Provincial Overdose Cohort: Population Data Linkage During an Overdose Crisis
Bibliographic record
Abstract
IntroductionIn 2016, the Provincial Overdose Cohort (ODC) was created following the declaration of the public health emergency in British Columbia (BC), Canada. The ODC is a set of longitudinal and linked administrative data which identifies illicit drug-related overdose events, including death, ambulance, emergency room, hospital, physician, and prescription drug records. Objectives and ApproachThe ODC was developed to better understand factors associated with overdose in order to support response activities, prevent overdose deaths, and identify trends and opportunities for interventions. Person-level linkages were conducted using provincial health insurance and health history data; socio-economic information, mental and physical illness diagnoses, and corrections history were also appended. The ODC currently includes people who have had a drug-related overdose between January 1 st 2015 and December 31 st 2017 as well as a 20% random sample of the general population. ResultsThe ODC contains 36,576 overdose episodes and 23,161 people who have a drug-related overdose between January 1 st , 2015 and December 31 st 2017. Of the 23,161 people, 3,604 (15.6%) had a fatal overdose and 19,557 (84.4%) had a non-fatal overdose. 49.9% of people in BC who had an overdose were 20-39 years of age and 67.4% were males. From 2015 to 2017, the proportion of people experiencing 3 or more overdoses a year increased from 3.6% to 8.7%, respectively. There were an increasing number of fatal and non-fatal drug-related overdoses in BC during this time period. Conclusion / ImplicationsLarge population data linkages can be invaluable tools during a public health emergency. Collaborative partnerships and a shared data governance across jurisdictions was central in building the ODC and understanding how various social determinants of health impact risk of overdose among people who use drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".