Characteristics of COVID-19 cases in Italy from a sex/gender perspective.

INTRODUCTION: Coronavirus disease 19 (COVID-19) is an infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To date, few data on clinical features and risk factors for disease severity and death by gender are available. AIM: The current study aims to describe from a sex/gender perspective the characteristics of the SARS-CoV-2 cases occurred in the Italian population from February 2020 until October 2021. METHOD AND RESULTS: We used routinely collected data retrieved from the Italian National Surveillance System. The highest number of cases occurred among women between 40 and 59 years, followed by men in the same age groups. The proportion of deaths due to COVID-19 was higher in men (56.46%) compared to women (43.54%). Most of the observed deaths occurred in the elderly. Considering the age groups, the clinical outcomes differed between women and men in particular in cases over 80 years of age; with serious or critical conditions more frequent in men than in women. CONCLUSIONS: Our data clearly demonstrate a similar number of cases in women and men, but with more severe disease and outcome in men, thus confirming the importance to analyse the impact of sex and gender in new and emerging diseases.

Assessing COVID-19-Related Excess Mortality Using Multiple Approaches-Italy, 2020-2021.

INTRODUCTION: Excess mortality (EM) is a valid indicator of COVID-19's impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations. METHODS: We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons. RESULTS: We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE. DISCUSSION: Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

[Integrated Surveillance in Italy: outputs and related activities]. / La sorveglianza integrata COVID-19 in Italia: output e attività correlate.

OBJECTIVES: to describe the integrated surveillance system of COVID-19 in Italy, to illustrate the outputs used to return epidemiological information on the spread of the epidemic to the competent public health bodies and to the Italian population, and to describe how the surveillance data contributes to the ongoing weekly regional monitoring and risk assessment system. METHODS: the COVID-19 integrated surveillance system is the result of a close and continuous collaboration between the Italian National Institute of Health (ISS), the Italian Ministry of Health, and the regional and local health authorities. Through a web platform, it collects individual data of laboratory confirmed cases of SARS-CoV-2 infection and gathers information on their residence, laboratory diagnosis, hospitalisation, clinical status, risk factors, and outcome. Results, for different levels of aggregation and risk categories, are published daily and weekly on the ISS website, and made available to national and regional public health authorities; these results contribute one of the information sources of the regional monitoring and risk assessment system. RESULTS: the COVID-19 integrated surveillance system monitors the space-time distribution of cases and their characteristics. Indicators used in the weekly regional monitoring and risk assessment system include process indicators on completeness and results indicators on weekly trends of newly diagnosed cases per Region. CONCLUSIONS: the outputs of the integrated surveillance system for COVID-19 provide timely information to health authorities and to the general population on the evolution of the epidemic in Italy. They also contribute to the continuous re-assessment of risk related to transmission and impact of the epidemic thus contributing to the management of COVID-19 in Italy.

A population-based cohort approach to assess excess mortality due to the spread of COVID-19 in Italy, January-May 2020.

AIMS: To assess the impact of the COVID-19 pandemic on all-cause mortality in Italy during the first wave of the epidemic, taking into consideration the geographical heterogeneity of the spread of COVID-19. METHODS: This study is a retrospective, population-based cohort study using national statistics throughout Italy. Survival analysis was applied to data aggregated by day of death, age groups, sex, and Italian administrative units (107 provinces). We applied Cox models to estimate the relative hazards (RH) of excess mortality, comparing all-cause deaths in 2020 with the expected deaths from all causes in the same time period. The RH of excess deaths was estimated in areas with a high, moderate, and low spread of COVID-19. We reported the estimate also restricting the analysis to the period of March-April 2020 (first peak of the epidemic). RESULTS: The study population consisted of 57,204,501 individuals living in Italy as of January 1, 2020. The number of excess deaths was 36,445, which accounts for 13.4% of excess mortalities from all causes during January-May 2020 (i.e., RH = 1.134; 95% confidence interval (CI): 1.129-1.140). In the macro-area with a relatively higher spread of COVID-19 (i.e., incidence rate, IR): 450-1,610 cases per 100,000 residents), the RH of excess deaths was 1.375 (95% CI: 1.364-1.386). In the area with a relatively moderate spread of COVID-19 (i.e., IR: 150-449 cases) it was 1.049 (95% CI: 1.038-1.060). In the area with a relatively lower spread of COVID-19 (i.e., IR: 30-149 cases), it was 0.967 (95% CI: 0.959-0.976). Between March and April (peak months of the first wave of the epidemic in Italy), we estimated an excess mortality from all causes of 43.5%. The RH of all-cause mortality for increments of 500 cases per 100,000 residents was 1.352 (95% CI: 1.346-1.359), corresponding to an increase of about 35%. CONCLUSIONS: Our analysis, making use of a population-based cohort model, estimated all-cause excess mortality in Italy taking account of both time period and of COVID-19 geographical spread. The study highlights the importance of a temporal/geographic framework in analyzing the risk of COVID-19-epidemy related mortality.

