The impact of poisoning in British Columbia: a cost analysis
Bibliographic record
Abstract
BACKGROUND: Poisoning, from substances such as illicit drugs, prescribed and over-the-counter medications, alcohol, pesticides, gases and household cleaners, is the leading cause of injury-related death and the second leading cause for injury-related hospital admission in British Columbia. We examined the health and economic costs of poisoning in BC for 2016, using a societal perspective, to support public health policies aimed at minimizing losses to society. METHODS: Costs by intent, sex and age group were calculated in Canadian dollars using a classification and costing framework based on existing provincial injury data combined with data from the published literature. Direct cost components included fatal poisonings, hospital admissions, emergency department visits, ambulance attendance without transfer to hospital and calls to the British Columbia Drug and Poison Information Centre (BC DPIC) not resulting in ambulance attendance, emergency care or transfer to hospital. Indirect costs, measured as loss of earnings and informal caregiving costs, were also calculated. RESULTS: We estimate that poisonings in BC totalled $812.5 million in 2016 with $108.9 million in direct health care costs and $703.6 million in indirect costs. Unintentional poisoning injuries accounted for 84% of total costs, 46% of direct costs and 89% of indirect costs. Males accounted for higher proportions of direct costs for all patient dispositions except hospital admissions. Patients aged 25-64 years accounted for higher proportions of direct costs except for calls to BC DPIC, where proportions were highest for children younger than 15 years. INTERPRETATION: Hospital care expenditures represented the largest direct cost of poisoning, and lost productivity following death represented the largest indirect cost. Quantifying and understanding the financial burden of poisoning has implications not only for government and health care, but also for society, employers, patients and families.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".