Using Linked Data to Identify Pathways of Reporting Overdose Events in British Columbia, 2015 - 2017
Bibliographic record
Abstract
Introduction: Overdose events related to illicit opioids and other substances are a public health crisis in Canada. The BC Provincial Overdose Cohort is a collection of linked datasets identifying drug-related toxicity events, including death, ambulance, emergency room, hospital, and physician records. The datasets were brought together to understand factors associated with drug-related overdose and can also provide information on pathways of care among people who experience an overdose. Objectives: To describe pathways of recorded healthcare use for overdose events in British Columbia, Canada and discrepancies between data sources. Methods: Using the BC Provincial Overdose Cohort spanning 2015 to 2017, we examined pathways of recorded health care use for overdose through the framework of an injury reporting pyramid. We also explored differences in event capture between linked datasets. Results: In the cohort, a total of 34,113 fatal and non-fatal overdose events were identified. A total of 3,056 people died of overdose. Nearly 80% of these deaths occurred among those with no contact with the healthcare system. The majority of events with healthcare records included contact with EHS services (72%), while 39% were seen in the ED and only 7% were hospitalized. Pathways of care from EHS services to ED and hospitalization were generally observed. However, not all ED visits had an associated EHS record and some hospitalizations following an ED visit were for other health issues. Conclusions: These findings emphasize the importance of accessing timely healthcare for people experiencing overdose. These findings can be applied to understanding pathways of care for people who experience overdose events and estimating the total burden of healthcare-attended overdose events. Highlights: In British Columbia, Canada:Multiple sources of linked administrative health data were leveraged to understand recorded healthcare use among people with fatal and non-fatal overdose eventsThe majority of fatal overdose events occurred with no contact with the healthcare system and only appear in mortality dataMany non-fatal overdose events were captured in data from emergency health services, emergency departments, and hospital recordsAccessing timely healthcare services is critical for people experiencing overdose.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".