Excess Mortality in Italy During the COVID-19 Pandemic: Assessing the Differences Between the First and the Second Wave, Year 2020.

COVID-19 dramatically influenced mortality worldwide, in Italy as well, the first European country to experience the Sars-Cov2 epidemic. Many countries reported a two-wave pattern of COVID-19 deaths; however, studies comparing the two waves are limited. The objective of the study was to compare all-cause excess mortality between the two waves that occurred during the year 2020 using nationwide data. All-cause excess mortalities were estimated using negative binomial models with time modeled by quadratic splines. The models were also applied to estimate all-cause excess deaths "not directly attributable to COVD-19", i.e., without a previous COVID-19 diagnosis. During the first wave (25th February-31st May), we estimated 52,437 excess deaths (95% CI: 49,213-55,863) and 50,979 (95% CI: 50,333-51,425) during the second phase (10th October-31st December), corresponding to percentage 34.8% (95% CI: 33.8%-35.8%) in the second wave and 31.0% (95%CI: 27.2%-35.4%) in the first. During both waves, all-cause excess deaths percentages were higher in northern regions (59.1% during the first and 42.2% in the second wave), with a significant increase in the rest of Italy (from 6.7% to 27.1%) during the second wave. Males and those aged 80 or over were the most hit groups with an increase in both during the second wave. Excess deaths not directly attributable to COVID-19 decreased during the second phase with respect to the first phase, from 10.8% (95% CI: 9.5%-12.4%) to 7.7% (95% CI: 7.5%-7.9%), respectively. The percentage increase in excess deaths from all causes suggests in Italy a different impact of the SARS-CoV-2 virus during the second wave in 2020. The decrease in excess deaths not directly attributable to COVID-19 may indicate an improvement in the preparedness of the Italian health care services during this second wave, in the detection of COVID-19 diagnoses and/or clinical practice toward the other severe diseases.

[Temporal variations in excess mortality during phase 1 and phase 2 of the COVID-19 epidemic in Italy]. / Variazioni temporali della mortalità totale e nei decessi per COVID-19 durante la fase 1 e la fase 2 dell'epidemia in Italia.

OBJECTIVES: to assess the temporal variation in excess total mortality and the portion of excess explained by COVID-19 deaths by geographical area, gender, and age during the COVID-19 epidemic. DESIGN: descriptive analysis of temporal variations of total excess deaths and COVID-19 deaths in the phase 1 and phase 2 of the epidemic in Italy. SETTING AND PARTICIPANTS: 12 Northern cities and 20 Central-Southern cities from December 2019 to June 2020: daily mortality from the National Surveillance System of Daily Mortality (SiSMG) and COVID-19 deaths from the integrated COVID-19 surveillance system. MAIN OUTCOME MEASURES: total mortality excess and COVID-19 deaths, defined as deaths in microbiologically confirmed cases of SARS-CoV-2, by gender and age groups. RESULTS: the largest excess mortality was observed in the North and during the first phase of the epidemic. The portion of excess mortality explained by COVID-19 decreases with age, decreasing to 51% among the very old (>=85 years). In phase 2 (until June 2020), the impact was more contained and totally attributable to COVID-19 deaths and this suggests an effectiveness of social distancing measures. CONCLUSIONS: mortality surveillance is a sensible information basis for the monitoring of health impact of the different phases of the epidemic and supporting decision making at the local and national level on containment measures to put in place in coming months.

Epidemiological characteristics of COVID-19 cases in non-Italian nationals notified to the Italian surveillance system.

BACKGROUND: International literature suggests that disadvantaged groups are at higher risk of morbidity and mortality from SARS-CoV-2 infection due to poorer living/working conditions and barriers to healthcare access. Yet, to date, there is no evidence of this disproportionate impact on non-national individuals, including economic migrants, short-term travellers and refugees. METHODS: We analyzed data from the Italian surveillance system of all COVID-19 laboratory-confirmed cases tested positive from the beginning of the outbreak (20th of February) to the 19th of July 2020. We used multilevel negative-binomial regression models to compare the case fatality and the rate of admission to hospital and intensive care unit (ICU) between Italian and non-Italian nationals. The analysis was adjusted for differences in demographic characteristics, pre-existing comorbidities, and period of diagnosis. RESULTS: We analyzed 213 180 COVID-19 cases, including 15 974 (7.5%) non-Italian nationals. We found that, compared to Italian cases, non-Italian cases were diagnosed at a later date and were more likely to be hospitalized {[adjusted rate ratio (ARR)=1.39, 95% confidence interval (CI): 1.33-1.44]} and admitted to ICU (ARR=1.19, 95% CI: 1.07-1.32), with differences being more pronounced in those coming from countries with lower human development index (HDI). We also observed an increased risk of death in non-Italian cases from low-HDI countries (ARR=1.32, 95% CI: 1.01-1.75). CONCLUSIONS: A delayed diagnosis in non-Italian cases could explain their worse outcomes compared to Italian cases. Ensuring early access to diagnosis and treatment to non-Italians could facilitate the control of SARS-CoV-2 transmission and improve health outcomes in all people living in Italy, regardless of nationality.

