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Cost-effectiveness of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing and isolation strategies in nursing homes

Published online by Cambridge University Press:  15 February 2024

Sarah M. Bartsch ,
Colleen Weatherwax Open the ORCID record for Colleen Weatherwax [Opens in a new window] ,
Marie F. Martinez ,
Kevin L. Chin ,
Michael R. Wasserman ,
Raveena D. Singh ,
Jessie L. Heneghan Open the ORCID record for Jessie L. Heneghan [Opens in a new window] ,
Gabrielle M. Gussin Open the ORCID record for Gabrielle M. Gussin [Opens in a new window] ,
Sheryl A. Scannell Open the ORCID record for Sheryl A. Scannell [Opens in a new window]  and
Cameron White
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Sarah M. Bartsch
Affiliation:
Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
Colleen Weatherwax
Affiliation:
Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
Marie F. Martinez
Affiliation:
Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
Kevin L. Chin
Affiliation:
Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
Michael R. Wasserman
Affiliation:
Los Angeles Jewish Home, Reseda, California California Association of Long Term Care Medicine, Santa Clarita, California
Raveena D. Singh
Affiliation:
Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
Jessie L. Heneghan
Affiliation:
Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
Gabrielle M. Gussin
Affiliation:
Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
Sheryl A. Scannell
Affiliation:
Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
Cameron White
Affiliation:
Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
Bruce Leff
Affiliation:
Center for Transformative Geriatric Research, Division of Geriatric Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
Susan S. Huang
Affiliation:
Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
Bruce Y. Lee*
Affiliation:
Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, New York
*
Corresponding author: Bruce Y. Lee; Email: bruceleemdmba@gmail.com
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Abstract

Objective:

Nursing home residents may be particularly vulnerable to coronavirus disease 2019 (COVID-19). Therefore, a question is when and how often nursing homes should test staff for COVID-19 and how this may change as severe acute respiratory coronavirus virus 2 (SARS-CoV-2) evolves.

Design:

We developed an agent-based model representing a typical nursing home, COVID-19 spread, and its health and economic outcomes to determine the clinical and economic value of various screening and isolation strategies and how it may change under various circumstances.

Results:

Under winter 2023–2024 SARS-CoV-2 omicron variant conditions, symptom-based antigen testing averted 4.5 COVID-19 cases compared to no testing, saving $191 in direct medical costs. Testing implementation costs far outweighed these savings, resulting in net costs of $990 from the Centers for Medicare & Medicaid Services perspective, $1,545 from the third-party payer perspective, and $57,155 from the societal perspective. Testing did not return sufficient positive health effects to make it cost-effective [$50,000 per quality-adjusted life-year (QALY) threshold], but it exceeded this threshold in ≥59% of simulation trials. Testing remained cost-ineffective when routinely testing staff and varying face mask compliance, vaccine efficacy, and booster coverage. However, all antigen testing strategies became cost-effective (≤$31,906 per QALY) or cost saving (saving ≤$18,372) when the severe outcome risk was ≥3 times higher than that of current omicron variants.

Conclusions:

SARS-CoV-2 testing costs outweighed benefits under winter 2023–2024 conditions; however, testing became cost-effective with increasingly severe clinical outcomes. Cost-effectiveness can change as the epidemic evolves because it depends on clinical severity and other intervention use. Thus, nursing home administrators and policy makers should monitor and evaluate viral virulence and other interventions over time.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , First View , pp. 1 - 8
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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Footnotes

a

Authors of equal contribution.

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