Patterns of health care utilization among people who overdosed from illegal drugs: a descriptive analysis using the BC Provincial Overdose Cohort
Bibliographic record
Abstract
INTRODUCTION: British Columbia (BC) declared a public health emergency in April 2016 in response to a rapid rise in overdose deaths. Further understanding of health care utilization is needed to inform prevention strategies for individuals who overdose from illegal drugs. METHODS: The Provincial Overdose Cohort includes linked administrative data on health care utilization by individuals who experienced an illegal drug overdose event in BC between 1 January 2015 and 30 November 2016. Overdose cases were identified using data from ambulance services, coroners' investigations, poison control centre calls and hospital, emergency department and physician administrative records. In total, 10 455 overdose cases were identified and compared with 52 275 controls matched on age, sex and area of residence for a descriptive analysis of health care utilization. RESULTS: Two-thirds (66%) of overdose cases were male and about half (49%) were 20-39 years old. Over half of the cases (54%) visited the emergency department and about one-quarter (26%) were admitted to hospital in the year before the overdose event, compared with 17% and 9% of controls, respectively. Nevertheless, nearly onefifth (19%) of cases were recorded leaving the emergency department without being seen or against medical advice. High proportions of both cases (75%) and controls (72%) visited community-based physicians. Substance use and mental health-related concerns were the most common diagnoses among people who went on to overdose. CONCLUSION: People who overdosed frequently accessed the health care system in the year before the overdose event. In light of the high rates of health care use, there may be opportunities to identify at-risk individuals before they overdose and connect them with targeted programs and evidence-based interventions. Further work using the BC Provincial Overdose Cohort will focus on identifying risk factors for overdose events and death by 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".