Cardiovascular Risk Assessment after COVID-19 Infection before Resuming Sports Activities - Practical Flowchart and Meta-Analysis

Abstract Background: The risk of sports-related sudden cardiac arrest after COVID-19 infection can be a serious problem. There is an urgent need for evidence-based criteria to ensure patient safety before resuming exercise. Objective: To estimate the pooled prevalence of acute myocardial injury caused by COVID-19 and to provide an easy-to-use cardiovascular risk assessment toolkit prior to resuming sports activities after COVID-19 infection. Methods: We searched the Medline and Cochrane databases for articles on the prevalence of acute myocardial injury associated with COVID-19 infection. The pooled prevalence of acute myocardial injury was calculated for hospitalized patients treated in different settings (non-intensive care unit [ICU], ICU, overall hospitalization, and non-survivors). Statistical significance was accepted for p values <0.05. We propose a practical flowchart to assess the cardiovascular risk of individuals who recovered from COVID-19 before resuming sports activities. Results: A total of 20 studies (6,573 patients) were included. The overall pooled prevalence of acute myocardial injury in hospitalized patients was 21.7% (95% CI 17.3-26.5%). The non-ICU setting had the lowest prevalence (9.5%, 95% CI 1.5-23.4%), followed by the ICU setting (44.9%, 95% CI 27.7-62.8%), and the cohort of non-survivors (57.7% with 95% CI 38.5-75.7%). We provide an approach to assess cardiovascular risk based on the prevalence of acute myocardial injury in each setting. Conclusions: Acute myocardial injury is frequent and associated with more severe disease and hospital admissions. Cardiac involvement could be a potential trigger for exercise-induced clinical complications after COVID-19 infection. We created a toolkit to assist with clinical decision-making prior to resuming sports activities after COVID-19 infection.

CIRO: COVID-19 infection risk ontology.

Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not employed in countries with high patient volumes. Meanwhile, the Japanese government conducted this operation, thereby contributing to the control of infections, at the cost of arduous manual labor by public health officials. To ease the burden of the officials, this study attempted to automate the assessment of each person's infection risk through an ontology, called COVID-19 Infection Risk Ontology (CIRO). This ontology expresses infection risks of COVID-19 formulated by the Japanese government, toward automated assessment of infection risks of individuals, using Resource Description Framework (RDF) and SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we demonstrated that the knowledge graph built could infer the risks, formulated by the government. Moreover, we conducted reasoning experiments to analyze the computational efficiency. The experiments demonstrated usefulness of the knowledge processing, and identified issues left for deployment.

Informing Building Strategies to Reduce Infectious Aerosol Transmission Risk by Integrating DNA Aerosol Tracers with Quantitative Microbial Risk Assessment.

Using aerosol-based tracers to estimate risk of infectious aerosol transmission aids in the design of buildings with adequate protection against aerosol transmissible pathogens, such as SARS-CoV-2 and influenza. We propose a method for scaling a SARS-CoV-2 bulk aerosol quantitative microbial risk assessment (QMRA) model for impulse emissions, coughing or sneezing, with aerosolized synthetic DNA tracer concentration measurements. With point-of-emission ratios describing relationships between tracer and respiratory aerosol emission characteristics (i.e., volume and RNA or DNA concentrations) and accounting for aerosolized pathogen loss of infectivity over time, we scale the inhaled pathogen dose and risk of infection with time-integrated tracer concentrations measured with a filter sampler. This tracer-scaled QMRA model is evaluated through scenario testing, comparing the impact of ventilation, occupancy, masking, and layering interventions on infection risk. We apply the tracer-scaled QMRA model to measurement data from an ambulatory care room to estimate the risk reduction resulting from HEPA air cleaner operation. Using DNA tracer measurements to scale a bulk aerosol QMRA model is a relatively simple method of estimating risk in buildings and can be applied to understand the impact of risk mitigation efforts.

Healthcare workers exposure risk assessment in the context of the COVID-19: a survey among frontline workers in Qazvin, Iran.

