Comorbidity of Major Depression with Substance Use Disorders
Bibliographic record
Abstract
OBJECTIVES: In the Canadian adult population, we aimed to 1) estimate the 12-month prevalence of major depressive disorder (MDD) in persons with a diagnosis of harmful alcohol use, alcohol dependence, and drug dependence; 2) estimate the 12-month prevalence of harmful alcohol use, alcohol dependence, and drug dependence in persons with a 12-month and lifetime diagnosis of MDD; 3) identify socioeconomic correlates of substance use disorder-major depression comorbidity; 4) determine how comorbidity impacts the prevalence of suicidal thoughts; and 5) determine how comorbidity affects mental health care used. METHODS: We examined data from the Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2). RESULTS: The 12-month prevalences of MDD in persons with a substance use disorder (SUD) were 6.9% for harmful alcohol use (95% confidence interval [CI], 5.2 to 8.5), 8.8% for alcohol dependence (95%CI, 6.6 to 11.0), and 16.1% for drug dependence (95%CI, 10.3 to 21.9). Conversely, the 12-month prevalences of harmful alcohol use, alcohol dependence, and drug dependence in persons with a 12-month diagnosis of MDD were 12.3% (95%CI, 9.4 to 15.2), 5.8% (95%CI, 4.3 to 7.3), and 3.2% (95%CI, 2.0 to 4.4), respectively. Regression modelling did not identify any socioeconomic predictors of SUD-MDD comorbidity. Substance dependence and MDD independently predicted higher prevalence of suicidal thoughts and mental health treatment use. CONCLUSIONS: SUDs cooccur with a high frequency in cases of MDD. Clinicians and mental health services should consider routine assessment of SUDs in depression patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".