Assessment of SARS-CoV-2 airborne infection transmission risk in public buses

Public transport environments are thought to play a key role in the spread of SARS-CoV-2 worldwide. Indeed, high crowding indexes (i.e. high numbers of people relative to the vehicle size), inadequate clean air supply, and frequent extended exposure durations make transport environments potential hotspots for transmission of respiratory infections. During the COVID-19 pandemic, generic mitigation measures (e.g. physical distancing) have been applied without also considering the airborne transmission route. This is due to the lack of quantified data about airborne contagion risk in transport environments.In this study, we apply a novel combination of close proximity and room-scale risk assessment approaches for people sharing public transport environments to predict their contagion risk due to SARS-CoV-2 respiratory infection. In particular, the individual infection risk of susceptible subjects and the transmissibility of SARS-CoV-2 (expressed through the reproduction number) are evaluated for two types of buses, differing in terms of exposure time and crowding index: urban and long-distance buses. Infection risk and reproduction number are calculated for different scenarios as a function of the ventilation rates (both measured and estimated according to standards), crowding indexes, and travel times. The results show that for urban buses, the close proximity contribution significantly affects the maximum occupancy to maintain a reproductive number of <1. In particular, full occupancy of the bus would be permitted only for an infected subject breathing, whereas for an infected subject speaking, masking would be required. For long-distance buses, full occupancy of the bus can be maintained only if specific mitigation solutions are simultaneously applied. For example, for an infected person speaking for 1 h, appropriate filtration of the recirculated air and simultaneous use of FFP2 masks would permit full occupancy of the bus for a period of almost 8 h. Otherwise, a high percentage of immunized persons (>80%) would be needed.(c) 2022 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Leadership in public health crisis: a review to summarize lessons learned from COVID-19 pandemic : Angelica Valz Gris

Background During the COVID-19 pandemic, several public health challenges were faced, requiring worldwide leaders able to direct, guide, and establish appropriate strategies. The aim of this review was to summarize evidence on public health leadership during the COVID-19 era. Methods The systematic literature review was conducted according to the PRISMA 2020 checklist. A search of relevant articles was performed in the PubMed, Scopus, and Web of Science databases. Eligible articles were any type of publication, published between 2020 and 2022, that outlined one or more characteristics of effective public health leadership during the COVID-19 pandemic We excluded all articles that did not explicitly address the COVID-19 pandemic or had a different setting. Results A total of 2499 records were screened, and 45 articles were included. We identified 93 characteristics, clustered in six groups, that were reported as fundamental to be an effective leader in public health crises worldwide. Emotional intelligence and human traits (reported by 46.67% of the articles) were considered essential to build trust in the population and ensure cooperation with working groups. Communication skills (47%) are considered necessary to enable people to understand and accept measures. A supportive, multidisciplinary team and accountability mechanisms (33,33%) were highlighted as central elements, especially in the international field, to ensure reliability and consistency in action. Management skills (35,56%), adaptability (44,44%), and evidence-based approach (33,33%) were reported as key capabilities to ensure a prompt and rapid response to the challenges created by the pandemic. Conclusions The identification of the attributes of an effective public health leader conducted in this study is useful in choosing the key personalities who must lead public health today and in the training of tomorrow's European and worldwide leaders to be ready to face future threats. Key messages • Effective public health leaders in crisis are empathetic and trustworthy people, who have developed management and communication skills, and are able to make timely and evidence-based decisions. • In order to create leaders capable of facing future threats, more emphasis in the training of public health workforce on soft skills and management competencies should be recommended.