BACKGROUND: Healthcare workers perform various clinical procedures for COVID-19 patients facing an elevated risk of exposure to SARS-COV-2.This study aimed to assess the healthcare workers' exposure to COVID-19 in Qazvin, Iran in 2020. METHODS: We conducted this descriptive-analytical study among all healthcare workers on the frontline of exposure to COVID-19 in Qazvin province. We entered the participants into the study using a multi-stage stratified random sampling method. We utilized a questionnaire, "Health workers exposure risk assessment and management in the context of COVID-19 disease", designed by the World Health Organization (WHO) to collect data. We analyzed data using descriptive and analytical methods with SPSS software version 24. RESULTS: The results showed that all participants in the study had occupational exposure to the COVID-19 virus. So of 243 healthcare workers, 186 (76.5%) were at low risk and 57 (23.5%) at high risk of COVID-19 virus infection. Also, from the six domains mentioned in the questionnaire, health workers exposure risk assessment and management in the context of COVID-19 disease, the mean score of the domain of the type of healthcare worker interaction with a confirmed COVID-19 patient, the domain of health worker activities performed on a confirmed COVID-19 patient, the domain of the adherence to infection prevention and control (IPC) during health care interactions, and the domain of the adherence to IPC when performing aerosol-generating procedures in the high-risk group were more than the low-risk group. CONCLUSION: Despite strict WHO guidelines, many healthcare workers are exposed at contracting COVID-19. Therefore, healthcare managers, planners, and policymakers can revise the policies, provide appropriate and timely personal protective equipment, and plan for ongoing training for staff on the principles of infection prevention and control.

Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis.

BACKGROUND: Risk assessment of patients with acute COVID-19 in a telemedicine context is not well described. In settings of large numbers of patients, a risk assessment tool may guide resource allocation not only for patient care but also for maximum health care and public health benefit. OBJECTIVE: The goal of this study was to determine whether a COVID-19 telemedicine risk assessment tool accurately predicts hospitalizations. METHODS: We conducted a retrospective study of a COVID-19 telemedicine home monitoring program serving health care workers and the community in Atlanta, Georgia, with enrollment from March 24 to May 26, 2020; the final call range was from March 27 to June 19, 2020. All patients were assessed by medical providers using an institutional COVID-19 risk assessment tool designating patients as Tier 1 (low risk for hospitalization), Tier 2 (intermediate risk for hospitalization), or Tier 3 (high risk for hospitalization). Patients were followed with regular telephone calls to an endpoint of improvement or hospitalization. Using survival analysis by Cox regression with days to hospitalization as the metric, we analyzed the performance of the risk tiers and explored individual patient factors associated with risk of hospitalization. RESULTS: Providers using the risk assessment rubric assigned 496 outpatients to tiers: Tier 1, 237 out of 496 (47.8%); Tier 2, 185 out of 496 (37.3%); and Tier 3, 74 out of 496 (14.9%). Subsequent hospitalizations numbered 3 out of 237 (1.3%) for Tier 1, 15 out of 185 (8.1%) for Tier 2, and 17 out of 74 (23%) for Tier 3. From a Cox regression model with age of 60 years or older, gender, and reported obesity as covariates, the adjusted hazard ratios for hospitalization using Tier 1 as reference were 3.74 (95% CI 1.06-13.27; P=.04) for Tier 2 and 10.87 (95% CI 3.09-38.27; P<.001) for Tier 3. CONCLUSIONS: A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified populations with low, intermediate, and high risk of hospitalization.

Developing a risk assessment method for infectious diseases focusing on COVID-19 based on the Delphi method and fuzzy analytical hierarchy process.

