ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
Subject(s)
Antibodies, Neutralizing/blood , Antibodies, Viral/blood , COVID-19/blood , Immunoglobulin A/blood , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Adult , Aged , Aged, 80 and over , COVID-19/immunology , Female , HEK293 Cells , Hospitalization , Humans , Immunoglobulin G/blood , Immunoglobulin M/blood , Male , Middle Aged , Young AdultABSTRACT
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.