Opioid-related deaths in Northern Ontario in the early COVID-19 pandemic period
Bibliographic record
Abstract
OBJECTIVES: In the first year of pandemic measures, opioid-related deaths across Ontario's (ON) 34 public health units (PHUs) increased by 60%. Death rates for all seven Northern ON PHUs were above the provincial average. This study describes and compares factors surrounding opioid-related deaths before and after pandemic measures were introduced, for Northern ON compared to the rest of ON. METHODS: Aggregate data were provided for Northern ON and the rest of the province by the Office of the Chief Coroner/Ontario Forensic Pathology Services. Opioid-related deaths were cohorted by date of death for the year before and after pandemic measures were introduced on March 16, 2020. Chi-square tests were used to compare between cohorts and geographies to determine significant differences for each variable, and for dichotomized levels within variables. P-values < 0.05 were considered statistically significant a priori. RESULTS: In Northern ON, the number of opioid-related deaths approximately doubled from the pre-pandemic cohort (n = 185) to the early pandemic cohort (n = 365). Compared to the rest of ON, higher proportions of deaths occurred in Northern ON among individuals who lived and died in private residences, among women (although the majority of decedents were male) and among individuals employed in mining, quarrying, and oil and gas industries. Compared to the pre-pandemic year, in Northern ON, higher proportions of opioid-related deaths involved fentanyl and stimulants as direct contributors, and the majority involved evidence of inhaled drugs. CONCLUSION: Differences between the circumstances of death in Northern ON and in the rest of ON suggest opportunities to tailor interventions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".