A Retrospective Cohort Study Comparing In-Person and Telemedicine-Based Opioid Agonist Treatment in Ontario, Canada, Using Administrative Health Data
Bibliographic record
Abstract
BACKGROUND: This study evaluated how telemedicine as a modality for opioid agonist treatment compares to in-person care. METHODS: We conducted a retrospective cohort study of patients enrolled in opioid agonist treatment between January 1, 2011, and December 31, 2015, in Ontario, Canada. We compared patients who received opioid agonist treatment predominantly in person, mixed, and predominantly by telemedicine. We used a logistic regression model to evaluate mortality, a Cox proportional hazard model to assess retention, and a negative binomial regression model to evaluate emergency department visits and hospitalizations. The study was performed using administrative health data with physician billing data from the Ontario Health Insurance Plan and prescription data from the Ontario Drug Benefit databases. RESULTS: A total of 55,924 individuals were included in the study. Receiving opioid agonist treatment by predominantly telemedicine was not associated with all-cause mortality (OR = 0.9, 95% CI: 0.8-1.0), 1-year treatment retention (OR = 1.0, 95% CI: 0.9-1.1), or opioid-related emergency department visits and hospitalizations when compared to in-person care. The rate of emergency department visits (IRR = 1.4), the rate of mental health-related emergency department visits (IRR = 1.5), and the rate of mental health-related hospitalizations per year (IRR = 1.2) was higher for patients who received opioid agonist treatment predominantly by telemedicine compared to in person. CONCLUSION: Our findings support the conclusion that telemedicine is equal to in-person care regarding mortality opioid-related emergency department visits and retention, and is a viable option for those seeking opioid agonist treatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| 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".