HLA and AB0 Polymorphisms May Influence SARS-CoV-2 Infection and COVID-19 Severity.

BACKGROUND: SARS-CoV-2 infection is heterogeneous in clinical presentation and disease evolution. To investigate whether immune response to the virus can be influenced by genetic factors, we compared HLA and AB0 frequencies in organ transplant recipients and waitlisted patients according to presence or absence of SARS-CoV-2 infection. METHODS: A retrospective analysis was performed on an Italian cohort composed by transplanted and waitlisted patients in a January 2002 to March 2020 time frame. Data from this cohort were merged with the Italian registry of COVID+ subjects, evaluating infection status of transplanted and waitlisted patients. A total of 56 304 cases were studied with the aim of comparing HLA and AB0 frequencies according to the presence (n = 265, COVID+) or absence (n = 56 039, COVID-) of SARS-CoV-2 infection. RESULTS: The cumulative incidence rate of COVID-19 was 0.112% in the Italian population and 0.462% in waitlisted/transplanted patients (OR = 4.2; 95% CI, 3.7-4.7; P < 0.0001). HLA-DRB1*08 was more frequent in COVID+ (9.7% and 5.2%: OR = 1.9, 95% CI, 1.2-3.1; P = 0.003; Pc = 0.036). In COVID+ patients, HLA-DRB1*08 was correlated to mortality (6.9% in living versus 17.5% in deceased: OR = 2.9, 95% CI, 1.15-7.21; P = 0.023). Peptide binding prediction analyses showed that these DRB1*08 alleles were unable to bind any of the viral peptides with high affinity. Finally, blood group A was more frequent in COVID+ (45.5%) than COVID- patients (39.0%; OR = 1.3; 95% CI, 1.02-1.66; P = 0.03). CONCLUSIONS: Although preliminary, these results suggest that HLA antigens may influence SARS-CoV-2 infection and clinical evolution of COVID-19 and confirm that blood group A individuals are at greater risk of infection, providing clues on the spread of the disease and indications about infection prognosis and vaccination strategies.

Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020.

BackgroundOn 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak.AimOur aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures.MethodsWe analysed all RT-PCR-confirmed COVID-19 cases reported to the national integrated surveillance system until 31 March 2020. We provide a descriptive epidemiological summary and estimate the basic and net reproductive numbers by region.ResultsOf the 98,716 cases of COVID-19 analysed, 9,512 were healthcare workers. Of the 10,943 reported COVID-19-associated deaths (crude case fatality ratio: 11.1%) 49.5% occurred in cases older than 80 years. Male sex and age were independent risk factors for COVID-19 death. Estimates of R0 varied between 2.50 (95% confidence interval (CI): 2.18-2.83) in Tuscany and 3.00 (95% CI: 2.68-3.33) in Lazio. The net reproduction number Rt in northern regions started decreasing immediately after the first detection.ConclusionThe COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R0 at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, Rt in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures.

Impact of a Nationwide Lockdown on SARS-CoV-2 Transmissibility, Italy.

On March 11, 2020, Italy imposed a national lockdown to curtail the spread of severe acute respiratory syndrome coronavirus 2. We estimate that, 14 days after lockdown, the net reproduction number had dropped below 1 and remained stable at ¼0.76 (95% CI 0.67-0.85) in all regions for >3 of the following weeks.

COVID-19 Disease Severity Risk Factors for Pediatric Patients in Italy.

OBJECTIVES: To describe the epidemiological and clinical characteristics of coronavirus disease (COVID-19) pediatric patients aged <18 years in Italy. METHODS: Data from the national case-based surveillance system of confirmed COVID-19 infections until May 8, 2020, were analyzed. Demographic and clinical characteristics of subjects were summarized by age groups (0-1, 2-6, 7-12, 13-18 years), and risk factors for disease severity were evaluated by using a multilevel (clustered by region) multivariable logistic regression model. Furthermore, a comparison among children, adults, and elderly was performed. RESULTS: Pediatric patients (3836) accounted for 1.8% of total infections (216 305); the median age was 11 years, 51.4% were male, 13.3% were hospitalized, and 5.4% presented underlying medical conditions. The disease was mild in 32.4% of cases and severe in 4.3%, particularly in children ≤6 years old (10.8%); among 511 hospitalized patients, 3.5% were admitted in ICU, and 4 deaths occurred. Lower risk of disease severity was associated with increasing age and calendar time, whereas a higher risk was associated with preexisting underlying medical conditions (odds ratio = 2.80, 95% confidence interval = 1.74-4.48). Hospitalization rate, admission in ICU, disease severity, and days from symptoms onset to recovery significantly increased with age among children, adults and elderly. CONCLUSIONS: Data suggest that pediatric cases of COVID-19 are less severe than adults; however, age ≤1 year and the presence of underlying conditions represent severity risk factors. A better understanding of the infection in children may give important insights into disease pathogenesis, health care practices, and public health policies.