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Background Arsenic in drinking water is a global public health concern. This study aims to investigate the association between chronic low-level exposure to arsenic in drinking water and health outcomes in the volcanic area of Mt. Amiata in Italy, using a residential cohort study design. Methods Chronic exposure to arsenic in drinking water was evaluated using monitoring data collected by the water supplier. A time-weighted average arsenic exposure was estimated for the period 2005–2010. The population-based cohort included people living in five municipalities in the Mt. Amiata area between 01/01/1998 and 31/12/2019. Residence addresses were georeferenced and each subject was matched with arsenic exposure and socio-economic status. Mortality and hospital discharge data were selected from administrative health databases. Cox proportional hazard models were used to test the associations between arsenic exposure and outcomes, with age as the temporal axis and adjusting for gender, socio-economic status and calendar period. Results The residential cohort was composed of 30,910 subjects for a total of 407,213 person-years. Analyses reported risk increases associated with exposure to arsenic concentrations in drinking water > 10 µg/l for non-accidental mortality (HR = 1.07 95%CI:1.01–1.13) and malignant neoplasms in women (HR = 1.14 95%CI:0.97–1.35). Long-term exposure to arsenic concentrations > 10 µg/l resulted positively associated with several hospitalization outcomes: non-accidental causes (HR = 1.06 95%CI:1.03–1.09), malignant neoplasms (HR = 1.10 95%CI:1.02–1.19), lung cancer (HR = 1.85 95%CI:1.14–3.02) and breast cancer (HR = 1.23 95%CI:0.99–1.51), endocrine disorders (HR = 1.13 95%CI:1.02–1.26), cardiovascular (HR = 1.12 95%CI:1.06–1.18) and respiratory diseases (HR = 1.10 95%CI:1.03–1.18). Some risk excesses were also observed for an exposure to arsenic levels below the regulatory standard, with evidence of exposure-related trends. Conclusions Our population-based cohort study in the volcanic area of Mt. Amiata showed that chronic exposure to arsenic concentrations in drinking water above the current regulatory limit was associated with a plurality of outcomes, in terms of both mortality and hospitalization. Moreover, some signs of associations emerge even at very low levels of exposure, ​​below the current regulatory limit, highlighting the need to monitor arsenic concentrations continuously and implement policies to reduce concentrations in the environment as far as possible.

Background Per- and polyfluoroalkyl substances (PFAS) are associated with many adverse health conditions. Among the main effects is carcinogenicity in humans, which deserves to be further clarified. An evident association has been reported for kidney cancer and testicular cancer. In 2013, a large episode of surface, ground and drinking water contamination with PFAS was uncovered in three provinces of the Veneto Region (northern Italy) involving 30 municipalities and a population of about 150,000. We report on the temporal evolution of all-cause mortality and selected cause-specific mortality by calendar period and birth cohort in the local population between 1980 and 2018. Methods The Italian National Institute of Health pre-processed and made available anonymous data from the Italian National Institute of Statistics death certificate archives for residents of the provinces of Vicenza, Padua and Verona (males, n  = 29,629; females, n  = 29,518) who died between 1980 and 2018. Calendar period analysis was done by calculating standardised mortality ratios using the total population of the three provinces in the same calendar period as reference. The birth cohort analysis was performed using 20–84 years cumulative standardised mortality ratios. Exposure was defined as being resident in one of the 30 municipalities of the Red area , where the aqueduct supplying drinking water was fed by the contaminated groundwater. Results During the 34 years between 1985 (assumed as beginning date of water contamination) and 2018 (last year of availability of cause-specific mortality data), in the resident population of the Red area we observed 51,621 deaths vs. 47,731 expected (age- and sex-SMR: 108; 90% CI: 107–109). We found evidence of raised mortality from cardiovascular disease (in particular, heart diseases and ischemic heart disease) and malignant neoplastic diseases, including kidney cancer and testicular cancer. Conclusions For the first time, an association of PFAS exposure with mortality from cardiovascular disease was formally demonstrated. The evidence regarding kidney cancer and testicular cancer is consistent with previously reported data.

The geothermal power plants for electricity production currently active in Italy are all located in Mt. Amiata area in the Tuscany region. A cross-sectional survey was conducted in the framework of the regional project \"InVETTA-Biomonitoring Survey and Epidemiological Evaluations for the Protection of Health in the Amiata Territories\", using objective measures of lung function to investigate the role of hydrogen sulphide (H.sub.2 S) in affecting the respiratory health of the population living in this area. 2018 adults aged 18-70 were enrolled during 2017-2019. Home and workplace addresses of participants were geocoded. Dispersion modelling was used to evaluate the spatial variability of exposure to H.sub.2 S from the geothermal power plants' emissions. We estimated average long-term historical exposure to H.sub.2 S and more recent exposure indicators. Chronic Obstructive Pulmonary Disease (COPD) was defined according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD). Multivariable logistic regressions were performed to investigate associations between outcome and exposure. Our findings did not showed any evidence of an association between increasing H.sub.2 S exposure and lung function impairments. Some risk reductions were observed: a -32.8% (p = 0.003) for FEV1<80% and a -51.7% (p = 0.001) risk decrease for FVC<80% were associated with interquartile increase (13.8 [mu]g/m.sup.3) of H.sub.2 S levels. Our study provides no evidence that chronic exposure to low levels of H.sub.2 S is associated with decrements in pulmonary function, suggesting that ambient H.sub.2 S exposure may benefit lung function.