BACKGROUND: Given the coronavirus 2019 (COVID-19) risk, it is essential to develop a comprehensive risk assessment method to manage the risk of the infectious diseases. OBJECTIVE: This study aimed to develop a risk assessment method for infectious diseases focusing on COVID-19. METHOD: This study was based on the fuzzy Delphi method (FDM) and fuzzy analytical hierarchical process (FAHP) in three steps: (a) designing the preliminary risk assessment algorithm by reviewing the literature, (b) corroborating the designed structure based on the majority opinions of the expert panel and assigning scores to different factors according to the Delphi method, and (c) determining the weight of components and their factors based on the FAHP. RESULTS: The COVID-19 risk index (CVRI) was found to be affected by four components and 19 factors. The four components consisted of the probability of getting sick (5 factors), disease severity (4 factors), health beliefs level (3 factors), and exposure rate (6 factors). The identified components and their relevant factors had different weights and effects on the CVIR. The weights of probability, severity, health beliefs level, and exposure rate components were 0.27, 0.20, 0.14, and 0.38, respectively. The CVRI was found to range from 0.54 to 0.82, defined in three levels. CONCLUSION: Given the significant effects of identified components, factors, and parameters on the incidence of COVID-19 on the one hand and using the FDM and FAHP on the other, the proposed method can be considered as an appropriate method for managing the risk of COVID-19 and other infectious diseases.

Performance of prognostic scores in prediction of 30-day postoperative mortality in COVID-19 patients after emergency surgery: A retrospective cohort study.

Background: : Risk assessment with prognostic scoring, though important, is scarcely studied in emergency surgical patients with COVID-19 infection. Methods and Material: We conducted a retrospective cohort study on adult emergency surgical patients with COVID-19 infection in our institute from 1 May 2020 to 31 October 2021 to find the 30-day postoperative mortality and predictive accuracy of prognostic scores. We assessed the demographic data, prognostic risk scores (American Society of Anesthesiologists-Physical Classification (ASA-PS), Sequential Organ Failure Assessment (SOFA), Quick SOFA (qSOFA), Physiologic and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Portsmouth-POSSUM (P-POSSUM) scores), surgical and anesthetic factors. We assessed the postoperative morbidity using the Clavien-Dindo scale and recorded the 30-day mortality. Correlation of prognostic scores and mortality was evaluated using Univariate Cox proportional hazards regression, receiver operating characteristic curve (ROC), Youden's index and Hosmer- Lemeshow goodness of fit model. Results: Emergency surgery was performed in 67 COVID-19 patients with postoperative complication and 30-day mortality rate of 33% and 19%, respectively. A positive qSOFA and ASAPS IIIE/IVE had a 9.03- and 12.7-times higher risk of mortality compared to a negative qSOFA and ASA-PS IE/IIE (P < 0.001), respectively. Every unit increase of SOFA, POSSUM and P-POSSUM scores was associated with a 50%, 18% and 17% higher risk of mortality, respectively. SOFA, POSSUM and P-POSSUM AUCROC curves showed good discrimination between survivors and non-survivors (AUC 0.8829, 0.85 and 0.86, respectively). Conclusions: SOFA score has a higher sensitivity to predict 30-day postoperative mortality as compared to POSSUM and P-POSSUM. However, in absence of a control group of non-COVID-19 patients, actual risk attributable to COVID-19 infection could not be determined.

Prehospital risk assessment in patients suspected of non-ST-segment elevation acute coronary syndrome: a systematic review and meta-analysis.

