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Record W4311736927 · doi:10.1093/ofid/ofac492.086

1524. SARS-CoV-2 Infection Incidence Following Exposure Assessments for Healthcare Workers

2022· article· en· W4311736927 on OpenAlexaff
A Vaisman, Rob A. Cairns, Danielle De Graeve, Tamara Dus, Susy Hota, John Granton

Bibliographic record

VenueOpen Forum Infectious Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineAbsenteeismHealth careIncidence (geometry)Coronavirus disease 2019 (COVID-19)Personal protective equipmentSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Healthcare workerEnvironmental healthOccupational exposureInfection controlHealth workerEmergency medicineInternal medicineIntensive care medicineHealth servicesPopulation

Abstract

fetched live from OpenAlex

Abstract Background Although healthcare worker (HCW) absenteeism due to COVID-19 exposure represents a significant challenge, there are currently no evidence-based criteria for assessing infection risk based on COVID-19 exposure type. We aimed to identify the incidence of acquiring infection following varying exposures to COVID-19 to guide safe return-to-work policies for staff in healthcare settings. Methods We analyzed prospectively collected data at an academic centre with approximately 17 000 active staff between January 1 - April 30, 2022 during a large BA.1 Omicron surge. More than 99% of staff received >2 vaccine doses. All staff self-reporting household, community, and workplace exposure to confirmed cases of COVID-19 submitted attestation to the Occupational Health department detailing the nature of the exposure, the duration, and setting. Staff were required to report all positive test results by rapid antigen or PCR testing. Results A total of 3209 staff submitted exposure reports (2493 household, 539 community, and 177 workplace). Of these, 1008 (31.4%) tested positive 2 days prior to or 14 days after the exposure (36% household; 19% community, 7% workplace). In the community exposure group, 19% tested positive due to a discrete exposure of < 4 hours and 21% tested positive with an exposure >4 hours. For household exposures and workplace exposures, these values were 25%/27% and 6%/10%, respectively (Figure 1). The median time to testing positive was 2 days for household exposures and 3 days for community and workplace exposures (Figure 2, Panels A-C). By day 4 post-exposure, more than 80% of positive results were reported (Figure 2, Panel D). Risk of testing positive differed based on baseline symptom status at the time of reporting (Table 1). Risk of infection during the peri-exposure period (2 days before reported exposure and 14 days after) according to type and duration of exposure. Background rate of infection based on regional incidence of disease due to BA.1 Omicron wave. SARS-CoV-2 Infection Risk (A - household; B - Community; C - Workplace) after exposure. Time to infection in all groups is shown in panel D. The risk of infection amongst healthcare workers reporting exposures, according to their symptom status at the time of reporting their exposures. Conclusion Our data suggests that the highest risk of acquiring SARS-CoV-2 was via household contacts, regardless of exposure duration, with workplace exposures carrying less risk. Using a cut-off of 4 hours for exposure duration to delineate risk may be of limited value. These data could help workplaces predict infection risk following exposure and guide return-to-work policies that balance the need to staff workplaces, including hospitals, with reducing risk of on-site transmission during periods of increased community transmission (Figure 3). Risk of Infection in the Context of Background Infection Background general population infection risk based on regional incidence of disease due to BA.1 Omicron wave. Disclosures All Authors: No reported disclosures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.034
GPT teacher head0.376
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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