The impact of the novel coronavirus disease (COVID-19) pandemic on drug overdose-related deaths in the United States and Canada: a systematic review of observational studies and analysis of public health surveillance data
Bibliographic record
Abstract
BACKGROUND: There are preliminary indications that the trajectory of drug overdose-related deaths in North America has been exacerbated due to the novel coronavirus disease pandemic (COVID-19). As such, the impact of COVID-19 on drug overdose-related deaths was examined through a systematic review of the literature and percentage change analyses of surveillance data. METHODS: Systematic searches in electronic databases were conducted, a topical issue brief and bibliography were reviewed, reference lists of included studies were searched and expert consultations were held to identify studies (Registration # CRD42021230223). Observational studies from the United States and Canada were eligible for inclusion if drug overdose-related deaths were assessed in quantitative or qualitative analyses onwards from at least March 2020. In addition, percentage changes comparing drug overdose-related deaths in the second annual quarter (Q2 2020 [April to June]) with the first annual quarter (Q1 2020 [January to March]) were generated using national and subnational data from public health surveillance systems and reports from jurisdictions in the United States and Canada. RESULTS: Nine studies were included in the systematic review, eight from the United States and one from Canada. The maximum outcome assessment period in the included studies extended until September 2020. Drug overdose-related deaths after the onset of COVID-19 were higher compared with the months leading up to the pandemic in 2020 and the comparative months in 2019. In additional percentage change analyses, drug overdose-related deaths increased by 2 to 60% in jurisdictions in the United States and by 58% in Canada when comparing Q2 2020 with Q1 2020. CONCLUSIONS: Drug overdose-related deaths increased after the onset of COVID-19. The current situation necessitates a multi-pronged approach, encompassing expanded access to substance use disorder treatment, undisrupted access to harm reduction services, emphasis on risk reduction strategies, provision of a safe drug supply and decriminalization of drug use.
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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 | 0.019 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".