Analysis of healthcare workers' mental health during the COVID-19 pandemic: Evidence from a Three-Wave Longitudinal Study.

The "Healthcare workers' wellbeing [Benessere Operatori]" project is an exploratory longitudinal study assessing healthcare workers' mental health at three different time points over a 14-month period during the COVID-19 pandemic. We collected socio-demographic and work-related information and assessed the perceived social support, coping strategies, and levels of depression, anxiety, insomnia, anger, burnout, and PTSD symptoms. In total, 325 Italian healthcare workers (i.e. physicians, nurses, other healthcare workers, and clerks) participated in the first initial survey and either the second or third subsequent survey. Participants reported subclinical levels of psychiatric symptoms that remained mostly unchanged across time, except for an increase in stress, depression, state anger, and emotional exhaustion symptoms. Despite subclinical levels, healthcare workers' distress can adversely affect the quality of care, patient satisfaction, and medical error rates. Therefore, implementing interventions to improve healthcare workers' wellbeing is required.

Profiling Covid-19 patients with respect to level of severity: an integrated statistical approach.

A full understanding of the characteristics of Covid-19 patients with a better chance of experiencing poor vital outcomes is critical for implementing accurate and precise treatments. In this paper, two different advanced data-driven statistical approaches along with standard statistical methods have been implemented to identify groups of patients most at-risk for death or severity of respiratory distress. First, the tree-based analysis allowed to identify profiles of patients with different risk of in-hospital death (by Survival Tree-ST analysis) and severity of respiratory distress (by Classification and Regression Tree-CART analysis), and to unravel the role on risk stratification of highly dependent covariates (i.e., demographic characteristics, admission values and comorbidities). The ST analysis identified as the most at-risk group for in-hospital death the patients with age > 65 years, creatinine [Formula: see text] 1.2 mg/dL, CRP [Formula: see text] 25 mg/L and anti-hypertensive treatment. Based on the CART analysis, the subgroups most at-risk of severity of respiratory distress were defined by patients with creatinine level [Formula: see text] 1.2 mg/dL. Furthermore, to investigate the multivariate dependence structure among the demographic characteristics, the admission values, the comorbidities and the severity of respiratory distress, the Bayesian Network analysis was applied. This analysis confirmed the influence of creatinine and CRP on the severity of respiratory distress.

Modifiable risk factors of COVID-19 in patients with multiple sclerosis: a single-centre case-control study.

BACKGROUND: Disease and treatment-associated immune system abnormalities may confer higher risk of Coronavirus disease 2019 (COVID-19) to people with multiple sclerosis (PwMS). We assessed modifiable risk factors associated with COVID-19 in PwMS. METHODS: Among patients referring to our MS Center, we retrospectively collected epidemiological, clinical and laboratory data of PwMS with confirmed COVID-19 between March 2020 and March 2021 (MS-COVID, n = 149). We pursued a 1:2 matching of a control group by collecting data of PwMS without history of previous COVID-19 (MS-NCOVID, n = 292). MS-COVID and MS-NCOVID were matched for age, expanded disability status scale (EDSS) and line of treatment. We compared neurological examination, premorbid vitamin D levels, anthropometric variables, life-style habits, working activity, and living environment between the two groups. Logistic regression and Bayesian network analyses were used to evaluate the association with COVID-19. RESULTS: MS-COVID and MS-NCOVID were similar in terms of age, sex, disease duration, EDSS, clinical phenotype and treatment. At multiple logistic regression, higher levels of vitamin D (OR 0.93, p < 0.0001) and active smoking status (OR 0.27, p < 0.0001) emerged as protective factors against COVID-19. In contrast, higher number of cohabitants (OR 1.26, p = 0.02) and works requiring direct external contact (OR 2.61, p = 0.0002) or in the healthcare sector (OR 3.73, p = 0.0019) resulted risk factors for COVID-19. Bayesian network analysis showed that patients working in the healthcare sector, and therefore exposed to increased risk of COVID-19, were usually non-smokers, possibly explaining the protective association between active smoking and COVID-19. CONCLUSIONS: Higher Vitamin D levels and teleworking may prevent unnecessary risk of infection in PwMS.

The "Healthcare Workers' Wellbeing [Benessere Operatori]" Project: A Longitudinal Evaluation of Psychological Responses of Italian Healthcare Workers during the COVID-19 Pandemic.

BACKGROUND: COVID-19 forced healthcare workers to work in unprecedented and critical circumstances, exacerbating already-problematic and stressful working conditions. The "Healthcare workers' wellbeing (Benessere Operatori)" project aimed at identifying psychological and personal factors, influencing individuals' responses to the COVID-19 pandemic. METHODS: 291 healthcare workers took part in the project by answering an online questionnaire twice (after the first wave of COVID-19 and during the second wave) and completing questions on socio-demographic and work-related information, the Depression Anxiety Stress Scale-21, the Insomnia Severity Index, the Impact of Event Scale-Revised, the State-Trait Anger Expression Inventory-2, the Maslach Burnout Inventory, the Multidimensional Scale of Perceived Social Support, and the Brief Cope. RESULTS: Higher levels of worry, worse working conditions, a previous history of psychiatric illness, being a nurse, older age, and avoidant and emotion-focused coping strategies seem to be risk factors for healthcare workers' mental health. High levels of perceived social support, the attendance of emergency training, and problem-focused coping strategies play a protective role. CONCLUSIONS: An innovative, and more flexible, data mining statistical approach (i.e., a regression trees approach for repeated measures data) allowed us to identify risk factors and derive classification rules that could be helpful to implement targeted interventions for healthcare workers.

