Preexisting mental health disorders and risk of opioid use disorder in young people: A case‐control study
Bibliographic record
Abstract
AIM: Opioid use disorder (OUD) is a leading cause of preventable mortality amongst young people worldwide. Early identification and intervention of modifiable risk factors may reduce future OUD risk. The aim of this study was to explore whether the onset of OUD is associated with preexisting mental health conditions such as anxiety and depressive disorders in young people. METHODS: A retrospective, population-based case-control study was conducted from 31 March 2018 until 01 January 2002. Provincial administrative health data were collected from Alberta, Canada. CASES: Individuals 18-25 years on 01 April 2018, with a previous record of OUD. CONTROLS: Individuals without OUD were matched to cases, on age/sex/index date. Conditional logistic regression analysis was used to control for additional covariates (e.g., alcohol-related disorders, psychotropic medications, opioid analgesics, and social/material deprivation). RESULTS: We identified N = 1848 cases and N = 7392 matched controls. After adjustment, OUD was associated with the following preexisting mental health conditions: Anxiety disorders, aOR = 2.53 (95% CI = 2.16-2.96); depressive disorders, aOR = 2.20 (95% CI = 1.80-2.70); alcohol-related disorders, aOR = 6.08 (95% CI, 4.86-7.61); anxiety and depressive disorders, aOR = 1.94 (95% CI = 1.56-2.40); anxiety and alcohol-related disorders, aOR = 5.22 (95% CI = 4.03-6.77); depressive and alcohol-related disorders, aOR = 6.47 (95% CI = 4.73-8.84); anxiety, depressive and alcohol-related disorders, aOR = 6.09 (95% CI = 4.41-8.42). DISCUSSION: Preexisting mental health conditions such as anxiety and depressive disorders are risk factors for future OUD in young people. Preexisting alcohol-related disorders showed the strongest association with future OUD and demonstrated an additive risk when concurrent with anxiety/depression. As not all plausible risk factors could be examined, more research is still needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".