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Reviews of "College campuses and COVID-19 mitigation: clinical and economic value"

Reviewers: Kathy Leung (The University of Hong Kong) | 📒📒📒 ◻️◻️ • David Kim (Tufts) | 📘📘📘📘📘 • Benjamin Linas (Boston University) | 📗📗📗📗◻️

Published onSep 30, 2020
Reviews of "College campuses and COVID-19 mitigation: clinical and economic value"
key-enterThis Pub is a Review of
College campuses and COVID-19 mitigation: clinical and economic value

Background: Decisions around US college and university operations will affect millions of students and faculty amidst the COVID-19 pandemic. We examined the clinical and economic value of different COVID-19 mitigation strategies on college campuses. Methods: We used the Clinical and Economic Analysis of COVID-19 interventions (CEACOV) model, a dynamic microsimulation that tracks infections accrued by students and faculty, accounting for community transmissions. Outcomes include infections, $/infection-prevented, and $/quality-adjusted-life-year ($/QALY). Strategies included extensive social distancing (ESD), masks, and routine laboratory tests (RLT). We report results per 5,000 students (1,000 faculty) over one semester (105 days). Results: Mitigation strategies reduced COVID-19 cases among students (faculty) from 3,746 (164) with no mitigation to 493 (28) with ESD and masks, and further to 151 (25) adding RLTq3 among asymptomatic students and faculty. ESD with masks cost $168/infection-prevented ($49,200/QALY) compared to masks alone. Adding RLTq3 ($10/test) cost $8,300/infection-prevented ($2,804,600/QALY). If tests cost $1, RLTq3 led to a favorable cost of $275/infection-prevented ($52,200/QALY). No strategies without masks were cost-effective. Conclusion: Extensive social distancing with mandatory mask-wearing could prevent 87% of COVID-19 cases on college campuses and be very cost-effective. Routine laboratory testing would prevent 96% of infections and require low cost tests to be economically attractive.

To read the original manuscript, click the link above.

Summary of Reviews: This is a comprehensive model that covers a timely topic; however, the many estimations that went into the model, as well as the use of "contact-hours" as a key parameter, may make the conclusions subject to uncertainty.

Reviewer 1 (Kathy Leung) | 📒📒📒 ◻️◻️

Reviewer 2 (David Kim) | 📘📘📘📘📘

Reviewer 2 (Benjamin Linas) | 📗📗📗📗◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

To read the reviews, click the links below.

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