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Objectives Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital.Design Single-site, observational cohort study.Setting An urban, academic hospital in Boston, Massachusetts, USA.Participants We enrolled adult hospital staff entering the hospital at a key ingress point.Interventions Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence.Outcome measures Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm.Results One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important.

Objectives To compare the impact of respirator extended use and reuse strategies with regard to cost and sustainability during the COVID-19 pandemic.Design Cost analysis.Setting USA.Participants All healthcare workers within the USA.Interventions Not applicable.Main outcome measures A model was developed to estimate usage, costs and waste incurred by several respirator usage strategies over the first 6 months of the pandemic in the USA. This model assumed universal masking of all healthcare workers. Estimates were taken from the literature, government databases and commercially available data from approved vendors.Results A new N95 respirator per patient encounter would require 7.41 billion respirators, cost $6.38 billion and generate 84.0 million kg of waste in the USA over 6 months. One respirator per day per healthcare worker would require 3.29 billion respirators, cost $2.83 billion and generate 37.22 million kg of waste. Decontamination by ultraviolet germicidal irradiation would require 1.64 billion respirators, cost $1.41 billion and accumulate 18.61 million kg of waste. H2O2 vapour decontamination would require 1.15 billion respirators, cost $1.65 billion and produce 13.03 million kg of waste. One reusable respirator with daily disposable filters would require 18 million respirators, cost $1.24 billion and generate 15.73 million kg of waste. Pairing a reusable respirator with H2O2 vapour-decontaminated filters would reduce cost to $831 million and generate 1.58 million kg of waste. The use of one surgical mask per healthcare worker per day would require 3.29 billion masks, cost $460 million and generate 27.92 million kg of waste.Conclusions Decontamination and reusable respirator-based strategies decreased the number of respirators used, costs and waste generated compared with single-use or daily extended-use of disposable respirators. Future development of low-cost,simple technologies to enable respirator and/or filter decontamination is needed to further minimise the economic and environmental costs of masks.

Objective To develop and test a new reusable, sterilisable N95 filtering facepiece respirator (FFR)-comparable face mask, known as the Injection Molded Autoclavable, Scalable, Conformable (iMASC) system, given the dire need for personal protective equipment within healthcare settings during the COVID-19 pandemic.Design Single-arm feasibility study.Setting Emergency department and outpatient oncology clinic.Participants Healthcare workers who have previously undergone N95 fit testing.Interventions Fit testing of new iMASC system.Primary and secondary outcome measures Primary outcome is success of fit testing using an Occupational Safety and Health Administration (OSHA)-approved testing method, and secondary outcomes are user experience with fit, breathability and filter replacement.Results Twenty-four subjects were recruited to undergo fit testing, and the average age of subjects was 41 years (range of 21–65 years) with an average body mass index of 26.5 kg/m2. The breakdown of participants by profession was 46% nurses (n=11), 21% attending physicians (n=5), 21% resident physicians (n=5) and 12% technicians (n=3). Of these participants, four did not perform the fit testing due to the inability to detect saccharin solution on premask placement sensitivity test, lack of time and inability to place mask over hair. All participants (n=20) who performed the fit test were successfully fitted for the iMASC system using an OSHA-approved testing method. User experience with the iMASC system, as evaluated using a Likert scale with a score of 1 indicating excellent and a score of 5 indicating very poor, demonstrated an average fit score of 1.75, breathability of 1.6, and ease of replacing the filter on the mask was scored on average as 2.05.Conclusions The iMASC system was shown to successfully fit multiple different face sizes and shapes using an OSHA-approved testing method. These data support further certification testing needed for use in the healthcare setting.