No Excess of Mortality from Lung Cancer during the COVID-19 Pandemic in an Area at Environmental Risk: Results of an Explorative Analysis.

BACKGROUND: The COVID-19 pandemic and the restrictive measures associated with it placed enormous pressure on health facilities and may have caused delays in the treatment of other diseases, leading to increases in mortality compared to the expected rates. Areas with high levels of air pollution already have a high risk of death from cancer, so we aimed to evaluate the possible indirect effects of the pandemic on mortality from lung cancer compared to the pre-pandemic period in the province of Taranto, a polluted site of national interest for environmental risk in the south of Italy. METHODS: We carried out a retrospective observational study on lung cancer data (ICD-10: C34) from the Registry of Mortality (ReMo) for municipalities in Taranto Province over the period of 1 January 2011 to 31 December 2021. Seasonal exponential smoothing, Holt-Winters additive, Holt-Winters multiplicative, and auto-regressive integrated moving average (ARIMA) models were used to forecast the number of deaths during the pandemic period. Data were standardized by sex and age via an indirect method and shown as monthly mortality rates (MRs), standardized mortality ratios (SMRs), and adjusted mortality rates (AMRs). RESULTS: In Taranto Province, 3108 deaths from lung cancer were recorded between 2011 and 2021. In the province of Taranto, almost all of the adjusted monthly mortality rates during the pandemic were within the confidence interval of the predicted rates, with the exception of significant excesses in March (+1.82, 95% CI 0.11-3.08) and August 2020 (+2.09, 95% CI 0.20-3.44). In the municipality of Taranto, the only significant excess rate was in August 2020 (+3.51, 95% CI 0.33-6.69). However, in total, in 2020 and 2021, the excess deaths from lung cancer were not significant both for the province of Taranto (+30 (95% CI -77; +106) for 2020 and +28 (95% CI -130; +133) for 2021) and for the municipality of Taranto alone (+14 (95% CI -47; +74) for 2020 and -2 (95% CI -86; +76) for 2021). CONCLUSIONS: This study shows that there was no excess mortality from lung cancer as a result of the COVID-19 pandemic in the province of Taranto. The strategies applied by the local oncological services during the pandemic were probably effective in minimizing the possible interruption of cancer treatment. Strategies for accessing care in future health emergencies should take into account the results of continuous monitoring of disease trends.

A Retrospective Observational Study to Assess the Effect of the COVID-19 Pandemic on Spontaneous and Voluntary Abortivity in the Apulia Region of Italy.

The spread of COVID-19 in Italy required urgent restrictive measures that led to delays in access to care and to hospital overloads and impacts on the quality of services provided by the national health service. It is likely that the area related to maternal and child health was also affected. The objective of the study was to evaluate the intensity of a possible variation in spontaneous abortion (SA) and voluntary termination of pregnancy (VTP) rates in relation to the different restrictive public health measures adopted during the pandemic period of 2020. The analysis concerned the data collected on the SAs and VTPs from public and private structures in Apulia that related to the years 2019 and 2020. The SRR (standardized rate ratio) between the standardized rates by age group in 2019 and those in 2020 were calculated using a multivariable Poisson model, and it was applied to evaluate the effect of public health restrictions on the number of SAs and VTPs, considering other possible confounding factors. The SSR was significantly lower in the first months of the pandemic compared to the same period of the previous year, both for SAs and for VTPs. The major decrease in SAs and VTPs occurred during the total lockdown phase. The results, therefore, highlight how the measures to reduce infection risk could also have modified the demand for assistance related to pregnancy interruption.

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19.

Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model's performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.

A Survey on Methodological Issues of Clinical Research Studies Reviewed by Independent Ethic Committees during the COVID-19 Pandemic in Italy.

