Socioeconomic factors and substances involved in poisoning-related emergency department visits in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Canada's opioid crisis has taken thousands of lives, increasing awareness of poisoning-related injuries as an important public health issue. However, in British Columbia (BC), where overdose mortality rates are the highest in Canada, studies have not yet identified which demographic populations most often visit emergency departments (ED) due to all poisonings, nor which substances are most commonly involved. The aim of this study was to explore these gaps, after developing a methodology for calculating ED visit rates in BC. METHODS: Poisoning-related ED visit rates during fiscal years 2012/13 to 2016/17, inclusive, were calculated by sex, age group, poisoning substance and socioeconomic status, using a novel methodology developed in this study. ED data were sourced from the National Ambulatory Care Reporting System and population data from Statistics Canada's 2016 (or 2011) census profiles. RESULTS: During the study period, there were an estimated 81 463 poisoning-related ED visits (351.2 per 100 000 population). Infants, toddlers, youth and those aged 20-64 years had elevated risks of poisoning-related ED visits. Rates were highest among those in neighbourhoods with the greatest material (607.8 per 100 000 population) or social (484.2 per 100 000 population) deprivation. Over time, narcotics and psychodysleptics became increasingly common poisoning agents, while alcohol remained problematic. CONCLUSION: A methodology for estimating ED visit rates in BC was developed and applied to determine poisoning-related ED visit rates among various demographic groups within BC. British Columbians most vulnerable to poisoning have been identified, emphasizing the need for efforts to limit drug overdoses and excessive alcohol intoxication to reduce rates of these preventable injuries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".