVID-19.
              Article 0 comments Cited 1215 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Krishna C. Bathina: Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Marijn ten Thij:
                ORCID: https://orcid.org/0000-0002-7186-7344
                Role: Formal analysisRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – review & editing
                Danny Valdez:
                ORCID: https://orcid.org/0000-0002-2355-9881
                Role: ConceptualizationRole: Data curationRole: Writing – original draft
                Lauren A. Rutter:
                ORCID: https://orcid.org/0000-0002-8852-7602
                Role: ResourcesRole: Writing – review & editing
                Johan Bollen: Role: Formal analysisRole: MethodologyRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing
                Flávia L. Osório: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 8 July 2021
                Publication date Collection: 2021
                Publication date PMC-release: 8 July 2021
                Volume: 16
                Issue: 7
                Electronic Location Identifier: e0254114
                Affiliations
                [1 ] Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States of America
                [2 ] Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands
                [3 ] School of Public Health, Indiana University, Bloomington, IN, United States of America
                [4 ] Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
                University of São Paulo, BRAZIL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-7186-7344
                https://orcid.org/0000-0002-2355-9881
                https://orcid.org/0000-0002-8852-7602
                Article
                Publisher ID: PONE-D-21-07348
                DOI: 10.1371/journal.pone.0254114
                PMC ID: 8266050
                PubMed ID: 34237087
                SO-VID: 1278c703-8ad9-47c0-ae05-a587ffd78cfc
                Copyright © © 2021 Bathina et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 31 March 2021
                Date accepted : 20 June 2021
                Page count
                Figures: 3, Tables: 0, Pages: 13
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Subject: Research Article
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Viral Diseases
                Subject: Covid 19
                Subject: Medicine and Health Sciences
                Subject: Mental Health and Psychiatry
                Subject: Medicine and Health Sciences
                Subject: Epidemiology
                Subject: Pandemics
                Subject: Social Sciences
                Subject: Sociology
                Subject: Communications
                Subject: Social Communication
                Subject: Social Media
                Subject: Twitter
                Subject: Computer and Information Sciences
                Subject: Network Analysis
                Subject: Social Networks
                Subject: Social Media
                Subject: Twitter
                Subject: Social Sciences
                Subject: Sociology
                Subject: Social Networks
                Subject: Social Media
                Subject: Twitter
                Subject: Social Sciences
                Subject: Sociology
                Subject: Communications
                Subject: Social Communication
                Subject: Social Media
                Subject: Computer and Information Sciences
                Subject: Network Analysis
                Subject: Social Networks
                Subject: Social Media
                Subject: Social Sciences
                Subject: Sociology
                Subject: Social Networks
                Subject: Social Media
                Subject: Earth Sciences
                Subject: Geography
                Subject: Human Geography
                Subject: Urban Geography
                Subject: Urban Areas
                Subject: Social Sciences
                Subject: Human Geography
                Subject: Urban Geography
                Subject: Urban Areas
                Subject: Earth Sciences
                Subject: Geography
                Subject: Geographic Areas
                Subject: Urban Areas
                Subject: Biology and Life Sciences
                Subject: Population Biology
                Subject: Population Metrics
                Subject: Population Density
                Subject: Earth Sciences
                Subject: Geography
                Subject: Human Geography
                Subject: Urban Geography
                Subject: Social Sciences
                Subject: Human Geography
                Subject: Urban Geography
                Custom metadata
                Data Availability In the spirit of transparency, raw but de-identified data can be found in the following GitHub repository: https://github.com/dvaldez44/COVID-19-Population-Sentiment. Data found on this repository conform to Twitter’s data use agreement and our IRB protocol.
                Outbreaks COVID-19

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

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