The struggle for information and the hasty search for answers caused by the COVID-19 pandemic threatened the possibility of lowering study quality, as well as ethical committees' review standards during the outbreak. Our investigation aimed to assess the impact of COVID-19 on the quality of clinical research studies submitted to Italian Ethics Committees in the period between April and July 2020. All 91 Italian ethics committees were contacted via email in order to collect anonymized information on the type and quality of COVID-19-related studies submitted to each committee during the study period. The present study summarizes the characteristics of the 184 study applications collected, pointing out, especially, how the quality of the study population and statistical analysis are crucial variables in determining the study approval. Nevertheless, despite the need for high-quality and open scientific information, especially exacerbated by this particular historical period, only a minority of the ethics committees (20.9%) agreed to share their data; such scarce participation, beyond biasing the representativeness of the results obtained by the present study, more importantly, hinders the broader goal of creating trust between researchers and the general public.

Impact of Socioeconomic Deprivation on the Local Spread of COVID-19 Cases Mediated by the Effect of Seasons and Restrictive Public Health Measures: A Retrospective Observational Study in Apulia Region, Italy.

The aim of this study was to investigate the spatiotemporal association between socioeconomic deprivation and the incidence of COVID-19 and how this association changes through the seasons due to the existence of restrictive public health measures. A retrospective observational study was conducted among COVID-19 cases that occurred in the Apulia region from 29 February 2020 to 31 December 2021, dividing the period into four phases with different levels of restrictions. A generalized estimating equation (GEE) model was applied to test the independent effect of deprivation on the incidence of COVID-19, taking into account age, sex, and regional incidence as possible confounding effects and covariates, such as season and levels of restrictions, as possible modifying effects. The highest incidence was in areas with a very high deprivation index (DI) in winter. During total lockdown, no rate ratio between areas with different levels of DI was significant, while during soft lockdown, areas with very high DI were more at risk than all other areas. The effects of social inequalities on the incidence of COVID-19 changed in association with the seasons and restrictions on public health. Disadvantaged areas showed a higher incidence of COVID-19 in the cold seasons and in the phases of soft lockdown.

Investigations on the Efficacy of Ozone as an Environmental Sanitizer in Large Supermarkets.

Awareness of the importance of the microbial contamination of air and surfaces has increased significantly during the COVID-19 pandemic. The aim of this study was to evaluate the presence of bacteria and fungi in the air and on surfaces within some critical areas of large supermarkets with and without an ozonation system. Surveys were conducted in four supermarkets belonging to the same commercial chain of an Apulian city in June 2021, of which two (A and B) were equipped with an ozonation system, and two (C and D) did not have any air-diffused remediation treatment. There was a statistically significant difference in the total bacterial count (TBC) and total fungal count (TFC) in the air between A/B and C/D supermarkets (p = 0.0042 and p = 0.0002, respectively). Regarding surfaces, a statistically significant difference in TBC emerged between A/B and C/D supermarkets (p = 0.0101). To the best of our knowledge, this is the first study evaluating the effect of ozone on commercial structures in Italy. Future investigations, supported by a multidisciplinary approach, will make it possible to deepen the knowledge on this method of sanitation, in light of any other epidemic/pandemic waves.

Tendency in Pulmonary Aspergillosis Investigation during the COVID-19 Era: What Is Changing?

Aspergillosis is a disease caused by Aspergillus, and invasive pulmonary aspergillosis (IPA) is the most common invasive fungal infection leading to death in severely immuno-compromised patients. The literature reports Aspergillus co-infections in patients with COVID-19 (CAPA). Diagnosing CAPA clinically is complex since the symptoms are non-specific, and performing a bronchoscopy is difficult. Generally, the microbiological diagnosis of aspergillosis is based on cultural methods and on searching for the circulating antigens galactomannan and 1,3-ß-D-glucan in the bronchoalveolar lavage fluid (bGM) or serum (sGM). In this study, to verify whether the COVID-19 period has stimulated clinicians to pay greater attention to IPA in patients with respiratory tract infections, we evaluated the number of requests for GM-Ag research and the number of positive tests found during the pre-COVID-19 and COVID-19 periods. Our data show a significant upward trend in GM-Ag requests and positivity from the pre-COVID to COVID period, which is attributable in particular to the increase in IPA risk factors as a complication of COVID-19. In the COVID period, parallel to the increase in requests, the number of positive tests for GM-Ag also increased, going from 2.5% in the first period of 2020 to 12.3% in the first period of 2021.

