The Epidemiological Surveillance of Mesothelioma Mortality in Italy as a Tool for the Prevention of Asbestos Exposure.

As part of a surveillance plan active since the early 1990s, this study evaluates malignant mesothelioma (MM) mortality for the time-window 2010-2019 in Italy, a country that banned asbestos in 1992. National and regional mortality rates for MM, and municipal standardized mortality ratios (all mesotheliomas, pleural (MPM) and peritoneal (MPeM)), by gender and age group were calculated. A municipal clustering analysis was also performed. There were 15,446 deaths from MM (11,161 males, 3.8 × 100,000; 4285 females, 1.1 × 100,000), of which 12,496 were MPM and 661 were MPeM. In the study period, 266 people ≤50 years died from MM. A slightly decreasing rate among males since 2014 was observed. The areas at major risk hosted asbestos-cement plants, asbestos mines (chrysotile in Balangero), shipyards, petrochemical and chemical plants, and refineries. Female mortality excesses particularly were found in municipalities with a fluoro-edenite-contaminated mine (Biancavilla) and textile facilities. Excesses were also found in a region with the presence of natural asbestos fibres and in males living in two small islands. The Italian National Prevention Plan stated recommendations to eliminate asbestos exposures and to implement health surveillance and healthcare for people exposed to asbestos.

Assessment of Excess Mortality in Italy in 2020-2021 as a Function of Selected Macro-Factors.

BACKGROUND: Excess mortality (EM) can reliably capture the impact of a pandemic, this study aims at assessing the numerous factors associated with EM during the COVID-19 pandemic in Italy. METHODS: Mortality records (ISTAT 2015-2021) aggregated in the 610 Italian Labour Market Areas (LMAs) were used to obtain the EM P-scores to associate EM with socioeconomic variables. A two-step analysis was implemented: (1) Functional representation of EM and clustering. (2) Distinct functional regression by cluster. RESULTS: The LMAs are divided into four clusters: 1 low EM; 2 moderate EM; 3 high EM; and 4 high EM-first wave. Low-Income showed a negative association with EM clusters 1 and 4. Population density and percentage of over 70 did not seem to affect EM significantly. Bed availability positively associates with EM during the first wave. The employment rate positively associates with EM during the first two waves, becoming negatively associated when the vaccination campaign began. CONCLUSIONS: The clustering shows diverse behaviours by geography and time, the impact of socioeconomic characteristics, and local governments and health services' responses. The LMAs allow to draw a clear picture of local characteristics associated with the spread of the virus. The employment rate trend confirmed that essential workers were at risk, especially during the first wave.

[Methodological approaches to evaluate the over-time mortality in SENTIERI epidemiological surveillance system: a focus on cohort analyses]. / Approcci metodologici per valutare l'andamento della mortalità nel lungo periodo nel sistema di sorveglianza SENTIERI: focus sull'analisi di coorte.

The SENTIERI Project analyses the health profile of the populations residing in Italian national priority contaminated sites in specific calendar periods using a cross-sectional approach. An aspect that has not been evaluated so far is the analysis over a long period, for understanding the changes in health profiles over time and studying them also in function of the changes occurred in the territories. This article studies temporal trends by birth cohort and calendar period for overall mortality and lung cancer mortality from 1980 to 2018, separately for men and women, for three sites: Priolo (Sicily Region, Southern Italy), Pitelli (Liguria Region, Northern Italy), and Terni-Papigno (Umbria Region, Central Italy). A method for selecting the temporal model that best fits the data is then proposed. General mortality presents complex temporal profiles when considering cumulative risks, and usually the most important temporal axis is the birth cohort for cumulative SMRs (i.e., after adjusting for trends in the reference population). For lung cancer, the most important time axis is the birth cohort and the age-cohort model is the most appropriate, in particular for men of Priolo and Terni.

Reduction of the risk of severe COVID-19 due to Omicron compared to Delta variant in Italy (November 2021 - February 2022).

OBJECTIVES: During 2022, Omicron became the dominant SARS-CoV-2 variant in Europe. This study aims to assess the impact of such variant on severe disease from SARS-CoV-2 compared with the Delta variant in Italy. METHODS: Using surveillance data, we assessed the risk of developing severe COVID-19 with Omicron infection compared with Delta in individuals aged ≥12 years using a multilevel negative binomial model adjusting for sex, age, vaccination status, occupation, previous infection, weekly incidence, and geographical area. We also analyzed the interaction between the sequenced variant, age, and vaccination status. RESULTS: We included 21,645 cases of SARS-CoV-2 infection where genome sequencing found Delta (10,728) or Omicron (10,917), diagnosed from November 15, 2021 to February 01, 2022. Overall, 3,021 cases developed severe COVID-19. We found that Omicron cases had a reduced risk of severe COVID-19 compared with Delta cases (incidence rate ratio [IRR] = 0.77; 95% confidence interval [CI]: 0.70-0.86). The largest difference was observed in cases aged 40-59 (IRR = 0.66; 95% CI: 0.55-0.79), while no protective effect was found in those aged 12-39 (IRR = 1.03; 95% CI: 0.79-1.33). Vaccination was associated with a lower risk of developing severe COVID-19 in both variants. CONCLUSION: The Omicron variant is associated with a lower risk of severe COVID-19 compared to infection with the Delta variant, but the degree of protection varies with age.

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.