An interdisciplinary intervention for health prevention and promotion in a Roman neighborhood : Alberto Lontano

Influencing behavioral patterns through primary prevention, possibly addressing more risk factors at a time, is the most effective means to tackle cardiovascular diseases. Many interdisciplinary prevention activities have been coordinated by community nurses outside of specialist centers, resulting in a more effective control of risk factors. Our study aims at describing the impact of an 18-month prevention and promotion, interdisciplinary intervention on lifestyle habits and cardiovascular risk. From December 2018 to May 2020, patients were recruited by 4 General Practitioners (GPs) in the Roman neighborhood of Torresina and received nutritional, physical and psychological counselling to learn healthy lifestyles. Until May 2020 patients had to self-manage their new healthy habits, but during this phase the SARS-CoV-2 pandemic broke out. Patients were assessed at baseline, 6, 12 and 18 months by a nutritionist, a physiotherapist, a psychologist, the 4 GPs and community nurses, and the cardiovascular risk score (CRS) was estimated at every examination. 76 patients were included, with a mean age of 54,6 years. Mean CRS showed a significant reduction between baseline and 12 months (from 4.9 to 3.8, p < 0.001), but this trend was not maintained at 18 months. As for variables included in the calculation of the cardiovascular risk score, both total cholesterol and systolic blood pressure significantly decreased at 6 months of follow up (respectively, from 211.1 to 192 (p < 0.001) and from 133.1 to 123.1(p < 0.001)). Nontheless, the reduction was maintained in the remaining points in time only for systolic blood pressure. Our interdisciplinary educational intervention in a primary care setting resulted in a CRS improvement at 12 months, but this changes where not maintained at 18 months. Community nurses were facilitators in improving health outcomes and patient's satisfaction in the described primary care setting. Key messages Our interdisciplinary educational intervention in a primary care setting resulted in a CRS improvement at 12 months, but this changes where not maintained at 18 months. Community nurses are facilitators in improving health outcomes and patient’s satisfaction in the described primary care setting.

Gender differences in death rates due to the covid-19 pandemic in italy

Italy was the first European country hit by the COVID-19 pandemic. As of March 3, 2021, the Italian national surveillance system had registered 2,953,120 cases and 96,977 deaths, with gender differences both in terms of cases and deaths. Therefore, the aim of this observational ecological study is to analyze gender differences during the first year of COVID-19 pandemic in Italy in terms of crude mortality rate (CMR) and case fatality rate (CFR). We collected data from the official database of the Italian National Institute of Health (Istituto Superiore di Sanità – ISS). We considered the two waves of the pandemic, using the date between the two peaks that had the trough (lowest number of deaths) for a 7-day average to separate the two periods). Then, we calculated the CMR and CFR by age group for males and females, considering three periods: overall, first wave and second wave. Our study shows that male gender reported both higher CMR and CFR than female, and this data is confirmed for all the age group. Although many demographic, socioeconom-ic, hormonal, genetic, and epigenetic factors may explain these differences, further studies are required in order to understand the pathophysiological mechanisms underlying gender differences in CMR and CFR due to COVID-19. © 2021, Il Pensiero Scientifico Editore s.r.l.. All rights reserved.

From infections to preventive actions monitoring in health facilities: an experience on MDROs

Healthcare-Associated Infections (HAIs) and Antimicrobial Resistance (AMR) involve high costs both in health and economic terms for patients and health systems. Implementing Infection Prevention and Control (IPC) programs is critical to decrease infectious agents' transmission in healthcare settings. The aim of this study is to assess if the monitoring of Contact Precautions could decrease the incidence of Multi-Drug Resistant Organisms (MRDOs) infections. This pilot study was conducted in a teaching hospital in Rome. A checklist of 16 items was developed to assess the compliance to Contact Precautions in 11 hospital wards in which MRDOs were detected between November and December 2020. It was administered on-site both interviewing healthcare professionals and through direct observation. A paired t-test with α = 5% was used to compare the number of alert organisms in the first quarter of 2020 respect to the first quarter of 2021 before and after implementing the surveillance checklist. A total of 30 checklists were analyzed. The rate of compliance to Contact Precautions was high for the proper use of personal protective equipment (100%), the intensified room cleaning (100%) and the presence of isolation mark (100%), while it was low for the active screening of contacts (53%). Mean MDROs infections rate decreased from 4.94 to 4.37 for every 1000 hospitalization day, with an average decrease of 0.57. However, the paired t-test showed that there was no statistically significant difference between the mean number of MDROs infections before and after the implementation of the checklist (p > 0.05). Despite the good adherence to IPC program, the low infection rate decrease, is probably due to the impact of COVID-19 on the HAIs surveillance and prevention practices. Even if the check-list administration could be a useful tool to reduce MDROs infections, it should be associated to other prevention strategies during the COVID-19 pandemic in order to achieve a successful outcome. Key messages Preventing Healthcare-Associated Infections represents a priority public health challenge in order to improve patient safety and health system economic sustainability. The COVID-19 pandemic has shown that healthcare facilities should enhance efforts in their IPC programs to reduce Healthcare-Associated Infections.

