COVID-19 cumulative incidence, intensive care, and mortality in Italian regions compared to selected European countries

Impact of vaccine coverage and disruption to health services on COVID-19 in Ukraine

COVID-19 surveillance in Ukraine ceased after the Russian invasion of the country in 2022, on a background of low vaccination rates of 34.5% for two doses at this time. We conducted a modelling study to estimate the epidemic trajectory of SARS-COV-2 in Ukraine after the start of the war. We use a COVID-19 deterministic Susceptible-Exposed-Infected-Recovered (SEIR) model for Ukraine to estimate the impact of increased vaccination coverage and masking as public health interventions. We fit the model output to case notification data between 6 January and 25 February 2022, then we forecast the COVID-19 epidemic trajectory in different scenarios of mask use and vaccine coverage. In the best-case scenario, 69% of the Ukrainian population would have been infected in the first half of 2022. Increasing mask use from 50 to 80% reduces cases and deaths by 17% and 30% respectively, while increasing vaccination rates to 60% and 9.6% for two and three doses respectively results in a 3% reduction in cases and 28% in deaths. However, if vaccination is increased to a higher coverage of 80% with two doses and 12.8% with three, or mask effectiveness is reduced to 40%, increasing vaccination coverage is more effective. The loss of health services, displacement, and destruction of infrastructure will amplify the risk of COVID-19 in Ukraine and make vaccine programs less feasible. Masks do not need the health infrastructure or cold-chain logistics required for vaccines and are more feasible for rapid epidemic control during war. However, increasing vaccine coverage will save more lives. Vaccination of refugees who have fled to other countries can be more feasibly achieved.

Nursing students' evaluation of clinical learning environment and supervision models before and during the COVID-19 pandemic: a comparative study

BACKGROUND AND AIM

The COVID-19 pandemic has required a rapid reorganization of clinical training and supervision models for nursing education. The aim of this study was to compare students' levels of perception of the quality of the Clinical Learning Environment (CLE) using two different clinical supervision models.

METHODS

A comparative design was implemented. A convenience sample of second and third-year undergraduate nursing students (n=127) in clinical training in the 2018/2019 Academic Year (AY) received the usual nursing staff supervision model, while during the COVID-19 pandemic in the following year, they (n=69) received an individualized supervision model. Data were collected using three instruments: the Clinical Learning Environmental Quality Evaluation Index (CLEQI); the Clinical Learning Environment, Supervision and Nurse Teacher Scale (CLES+T); and a socio-demographic tool.

RESULTS

The mean total scores of both scales had increased in the second survey (CLEQI: M=57.88±11.8 vs. M=60.88±9.3, p=0.035; CLES+T: M=148.4±23.3 vs. M=154.5±21.9, p=0.037). The nursing students reported high levels of CLE quality with both supervision models, even though the individualized supervision model was rated significantly higher by the students than the staff supervision model.

CONCLUSIONS

Students supported by a personal supervisor during clinical training had a more positive experience and rated the quality of the tutorial strategies, learning opportunities, safety and nursing care, leadership style of the ward manager and overall CLE more highly.

Emergency Medical Services Calls Analysis for Trend Prediction during Epidemic Outbreaks: Interrupted Time Series Analysis on 2020-2021 COVID-19 Epidemic in Lazio, Italy

(1) Background: During the COVID-19 outbreak in the Lazio region, a surge in emergency medical service (EMS) calls has been observed. The objective of present study is to investigate if there is any correlation between the variation in numbers of daily EMS calls, and the short-term evolution of the epidemic wave. (2) Methods: Data from the COVID-19 outbreak has been retrieved in order to draw the epidemic curve in the Lazio region. Data from EMS calls has been used in order to determine Excess of Calls (ExCa) in the 2020−2021 years, compared to the year 2019 (baseline). Multiple linear regression models have been run between ExCa and the first-order derivative (D’) of the epidemic wave in time, each regression model anticipating the epidemic progression (up to 14 days), in order to probe a correlation between the variables. (3) Results: EMS calls variation from baseline is correlated with the slope of the curve of ICU admissions, with the most fitting value found at 7 days (R2 0.33, p < 0.001). (4) Conclusions: EMS calls deviation from baseline allows public health services to predict short-term epidemic trends in COVID-19 outbreaks, and can be used as validation of current data, or as an independent estimator of future trends.

The Mediating Role of Gender, Age, COVID-19 Symptoms and Changing of Mansion on the Mental Health of Healthcare Workers Operating in Italy during the First Wave of the COVID-19 Pandemic

The COVID-19 pandemic has tested the performance of hospitals and intensive care units around the world. Health care workers (HCWs) have been used to developmental symptoms, but this was especially true during the COVID-19 pandemic when HCWs have been faced with many other sources of stress and anxiety that can usually be avoided. Moreover, long-term shifts and unprecedented population restrictions have weakened people’s ability to cope with stress. The research aims to observe the dynamic interplay between burnout, depression, distress, and anxiety in HCWs working in various settings, with specific a focus on emotional exhaustion, depersonalization, and a diminished sense of personal achievement in mediating a worse mental health status during the first wave of the COVID-19 pandemic in Italy. We performed a mediation analysis, which resulted in a strong correlation among depression, psychological distress, health perception and anxiety, and the impact of job burnout on anxiety, depression, and distress. Gender seemed to have a strong correlation with burnout, anxiety, and distress; the impact of the COVID-19 pandemic on Quality of Life seemed to affect anxiety and depression; the possible changes in job tasks and duties (intended as a change in work area or location and role change)influenced depression and job burnout. Encouraging supportive and educational strategies would be recommended to policymakers.

