COVID-19 infections among Healthcare Workers and Transmission within Households
Bibliographic record
Abstract
Abstract Importance Protecting healthcare workers (HCWs) from COVID-19 is a priority to maintain a safe and functioning healthcare system. The risk of transmitting COVID-19 to family members is a source of stress for many. Objective To describe and compare HCW and non-HCW COVID-19 cases in Ontario, Canada, as well as the frequency of COVID-19 among HCWs’ household members. Design, Setting, and Participants Using reportable disease data at Public Health Ontario which captures all COVID-19 cases in Ontario, Canada, we conducted a population-based cross-sectional study comparing demographic, exposure, and clinical variables between HCWs and non-HCWs with COVID-19 as of 14 May 2020. We calculated rates of infections over time and determined the frequency of within household transmissions using natural language processing based on residential address. Exposures and Outcomes We contrasted age, gender, comorbidities, clinical presentation (including asymptomatic and presymptomatic), exposure histories including nosocomial transmission, and clinical outcomes between HCWs and non-HCWs with confirmed COVID-19. Results There were 4,230 (17.5%) HCW COVID-19 cases in Ontario, of whom 20.2% were nurses, 2.3% were physicians, and the remaining 77.4% other specialties. HCWs were more likely to be between 30-60 years of age and female. HCWs were more likely to present asymptomatically (8.1% versus 7.0%, p=0.010) or with atypical symptoms (17.8% versus 10.5%, p<0.001). The mortality among HCWs was 0.2% compared to 10.5% of non-HCWs. HCWs commonly had exposures to a confirmed case or outbreak (74.1%), however only 3.1% were confirmed to be nosocomial. The rate of new infections was 5.5 times higher in HCWs than non-HCWs, but mirrored the epidemic curve. We identified 391 (9.8%) probable secondary household transmissions and 143 (3.6%) acquisitions. Children < 19 years comprised 14.6% of secondary cases compared to only 4.2% of the primary cases. Conclusions and Relevance HCWs represent a disproportionate number of COVID-19 cases in Ontario but with low confirmed numbers of nosocomial transmission. The data support substantial testing bias and under-ascertainment of general population cases. Protecting HCWs through appropriate personal protective equipment and physical distancing from colleagues is paramount. Key Points Question What are the differences between healthcare workers and non-healthcare workers with COVID-19? Findings In this population-based cross-sectional study there were 4,230 healthcare workers comprising 17.5% of COVID-19 cases. Healthcare workers were diagnosed with COVID-19 at a rate 5.5 times higher than the general population with 0.8% of all healthcare workers, compared to 0.1% of non-healthcare workers. Meaning High healthcare worker COVID-19 burden highlights the importance of physical distancing from colleagues, appropriate personal protective equipment, as well as likely substantial testing bias and under-ascertainment of COVID-19 in the general population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".