Surveillance of laboratory exposures to human pathogens and toxins: Canada 2016
Bibliographic record
Abstract
Background: Canada recently enacted legislation to authorize the collection of data on laboratory incidents involving a biological agent.This is done by the Public Health Agency of Canada (PHAC) as part of a comprehensive national program that protects Canadians from the health and safety risks posed by human and terrestrial animal pathogens and toxins.Objective: To describe the first year of data on laboratory exposure incidents and/or laboratory-acquired infections in Canada since the Human Pathogens and Toxins Regulations came into effect.Methods: Incidents that occurred between January 1 and December 31, 2016 were self-reported by federally-regulated parties across Canada using a standardized form from the Laboratory Incident Notification Canada (LINC) surveillance system.Exposure incidents were described by sector, frequency of occurrence, timeliness of reporting, number of affected persons, human pathogens and toxins involved, causes and corrective actions taken.Microsoft Excel 2010 was used for basic descriptive analyses.Results: In 2016, 46 exposure incidents were reported by holders of 835 active licences in Canada representing 1,352 physical areas approved for work involving a biological agent, for an overall incidence of 3.4%.The number of incidents was highest in the academic (n=16; 34.8%) and hospital (n=12; 26.1%) sectors, while the number of reported incidents was relatively low in the private industry sector.An average of four to five incidents occurred each month; the month of September presented as an outlier with 10 incidents.A total of 100 people were exposed, with no reports of secondary exposure.Four incidents led to suspected (n=3) or confirmed (n=1) cases of laboratory-acquired infection.Most incidents involved pathogens classified at a risk group 2 level that were manipulated in a containment level 2 laboratory (91.3%).Over 22 different species of human pathogens and toxins were implicated, with bacteria the most frequent (34.8%), followed by viruses (26.1%).Eleven (23.9%) incidents involved a security sensitive biologic agent.Procedure breaches (n=15) and sharps-related incidents (n=14) were the most common antecedents to an exposure.In 10 (21.7%) cases, inadvertent possession (i.e., isolation of an unexpected biological agent during routine work) played a role.Possible improvements to standard operating procedures were cited in 71.7% of incidents.Improvements were also indicated for communication (26.1%) and management (23.9%). Conclusions:The Laboratory Incident Notification Canada is one of the first surveillance systems in the world to gather comprehensive data on laboratory incidents involving human pathogens and toxins.Exposure incidents reported in the first year were relatively rare, occurring in less than 4% of containment zones within laboratory settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".