Substance-Related Acute Toxicity Deaths by Area-Based Characteristics: A Descriptive Analysis of a National Chart Review Study of Coroner and Medical Examiner Data
Bibliographic record
Abstract
Abstract Over the last decade, Canada has experienced a substantial increase in people dying from substance-related acute toxicity. Examining mortality rates by area-level characteristics can identify disproportionately affected populations and inform strategies to reduce substance-related acute toxicity deaths (ATDs). Using area-based methods, this study sought to examine substance-related acute toxicity mortality rates for varying community population sizes, levels of community remoteness, and indicators of deprivation in Canada from 2016 to 2017. Age-standardized mortality rates and rate ratios were calculated and disaggregated by sex. Mortality rates were highest in mid-sized urban communities with populations of 100,000 to 499,999 residents (15.9 per 100,000 population), followed by larger cities of 500,000 to 1,499,999 (15.1 per 100,000 population). The distribution of people who died also varied by community remoteness, with the highest mortality rates observed in accessible areas (14.9 per 100,000 population), followed by very remote areas (14.7 per 100,000 population). Neighbourhoods with the highest levels of deprivation, including high residential instability, economic dependency, and situational vulnerability, experienced 1.5 to 3.2 times more ATDs compared to neighbourhoods with the lowest levels of deprivation. Reported trends were similar among males and females, with higher mortality rates for males across all area-level characteristics. This study provides novel evidence on the context surrounding deaths to inform responses to reduce ATDs in Canada and serves as an important baseline that can be used to measure future progress.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".