An ecological study of the correlation between COVID-19 support payments and overdose events in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Pandemic income support payments have been speculatively linked to an increased incidence of illicit drug poisoning (overdose). However, existing research is limited. METHODS: Collating Canadian Emergency Response Benefit (CERB) payment data with data on paramedic attended overdose and illicit drug toxicity deaths for the province of British Columbia at the Local Health Area (LHA) level, we conducted a correlation analysis to compare overdose rates before, during and after active CERB disbursement. RESULTS: There were 20,014,270 CERB-entitled weeks identified among residents of British Columbia for the duration of the pandemic response program. Approximately 52 % of all CERB entitled weeks in the study were among females and approximately 48 % were among males. Paramedic-attended overdoses increased uniformly across the pre-CERB, CERB and post-CERB periods, while illicit drug toxicity deaths sharply increased and then remained high over the period of the study. Correlation analyses between overdose and CERB-entitled weeks approached zero for both paramedic-attended overdoses and illicit drug toxicity deaths. CONCLUSIONS: These findings suggest that attributing the pandemic increase in overdose to income support payments is unfounded. Sustained levels of unacceptably high non-fatal and fatal drug poisonings that further increased at the start of the pandemic are reflective of complex pre-existing and pandemic-driven changes to overdose 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".