Development and Characteristics of the Provincial Overdose Cohort in British Columbia, Canada
Bibliographic record
Abstract
IntroductionBritish Columbia has the highest rate of opioid overdose in Canada, driven by the use of illegal opioids such as fentanyl. In addition to ongoing surveillance, there is a need for more comprehensive data to identify risk factors, inform the development of interventions, and evaluate the public health emergency response. Objectives and ApproachThe Provincial Overdose Cohort is a linked administrative dataset based on information from hospital admissions, physician visits, prescription dispensations, poison centre calls, ambulance, emergency department, coroner’s data, and First Nations Client File. Overdoses in the province were identified for the period January 2015-November 2016. Overdoses occurring within a 24 hour period across data sources were grouped as a single episode. For identified cases and for a control population (a 20% random sample of the BC residents), health care and prescribing history was appended dating back to 2010. Initial analyses were conducted based on a prioritization process with knowledge users. ResultsIntegration of distinct data sources about overdose events provided a more complete understanding of the extent of the opioid crisis than use of a single dataset alone. Between January 1, 2015 and November 30, 2016 10,456 overdoses occurred in BC. Overdose deaths represented only 13% of individuals overdosing; 54% of all overdoses were captured through ambulance records and 46\% through emergency and hospital records, with some overlap between the datasets. Most cases had contact with the health care system in the year before overdose suggesting opportunities for intervention. Some demographic differences were noted when comparing fatal and non-fatal overdoses, but few differences in health or prescribing histories were identifiable using administrative data. Conclusion/ImplicationsThe Provincial Overdose Cohort is a uniquely comprehensive dataset in a jurisdiction at the forefront of the opioid overdose response. Jurisdictions developing surveillance systems should consider the inclusion of ambulance, emergency room and hospital data in order to more completely characterize the population at risk.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".