Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff

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.

Patient and Health Care Worker Perceptions of Communication and Ability to Identify Emotion When Wearing Standard and Transparent Masks

IMPORTANCE

Adoption of mask wearing in response to the COVID-19 pandemic alters daily communication.

OBJECTIVE

To assess communication barriers associated with mask wearing in patient-clinician interactions and individuals who are deaf and hard of hearing.

DESIGN, SETTING, AND PARTICIPANTS

This pilot cross-sectional survey study included the general population, health care workers, and health care workers who are deaf or hard of hearing in the United States. Volunteers were sampled via an opt-in survey panel and nonrandomized convenience sampling. The general population survey was conducted between January 5 and January 8, 2021. The health care worker surveys were conducted between December 3, 2020, and January 3, 2021. Respondents viewed 2 short videos of a study author wearing both a standard and transparent N95 mask and answered questions regarding mask use, communication, preference, and fit. Surveys took 15 to 20 minutes to complete.

MAIN OUTCOMES AND MEASURES

Participants' perceptions were assessed surrounding the use of both mask types related to communication and the ability to express emotions.

RESULTS

The national survey consisted of 1000 participants (mean [SD] age, 48.7 [18.5] years; 496 [49.6%] women) with a response rate of 92.25%. The survey of general health care workers consisted of 123 participants (mean [SD] age, 49.5 [9.0] years; 84 [68.3%] women), with a response rate of 11.14%. The survey of health care workers who are deaf or hard of hearing consisted of 45 participants (mean [SD] age, 54.5 [9.0] years; 30 [66.7%] women) with a response rate of 23.95%. After viewing a video demonstrating a study author wearing a transparent N95 mask, 781 (78.1%) in the general population, 109 general health care workers (88.6%), and 38 health care workers who are deaf or hard of hearing (84.4%) were able to identify the emotion being expressed, in contrast with 201 (20.1%), 25 (20.5%), and 11 (24.4%) for the standard opaque N95 mask. In the general population, 450 (45.0%) felt positively about interacting with a health care worker wearing a transparent mask; 76 general health care workers (61.8%) and 37 health care workers who are deaf or hard of hearing (82.2%) felt positively about wearing a transparent mask to communicate with patients.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that transparent masks could help improve communication during the COVID-19 pandemic, particularly for individuals who are deaf and hard of hearing.

Thinking green: modelling respirator reuse strategies to reduce cost and waste

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.