Independent associations and effect modification between lifetime substance use and recent mood disorder diagnosis with household food insecurity
Bibliographic record
Abstract
Poor mental health and substance use are associated with food insecurity, however, their potential combined effects have not been studied. This study explored independent associations and effect modification between lifetime substance use and mood disorder in relation to food insecurity. Poisson regression analysis of data from British Columbia respondents (n = 13,450; 12 years+) in the 2007/08 Canadian Community Health Survey was conducted. Measures included The Household Food Security Survey Module (7.3% food insecure), recent diagnosis of a mood disorder (self-reported; 9.5%), lifetime use of cannabis, cocaine/crack, ecstasy, hallucinogens, and speed, any lifetime substance use, sociodemographic covariates, and the interaction terms of mood disorder by substance. For those with recent diagnosis of a mood disorder the prevalence of lifetime substance use ranged between 1.2 to 5.7% and were significantly higher than those without recent mood disorder diagnosis or lifetime use of substances (p's < 0.05). For respondents with a recent mood disorder diagnosis or who used cannabis, food insecurity prevalence was higher compared to the general sample (p < 0.001); prevalence was lower for cocaine/crack use (p < 0.05). Significant effect modification was found between mood disorder with cannabis, ecstasy, hallucinogen and any substance use over the lifetime (PRs 0.51 to 0.64, p's 0.022 to 0.001). Independent associations were found for cocaine/crack and speed use (PRs 1.68, p's < 0.001) and mood disorder (PRs 2.02, p's < 0.001). Based on these findings and the existing literature, future study about coping and resilience in the context of substance use, mental health, and food insecurity may lead to the development of relevant interventions aimed at mental well-being and food security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".