Opioid-related deaths in Kingston, Frontenac, Lennox and Addington in Ontario, Canada: the shadow epidemic
Bibliographic record
Abstract
INTRODUCTION: In the Kingston, Frontenac, Lennox and Addington (KFL&A) health unit, opioid overdoses are an important preventable cause of death. The KFL&A region differs from larger urban centres in its size and culture; the current overdose literature that is focussed on these larger areas is less well suited to aid in understanding the context within which overdoses take place in smaller regions. This study characterized opioidrelated mortality in KFL&A, to enhance understanding of opioid overdoses in these smaller communities. METHODS: We analyzed opioid-related deaths that occurred in the KFL&A region between May 2017 and June 2021. Descriptive analyses (number and percentage) were performed on factors conceptually relevant in understanding the issue, including clinical and demographic variables, as well as substances involved, locations of deaths and whether substances were used while alone. RESULTS: A total of 135 people died of opioid overdose. The mean age was 42 years, and most participants were White (94.8%) and male (71.1%). Decedents often had the following characteristics: being currently or previously incarcerated; using substances alone; not using opioid substitution therapy; and having a prior diagnosis of anxiety and depression. CONCLUSION: Specific characteristics such as incarceration, using alone and not using opioid substitution therapy were represented in our sample of people who died of an opioid overdose in the KFL&A region. A robust approach to decreasing opioid-related harm integrating telehealth, technology and progressive policies including providing a safe supply would assist in supporting people who use opioids and in preventing deaths.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".