02 Leveraging paramedic data to investigate the effect of COVID-19 on community opioid overdoses
Bibliographic record
Abstract
Background Opioid overdoses in Canada have shown dramatic increases over recent years, despite significant investments in harm reduction. Most community monitoring currently relies on emergency department and coroner data. Our team has previously shown that paramedic data can be a useful addition to the current metrics as paramedics regularly interact with opioid overdose patients. This study examines paramedic data to investigate the changes to community opioid overdoses in the era of COVID-19 to better support our strategic partners in their battle against the opioid crisis. Methods The electronic ambulance call report database of Peterborough Paramedics (Ontario, Canada) was examined. De-identified records for patients from 2017-2020 with documented problem codes of ‘Opioid Overdose’ were extracted. Patients receiving paramedic naloxone were also included. The data was cleaned and analysed, and incomplete records were removed. Statistical models including chi-squared tests of goodness-of-fit and post hoc pairwise t-tests were applied to the data. Ethics approval for this study was granted by the Trent University’s Research Ethics Board. Results 788 opioid overdoses were identified out of 72,737 patients. There were 263 opioid overdoses found in 2020 representing 1.4% patients, a significant increase from 2017-2019 (p value: 0.006). The proportion of patients receiving paramedic naloxone was significantly increased from previous years (p value: 0.005) while bystander naloxone administration was significantly decreased (p value 0.002). Age, gender, and pick-up location types were not significantly different between 2020 and previous years. Conclusion Despite reduced overall call volumes in 2020, paramedics observed an increase in opioid overdoses. The increase in paramedic naloxone administration and decrease in bystander naloxone administration may indicate changes in usage practices of community opioid users or an instability in the drug supply. These factors must be considered in future opioid harm reduction strategies and public health COVID-19 containment measures.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".