OBJECTIVE: To review, inventory and compare available diagnostic tools and investigate which tool has the best performance for prehospital risk assessment in patients suspected of non-ST-segment elevation acute coronary syndrome (NSTE-ACS). METHODS: Systematic review and meta-analysis. Medline and Embase were searched up till 1 April 2021. Prospective studies with patients, suspected of NSTE-ACS, presenting in the primary care setting or by emergency medical services (EMS) were included. The most important exclusion criteria were studies including only patients with ST-elevation myocardial infarction and studies before 1995, the pretroponin era. The primary end point was the final hospital discharge diagnosis of NSTE-ACS or major adverse cardiac events (MACE) within 6 weeks. Risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies Criteria. MAIN OUTCOME AND MEASURES: Sensitivity, specificity and likelihood ratio of findings for risk stratification in patients suspected of NSTE-ACS. RESULTS: In total, 15 prospective studies were included; these studies reflected in total 26 083 patients. No specific variables related to symptoms, physical examination or risk factors were useful in risk stratification for NSTE-ACS diagnosis. The most useful electrocardiographic finding was ST-segment depression (LR+3.85 (95% CI 2.58 to 5.76)). Point-of-care troponin was found to be a strong predictor for NSTE-ACS in primary care (LR+14.16 (95% CI 4.28 to 46.90) and EMS setting (LR+6.16 (95% CI 5.02 to 7.57)). Combined risk scores were the best for risk assessment in an NSTE-ACS. From the combined risk scores that can be used immediately in a prehospital setting, the PreHEART score, a validated combined risk score for prehospital use, derived from the HEART score (History, ECG, Age, Risk factors, Troponin), was most useful for risk stratification in patients with NSTE-ACS (LR+8.19 (95% CI 5.47 to 12.26)) and for identifying patients without ACS (LR-0.05 (95% CI 0.02 to 0.15)). DISCUSSION: Important study limitations were verification bias and heterogeneity between studies. In the prehospital setting, several diagnostic tools have been reported which could improve risk stratification, triage and early treatment in patients suspected for NSTE-ACS. On-site assessment of troponin and combined risk scores derived from the HEART score are strong predictors. These results support further studies to investigate the impact of these new tools on logistics and clinical outcome. FUNDING: This study is funded by ZonMw, the Dutch Organisation for Health Research and Development. TRIAL REGISTRATION NUMBER: This meta-analysis was published for registration in PROSPERO prior to starting (CRD York, CRD42021254122).

Evaluating the Preparedness of Indian States against COVID-19 Pandemic Risk: A Fuzzy Multi-criteria Decision-Making Approach.

The preparedness of Indian states and union territories (UTs) against the COVID-19 pandemic has been evaluated. Ten parameters related to demographic, socioeconomic, and healthcare aspects have been considered and the performances of 27 states and three UTs have been evaluated applying the Fuzzy Analytic Hierarchy Process. Opinions of medical experts have been considered to ascertain the relative importance of decision criteria as well as subcriteria. The scores of various states and UTs in each of the decision subcriteria have been calculated by using the secondary data collected from authentic sources. It is found that Kerala and Bihar are the best prepared and worst prepared states, respectively, to combat COVID-19 pandemic. Karnataka, Goa, and Tamil Nadu have very good preparedness whereas Chhattisgarh, Jharkhand, and Bihar have very poor preparedness. Maharashtra, the most affected state in India, has average preparedness. As around 650 million people are vulnerable due to the poor and very poor preparedness of their states, the country needs to make region specific mitigation strategies to combat the COVID-19 pandemic and the preparedness map will be helpful in that direction.

A Pandemic Risk Perception Scale.

We propose a Pandemic Risk Perception Scale. Our scale comprises two constructs, Dread Risk and Personal Exposure, divided into five dimensions: Infection Risk, Emotional Health Risk, Health System Risk,Financial Risk, and Alimentary Risk. Using multidimensional item response theory, confirmatory factor analysis, and structural equation modeling on two samples of respondents, our results show that Alimentary Risk, Health System Risk, and Emotional Health Risk are the main dimensions of risk perception for the COVID-19 pandemic. Furthermore, Infection Risk has a minor impact on the pandemic's risk perception, suggesting the presence of different dynamics between personal and general risk perceptions for the COVID-19 pandemic.

Effective Lockdown and Role of Hospital-Based COVID-19 Transmission in Some Indian States: An Outbreak Risk Analysis.