Reduced COVID-19 mortality linked with early antibodies against SARS-CoV-2, irrespective of age.

BACKGROUND: COVID-19 pandemic has generated a million deaths worldwide. The efficiency of the immune system can modulate individual vulnerability with variable outcomes. However, the relationships between disease severity and the titer of antibodies produced against SARS-CoV-2 in non-vaccinated, recently infected subjects need to be fully elucidated. METHODS: A total of 99 patients admitted to a COVID-unit underwent clinical assessment and measurement of serum levels of anti-spike protein (S1) IgM, and anti-nucleocapsid protein IgG. Patients were stratified according to the clinical outcome (i.e., discharged at home or in-hospital death). RESULTS: Following hospitalization, 18 died during the hospital stay. They were older, had lymphopenia, a higher co-morbidity rate, and longer hospital stay than 81 patients who were discharged after healing. Patients in this latter group had, at hospital admittance, 7.9-fold higher serum concentration of IgM, and 2.4-fold higher IgG levels. Multivariate Cox regression models indicated age and anti-nucleocapsid protein IgG concentration at admission as independently associated with the risk of in-hospital death. CONCLUSIONS: An efficient immunological response during the early phase of COVID-19 protects from mortality, irrespective of age. Advanced age is a critical risk factor for poor outcome in infected subjects. Further studies must explore potential therapeutic strategies able to restore a valid functional humoral immunity in elderly patients with poor antibody response during the early stage of COVID-19 infection.

COVID-19 Infection in Pregnancy: Obstetrical Risk Factors and Neonatal Outcomes-A Monocentric, Single-Cohort Study.

The effects of coronavirus disease 2019 caused by severe acute respiratory syndrome coronavirus 2 on pregnant women and neonates are mainly unknown, since limited data are available in the literature. We conducted a monocentric and cross-sectional study enrolling 122 un-vaccinated pregnant women with COVID-19 infection tested by RT-PCR nasopharyngeal swab. Only 4.1% of the patients had severe COVID-19 symptoms together with major respiratory symptoms and intensive care unit admission, whereas 35.25% of women had comorbidities and two-thirds of them were overweight or obese. COVID-19 was detected mainly in the third trimester (98.36%) and multiparous women (59.02%). The mode of delivery was influenced by mild-severe COVID-19 symptoms, with a higher number of urgent or emergent cesarean sections than spontaneous or operative vaginal births. Preterm births were associated with high BMI, mode of delivery (higher among cesarean sections), nulliparity, and severe COVID-19 symptoms. In cases of severe COVID-19 symptoms, there was a higher rate of respiratory distress syndrome among newborns. In the end, only the presence of a severe COVID-19 infection worsened the obstetrical and neonatal outcomes, with higher rates of urgent or emergent cesarean section, preterm births, and neonatal respiratory distress syndrome.

In-Hospital Mortality in Non-COVID-19-Related Diseases before and during the Pandemic: A Regional Retrospective Study.