Profiling Antibody Response Patterns in COVID-19: Spike S1-Reactive IgA Signature in the Evolution of SARS-CoV-2 Infection.

This contribution explores in a new statistical perspective the antibody responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 141 coronavirus disease 2019 (COVID-19) patients exhibiting a broad range of clinical manifestations. This cohort accurately reflects the characteristics of the first wave of the SARS-CoV-2 pandemic in Italy. We determined the IgM, IgA, and IgG levels towards SARS-CoV-2 S1, S2, and NP antigens, evaluating their neutralizing activity and relationship with clinical signatures. Moreover, we longitudinally followed 72 patients up to 9 months postsymptoms onset to study the persistence of the levels of antibodies. Our results showed that the majority of COVID-19 patients developed an early virus-specific antibody response. The magnitude and the neutralizing properties of the response were heterogeneous regardless of the severity of the disease. Antibody levels dropped over time, even though spike reactive IgG and IgA were still detectable up to 9 months. Early baseline antibody levels were key drivers of the subsequent antibody production and the long-lasting protection against SARS-CoV-2. Importantly, we identified anti-S1 IgA as a good surrogate marker to predict the clinical course of COVID-19. Characterizing the antibody response after SARS-CoV-2 infection is relevant for the early clinical management of patients as soon as they are diagnosed and for implementing the current vaccination strategies.

CXCL10 levels at hospital admission predict COVID-19 outcome: hierarchical assessment of 53 putative inflammatory biomarkers in an observational study.

BACKGROUND: Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment. METHODS: We studied in 111 COVID-19 patients prospectively followed at a single reference Hospital fifty-three potential biomarkers including alarmins, cytokines, adipocytokines and growth factors, humoral innate immune and neuroendocrine molecules and regulators of iron metabolism. Biomarkers at hospital admission together with age, degree of hypoxia, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP) and creatinine were analysed within a data-driven approach to classify patients with respect to survival and ICU outcomes. Classification and regression tree (CART) models were used to identify prognostic biomarkers. RESULTS: Among the fifty-three potential biomarkers, the classification tree analysis selected CXCL10 at hospital admission, in combination with NLR and time from onset, as the best predictor of ICU transfer (AUC [95% CI] = 0.8374 [0.6233-0.8435]), while it was selected alone to predict death (AUC [95% CI] = 0.7334 [0.7547-0.9201]). CXCL10 concentration abated in COVID-19 survivors after healing and discharge from the hospital. CONCLUSIONS: CXCL10 results from a data-driven analysis, that accounts for presence of confounding factors, as the most robust predictive biomarker of patient outcome in COVID-19.

SARS-CoV-2 Antibody Rapid Tests: Valuable Epidemiological Tools in Challenging Settings.

During the last year, mass screening campaigns have been carried out to identify immunological response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and establish a possible seroprevalence. The obtained results gained new importance with the beginning of the SARS-CoV-2 vaccination campaign, as the lack of doses has persuaded several countries to introduce different policies for individuals who had a history of COVID-19. Lateral flow assays (LFAs) may represent an affordable tool to support population screening in low-middle-income countries, where diagnostic tests are lacking and epidemiology is still widely unknown. However, LFAs have demonstrated a wide range of performance, and the question of which one could be more valuable in these settings still remains. We evaluated the performance of 11 LFAs in detecting SARS-CoV-2 infection, analyzing samples collected from 350 subjects. In addition, samples from 57 health care workers collected at 21 to 24 days after the first dose of the Pfizer-BioNTech vaccine were also evaluated. LFAs demonstrated a wide range of specificity (92.31% to 100%) and sensitivity (50% to 100%). The analysis of postvaccination samples was used to describe the most suitable tests to detect IgG response against S protein receptor binding domain (RBD). Tuberculosis (TB) therapy was identified as a potential factor affecting the specificity of LFAs. This analysis identified which LFAs represent a valuable tool not only for the detection of prior SARS-CoV-2 infection but also for the detection of IgG elicited in response to vaccination. These results demonstrated that different LFAs may have different applications and the possible risks of their use in high-TB-burden settings. IMPORTANCE Our study provides a fresh perspective on the possible employment of SARS-CoV-2 LFA antibody tests. We developed an in-depth, large-scale analysis comparing LFA performance to enzyme-linked immunosorbent assay (ELISA) and electrochemiluminescence immunoassay (ECLIA) and evaluating their sensitivity and specificity in identifying COVID-19 patients at different time points from symptom onset. Moreover, for the first time, we analyzed samples of patients undergoing treatment for endemic poverty-related diseases, especially tuberculosis, and we evaluated the impact of this therapy on test specificity in order to assess possible performance in TB high-burden countries.

A data-driven approach to identify risk profiles and protective drugs in COVID-19.

As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensin-converting enzyme 2 (ACE2), but whether or not renin-angiotensin-aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19.