Quantifying the escalating impact of paramedic transported emergency department visits for opioid-related conditions in Ontario, Canada: A population-based cohort study
Bibliographic record
Abstract
INTRODUCTION: While overdoses comprise the majority of opioid research, the comprehensive impact of the opioid crisis on emergency departments (EDs) and paramedic services has not been reported. We examined temporal changes in population-adjusted incidence rates of ED visits and paramedic transports due to opioid-related conditions. MATERIALS AND METHODS: We conducted a population-based cohort study of all ED visits in the National Ambulatory Care Reporting System from January 1, 2009 to December 31, 2019 in Ontario, Canada. We included all patients with a primary diagnosis naming opioids as the underlying cause for the visit, without any other drugs or substances. We clustered geographic regions using Local Health Integration Network boundaries. Descriptive statistics, incidence rate ratios (IRR) and 95% confidence intervals (CIs) were calculated to analyze population-adjusted temporal changes. RESULTS: Overall, 86,403 ED visits were included in our study. Incidence of opioid-related ED visits increased by 165% in the study timeframe, with paramedic transported patients increasing by 429%. Per 100,000 residents, annual ED visits increased from 40.4 to 97.2, and paramedic transported patients from 12.1 to 67.9. The proportion of opioid-related ED visits transported by paramedics increased from 35.0% to 69.9%. The medical acuity of opioid-related ED visits increased throughout the years (IRR 6.8. 95% CI 5.9-7.7), though the proportion of discharges remained constant (~75%). The largest increases in ED visits and paramedic transports were concentrated to urbanized regions. DISCUSSION: Opioid-related ED visits and paramedic transports increased substantially between 2009 and 2019. The proportion of ED visits transported by paramedics doubled. Our findings could provide valuable support to health stakeholders in implementing timely strategies aimed at safely reducing opioid-related ED visits. The increased use of paramedics followed by high rates of ED discharge calls for exploration of alternative care models within paramedic systems, such as direct transport to specialized substance abuse centres.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".