Italy was one of the nations most affected by SARS-CoV-2. During the pandemic period, the national government approved some restrictions to reduce diffusion of the virus. We aimed to evaluate changes in in-hospital mortality and its possible relation with patient comorbidities and different restrictive public health measures adopted during the 2020 pandemic period. We analyzed the hospital discharge records of inpatients from public and private hospitals in Apulia (Southern Italy) from 1 January 2019 to 31 December 2020. The study period was divided into four phases according to administrative restriction. The possible association between in-hospital deaths, hospitalization period, and covariates such as age group, sex, Charlson comorbidity index (CCI) class, and length of hospitalization stay (LoS) class was evaluated using a multivariable logistic regression model. The risk of death was slightly higher in men than in women (OR 1.04, 95% CI: 1.01-1.07) and was lower for every age group below the >75 years age group. The risk of in-hospital death was lower for hospitalizations with a lower CCI score. In summary, our analysis shows a possible association between in-hospital mortality in non-COVID-19-related diseases and restrictive measures of public health. The risk of hospital death increased during the lockdown period.

COVID19 outbreak in Lombardy, Italy: An analysis on the short-term relationship between air pollution, climatic factors and the susceptibility to SARS-CoV-2 infection.

Short-term exposure to air pollution, as well as to climate variables have been linked to a higher incidence of respiratory viral diseases. The study aims to assess the short-term influence of air pollution and climate on COVID19 incidence in Lombardy (Italy), during the early stage of the outbreak, before the implementation of the lockdown measures. The daily number of COVID19 cases in Lombardy from February 25th to March 10th, 2020, and the daily average concentrations up to 15 days before the study period of particulate matter (PM10, PM2.5), O3, SO2, and NO2 together with climate variables (temperature, relative humidity - RH%, wind speed, precipitation), were analyzed. A univariable mixed model with a logarithm transformation as link function was applied for each day, from 15 days (lag15) to one day (lag1) before the day of detected cases, to evaluate the effect of each variable. Additionally, change points (Break Points-BP) in the relationship between incident cases and air pollution or climatic factors were estimated. The results did not show a univocal relationship between air quality or climate factors and COVID19 incidence. PM10, PM2.5 and O3 concentrations in the last lags seem to be related to an increased COVID19 incidence, probably due to an increased susceptibility of the host. In addition, low temperature and low wind speed in some lags resulted associated with increased daily COVID19 incidence. The findings observed suggest that these factors, in particular conditions and lags, may increase individual susceptibility to the development of viral infections such as SARS-CoV-2.

Short-term forecast in the early stage of the COVID-19 outbreak in Italy. Application of a weighted and cumulative average daily growth rate to an exponential decay model.

To estimate the size of the novel coronavirus (COVID-19) outbreak in the early stage in Italy, this paper introduces the cumulated and weighted average daily growth rate (WR) to evaluate an epidemic curve. On the basis of an exponential decay model (EDM), we provide estimations of the WR in four-time intervals from February 27 to April 07, 2020. By calibrating the parameters of the EDM to the reported data in Hubei Province of China, we also attempt to forecast the evolution of the outbreak. We compare the EDM applied to WR and the Gompertz model, which is based on exponential decay and is often used to estimate cumulative events. Specifically, we assess the performance of each model to short-term forecast of the epidemic, and to predict the final epidemic size. Based on the official counts for confirmed cases, the model applied to data from February 27 until the 17th of March estimate that the cumulative number of infected in Italy could reach 131,280 (with a credibility interval 71,415-263,501) by April 25 (credibility interval April 12 to May 3). With the data available until the 24st of March the peak date should be reached on May 3 (April 23 to May 23) with 197,179 cumulative infections expected (130,033-315,269); with data available until the 31st of March the peak should be reached on May 4 (April 25 to May 18) with 202,210 cumulative infections expected (155.235-270,737); with data available until the 07st of April the peak should be reached on May 3 (April 26 to May 11) with 191,586 (160,861-232,023) cumulative infections expected. Based on the average mean absolute percentage error (MAPE), cumulated infections forecasts provided by the EDM applied to WR performed better across all scenarios than the Gompertz model. An exponential decay model applied to the cumulated and weighted average daily growth rate appears to be useful in estimating the number of cases and peak of the COVID-19 outbreak in Italy and the model was more reliable in the exponential growth phase.