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An aerosol-generating medical procedure (AGMP) is any procedure performed on a patient that can induce the production of aerosols of various sizes, including droplet nuclei. AGMPs have become a subject of increasing interest during the COVID-19 pandemic for two critical reasons. First, AGMP likely increases the risk of transmission from patients infected with respiratory infections to healthcare personnel and other patients in their environment. Second, special risk mitigation strategies, including selection of specific types of personal protective equipment and environmental controls, are necessary to protect staff during the performance of AGMPs. Heightened awareness for AGMPs began during the 2003 severe acute respiratory syndrome (SARS) pandemic, where it was noted that, in outbreaks, many frontline HCWs had increased risk of contracting the virus related to certain procedures performed on the respiratory tract (Tran et al. PLoS One 7:e35797, 2012). Numerous clinical guidelines were published attempting to categorize and classify the risk associated with various AGMP. However, while numerous procedures have been identified as "aerosol generating, " the scientific evidence for the creation of aerosols associated with these procedures, the burden of potential viable microbes within the created aerosols, and the mechanism of transmission to the host have not been well studied (Davies et al. J Infect Prev 10:122-6, 2009). Almost 20 years later, there are still large gaps in knowledge around AGMPs - what defines them, what is the added risk associated with them, and which strategies are most effective at mitigating the risks associated with them. Here, we summarize the current knowledge around AGMPs including the types, risk, and mitigation strategies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Background. Although healthcare worker (HCW) absenteeism due to COVID-19 exposure represents a significant challenge, there are currently no evidence-based criteria for assessing infection risk based on COVID-19 exposure type.We aimed to identify the incidence of acquiring infection following varying exposures to COVID-19 to guide safe return-to-work policies for staff in healthcare settings. Methods. We analyzed prospectively collected data at an academic centre with approximately 17 000 active staff between January 1 - April 30, 2022 during a large BA.1 Omicron surge. More than 99% of staff received >2 vaccine doses. All staff selfreporting household, community, and workplace exposure to confirmed cases of COVID-19 submitted attestation to the Occupational Health department detailing the nature of the exposure, the duration, and setting. Staff were required to report all positive test results by rapid antigen or PCR testing. Results. A total of 3209 staff submitted exposure reports (2493 household, 539 community, and 177 workplace). Of these, 1008 (31.4%) tested positive 2 days prior to or 14 days after the exposure (36% household;19% community, 7% workplace). In the community exposure group, 19% tested positive due to a discrete exposure of < 4 hours and 21% tested positive with an exposure >4 hours. For household exposures and workplace exposures, these values were 25%/27% and 6%/10%, respectively (Figure 1). The median time to testing positive was 2 days for household exposures and 3 days for community and workplace exposures (Figure 2, Panels A-C). By day 4 post-exposure, more than 80% of positive results were reported (Figure 2, Panel D). Risk of testing positive differed based on baseline symptom status at the time of reporting (Table 1). The risk of infection amongst healthcare workers reporting exposures, according to their symptom status at the time of reporting their exposures. Conclusion. Our data suggests that the highest risk of acquiring SARS-CoV-2 was via household contacts, regardless of exposure duration, with workplace exposures carrying less risk. Using a cut-off of 4 hours for exposure duration to delineate risk may be of limited value. These data could help workplaces predict infection risk following exposure and guide return-to-work policies that balance the need to staff workplaces, including hospitals, with reducing risk of on-site transmission during periods of increased community transmission (Figure 3). (Figure Presented).
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Analysis of chest X-ray images of COVID infected patients is one of the important diagnostic strategies. The manual identification of these images may be erroneous and faulty. So the computer-aided diagnosis of COVID infections using deep learning techniques is becoming useful. In this paper, the classification of chest X-ray images using CNN is conducted, and the performance of different optimizers is studied. The dataset containing chest X-ray images of normal and COVID infected patients is collected from Kaggle. The experimental study suggested that Adam optimizer achieved 95.83% classification accuracy, and it outperformed the other three optimizers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background. Hand hygiene (HH) is a standard infection prevention and control precaution to be applied in healthcare settings to prevent transmission of COVID-19. Many healthcare institutions observed significant improvements in HH performance during wave one of the COVID-19 pandemic but the sustainability of this change is unknown. Our aim was to evaluate long-term HH performance throughout subsequent waves of the pandemic across acute care hospitals in Ontario, Canada. Methods. HH adherence was measured using a previously validated group electronic monitoring system which was installed on all alcohol handrub and sink soap dispensers inside and outside each patient room across 56 inpatient units (35 wards and 21 critical care units) spanning 13 acute care hospitals (6 university and 7 community teaching hospitals) from 1 November 2019 to 31 May 2021. Daily HH adherence was compared with daily COVID-19 case count across Ontario. During this period, weekly performance continued to be reported to units but unit-based quality improvement discussions were inconsistent due to the COVID-19 response. Results. Figure 1 depicts daily aggregate HH adherence plotted against the new daily COVID-19 case count across Ontario. An elevation in HH adherence was seen prior to the start of the first wave, rising almost to 80% and then remained above 70% for the peak of wave one. During waves two and three, peak COVID-19 case counts were associated with a maximum HH adherence of 51%, only marginally above the pre-pandemic baseline. After the end of wave one (from 1 July 2020 to 31 May 2021) the median HH performance was only 49% (interquartile range 47%-50%). Conclusion. Initial improvements in HH adherence preceding the start of the COVID-19 pandemic were not sustained, possibly due to increasing comfort and reduced anxiety associated with providing care to COVID-19 patients leading to a perception of reduced COVID-19 transmission risk. These findings highlight the need for HH monitoring to be tied to longitudinal unit-led quality improvement in order to achieve durable changes in practice.