Cross-sectional analysis of the humoral response after SARS-CoV-2 vaccination in Sardinian multiple sclerosis patients, a follow-up study.

Monitoring immune responses to SARS-CoV-2 vaccination and its clinical efficacy over time in Multiple Sclerosis (MS) patients treated with disease-modifying therapies (DMTs) help to establish the optimal strategies to ensure adequate COVID-19 protection without compromising disease control offered by DMTs. Following our previous observations on the humoral response one month after two doses of BNT162b2 vaccine (T1) in MS patients differently treated, here we present a cross-sectional and longitudinal follow-up analysis six months following vaccination (T2, n=662) and one month following the first booster (T3, n=185). Consistent with results at T1, humoral responses were decreased in MS patients treated with fingolimod and anti-CD20 therapies compared with untreated patients also at the time points considered here (T2 and T3). Interestingly, a strong upregulation one month after the booster was observed in patients under every DMTs analyzed, including those treated with fingolimod and anti-CD20 therapies. Although patients taking these latter therapies had a higher rate of COVID-19 infection five months after the first booster, only mild symptoms that did not require hospitalization were reported for all the DMTs analyzed here. Based on these findings we anticipate that additional vaccine booster shots will likely further improve immune responses and COVID-19 protection in MS patients treated with any DMT.

Violence Against Healthcare: A Public Health Issue beyond Conflict Settings.

A 3-year analysis released in August 2021 by the WHO indicated that more than 700 healthcare workers and patients have died (2,000 injured) as a result of attacks against health facilities since 2017. The COVID-19 pandemic has made the risks even worse for doctors, nurses, and support staff, unfortunately. According to the latest figures from the International Committee of the Red Cross, 848 COVID-19-related violent incidents were recorded in 2020, and this is likely an underrepresentation of a much more widespread phenomenon. In response to rises in COVID-19-related attacks against healthcare, some countries have taken action. In Algeria, for instance, the penal code was amended to increase protection for healthcare workers against attacks and to punish individuals who damage health facilities. In the United Kingdom, the police, crime, sentencing, and courts bill proposed increased the maximum penalty from 12 months to 2 years in prison for anyone who assaults an emergency worker. Measures taken by countries represent a good practical way to counteract this crisis within COVID-19. However, we stress the importance of primary prevention with the use of communication: social media and other communication channels are fundamentally important to combat violence against health professionals, both to inform the population with quality data and to disseminate campaigns to prevent these acts.

Are "cases", "waves", "tests" and "modeling" deceiving indicators to describe the COVID-19 pandemic?

This commentary elaborates on different methodological aspects complicating the interpretation of epidemiological data related to the current COVID-19 pandemic, thus preventing reliable within and across-country estimates. Firstly, an inaccuracy of epidemiological data maybe arguably be attributed to passive surveillance, a relatively long incubation period during which infected individuals can still shed high loads of virus into the surrounding environment and the very high proportion of cases not even developing signs and/or symptoms of COVID-19. The latter is also the major reason for the inappropriateness of the abused "wave" wording, which gives the idea that health system starts from scratch to respond between "peaks". Clinical data for case-management on the other hand often requires complex technology in order to merge and clean data from health care facilities. Decision-making is often further derailed by the overuse of epidemiological modeling: precise aspects related to transmissibility, clinical course of COVID-19 and effectiveness of the public health and social measures are heavily influenced by unbeknownst and unpredictable human behaviors and modelers try to overcome missing epidemiological information by relying on poorly precise or questionable assumptions. Therefore the COVID-9 pandemic may provide a valuable opportunity to rethink how we are dealing with the very basic principles of epidemiology as well as risk communication issues related to such an unprecedented emergency situation.

Non-COVID-19 patients in times of pandemic: Emergency department visits, hospitalizations and cause-specific mortality in Northern Italy.

