A cohort study evaluating the association between concurrent mental disorders, mortality, morbidity, and continuous treatment retention for patients in opioid agonist treatment (OAT) across Ontario, Canada, using administrative health data
Bibliographic record
Abstract
Abstract Background Due to the high prevalence of mental disorders among people with opioid use disorder, the objective of this study was to determine the association between concurrent mental disorders, mortality, morbidity, and continuous treatment retention for patients in opioid agonist treatment in Ontario, Canada. Methods We conducted a retrospective cohort study of patients enrolled in opioid agonist treatment between January 1, 2011, and December 31, 2015. Patients were stratified into two groups: those diagnosed with concurrent mental disorders and opioid use disorder and those with opioid use disorder only, using data from the Ontario Health Insurance Plan Database, Ontario Drug Benefit Plan Database. The primary outcome studied was all-cause mortality using data from the Registered Persons Database. Emergency department visits from the National Ambulatory Care Database, hospitalizations Discharge Abstract Database, and continuous retention in treatment, defined as 1 year of uninterrupted opioid agonist treatment using data from the Ontario Drug Benefit Plan Database were measured as secondary outcomes. Encrypted patient identifiers were used to link information across databases. Results We identified 55,924 individuals enrolled in opioid agonist treatment, and 87% had a concurrent mental disorder diagnosis during this period. We observed that having a mental disorder was associated with an increased likelihood of all-cause mortality (odds ratio (OR) 1.4; 95% confidence interval (CI) 1.2–1.5). For patients diagnosed with mental disorders, the estimated rate of ED visits per year was 2.25 times higher and estimated rate of hospitalization per year was 1.67 times higher than for patients with no mental disorders. However, there was no association between having a diagnosis of a mental disorder and 1-year treatment retention in OAT-adjusted hazard ratio (HR) = 1.0; 95% CI 0.9 to 1.1. Conclusion Our findings highlight the consequences of the high prevalence of mental disorders for individuals with opioid use disorder in Ontario, Canada.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".