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Spatial and temporal effects on severe acute respiratory coronavirus virus 2 (SARS-CoV-2) contamination of the healthcare environment

Part of: SARS-CoV-2/COVID-19

Published online by Cambridge University Press:  27 December 2021

Matthew J. Ziegler Open the ORCID record for Matthew J. Ziegler [Opens in a new window] ,
Elizabeth Huang ,
Selamawit Bekele ,
Emily Reesey ,
Pam Tolomeo Open the ORCID record for Pam Tolomeo [Opens in a new window] ,
Sean Loughrey ,
Michael Z. David ,
Ebbing Lautenbach ,
Brendan J. Kelly Open the ORCID record for Brendan J. Kelly [Opens in a new window]  and
for the CDC Prevention Epicenters Program
Matthew J. Ziegler*
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Elizabeth Huang
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Selamawit Bekele
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Emily Reesey
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Pam Tolomeo
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Sean Loughrey
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Michael Z. David
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Ebbing Lautenbach
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Brendan J. Kelly
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
*
Author for correspondence: Matthew J. Ziegler, E-mail: [email protected]
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Abstract

Background:

The spatial and temporal extent of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) environmental contamination has not been precisely defined. We sought to elucidate contamination of different surface types and how contamination changes over time.

Methods:

We sampled surfaces longitudinally within COVID-19 patient rooms, performed quantitative RT-PCR for the detection of SARS-CoV-2 RNA, and modeled distance, time, and severity of illness on the probability of detecting SARS-CoV-2 using a mixed-effects binomial model.

Results:

The probability of detecting SARS-CoV-2 RNA in a patient room did not vary with distance. However, we found that surface type predicted probability of detection, with floors and high-touch surfaces having the highest probability of detection: floors (odds ratio [OR], 67.8; 95% credible interval [CrI], 36.3–131) and high-touch elevated surfaces (OR, 7.39; 95% CrI, 4.31–13.1). Increased surface contamination was observed in room where patients required high-flow oxygen, positive airway pressure, or mechanical ventilation (OR, 1.6; 95% CrI, 1.03–2.53). The probability of elevated surface contamination decayed with prolonged hospitalization, but the probability of floor detection increased with the duration of the local pandemic wave.

Conclusions:

Distance from a patient’s bed did not predict SARS-CoV-2 RNA deposition in patient rooms, but surface type, severity of illness, and time from local pandemic wave predicted surface deposition.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 43 , Issue 12 , December 2022 , pp. 1773 - 1778
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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References

Karan, A, Klompas, M, Tucker, R, et al. The risk of SARS-CoV-2 transmission from patients with undiagnosed COVID-19 to roommates in a large academic medical center. Clin Infect Dis 2021. doi: 10.1093/cid/ciab564.Google Scholar
Morawska, L, Allen, J, Bahnfleth, W, et al. A paradigm shift to combat indoor respiratory infection. Science 2021; 372: 689–691.CrossRefGoogle ScholarPubMed
Ong, SWX, Lee, PH, Tan, YK, et al. Environmental contamination in a coronavirus disease 2019 (COVID-19) intensive care unit—what is the risk? Infect Control Hosp Epidemiol 2021; 42: 669–677.CrossRefGoogle Scholar
Ryu, BH, Cho, Y, Cho, OH, Hong, SI, Kim, S, Lee, S. Environmental contamination of SARS-CoV-2 during the COVID-19 outbreak in South Korea. Am J Infect Control 2020; 48: 875–879.Google ScholarPubMed
Moore, G, Rickard, H, Stevenson, D, et al. Detection of SARS-CoV-2 within the healthcare environment: a multicentre study conducted during the first wave of the COVID-19 outbreak in England. J Hosp Infect 2021; 108: 189–196.CrossRefGoogle ScholarPubMed
Coil, DA, Albertson, T, Banerjee, S, et al. SARS-CoV-2 detection and genomic sequencing from hospital surface samples collected at UC Davis. PLoS One 2021;16:e0253578.Google ScholarPubMed
Redmond, SN, Dousa, KM, Jones, LD, et al. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid contamination of surfaces on a coronavirus disease 2019 (COVID-19) ward and intensive care unit. Infect Control Hosp Epidemiol 2021; 42: 215–217.CrossRefGoogle ScholarPubMed
Ong, SWX, Tan, YK, Chia, PY, et al. Air, surface environmental, and personal protective equipment contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from a symptomatic patient. JAMA 2020; 323: 1610–1612.Google ScholarPubMed
Mody, L, Akinboyo, IC, Babcock, HM, et al. Coronavirus disease 2019 (COVID-19) research agenda for healthcare epidemiology. Infect Control Hosp Epidemiol 2021. doi: 10.1017/ice.2021.25.Google ScholarPubMed
Kelly, BJ, Bekele, S, Loughrey, S, et al. Healthcare microenvironments define multidrug-resistant organism persistence. Infect Control Hosp Epidemiol 2021. doi: 10.1017/ice.2021.323.Google ScholarPubMed
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.Google Scholar
Wickham, H. Ggplot2: Elegant Graphics for Data Analysis, 1st edition, 2009, corr. 3rd printing, 2010 edition. New York: Springer; 2016.Google Scholar
Carpenter, B, Gelman, A, Hoffman, MD, et al. Stan: a probabilistic programming language. J Stat Softw 2017; 76: 1–32.CrossRefGoogle Scholar
Bürkner, PC. Brms: an R package for Bayesian multilevel models using stan. J Stat Softw 2017; 80: 1–28.CrossRefGoogle Scholar
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. 1 edition. Boca Raton, FL: CRC Press/Taylor & Francis Group; 2016.Google Scholar
Gabry, J, Simpson, D, Vehtari, A, Betancourt, M, Gelman, A. Visualization in Bayesian workflow. J Roy Stat Soc A 2019; 182: 389–402.Google Scholar
Gelman, A, Vehtari, A, Simpson, D, et al. Bayesian workflow. Cornell University website. https://arxiv.org/abs/2011.01808. Published November 2020. Accessed January 4, 2022.Google Scholar
Chia, PY, Coleman, KK, Tan, YK, et al. Detection of air and surface contamination by SARS-CoV-2 in hospital rooms of infected patients. Nat Commun 2020;11:2800.CrossRefGoogle ScholarPubMed
Guo, ZD, Wang, ZY, Zhang, SF, et al. Aerosol and surface distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards, Wuhan, China, 2020. Emerg Infect Dis 2020; 26: 1583–1591.Google ScholarPubMed
Abdulrahman, A, Mallah, SI, Alqahtani, M. COVID-19 viral load not associated with disease severity: findings from a retrospective cohort study. BMC Infect Dis 2021;21:688.CrossRefGoogle Scholar
Healthcare workers. Centers for Disease Control and Prevention website. https://www.cdc.gov/coronavirus/2019-ncov/hcp/infection-control-recommendations.html. Published September 2021. Accessed January 4, 2022.Google Scholar
Rutala, WA, Gergen, MF, Weber, DJ. Microbiologic evaluation of microfiber mops for surface disinfection. Am J Infect Control 2007; 35: 569–573.CrossRefGoogle ScholarPubMed
Lednicky, JA, Lauzard, M, Fan, ZH, et al. Viable SARS-CoV-2 in the air of a hospital room with COVID-19 patients. Int J Infect Dis 2020; 100: 476–482.CrossRefGoogle ScholarPubMed
Santarpia, JL, Rivera, DN, Herrera, VL, et al. Aerosol and surface contamination of SARS-CoV-2 observed in quarantine and isolation care. Sci Rep 2020;10:12732.Google ScholarPubMed
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