Self-Reported Mental Health During the COVID-19 Pandemic and its Association with Alcohol and Cannabis Use: A Latent Class Analysis
Bibliographic record
Abstract
Abstract Background: Mental health problems and substance use co-morbidities during the COVID-19 pandemic are a public health priority. Identifying individuals at high-risk of developing these problems can directly 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 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: 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 (high-symptoms). Those at greater risk of being in the high-symptoms class were likely to be women (adjusted odds ratio (aOR) =1.34, 95%CI:1.18-1.52), people worried about getting COVID-19 (aOR=2.39, 95%CI:2.02-2.82), and those with post-secondary education (aOR=1.26, 95%CI:1.02-1.55). Asian ethnicity (aOR=0.78, 95%CI:0.62-0.97), married status (aOR=0.71, 95%CI:0.59-0.85), seniors (aOR=0.38, 95%CI:0.32-0.47), individuals in households with income higher than CAD$40,000: $40,000-$79,000 (aOR=0.73, 95%CI:0.60-0.90), $80,000-$119,000 (aOR=0.60, 95%CI:0.48-0.74) and $120,000+ (aOR=0.47, 95%CI:0.37-0.59) were at lower odds of being in the high-symptoms class. Individuals in the high-symptoms class were more likely to use cannabis at least once a week (aOR=2.25, 95%CI:1.90-2.67), drink alcohol heavily (aOR=1.69, 95%CI:1.47-1.95); and increase the use of cannabis (aOR=3.48, 95%CI:2.79-4.35) and alcohol (aOR=2.37, 95%CI:2.05-2.73) during the pandemic. Women in the high-symptoms class had higher odds of increasing alcohol use than men.Conclusions: We identified the determinants of experiencing high-symptoms of anxiety, depression, and loneliness, 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, particularly regarding alcohol use in women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".