Self-reported mental health during the COVID-19 pandemic and its association with alcohol and cannabis use: a latent class analysis

Author(s): Nibene Habib Somé ¹ ^, ² ^, ³ ^, ⁴ ^, ⁵ ^, , Samantha Wells ¹ ^, ² ^, ⁴ ^, ⁵ ^, ⁶ ^, ⁷ , Daniel Felsky ⁵ ^, ⁶ ^, ⁸ ^, ⁹ , Hayley A. Hamilton ¹ ^, ² ^, ⁵ , Shehzad Ali ⁴ ^, ¹⁰ ^, ¹¹ ^, ¹² ^, ¹³ , Tara Elton-Marshall ¹ ^, ² ^, ⁴ ^, ⁵ ^, ¹⁴ ^, ¹⁵ , Jürgen Rehm ¹ ^, ² ^, ⁵ ^, ⁶ ^, ⁸ ^, ¹⁶

Publication date (Electronic): 30 April 2022

Journal: BMC Psychiatry

Publisher: BioMed Central

Keywords: COVID-19, Latent class analysis, Mental health, Alcohol, cannabis

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Abstract

Background

Mental health problems and substance use co-morbidities during and after the COVID-19 pandemic are a public health priority. Identifying individuals at high-risk of developing mental health problems and potential sequela can inform mitigating strategies. We aimed to identify distinct groups of individuals (i.e., latent classes) based on patterns of self-reported mental health symptoms and investigate their associations with alcohol and cannabis use.

Methods

We used data from six successive waves of a web-based cross-sectional survey of adults aged 18 years and older living in Canada (6,021 participants). We applied latent class analysis to three domains of self-reported mental health most likely linked to effects of the pandemic: anxiety, depression, and loneliness. Logistic regression was used to characterize latent class membership, estimate the association of class membership with alcohol and cannabis use, and perform sex-based analyses.

Results

We identified two distinct classes: (1) individuals with low scores on all three mental health indicators (no/low-symptoms) and (2) those reporting high scores across the three measures (high-symptoms). Between 73.9 and 77.1% of participants were in the no/low-symptoms class and 22.9–26.1% of participants were in the high-symptom class. We consistently found across all six waves that individuals at greater risk of being in the high-symptom class were more likely to report worrying about getting COVID-19 with adjusted odds ratios (aORs) between 1.72 (95%CI:1.17–2.51) and 3.51 (95%CI:2.20–5.60). Those aged 60 + were less likely to be in this group with aORs (95%CI) between 0.26 (0.15–0.44) and 0.48 (0.29–0.77) across waves. We also found some factors associated with class membership varied at different time points. Individuals in the high-symptom class were more likely to use cannabis at least once a week (aOR = 2.28, 95%CI:1.92–2.70), drink alcohol heavily (aOR = 1.71, 95%CI:1.49–1.96); and increase the use of cannabis (aOR = 3.50, 95%CI:2.80–4.37) and alcohol (aOR = 2.37, 95%CI:2.06–2.74) during the pandemic. Women in the high-symptom class had lower odds of drinking more alcohol during the pandemic than men.

Conclusions

We identified the determinants of experiencing high anxiety, depression, and loneliness symptoms and found a significant association with alcohol and cannabis consumption. This suggests that initiatives and supports are needed to address mental health and substance use multi-morbidities.

Supplementary information

The online version contains supplementary material available at 10.1186/s12888-022-03917-z.

Related collections

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Author and article information

Contributors

Nibene Habib Somé: Nibene.Some@camh.ca

Journal

Journal ID (nlm-ta): BMC Psychiatry

Journal ID (iso-abbrev): BMC Psychiatry

Title: BMC Psychiatry

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-244X

Publication date (Electronic): 30 April 2022

Publication date PMC-release: 30 April 2022

Publication date Collection: 2022

Volume: 22

Electronic Location Identifier: 306

Affiliations

[1 ]GRID grid.155956.b, ISNI 0000 0000 8793 5925, Institute for Mental Health Policy Research, , Centre for Addiction and Mental Health, ; 100 Collip Circle, Suite 200, ON N6G 4X8 London, Canada

[2 ]GRID grid.155956.b, ISNI 0000 0000 8793 5925, Campbell Family Mental Health Research Institute, , Centre for Addiction and Mental Health, ; Toronto, Ontario Canada

[3 ]GRID grid.418647.8, ISNI 0000 0000 8849 1617, Institute for Clinical Evaluative Sciences, ; Toronto, Ontario Canada

[4 ]GRID grid.39381.30, ISNI 0000 0004 1936 8884, Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, , Western University, ; London, Ontario Canada

[5 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Dalla Lana School of Public Health, , University of Toronto, ; Toronto, Ontario Canada

[6 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Psychiatry, , University of Toronto, ; Toronto, Ontario Canada

[7 ]GRID grid.1021.2, ISNI 0000 0001 0526 7079, School of Psychology, , Deakin University, ; Victoria, Australia

[8 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Institute of Medical Science, , University of Toronto, ; Toronto, Ontario Canada

[9 ]GRID grid.155956.b, ISNI 0000 0000 8793 5925, Krembil Centre for Neuroinformatics, , Centre for Addiction and Mental Health, ; Toronto, Ontario Canada

[10 ]GRID grid.39381.30, ISNI 0000 0004 1936 8884, Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, , Western University, ; London, Canada

[11 ]GRID grid.5685.e, ISNI 0000 0004 1936 9668, Department of Health Sciences, , University of York, ; York, UK

[12 ]GRID grid.1004.5, ISNI 0000 0001 2158 5405, Department of Psychology, , Macquarie University, ; Sydney, Australia

[13 ]GRID grid.418792.1, ISNI 0000 0000 9064 3333, Bruyere Research Institute, ; Ottawa, Canada

[14 ]GRID grid.28046.38, ISNI 0000 0001 2182 2255, School of Epidemiology and Public Health, Faculty of Medicine, , University of Ottawa, ; Ottawa, Canada

[15 ]GRID grid.258900.6, ISNI 0000 0001 0687 7127, Department of Health Sciences, , Lakehead University, ; Thunder Bay, Ontario Canada

[16 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Institute for Clinical Psychology and Psychotherapy, , TU Dresden, ; Dresden, Germany

Article

Publisher ID: 3917

DOI: 10.1186/s12888-022-03917-z

PMC ID: 9055215

PubMed ID: 35490222

SO-VID: 4f8779e7-2f90-4fd5-bfe5-da1e10b57f82

License:

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