Chandelier
Senior Member (Voting Rights)
Comparative effectiveness of proactive exposure management and illness-based exclusion strategies for COVID-19 in healthcare personnel
Amanda Brown Marusiak 1 , Hilary Babcock 2 , Emily Sickbert-Bennett 1 , Daniel Westreich 1 , Justin Lessler 1 , David J Weber
Design: Retrospective cohort study.
Setting: Large academic medical center, ambulatory and acute care.
Participants: HCP COVID-19 exposure and case records (N = 30,102) collected at the University of North Carolina Medical Center, March 2020 through June 2021.
Methods: Among COVID-positive HCP, we measured hours worked while infectious and symptomatic, onward exposures, and secondary cases caused. Using negative binomial regression, we estimated mean count differences for each outcome comparing contact-tracing and self-reporting to standard illness management.
We calculated the "number of encounters needed" to prevent one additional outcome (e.g., single onward exposure) by multiplying count differences by the proportion of exposure management encounters that identified a case.
Results: Contact tracing and self-reporting exposures captured a higher proportion of asymptomatic cases (14 and 18%) than standard illness management (5%). Both strategies reduced infectious work time by ~8 hours and presenteeism by ~3.5 hours. We found large relative reductions in onward exposures (62% contact tracing, 66% self-report) and secondary cases (79%, 100%), but small absolute reductions. Preventing one secondary case required identifying >1,000 traced contacts or >350 self-reports.
Conclusions: Compared with standard illness management, COVID-19 exposure management reduced infectious work time, presenteeism and onward exposures, but preventing secondary cases was highly inefficient.
Layered with comprehensive institutional mitigation strategies, exposure management provided limited case prevention benefits. Presenteeism was common and remains an important healthcare safety challenge.
Web | DOI
Amanda Brown Marusiak 1 , Hilary Babcock 2 , Emily Sickbert-Bennett 1 , Daniel Westreich 1 , Justin Lessler 1 , David J Weber
Abstract
Objective: To evaluate whether COVID-19 exposure management in healthcare personnel (HCP) reduced presenteeism, occupational exposures and cases, comparing contact tracing and self-reporting exposure to standard illness management (i.e. calling out sick).Design: Retrospective cohort study.
Setting: Large academic medical center, ambulatory and acute care.
Participants: HCP COVID-19 exposure and case records (N = 30,102) collected at the University of North Carolina Medical Center, March 2020 through June 2021.
Methods: Among COVID-positive HCP, we measured hours worked while infectious and symptomatic, onward exposures, and secondary cases caused. Using negative binomial regression, we estimated mean count differences for each outcome comparing contact-tracing and self-reporting to standard illness management.
We calculated the "number of encounters needed" to prevent one additional outcome (e.g., single onward exposure) by multiplying count differences by the proportion of exposure management encounters that identified a case.
Results: Contact tracing and self-reporting exposures captured a higher proportion of asymptomatic cases (14 and 18%) than standard illness management (5%). Both strategies reduced infectious work time by ~8 hours and presenteeism by ~3.5 hours. We found large relative reductions in onward exposures (62% contact tracing, 66% self-report) and secondary cases (79%, 100%), but small absolute reductions. Preventing one secondary case required identifying >1,000 traced contacts or >350 self-reports.
Conclusions: Compared with standard illness management, COVID-19 exposure management reduced infectious work time, presenteeism and onward exposures, but preventing secondary cases was highly inefficient.
Layered with comprehensive institutional mitigation strategies, exposure management provided limited case prevention benefits. Presenteeism was common and remains an important healthcare safety challenge.
Web | DOI