Evidence map›Paper›PMID 35490222›Full record

ArticleBMC psychiatry2022

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

Nibene Habib Somé, Samantha Wells, Daniel Felsky, Hayley A Hamilton, Shehzad Ali, Tara Elton-Marshall, Jürgen Rehm

Open access · goldAbstract read
In one paragraph

Article in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
5.6field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 29 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 4 countries.

Nibene Habib SoméInstitute for Mental Health Policy Research, Centre for Addiction and Mental Health, 100 Collip Circle, Suite 200, ON, N6G 4X8, London, Canada. Nibene.Some@camh.ca.
Samantha WellsInstitute for Mental Health Policy Research, Centre for Addiction and Mental Health, 100 Collip Circle, Suite 200, ON, N6G 4X8, London, Canada.
Daniel FelskyDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Hayley A HamiltonInstitute for Mental Health Policy Research, Centre for Addiction and Mental Health, 100 Collip Circle, Suite 200, ON, N6G 4X8, London, Canada.
Shehzad AliDepartment of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
Tara Elton-MarshallInstitute for Mental Health Policy Research, Centre for Addiction and Mental Health, 100 Collip Circle, Suite 200, ON, N6G 4X8, London, Canada.
Jürgen RehmInstitute for Mental Health Policy Research, Centre for Addiction and Mental Health, 100 Collip Circle, Suite 200, ON, N6G 4X8, London, Canada.
University of Toronto · CACentre for Addiction and Mental Health · CATU Dresden · DEWestern University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMental 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.

methodsWe 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.

resultsWe 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.

conclusionsWe 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.

Indexed as

CannabisCOVID-19Substance-Related DisordersAdultCross-Sectional StudiesFemaleHumansLatent Class AnalysisMaleMental HealthPandemicsSelf ReportAlcoholcannabisCOVID-19Latent class analysisMental health

Identifiers

PMID35490222
PMCPMC9055215
OpenAlexW4225275375

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.