Development and characteristics of the Provincial Overdose Cohort in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: British Columbia (BC), Canada declared a public health emergency in April 2016 for opioid overdose. Comprehensive data was needed to identify risk factors, inform interventions, and evaluate response actions. We describe the development of an overdose cohort, including linkage strategy, case definitions, and data governance model, and present the resulting characteristics, including data linkage yields and case overlap among data sources. METHODS: Overdose events from hospital admissions, physician visits, poison centre and ambulance calls, emergency department visits, and coroner's data were grouped into episodes if records were present in multiple sources. A minimum of five years of universal health care records (all prescription dispensations, fee-for-service physician billings, emergency department visits and hospitalizations) were appended for each individual. A 20% random sample of BC residents and a 1:5 matched case-control set were generated. Consultation and prioritization ensured analysts worked to address questions to directly inform public health actions. RESULTS: 10,456 individuals suffered 14,292 overdoses from January 1, 2015 to Nov 30, 2016. Only 28% of overdose events were found in more than one dataset with the unique contribution of cases highest from ambulance records (32%). Compared with fatal overdoses, non-fatal events more often involved females, younger individuals (20 to 29 years) and those 60 or older. In 78% of illegal drug deaths, there was no associated ambulance response. In the year prior to first recorded overdose, 60% of individuals had at least one ED visit, 31% at least one hospital admission, 80% at least one physician visit, and 87% had filled at least one prescription in a community pharmacy. CONCLUSION: While resource-intensive to establish, a linked cohort is useful for characterizing the full extent of the epidemic, defining sub-populations at risk, and patterns of contact with the health system. Overdose studies in other jurisdictions should consider the inclusion of multiple data sources.
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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 | 0.000 |
| 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".