ABSTRACT
Background: The need to contain the spread of the SARS-CoV-2 pandemic has forced national and local organizations to define and implement targeted emergency response and management measures. As the knowledge about the infection grew, a wider range of organizational measures were deployed. Methods: This study involves the SARS-CoV-2 infected people managed by the Local Health Authority of Rieti (Italy). Diagnostic test waiting times and hospital admission rates in the Province of Rieti are investigated as the pandemic evolved. Trends were analyzed in relation to the tempora spreading of SARS-CoV-2, to the organizational actions taken by the Local Health Authority of Rieti, and to the deployment of actions across the territory. A municipalities classification of the province of Rieti was conducted after a cluster analysis based on the diagnostic test waiting times and the hospital admission rates. Results: Our findings show a declining trend, thus indicating a possible positive effect of the measures taken to contain the pandemic. The cluster analysis of the municipalities of the Province of Rieti makes evident an inhomogeneous geographical distribution of examined parameters (diagnostic test waiting times and the hospital admission rates), demonstrating the capability of Local Health Authority of Rieti to reach even the most disadvantaged areas and implying that the differences are due to the demographical variabilities. Conclusion: Despite some limitations, this study outlines the importance of management measures in response of the pandemic. These measures should adapt to social, cultural and geographical nature of the territory involved. The findings of the present study will contribute to the update of further pandemic preparedness plans of the Local Health Authorities.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , COVID-19/epidemiology , Pandemics , Italy/epidemiology , Delivery of Health CareABSTRACT
The COVID-19 pandemic remains a concrete challenge, especially in communities and rural areas where health resources are scarce. We recently developed several classifiers, useful to predict safe discharge, disease severity, and mortality risk from COVID-19, fed by routine analyses collected in the Emergency Department. In this paper, we discuss a system, made up of an app and a server, that enables doctors to use these models during the management of COVID-19 patients. The app has been developed involving the doctors since the early phases of the app design, then revised in the light of two usability cycles. We report its main features and its ease of use. So far, it has been used during the fourth wave, producing accurate results with patients that did not complete the vaccination protocol (i.e., up to the second dose). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Teaching medical statistics online poses several difficulties related both to the subject and the use of technology. Due to the Covid-19 pandemic, a traditional medical statistics course was forced to be held online. The study investigates the impact of such a transformation in terms of both the student satisfaction and the stress at the end of the course, by taking into account the attitudes toward statistics before and after the academic course. The results show that the students' learning satisfaction was on average 30.1 (5.8). Analyzing the singles items: over 70% of students were satisfied of the technology used, even if only about 18% would participate in a new online course. Students were stressed and half of them reported high levels of stress. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic remains a concrete challenge, especially in communities and rural areas where health resources are scarce. We recently developed several classifiers, useful to predict safe discharge, disease severity, and mortality risk from COVID-19, fed by routine analyses collected in the Emergency Department. In this paper, we discuss a system, made up of an app and a server, that enables doctors to use these models during the management of COVID-19 patients. The app has been developed involving the doctors since the early phases of the app design, then revised in the light of two usability cycles. We report its main features and its ease of use. So far, it has been used during the fourth wave, producing accurate results with patients that did not complete the vaccination protocol (i.e., up to the second dose). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
To implement the protocol contrasting the diffusion of Covid-19, the employer is required, to ensure the safety and health of the worker at work, to adopt measures related to the control of body temperature (with respect for privacy), the minimum distance during work and all other activities such as breaks, canteen breaks, access to toilets, in addition to the adoption of specifically developed safety procedures, such as e.g. the use of man-down detection devices. In this context, the project aims to illustrate a system able of providing support in the safeguarding of workers' health on construction sites. This system, based on information received from sensors capable of identifying workers' positions (e.g., if less than 1m away) and their vital parameters (e.g., body temperature, gasped breathing), as well as moving objects inside the construction site area (e.g., to check if a worker is passing under a moving crane), will raise early alerts directly to the workers and/or to the central software, with respect for privacy, to immediately activate all the necessary measures to mitigate the risk. The system, based on the data communicated by the various sensors, will store and process them for the purpose of extracting useful information for risk management. The proposed system configured itself as a new product taking advantage from a high Technology Readiness Level maturated from the Smart Safety Belt already developed by some of the authors.