Machine learning and COVID-19: a tool for healthcare setting choice by primary care physicians

Background Primary care physicians have a crucial role in determining the appropriate healthcare setting for their confirmed or suspect COVID-19 patients. Machine learning provides science-based tools that can be used for clinical decision-making which have already been applied to the fight against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) both in the therapeutic and the prevention area. The aim of this study was to develop a machine learning-based tool to support primary care physicians to decide between home monitoring and hospitalization for their patients before diagnostic test results are available. Methods A retrospective cohort study with data from a hospital setting was performed. Patients' medical history and clinical, laboratory and radiological findings were collected and the dataset was used to train a predictive model for COVID-19 severity. The patients were divided between confirmed and suspect cases on the basis of the positivity of the nasopharyngeal RT-PCR test results. A splitting algorithm was recursively used to choose the predictor. A decision tree was built. Results A total of 198 subjects were enrolled for the study. Out of them, 28 cases were classified as mild disease, 62 as moderate disease, 64 as severe disease, and 44 as critical disease, according to WHO guidelines. The G2 value was used to determine the contribution of each obtained value to build the decision tree. The tree was, therefore, built choosing values that maximized G2 and LogWorth. SpO2 (cut point = 92%) was chosen for the optimal first split. The correspondence between inputs and outcomes was validated. Conclusions Our tool provides accurate clinical severity prediction for both confirmed and suspect COVID-19 patients. We, therefore, propose its implementation in the everyday life challenges of primary care physicians to support their clinical decision-making in providing appropriate and timely care for their patients. Key messages Primary care physicians have a crucial role in determining the appropriate healthcare setting for their confirmed or suspect COVID-19 patients. We propose a tool that provides an accurate clinical severity prediction for both confirmed and suspect COVID-19 patients to help choosing the appropriate healthcare setting for them.

Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study.

OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring. PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity. RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated. CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.

[Transmission of Sars-Cov-2 and ventilation of indoor environments. Technical notes and preventive measures]

Knowledge about the new infectious disease COVID-19, which first spread in the city of Wuhan in China, in December 2019, is based on the evidence retrieved from coronaviruses previously known to humans. The main transmission ways of the new SARS-CoV-2 virus are respiratory droplets and direct and close contact with infected individuals and contaminated surfaces. To date, some scientific publications provide initial evidence that SARS-CoV-2 can be detected in the air, thus assuming a further route of infection, that airborne, although these results are to be considered preliminary and they need careful interpretation. In support of this hypothesis, ventilation systems, aimed to improve indoor air, could represent an easy way to spread and promote the virus infection especially in hospitals and in all health facilities where the presence of infected individuals is potentially high as well as the possibility of infection by air. Indeed, by generating jets of air at different speeds, they can interfere with the mission of respiratory particles and determine an environmental diffusion of the potentially contaminating droplet. Therefore, ventilation systems could provide a potential transmission channel for the viral load able to spread out in indoor air. Nonetheless, good management, technical and operational practices may lead to a low risk of contagion, both in community and health environments.