Analysis of Risk Factors of Laboratory-acquired Infections in Canada: 2016–2023 Data from the Laboratory Incident Notification Canada Surveillance System
Bibliographic record
Abstract
Introduction: Laboratory-acquired infections (LAIs) remain a significant occupational hazard worldwide, with the potential for public health risks beyond the laboratory. This study examined 2016 to 2023 data from the Laboratory Incident Notification Canada (LINC) surveillance system to identify risk factors associated with LAIs in Canadian laboratories. Methods: LINC incident reports, focusing on LAIs resulting from exposures to human pathogens or toxins, were analyzed. Potential risk factors contributing to LAIs were identified through univariate, bivariate, and multivariate analyses. Logistic regression was used to assess the association between potential risk factors and the incidence of LAIs. Results: Between 2016 and 2023, there were eight LAI exposure incidents that met the inclusion criteria and 354 non-LAI exposure incidents. Bivariate analyses between 10 potential risk factors and LAI occurrence only identified failure of or inadequate personal protective equipment (PPE) to be statistically significantly associated with LAIs ( p = 0.027). Regression analysis demonstrated the importance of PPE, where failure of or inadequate PPE was associated with increased odds of LAI (odds ratio = 4.53, 95% confidence interval: 1.07, 19.28), having adjusted for other potential risk factors. The time trend revealed some variance in the total number of affected persons, with a particular peak in 2018. Conclusion: Failure of or inadequate PPE was a significant risk factor for LAIs in Canadian laboratories, thus reinforcing the importance of safety protocol adherence, ongoing training, and targeted interventions to reduce the risk of LAIs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| 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".