Several reports in India indicate hospitals and quarantined centers are COVID-19 hotspots. To study the transmission occurring from the hospitals and as well as from the community, we developed a mechanistic model with a lockdown effect. Using daily COVID-19 cases data from six states and overall India, we estimated several important parameters of our model. Moreover, we provided an estimation of the effective (RT ), the basic (R0 ), the community (RC ), and the hospital (RH ) reproduction numbers. We forecast COVID-19 notified cases from May 3, 2020, till May 20, 2020, under five different lockdown scenarios in the seven locations. Our analysis suggests that 65% to 99% of the new COVID-19 cases are currently asymptomatic in those locations. Besides, about 1-16% of the total COVID-19 transmission are currently occurring from hospital-based contact and these percentage can increase up to 69% in some locations. Furthermore, the hospital-based transmission rate (ß2 ) has significant positive (0.65 to 0.8) and negative (-0.58 to -0.23) correlation with R0 and the effectiveness of lockdown, respectively. Therefore, a much larger COVID-19 outbreak may trigger from the hospital-based transmission. In most of the locations, model forecast from May 3, 2020, till May 20, 2020, indicates a two-times increase in cumulative cases in comparison to total observed cases up to April 29, 2020. Based on our results, we proposed a containment policy that may reduce the threat of a larger COVID-19 outbreak in the future.

Integrating Risk Assessment and Decision-Making Methods in Analyzing the Dynamics of COVID-19 Epidemics in Davao City, Mindanao Island, Philippines.

The COVID-19 pandemic has become a public health crisis in the Philippines and the attention of national and local health authorities is focused on managing the fluctuating COVID-19 cases. This study presents a method that integrates risk management tools into health care decision-making processes to enhance the understanding and utilization of risk-based thinking in public health decision making. The risk assessment consists of the identification of the key risk factors of the COVID-19 contagion via bow-tie diagrams. Second, the safety controls for each risk factor relevant to the Davao City context are taken into account and are identified as barriers in the bow-tie. After which, the prioritization of the identified COVID-19 risks, as well as the effectiveness of the proposed interventions, is performed using the analytic hierarchy process. Consequently, the dynamics of COVID-19 management initiatives were explored using these priorities and a system of ordinary differential equations. Our results show that reducing the number of COVID-19 fatalities should be the top priority of the health authorities. In turn, we predict that the COVID-19 contagion can be controlled and eliminated in Davao city in three-month time after prioritizing the fatalities. In order to reduce the COVID-19 fatalities, health authorities should ensure an adequate number of COVID-ready ICU facilities. The general public, on the other hand, should follow medical and science-based advice and suspected and confirmed COVID-19 patients should strictly follow isolation protocols. Overall, an informed decision-making is necessary to avoid the unwanted consequences of an uncontrolled contagion.

Risk Stratification Models for Stroke in Patients Hospitalized with COVID-19 Infection.

OBJECTIVES: To derive models that identify patients with COVID-19 at high risk for stroke. MATERIALS AND METHODS: We used data from the AHA's Get With The Guidelines® COVID-19 Cardiovascular Disease Registry to generate models for predicting stroke risk among adults hospitalized with COVID-19 at 122 centers from March 2020-March 2021. To build our models, we used data on demographics, comorbidities, medications, and vital sign and laboratory values at admission. The outcome was a cerebrovascular event (stroke, TIA, or cerebral vein thrombosis). First, we used Cox regression with cross validation techniques to identify factors associated with the outcome in both univariable and multivariable analyses. Then, we assigned points for each variable based on corresponding coefficients to create a prediction score. Second, we used machine learning techniques to create risk estimators using all available covariates. RESULTS: Among 21,420 patients hospitalized with COVID-19, 312 (1.5%) had a cerebrovascular event. Using traditional Cox regression, we created/validated a COVID-19 stroke risk score with a C-statistic of 0.66 (95% CI, 0.60-0.72). The CANDLE score assigns 1 point each for prior cerebrovascular disease, afebrile temperature, no prior pulmonary disease, history of hypertension, leukocytosis, and elevated systolic blood pressure. CANDLE stratified risk of an acute cerebrovascular event according to low- (0-1: 0.2% risk), medium- (2-3: 1.1% risk), and high-risk (4-6: 2.1-3.0% risk) groups. Machine learning estimators had similar discriminatory performance as CANDLE: C-statistics, 0.63-0.69. CONCLUSIONS: We developed a practical clinical score, with similar performance to machine learning estimators, to help stratify stroke risk among patients hospitalized with COVID-19.

Violence Risk Assessment and Risk Management: Case-Study of Filicide in an Italian Woman.