Quantitative assessment of the impact of COVID-19 pandemic on pancreatic surgery: an Italian multicenter analysis of 1423 cases from 10 tertiary referral centers

Few evidences are present on the consequences of coronavirus disease 2019 (COVID-19) pandemic on pancreatic surgery. Aim of this study is to evaluate how COVID-19 influenced the diagnostic and therapeutic pathways of surgical pancreatic diseases. A comparative analysis of surgical volumes and clinical, surgical and perioperative outcomes in ten Italian referral centers was conducted between the first semester 2020 and 2019. One thousand four hundred and twenty-three consecutive patients were included in the analysis: 638 from 2020 and 785 from 2019. Surgical volume in 2020 decreased by 18.7% (p < 0.0001). Benign/precursors diseases (− 43.4%; p < 0.0001) and neuroendocrine tumors (− 33.6%; p = 0.008) were the less treated diseases. No difference was reported in terms of discussed cases at the multidisciplinary tumor board (p = 0.43), mean time between diagnosis and neoadjuvant treatment (p = 0.91), indication to surgery and surgical resection (p = 0.35). Laparoscopic and robot-assisted procedures dropped by 45.4% and 61.9%, respectively, during the lockdown weeks of 2020. No difference was documented for post-operative intensive care unit accesses (p = 0.23) and post-operative mortality (p = 0.06). The surgical volume decrease in 2020 will potentially lead, in the near future, to the diagnosis of a higher rate of advanced stage diseases. However, the reassessment of the Italian Health Service kept guarantying an adequate level of care in tertiary referral centers. Clinicaltrials.gov ID: NCT04380766.

COVID-19 Pandemic: Influence of Schools, Age Groups, and Virus Variants in Italy

The estimated smooth curve of the percentage of subjects positive to SARS-CoV-2 started decreasing in Italy at the beginning of January 2021, due to the government containment measures undertaken from Christmas until 7 January. Approximately two weeks after releasing the measures, the curve stopped to decrease and remained approximately constant for four weeks to increase again in the middle of February. This epidemic phase had a public health care impact since, from the beginning of the fourth week of February, the curve of the intensive care unit’s occupancy started to grow. This wave of infection was characterized by the presence of new virus variants, with a higher than 80% dominance of the so-called “English” variant, since 15 April. School activities in Italy started at different times from 7 January until 8 February, depending on every region’s decision. Our present data on the incidence of SARS-CoV-2 in different age groups in Italy are in agreement with literature reports showing that subjects older than 10 years are involved in virus transmission. More importantly, we provide evidence to support the hypothesis that also individuals of age 0–9 years can significantly contribute to the spread of SARS-CoV-2.

The bimodal "rise and fall" ACS curve overlapping COVID-19 pandemic peaks

BACKGROUND

The COVID-19 outbreak harmed acute coronary syndromes. During the national lockdown in Italy, the fear of post-admission contagion translated into significant delays in seeking medical help among STEMI (ST-elevation myocardial infarction) patients.

OBJECTIVE AND METHODS

Our analysis aimed to assess the ACS (Acute Coronary Syndromes) admissions during the pandemic, together with time to presentation and clinical outcomes compared to 2019 in a cardiovascular hub in Milan. Data of ACS patients admitted during the pandemic year 2020 were extracted by the hospital's database and compared to a historical cohort of patients admitted for the same clinical indications in 2019.

RESULTS

A total of 599 ACS cases were recorded in 2020 vs. 386 cases in 2019, with a net 55% increase, associated with late clinical presentations, a threefold increase in cardiogenic shock, and a more than two-fold higher mortality rate.

CONCLUSIONS

The ultimate goal of this analysis is to preserve the life-saving focus on universal and prompt STEMI diagnosis and treatment, even in a time of dynamic global crisis.

Evaluation and review of preventive measures applied during COVID-19 pandemic: strategies adopted by European countries

The COVID-19 pandemic has greatly jeopardized the European continent and the spread of SARS-COV-2 has led European countries to implement a series of preventive interventions aimed at decreasing the incidence rate of the disease, in consideration of the lack of specific therapies and of a vaccine. Each European country has behaved in different ways and timing accordingly to the epidemiological trend and to different political strategies. The main purpose of preventive measures is to lower the incidence rate of the disease, avoiding the collapse of health systems and limiting the total number of severe cases and deaths. All these targets should fit with needs that go beyond scientific evidence such as economic interests, decisions of neighboring countries and specific socio-political factors for each country. The objective of this research is to clarify which preventive measures have been recommended and applied in different European countries.

Spatiotemporal Analysis of COVID-19 Incidence Data

(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model's parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.

Vaccination Criteria Based on Factors Influencing COVID-19 Diffusion and Mortality

SARS-CoV-2 is highly contagious, rapidly turned into a pandemic, and is causing a relevant number of critical to severe life-threatening COVID-19 patients. However, robust statistical studies of a large cohort of patients, potentially useful to implement a vaccination campaign, are rare. We analyzed public data of about 19,000 patients for the period 28 February to 15 May 2020 by several mathematical methods. Precisely, we describe the COVID-19 evolution of a number of variables that include age, gender, patient’s care location, and comorbidities. It prompts consideration of special preventive and therapeutic measures for subjects more prone to developing life-threatening conditions while affording quantitative parameters for predicting the effects of an outburst of the pandemic on public health structures and facilities adopted in response. We propose a mathematical way to use these results as a powerful tool to face the pandemic and implement a mass vaccination campaign. This is done by means of priority criteria based on the influence of the considered variables on the probability of both death and infection.