What was the impact of COVID-19 restrictions on unintentional injuries, in Canada and globally? A scoping review investigating how lockdown measures impacted the global burden of unintentional injury
Bibliographic record
Abstract
Background Injuries are among the leading causes for hospitalizations and emergency department (ED) visits. COVID-19 restrictions ensured safety to Canadians, but also negatively impacted health outcomes, including increasing rates of certain injuries. These differences in trends have been reported internationally however the evidence is scattered and needs to be better understood to identify opportunities for public education and to prepare for future outbreaks. Objective A scoping review was conducted to synthesize evidence regarding the impact of COVID-19 restrictions on unintentional injuries in Canada, compared to other countries. Methods Studies investigating unintentional injuries among all ages during COVID-19 from any country, published in English between December 2019 and July 2021, were included. Intentional injuries and/or previous pandemics were excluded. Four databases were searched (MEDLINE, Embase, Web of Science, SPORTDiscus), and a gray literature search was also conducted. Results The search yielded 3,041 results, and 189 articles were selected for extraction. A total of 41 reports were included from the gray literature search. Final studies included research from: Europe (n = 85); North America (n = 44); Asia (n = 32); Oceania (n = 12); Africa (n = 8); South America (n = 4); and multi-country (n = 4). Most studies reported higher occurrence of injuries/trauma among males, and the average age across studies was 46 years. The following mechanisms of injury were reported on most frequently: motor vehicle collisions (MVCs; n = 134), falls (n = 104), sports/recreation (n = 65), non-motorized vehicle (n = 31), and occupational (n = 24). Injuries occurring at home (e.g., gardening, home improvement projects) increased, and injuries occurring at schools, workplaces, and public spaces decreased. Overall, decreases were observed in occupational injuries and those resulting from sport/recreation, pedestrian-related, and crush/trap incidents. Decreases were also seen in MVCs and burns, however the severity of injury from these causes increased during the pandemic period. Increases were observed in poisonings, non-motorized vehicle collisions, lacerations, drownings, trampoline injuries; and, foreign body ingestions. Implications Findings from this review can inform interventions and policies to identify gaps in public education, promote safety within the home, and decrease the negative impact of future stay-at-home measures on unintentional injury among Canadians and populations worldwide.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".