Suicide and drug toxicity mortality in the first year of the COVID-19 pandemic: use of medical examiner data for public health in Nova Scotia
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic and governmental responses have raised concerns about any corresponding rise in suicide and/or drug toxicity mortality due to exacerbations of mental illness, economic issues, changes to drug supply, ability to access harm reduction services, and other factors. METHODS: Data were obtained from the Nova Scotia Medical Examiner Service. Case definitions were developed, and their performance characteristics assessed. Pre-pandemic trends in monthly suicide and drug toxicity deaths were modelled and the observed numbers of deaths in the pandemic year compared to expected numbers. RESULTS: There was a significant reduction in suicide deaths in the first year of the COVID-19 pandemic in Nova Scotia, with about 21 fewer non-drug toxicity suicide deaths than expected in March 2020 to February 2021 (risk ratio = 0.82). No change in drug toxicity mortality was detected. Case definitions were successfully applied to free-text cause of death statements and cases where cause and manner of death remained under investigation. CONCLUSION: Processes for case classification and monitoring can be implemented in collaboration with medical examiners/coroners for timely, ongoing public health surveillance of suicide and drug toxicity mortality. Medical examiners and coroners are the stewards of a wealth of data that could inform the prevention of further deaths; it is time to engage these systems in public health surveillance.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".