Estimating the risk of SARS-CoV-2 transmission to pediatric anesthesiologists: a microsimulation model
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CORRESPONDENCE
Estimating the risk of SARS-CoV-2 transmission to pediatric anesthesiologists: a microsimulation model Kazuyoshi Aoyama, MD, PhD . Anna Heath, PhD . Alan Yang, MSc . Jason T. Maynes, PhD, MD . Guy Petroz, MD . James Robertson, MD . Conor Mc Donnell, MB, MD . Russanthy Velummailum, MPH . Elizabeth Bond, MPH . Petros Pechlivanoglou, PhD
Received: 17 July 2020 / Revised: 19 July 2020 / Accepted: 19 July 2020 Ó Canadian Anesthesiologists’ Society 2020
To the Editor, Anesthesiologists are at high risk of aerosol-transmitted infection by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) during their airway management of patients with coronavirus disease (COVID-19). Adding to this, many patients with COVID19 are asymptomatic, especially children,1 and aerosol transmission can occur despite the use of full personal protective equipment.2 Most acute-care hospitals in North America suspended elective surgeries soon after the pandemic was declared to reduce the spread of SARSCoV-2 in patients and healthcare workers (HCWs), as well as to conserve hospital resources for the anticipated surge of COVID-19 patients. It remains unknown how cancelling elective surgeries initially impacted, and will continue to impact, the risk of SARS-CoV-2 transmission from a patient to anesthesiologists, or the use of hospital resources. Thus, we created an open source, online
microsimulation model (https://pechlilab.shinyapps.io/ COMPS-GTA/) to estimate the effect of cancelling elective surgeries on the risk of SARS-CoV-2 transmission from a pediatric patient to anesthesiologists during the COVID-19 pandemic. The Hospital for Sick Children Research Ethics Board approved this study; no.1000070090). The microsimulation model allowed us to explore pivotal factors while resuming elective surgeries. The microsimulation model is based on principles and methods previously described.3,4 A userfriendly interface enhances access to the model and allows customized use by other hospitals through the adjustment of parameters for local settings. Specific assumptions and input parameters used in the model are described within the interface, which we will continue to update for more accuracy and validation as additional evidence becomes available. Pre-defined inputs and assumptions derived from our institution and the existing literature were used to
K. Aoyama, MD, PhD (&) Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, ON, Canada e-mail: [email protected]
J. T. Maynes, PhD, MD Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, ON, Canada
Program in Child Health Evaluative Sciences, SickKids Research Institute, Toronto, ON, Canada
Program in Molecular Medicine, SickKids Research Institute, Toronto, ON, Canada
A. Heath, PhD Program in Child Health Evaluative Sciences, SickKids Research Institute, Toronto, ON, Canada
G. Petroz, MD J. Robertson, MD C. Mc Donnell, MB, MD Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Tor
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