Incidence and predictors of mental health disorder diagnoses among people who inject drugs in a Canadian setting
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Limited attention has been given to the predictors of mental health diagnoses among people who inject drugs (PWID) in community settings. Therefore, we sought to longitudinally examine the prevalence, incidence and predictors of mental disorder diagnosis among a community-recruited cohort of PWID. DESIGN AND METHODS: Data were derived from two prospective cohort studies of PWID (VIDUS and ACCESS) in Vancouver, Canada between December 2005 and May 2015. We used multivariable extended Cox regression to identify factors independently associated with self-reported mental disorder diagnosis during follow-up among those without a history of such diagnoses at baseline. RESULTS: Among the 923 participants who did not report a mental disorder at baseline, 206 (22.3%) reported a first diagnosis of a mental disorder during follow-up for an incidence density of 4.29 [95% confidence interval (CI) 3.72-4.91] per 100 person-years. In the multivariable analysis, female sex [adjusted hazards ratio (AHR) = 1.74, 95% CI 1.29-2.33], experiencing non-fatal overdose (AHR = 2.33, 95% CI 1.38-3.94), accessing any drug or alcohol treatment (AHR = 1.68, 95% CI 1.24-2.27), accessing any community health or social services (AHR = 1.53, 95% CI 1.02-2.28) and experiencing violence (AHR = 1.60, 95% CI 1.12-2.29) were independently associated with a mental disorder diagnosis at follow-up. DISCUSSION AND CONCLUSIONS: We observed a high prevalence and incidence of mental disorders among our community-recruited sample of PWID. The validity and implication of these diagnoses for key substance use and public health outcomes are an urgent priority.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".