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Different Mental Health Responses to the COVID-19 Pandemic: Latent Class Trajectory Analysis Using Longitudinal UK Data

25 Pages Posted: 17 Feb 2021

See all articles by Matthias Pierce

Matthias Pierce

The University of Manchester - Centre for Women’s Mental Health

Sally McManus

National Centre for Social Research (NatCen)

Holly Hope

The University of Manchester - Centre for Women’s Mental Health

Matthew Hotopf

King’s College London - Department of Psychological Medicine

Tamsin Ford

University of Cambridge - Department of Psychiatry

Stephani Hatch

King’s College London - Department of Psychological Medicine

Ann John

Swansea University - Population Data Science

Evangelos Kontopantelis

University of Manchester - Division of Informatics

Roger T. Webb

The University of Manchester - Division of Psychology and Mental Health; The University of Manchester - NIHR Greater Manchester Patient Safety Translational Research Centre (Greater Manchester PSTRC)

Simon Wessely

King’s College London - Department of Psychological Medicine

Kathryn Abel

The University of Manchester - Centre for Women’s Mental Health; The University of Manchester - Manchester Academic Health Science Centre (MAHSC)

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Abstract

Background: The UK population’s mental health declined at the pandemic onset. Convenience sample surveys indicate recovery began soon after. Using a probability sample, we tracked average mental health during the pandemic, characterised distinct mental health trajectories and identified predictors of deterioration.

Methods: Secondary analysis of five waves of UK Household Longitudinal Survey from late April-early October 2020 and pre-pandemic data, 2018-2019. Mental health was assessed in 19,763 adults (≥16 years) using 12-item General Health Questionnaire. Latent class growth models identified discrete mental health trajectories and fixed-effects regression identified predictors of change in mental health.

Findings: Average population mental health deteriorated with onset of the pandemic and did not begin improving until July 2020. Latent class analysis identified six distinct mental health trajectories up to October 2020. Three-quarters had consistently good (46·2%) or very good (30·9%) mental health. Two ‘recovery’ groups (15·8%) initially experienced marked declines in mental health, improving to their pre-pandemic levels by October. For 4·8%, mental health steadily deteriorated and for 2·3% it was very poor throughout. These two groups were more likely to have pre-existing mental or physical ill-health, live in deprived neighbourhoods and be non-white. Infection with COVID-19, local lockdown and financial difficulties all predicted subsequent mental health deterioration.

Interpretation: Between April-October 2020, the mental health of most UK adults remained resilient or returned to pre-pandemic levels. One-in-fourteen experienced deteriorating or consistently poor mental health. People living in areas affected by lockdown, struggling financially, with pre-existing conditions or COVID infection might benefit most from early intervention.

Funding Statement: None.

Declaration of Interests: None.

Ethics Approval Statement: Ethics approval was granted by the University of Essex Ethics Committee for the COVID-19 web and telephone surveys (ETH1920-1271).

Keywords: COVID-19; trajectories; mental health; inequalities

Suggested Citation

Pierce, Matthias and McManus, Sally and Hope, Holly and Hotopf, Matthew and Ford, Tamsin and Hatch, Stephani and John, Ann and Kontopantelis, Evangelos and Webb, Roger T. and Wessely, Simon and Abel, Kathryn, Different Mental Health Responses to the COVID-19 Pandemic: Latent Class Trajectory Analysis Using Longitudinal UK Data. Available at SSRN: https://ssrn.com/abstract=3784647 or http://dx.doi.org/10.2139/ssrn.3784647

Matthias Pierce (Contact Author)

The University of Manchester - Centre for Women’s Mental Health

United Kingdom

Sally McManus

National Centre for Social Research (NatCen) ( email )

London EC1V OAX
United Kingdom

Holly Hope

The University of Manchester - Centre for Women’s Mental Health ( email )

United Kingdom

Matthew Hotopf

King’s College London - Department of Psychological Medicine

Strand
London, England WC2R 2LS
United Kingdom

Tamsin Ford

University of Cambridge - Department of Psychiatry

Herchel Smith Bldg
Robinson Way
Cambridge, CB2 0SZ
United Kingdom

Stephani Hatch

King’s College London - Department of Psychological Medicine

Strand
London, England WC2R 2LS
United Kingdom

Ann John

Swansea University - Population Data Science ( email )

Singleton Park
Swansea, Wales SA2 8PP
United Kingdom

Evangelos Kontopantelis

University of Manchester - Division of Informatics

United Kingdom

Roger T. Webb

The University of Manchester - Division of Psychology and Mental Health ( email )

United Kingdom

The University of Manchester - NIHR Greater Manchester Patient Safety Translational Research Centre (Greater Manchester PSTRC) ( email )

United Kingdom

Simon Wessely

King’s College London - Department of Psychological Medicine

Strand
London, England WC2R 2LS
United Kingdom

Kathryn Abel

The University of Manchester - Centre for Women’s Mental Health

United Kingdom

The University of Manchester - Manchester Academic Health Science Centre (MAHSC) ( email )

46 Grafton Street
Manchester, M13 9NT
United Kingdom