BACKGROUND: At an international level, the risk assessment and management process of violent offenders follows a standard method that implies well-defined theoretical models and the use of scientifically validated tools. In Italy, this process is still highly discretionary. The aim of this study is to highlight the advantages deriving from the use of risk assessment tools within the framework of a single case study; Methods: Recidivism risk and social dangerousness of an Italian woman perpetrator of filicide were assessed through the administration of the Level of Service/Case Management Inventory (LS/CMI) instrument supported by Historical Clinical Risk-20 Version 3 (HCR-20 V3); Results: The administration of LS/CMI showed that, in this single case, the subcomponents represent a criminogenic risk/need factor are: Family/Marital, Companions, Alcohol and Drug Problem and Leisure; while constituting strengths: employment and the absence of a Pro-criminal Orientation and an Antisocial Pattern; Conclusions: Data collected through LS/CMI indicated life areas of a single case, which should be emphasised not only to assess the risk of re-offending and social dangerousness but also for a social rehabilitation programme more suited to the subject. This study demonstrates that the LS/CMI assessment tool is suitable for the Italian context.

Complications of Adenoviral Keratoconjunctivitis in Ophthalmologists and Orthoptists: Epidemiology and Risk Factor, A Retrospective Questionnaire Analysis.

PURPOSE: The purpose of this study was to assess the medical history of adenoviral keratoconjunctivitis (AK) and subepithelial infiltrates (SEIs) among French ophthalmologists and orthoptists and the frequency of unreported occupational diseases. We also described short-term and long-term consequences of AK and evaluated associated factors. METHODS: The REDCap questionnaire was diffused online several times over 7 consecutive months, from October 2019 to May 2020, through mailing lists (French Society of Ophthalmology, residents, and hospital departments), social networks, and by word of mouth. RESULTS: Seven hundred ten participants were included with a response rate of 6.2% for ophthalmologists, 3.8% for orthoptists, and 28.3% for ophthalmology residents. The medical history of AK was found in 24.1% (95% confidence interval 21%-27.2%) of respondents and SEI in 43.9% (36.5%-51.3%) of the AK population. In total, 87.1% (82.1%-92.1%) of AK occupational diseases were not declared. In total, 57.7% of respondents took 9.4 ± 6.2 days of sick leave, mostly unofficial, and 95.7% stopped surgeries for 13.0 ± 6.6 days. Among the AK population, 39.8% had current sequelae, with 17.5% having persistent SEIs, 19.9% using current therapy, and 16.4% experiencing continuing discomfort. SEIs were associated with wearing contact lenses (odds ratio 3.31, 95% confidence interval 1.19-9.21) and smoking (4.07, 1.30-12.8). Corticosteroid therapy was associated with a greater number of sequelae (3.84, 1.51-9.75). CONCLUSIONS: AK and SEI affect a large proportion of ophthalmologists and orthoptists, possibly for years, with high morbidity leading to occupational discomfort. Few practitioners asked for either to be recognized as an occupational disease. Associated factors would require a dedicated study.

Risk stratification of patients with SARS-CoV-2 by tissue factor expression in circulating extracellular vesicles.

Inflammatory response following SARS-CoV-2 infection results in substantial increase of amounts of intravascular pro-coagulant extracellular vesicles (EVs) expressing tissue factor (CD142) on their surface. CD142-EV turned out to be useful as diagnostic biomarker in COVID-19 patients. Here we aimed at studying the prognostic capacity of CD142-EV in SARS-CoV-2 infection. Expression of CD142-EV was evaluated in 261 subjects admitted to hospital for pneumonia and with a positive molecular test for SARS-CoV-2. The study population consisted of a discovery cohort of selected patients (n = 60) and an independent validation cohort including unselected consecutive enrolled patients (n = 201). CD142-EV levels were correlated with post-hospitalization course of the disease and compared to the clinically available 4C Mortality Score as referral. CD142-EV showed a reliable performance to predict patient prognosis in the discovery cohort (AUC = 0.906) with an accuracy of 81.7%, that was confirmed in the validation cohort (AUC = 0.736). Kaplan-Meier curves highlighted a high discrimination power in unselected subjects with CD142-EV being able to stratify the majority of patients according to their prognosis. We obtained a comparable accuracy, being not inferior in terms of prediction of patients' prognosis and risk of mortality, with 4C Mortality Score. The expression of surface vesicular CD142 and its reliability as prognostic marker was technically validated using different immunocapture strategies and assays. The detection of CD142 on EV surface gains considerable interest as risk stratification tool to support clinical decision making in COVID-19.

