Silicosis mortality in Italy: temporal trends 1990-2012 and spatial patterns 2000-2012

The present study investigates the occurrence of silicosis in Italy and its temporal trends and spatial patterns using mortality data. The aim is to give a contribution, albeit with a conservative estimate inferred from mortality data, to epidemiological knowledge of silicosis in Italy. Trends in mortality due to silicosis from 1990 to 2012 were evaluated and a municipal cluster analysis was performed. It shows that mortality due to silicosis is declining but still not eradicated and that one of its main features is regional variability: in this respect, the cluster analysis performed allowed to identify 34 different geographic areas. The results obtained may help display a more detailed picture of silicosis epidemiology and contribute to the fight against exposure to silica, an undisputable public health commitment

Health impact of the exposure to fibres with fluoro-edenitic composition on the residents in Biancavilla (Sicily, Italy): mortality and hospitalization from current data

Introduction. The objective of this chapter is to study the health impact of the exposure to fibres with fluoro-edenitic composition on the residents in Biancavilla (Sicily, Italy), in terms of mortality and hospitalization. The diseases which international scientific literature indicates as associated with asbestos exposure were taken into consideration: mesothelioma of pleura, peritoneum, pericardium and tunica vaginalis testis, malignant neoplasm of larynx, malignant neoplasm of trachea, bronchus and lung, malignant neoplasm of ovary, pneumoconiosis; moreover, in order to describe the health profile of the study population, large groups of diseases were taken into consideration.Material and methods. Current data (available in the Data Bases of the Unit of Statistics of ISS) regarding mortality and hospitalization were analyzed. Standardized Mortality Ratios, Standardized Hospitalization Ratios and Age-standardized Death Rates were calculated. The demographic background of the population residing in Biancavilla was also outlined.Conclusions. Our findings support the etiologic role of fibres with fluoro-edenitic composition in the occurrence of the above mentioned diseases, already observed in other studies

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

mesothelioma mortality surveillance and asbestos exposure tracking in italy

INTRODUCTION: Spatial distribution of mortality from pleural mesothelioma (which in the ICD-10 Revision has a specific code: C45.0) in Italy for the period 2003-2009 is described. Previous mortality studies at national level employed the topographic code "Malignant neoplasms of pleura", because of unavailability of a specific code in ICD-9 Revision for pleural mesothelioma. METHODS: Standardized mortality ratios were computed for all municipalities, using each regional population as reference; for municipalities in Regions with rate higher than the national rate, the latter has been used as reference. SMRs were computed specifically also for each Italian Polluted Sites "of national concern for environmental remediation" (IPS) with asbestos exposure sources, composed by one or more municipalities, using regional rate as reference. Spatial Scan Statistics procedure, using SatScan software, was applied in cluster analysis: the country was divided into geographic macro-areas and the relative risks (RR) express the ratio of risk within the cluster to the risk of the macro-area outside the cluster. Clusters with p-value < 0.10 were selected. RESULTS: The national standardized annual mortality rate was 1.7 cases per 100 000. Several areas with evident burden of asbestos-related disease were detected. Significant clusters were found in correspondence to asbestos-cement industries (e.g. Casale Monferrato, women: RR = 28.7), shipyards (e.g. Trieste, men: RR = 4.8), petrochemical industries (e.g. Priolo, men: RR = 6.9) and a stone quarry contaminated by fluoro-edenite fibres (Biancavilla, women: RR = 25.9). Some of the increased clusters correspond to IPS. CONCLUSIONS: The results may contribute to detect asbestos exposure and to set priorites for environmental remediation

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

Merkel cell carcinoma: a population-based study on mortality and the association with other cancers

Few population-based epidemiological data are available on Merkel cell carcinoma (MCC), a rare lethal non-melanoma skin cancer. We analysed multiple-cause-of-death records to describe MCC mortality and trends and the association with other primary cancers. We reviewed all 6,713,059 death certificates in Italy (1995-2006) to identify those mentioning MCC. We evaluated the association with other primary cancers by calculating the ratio of observed to expected deaths, using a standardized mortality ratio (SMR)-like analysis. We also evaluated the geographic distribution of deaths. We identified 351 death certificates with the mention of MCC. The age-adjusted mortality was 0.031/100,000, with a significant trend of increase and a slight north-south gradient. There was a significant deficit for solid cancers (SMR = 0.15) and a non-significant excess for lymphohematopoietic malignancies (SMR = 1.62). There were significant excesses for chronic lymphocytic leukemia (SMR = 4.07) and Waldenstrom's macroglobulinemia (SMR = 27.2) and a non-significant excess for chronic myeloid leukemia (SMR = 5.12). The increase in MCC mortality reflects the incidence trend in the literature. The association with chronic lymphocytic leukemia confirms the importance of immunologic factors in MCC. Regarding Waldenstrom's macroglobulinemia, an association with MCC has never been reported

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

Assessment of Excess Mortality in Italy in 2020&ndash;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&ndash;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&rsquo; 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