Multiple time measurements of multidimensional psychiatric states from immediately before the COVID-19 pandemic to one year later: a longitudinal online survey of the Japanese population

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Abstract

The coronavirus disease 2019 (COVID-19) pandemic has profoundly affected the mental health of both infected and uninfected people. Although most psychiatric disorders have highly overlapping genetic and pathogenic backgrounds, most studies investigating the impact of the pandemic have examined only single psychiatric disorders. It is necessary to examine longitudinal trajectories of factors that modulate psychiatric states across multiple dimensions. About 2274 Japanese citizens participated in online surveys presented in December 2019 (before the pandemic), August 2020, Dec 2020, and April 2021. These surveys included nine questionnaires on psychiatric symptoms, such as depression and anxiety. Multidimensional psychiatric time-series data were then decomposed into four principal components. We used generalized linear models to identify modulating factors for the effects of the pandemic on these components. The four principal components can be interpreted as a general psychiatric burden, social withdrawal, alcohol-related problems, and depression/anxiety. Principal components associated with general psychiatric burden and depression/anxiety peaked during the initial phase of the pandemic. They were further exacerbated by the economic burden the pandemic imposed. In contrast, principal components associated with social withdrawal showed a delayed peak, with human relationships as an important risk modulating factor. In addition, being female was a risk factor shared across all components. Our results show that COVID-19 has imposed a large and varied burden on the Japanese population since the commencement of the pandemic. Although components related to the general psychiatric burden remained elevated, peak intensities differed between components related to depression/anxiety and those related to social withdrawal. These results underline the importance of using flexible monitoring and mitigation strategies for mental problems, according to the phase of the pandemic.

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The CES-D Scale: A Self-Report Depression Scale for Research in the General Population

L Radloff (1977)

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Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science

Emily A. Holmes, Rory C O'Connor, V. Perry … (2020)

Summary The coronavirus disease 2019 (COVID-19) pandemic is having a profound effect on all aspects of society, including mental health and physical health. We explore the psychological, social, and neuroscientific effects of COVID-19 and set out the immediate priorities and longer-term strategies for mental health science research. These priorities were informed by surveys of the public and an expert panel convened by the UK Academy of Medical Sciences and the mental health research charity, MQ: Transforming Mental Health, in the first weeks of the pandemic in the UK in March, 2020. We urge UK research funding agencies to work with researchers, people with lived experience, and others to establish a high level coordination group to ensure that these research priorities are addressed, and to allow new ones to be identified over time. The need to maintain high-quality research standards is imperative. International collaboration and a global perspective will be beneficial. An immediate priority is collecting high-quality data on the mental health effects of the COVID-19 pandemic across the whole population and vulnerable groups, and on brain function, cognition, and mental health of patients with COVID-19. There is an urgent need for research to address how mental health consequences for vulnerable groups can be mitigated under pandemic conditions, and on the impact of repeated media consumption and health messaging around COVID-19. Discovery, evaluation, and refinement of mechanistically driven interventions to address the psychological, social, and neuroscientific aspects of the pandemic are required. Rising to this challenge will require integration across disciplines and sectors, and should be done together with people with lived experience. New funding will be required to meet these priorities, and it can be efficiently leveraged by the UK's world-leading infrastructure. This Position Paper provides a strategy that may be both adapted for, and integrated with, research efforts in other countries.

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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 … (2021)

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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Author and article information

Contributors

Toshinori Chiba:

ORCID: http://orcid.org/0000-0002-2173-4519

t.chiba0906@gmail.com

Journal

Journal ID (nlm-ta): Transl Psychiatry

Journal ID (iso-abbrev): Transl Psychiatry

Title: Translational Psychiatry

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2158-3188

Publication date (Electronic): 11 November 2021

Publication date PMC-release: 11 November 2021

Publication date Collection: 2021

Volume: 11

Electronic Location Identifier: 573

Affiliations

[1 ]GRID grid.418163.9, ISNI 0000 0001 2291 1583, The Department of Decoded Neurofeedback, Computational Neuroscience Laboratories, , Advanced Telecommunications Research Institute International, ; Kyoto, Japan

[2 ]GRID grid.274841.c, ISNI 0000 0001 0660 6749, Department of Neuropsychiatry, , Faculty of Life Sciences, Kumamoto University, ; Kumamoto, Japan

[3 ]GRID grid.418163.9, ISNI 0000 0001 2291 1583, The Department of Brain robot interface, Computational Neuroscience Laboratories, , Advanced Telecommunications Research Institute International, ; Kyoto, Japan

[4 ]GRID grid.450318.b, ISNI 0000 0004 9495 9326, Collaborative AI Laboratory, KDDI Research, Inc., ; Fujimino, Japan

[5 ]GRID grid.418163.9, ISNI 0000 0001 2291 1583, The Department of Computational Brain Imaging, Computational Neuroscience Laboratories, , Advanced Telecommunications Research Institute International, ; Kyoto, Japan

[6 ]GRID grid.260493.a, ISNI 0000 0000 9227 2257, Mathematical Informatics Laboratory, , Nara Institute of Science and Technology (NAIST), ; Kyoto, Japan

[7 ]GRID grid.497100.b, ISNI 0000 0001 0712 8814, Technology Affairs Department, , Technology Strategy Division, KDDI CORPORATION, ; Tokyo, Japan

[8 ]GRID grid.450318.b, ISNI 0000 0004 9495 9326, Healthcare Medical Group, , Future Design Division 2, KDDI Research Atelier, KDDI Research, Inc., ; Fujimino, Japan

[9 ]GRID grid.54432.34, ISNI 0000 0004 0614 710X, Japan Society for the Promotion of Science, ; Tokyo, Japan

[10 ]GRID grid.416614.0, ISNI 0000 0004 0374 0880, The Department of Psychiatry, , National Defense Medical College, ; Tokorozawa, Japan

[11 ]The Department of Neuropsychiatry, Osaka Medical and Pharmaceutical University, Osaka, Japan

[12 ]GRID grid.418025.a, ISNI 0000 0004 0606 5526, The Florey Institute of Neuroscience and Mental Health, ; Parkville, VIC Australia

[13 ]GRID grid.416614.0, ISNI 0000 0004 0374 0880, Division of Behavioral Science, , National Defense Medical College Research Institute, ; Tokorozawa, Japan

[14 ]GRID grid.31432.37, ISNI 0000 0001 1092 3077, The Department of Psychiatry, , Kobe University Graduate School of Medicine, ; Kobe, Japan

[15 ]The Department of Psychiatry, Self-Defense Forces Hanshin Hospital, Kawanishi, Japan

Author information

Takatomi Kubo http://orcid.org/0000-0003-0192-7177

Aurelio Cortese http://orcid.org/0000-0003-4567-0924

Mitsuo Kawato http://orcid.org/0000-0001-8185-1197

Toshinori Chiba http://orcid.org/0000-0002-2173-4519

Article

Publisher ID: 1696

DOI: 10.1038/s41398-021-01696-x

PMC ID: 8581018

PubMed ID: 34759293

SO-VID: e8cf27cf-96de-4dde-8088-3f489f50a331

License:

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