Prevalence and Temporal Trends of Mental Disorders in Persons with Opioid Use Disorder and Concurrent Mental Disorders in British Columbia, Canada, Using Population-Level Administrative Data, 2013 to 2021: Prévalence et tendances temporelles des troubles mentaux chez les personnes souffrant d’un trouble lié à la consommation d’opioïdes et de troubles mentaux concomitants en Colombie-Britannique, au Canada, à partir de données administratives au niveau de la population, entre 2013 et 2021
Bibliographic record
Abstract
ObjectiveOpioid use is a major public health issue and associated with a broad range of comorbid mental disorders. Globally, there is considerable variability in reported rates of mental disorders among individuals with opioid use disorder (OUD), limiting timely intervention and evidence-based treatment among this population. We estimate the prevalence of specific mental disorders among individuals with a concurrent OUD using population-level administrative data in British Columbia, Canada.MethodA population-based retrospective observational study using individual-level linked health administrative data in British Columbia, Canada. Individuals with an OUD and concurrent mental disorder between January 1, 2013, and August 31, 2021, were included and followed from their first indication of OUD until censoring (death, administrative loss to follow-up, or August 31, 2021). We reported annual period (2013-2021) prevalence rates and age-standardized prevalence rates per 100,000 population (stratified by sex).ResultsThe population included 73,855 individuals (female 40.6%, median age, 36 [27-48]) with an OUD and concurrent mental disorder. During the observation period anxiety disorders were the most prevalent (91.7%) mental disorders followed by depression (73.6%), bipolar disorder (35.3%), schizophrenia spectrum disorders (20.4%), and personality disorders (19.5%). Among the population, the annual period prevalence of any mental disorder increased from 35,603 in 2013 to 60,940 in 2021, with an average annual percent difference of 7.0%, driven by increases in schizophrenia spectrum disorders and attention deficit/hyperactivity disorder. Overall, the annual age-standardized prevalence of any mental disorder was higher among males.ConclusionsOur findings demonstrate a steadily growing prevalence of people with OUD and a concurrent mental disorder and emphasize the need for access to mental disorder treatment among this population. Estimating specific mental disorder prevalence is a pragmatic step toward informing clinical guidelines, service needs, and health system planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".