SARS-CoV-2 infection among hospitalised pregnant women and impact of different viral strains on COVID-19 severity in Italy: a national prospective population-based cohort study.

OBJECTIVE: The primary aim of this article was to describe SARS-CoV-2 infection among pregnant women during the wild-type and Alpha-variant periods in Italy. The secondary aim was to compare the impact of the virus variants on the severity of maternal and perinatal outcomes. DESIGN: National population-based prospective cohort study. SETTING: A total of 315 Italian maternity hospitals. SAMPLE: A cohort of 3306 women with SARS-CoV-2 infection confirmed within 7 days of hospital admission. METHODS: Cases were prospectively reported by trained clinicians for each participating maternity unit. Data were described by univariate and multivariate analyses. MAIN OUTCOME MEASURES: COVID-19 pneumonia, ventilatory support, intensive care unit (ICU) admission, mode of delivery, preterm birth, stillbirth, and maternal and neonatal mortality. RESULTS: We found that 64.3% of the cohort was asymptomatic, 12.8% developed COVID-19 pneumonia and 3.3% required ventilatory support and/or ICU admission. Maternal age of 30-34 years (OR 1.43, 95% CI 1.09-1.87) and ≥35 years (OR 1.62, 95% CI 1.23-2.13), citizenship of countries with high migration pressure (OR 1.75, 95% CI 1.36-2.25), previous comorbidities (OR 1.49, 95% CI 1.13-1.98) and obesity (OR 1.72, 95% CI 1.29-2.27) were all associated with a higher occurrence of pneumonia. The preterm birth rate was 11.1%. In comparison with the pre-pandemic period, stillbirths and maternal and neonatal deaths remained stable. The need for ventilatory support and/or ICU admission among women with pneumonia increased during the Alpha-variant period compared with the wild-type period (OR 3.24, 95% CI 1.99-5.28). CONCLUSIONS: Our results are consistent with a low risk of severe COVID-19 disease among pregnant women and with rare adverse perinatal outcomes. During the Alpha-variant period there was a significant increase of severe COVID-19 illness. Further research is needed to describe the impact of different SARS-CoV-2 viral strains on maternal and perinatal outcomes.

Strategies to reduce risk perception among grocery shoppers in the US: A survey study.

During the COVID-19 lockdown in the US, many businesses were shut down temporarily. Essential businesses, most prominently grocery stores, remained open to ensure access to food and household essentials. Grocery shopping presents increased potential for COVID-19 infection because customers and store employees are in proximity to each other. This study investigated shoppers' perceptions of COVID-19 infection risks and put them in context by comparing grocery shopping to other activities outside home, and examined whether a proactive preventive action by grocery stores influence shoppers' perceived risk of COVID-19 infection. Our data were obtained via an anonymous online survey distributed between April 2 and 10, 2020 to grocery shoppers in New York State (the most affected by the pandemic at the time of the study) and Washington State (the first affected by the pandemic). We found significant factors associated with high levels of risk perception on grocery shoppers. We identified some effective preventive actions that grocery stores implement to alleviate anxiety and risk perception. We found that people are generally more concerned about in-store grocery shopping relative to other out-of-home activities. Findings suggest that a strict policy requiring grocery store employees to use facemasks and gloves greatly reduced shoppers' perceived risk rating of infection of themselves by 37.5% and store employees by 51.2%. Preventive actions by customers and businesses are critical to reducing the unwitting transmission of COVID-19 as state governments prepare to reopen the economy and relax restrictions on activities outside home.