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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".