Trends in Hospitalizations for Serious Infections Among People With Opioid Use Disorder in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Opioid use among people who inject drugs can lead to serious complications, including infections. We sought to study trends in rates of these complications among people with an opioid use disorder (OUD) and the sequelae of those hospitalizations. METHODS: We analyzed all inpatient hospitalizations for serious infections (infective endocarditis [IE], spinal infections, nonvertebral bone infections, and skin or soft tissue infections) among people with OUD in Ontario between 2013 and 2019. We reported the population adjusted rate of hospitalizations for serious infections annually, stratified by type of infection and prevalence of prior opioid agonist therapy and hydromorphone prescribing. We reported characteristics of hospitalizations and 30-day mortality in the most recent 2 years. RESULTS: Among people with OUD there was a 167% increase in rates of IE (7.7-20.6 per million residents; P < 0.01), a 394% increase in rates of spinal infections (3.4-16.8 per million residents; P < 0.01), a 191% increase in rates of nonvertebral bone infections (8.9 to 25.9 per million residents; P < 0.01), and a 147% increase in infections of the skin or soft tissue (32.1-79.4 per million residents; P < 0.01) over 7 years in Ontario. Death in-hospital and within 30 days of discharge was highest among those with IE (11.5% and 15.9%, respectively), and lower among those with other infections (<5%). CONCLUSIONS: Rates of serious infections among people with OUD are rising, placing a significant burden on patients. These findings suggest that early intervention and treatment of infections in this population are needed to prevent downstream harm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".