The COVID-19 pandemic forced healthcare services organization to adjust to mutating healthcare needs. Not exhaustive data are available on the consequences of this on non-COVID-19 patients. The aim of this study was to assess the impact of the pandemic on non-COVID-19 patients living in a one-million inhabitants' area in Northern Italy (Bologna Metropolitan Area-BMA), analyzing time trends of Emergency Department (ED) visits, hospitalizations and mortality. We conducted a retrospective observational study using data extracted from BMA healthcare informative systems. Weekly trends of ED visits, hospitalizations, in- and out-of-hospital, all-cause and cause-specific mortality between December 1st, 2019 to May 31st, 2020, were compared with those of the same period of the previous year. Non-COVID-19 ED visits and hospitalizations showed a stable trend until the first Italian case of COVID-19 has been recorded, on February 19th, 2020, when they dropped simultaneously. The reduction of ED visits was observed in all age groups and across all severity and diagnosis groups. In the lockdown period a significant increase was found in overall out-of-hospital mortality (43.2%) and cause-specific out-of-hospital mortality related to neoplasms (76.7%), endocrine, nutritional and metabolic (79.5%) as well as cardiovascular (32.7%) diseases. The pandemic caused a sudden drop of ED visits and hospitalizations of non-COVID-19 patients during the lockdown period, and a concurrent increase in out-of-hospital mortality mainly driven by deaths for neoplasms, cardiovascular and endocrine diseases. As recurrencies of the COVID-19 pandemic are underway, the scenario described in this study might be useful to understand both the population reaction and the healthcare system response at the early phases of the pandemic in terms of reduced demand of care and systems capability in intercepting it.

CovHos, a New Score to Predict the Need of Hospitalization for Coronavirus Disease 2019 (COVID-19) Patients at the Emergency Department.

INTRODUCTION AND AIM: As first receivers of suspected coronavirus disease 2019 (COVID-19) patients, clinicians of the Emergency Department (ED) have to rapidly perform the first clinical assessment evaluating the intensity of care needed. So far, clear management guidelines still lack. We identified variables associated with hospitalization in order to give a quick tool to assist clinicians in stratifying cases based on the severity at their arrival at the ED and in predicting the need for hospital care.  Methods: This is a monocentric observational prospective study enrolling COVID-19 patients. A score for hospitalization prediction (CovHos Score) was created using variables associated with hospitalization at multivariate analysis and then validated on an internal subsequent cohort. RESULTS: A total of 667 patients were included; 465 (69.7%) were hospitalized and 108 (16.2%) died at 30-days follow-up. In a multivariate analysis, male sex, age>65, alveolar-to-arterial oxygen gradient percentage increase compared to that expected for age, neutrophils/lymphocytes ratio and C-reactive protein levels were significantly associated with a higher rate of hospital admission. A CovHos score cut-off of 12 points predicted hospitalization with 85% sensitivity and 82.4 % specificity (area under a receiver operating characteristic curve [AUROC] = 0.909, 95% CI 0.884 - 0.935). Similar results were obtained in the validation court. A cut-off of 22 has 79% sensitivity and 77% specificity in predicting mortality (AUROC = 0.824; 95% CI 0.782-0.866); sensitivity and specificity were respectively 71.4% and 71.3% in the validation group. CONCLUSIONS: Although medical judgment still remains crucial, the CovHos score is an effective tool to assist emergency clinicians in predicting the need for hospitalization or to optimize allocation in a shortage of hospital resources.

Patterns of Emergency Department visits for acute and chronic diseases during the two pandemic waves in Italy.

BACKGROUND: Evidence is lacking about the impact of subsequent COVID-19 pandemic waves on Emergency Departments (ED). We analyzed the differences in patterns of ED visits in Italy during the two pandemic waves, focusing on changes in accesses for acute and chronic diseases. METHODS: We conducted a retrospective study using data from a metropolitan area in northern Italy that includes twelve ED. We analyzed weekly trends in non-COVID-19 ED visits during the first (FW) and second wave (SW) of the pandemic. Incidence rate ratios (IRRs) of triage codes, patient destination, and cause-specific ED visits in the FW and SW of the year 2020 vs. 2019 were estimated using Poisson regression models. MAIN FINDINGS: We found a significant decrease of ED visits by triage code, which was more marked for low priority codes and during the FW. We found an increased share of hospitalizations compared to home discharges both in the FW and in the SW. ED visits for acute and chronic conditions decreased during the FW, ranging, from -70% for injuries (IRR = 0.2862, p < 0.001) to -50% and - 60% for ischemic heart disease and heart failure. CONCLUSIONS: The two pandemic waves led to a selection of patients with higher and more urgent needs of acute hospital care. These findings should lead to investigate how to improve systems' capacity to manage changes in population needs.