The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their ability to predict the outcome of interest. The study involved patients admitted to PICUs between 2010 and 2020. Data were extracted from the Italian Network of Pediatric Intensive Care Units (TIPNet) registry. The algorithms considered were generalized linear model (GLM), recursive partition tree (RPART), random forest (RF), neural networks models, and extreme gradient boosting (XGB). Since the outcome is rare, upsampling and downsampling algorithms have been applied for imbalance control. For each approach, the main performance measures were reported. Among an overall sample of 29,494 subjects, only 399 developed HF during the PICU stay. The median age was about two years, and the male gender was the most prevalent. The XGB algorithm outperformed other MLTs in predicting HF development, with a median ROC measure of 0.780 (IQR 0.770–0.793). PIM 3, age, and base excess were found to be the strongest predictors of outcome. The present work provides insights for the prediction of HF development during PICU stay using machine-learning algorithms.

ObjectiveTo enhance the interpretation of occupational cancer risks within the BEST project (‘Big data and deep learning for the surveillance of occupational cancers’) by applying statistical methods that address multiple and selective inference, improving the prioritization of work sectors and cancer sites.Material and MethodsMortality data from the Italian National Statistics Institute (2005–2018) were linked to employment histories from the National Social Insurance Agency (1974–2018). Each individual was assigned the sector with the longest employment duration as a proxy for occupational exposure, applying a minimum latency of five years. Cancer Mortality Odds Ratios (CMORs) were estimated by cancer site and employment sector, adjusting for age, place of residence, year of death, and education level. Analyses were stratified by sex and occupational class, using the service sector as the reference. To address multiplicity and selective inference, synthesis methods included QQ-plots with guide rails, control of the Positive False Discovery Rate (q-values), and multivariate hierarchical Bayesian models for rank estimation and uncertainty quantification. These approaches allowed the generation of league tables of sector rankings with credibility intervals. This study is funded by the Italian Workers’ Compensation Authority (contract ID 56/2022), within the framework of the BEST project.ResultsIn male blue-collar workers, CMORs were computed for 43 sectors and 10 cancer sites. Lung cancer showed excess mortality in transport and construction and a deficit in agriculture; highlighted heterogeneity across sectors. Additional sector-site differences emerged from q-value analyses. Bayesian rankings placed lung cancer in construction consistently at the top, with narrow 80% credibility intervals.ConclusionRigorous synthesis methods that address multiplicity and selective reporting improve the interpretation of occupational cancer risks. Graphical and Bayesian approaches offer robust tools for identifying priority sectors and support evidence-based surveillance strategies.

ObjectivesSystematic reviews (SR) play a crucial role in synthesizing scientific literature for evidence-based decision-making, but traditional methods remain time-consuming and prone to inconsistencies. To enhance efficiency, we evaluated the feasibility of using GPT-based automated search techniques for retrieving occupational cancer-related studies from the PUBMED database.Material and MethodsThe assessment was conducted across seven neoplastic sites (i.e. nasopharynx, lymphomas, bladder, larynx, ovary, breast and multiple myeloma) each involving a review of 100 articles using an AI-generated search prompt. The evaluation focused on title and abstract screening, leveraging automated classification to identify relevant studies on occupational exposures and cancer risks. Only case-control, cohort, cross-sectional studies, and meta-analyses indicating an association between occupational sector and neoplasm were included in the selection.ResultsThe data extracted by GPT were subsequently reviewed by a human gold standard expert in occupational epidemiology. Compared to expert classification, GPT missed 5 out of 164 relevant studies (false negative rate: 3.0%) and flagged 69 irrelevant ones as relevant out of 536 true negatives (false positive rate: 11.4%), demonstrating the potential of AI-assisted literature screening in streamlining systematic reviews.ConclusionsThe findings suggest that GPT-based automated search techniques can effectively support the initial phases of systematic reviews by efficiently identifying relevant occupational cancer studies with a relatively low rate of false negatives. These results support the feasibility of using GPT-based tools for first-pass screening in occupational cancer SRs. While further refinement is needed to reduce false positives, this approach offers a promising balance between speed and sensitivity, with potential for broader integration into systematic review workflows

ObjectivesWe aimed to estimate the fraction of deaths from ovarian cancer attributable to asbestos exposure in Lombardy Region, Italy, using a novel approach that exploits the fact that ovarian cancer asbestos exposure is associated with pleural cancer and other risk factors for breast cancer.MethodsThis ecological study is based on the Italian National Institute of Statistics mortality data. We formulate a trivariate Bayesian joint disease model to estimate the attributable fraction (AF) and the number of ovarian cancer deaths attributable to asbestos exposure from the geographic distribution of ovarian, pleural and breast cancer mortality at the municipality level from 2000 to 2018. Expected deaths and standardised mortality ratios were calculated using regional rates.ResultsWe found shared dependencies between ovarian and pleural cancer, which capture risk factors common to the two diseases (asbestos exposure), and a spatially structured clustering component shared between ovarian and breast cancer, capturing other risk factors. Based on 10 462 ovarian cancer deaths, we estimated that 574 (95% credibility interval 388–819) were attributable to asbestos (AF 5.5%; 95% credibility interval 3.7–7.8). AF reaches 34%–47% in some municipalities with known heavy asbestos pollution.ConclusionsThe impact of asbestos on ovarian cancer occurrence can be relevant, particularly in areas with high asbestos exposure. Estimating attributable cases was possible only by using advanced Bayesian modelling to consider other risk factors for ovarian cancer. These findings are instrumental in tailoring public health surveillance programmes and implementing compensation and prevention policies.

Natural sources and anthropogenic activities are responsible for the widespread presence of heavy metals in the environment in the volcanic and geothermal area of Mt. Amiata (Tuscany, Italy). This study evaluates the extent of the population exposure to metals and describes the major individual and environmental determinants. A human biomonitoring survey was carried out to determine the concentrations of arsenic (As), mercury (Hg), thallium (Tl), antimony (Sb), cadmium (Cd), nickel (Ni), chromium (Cr), cobalt (Co), vanadium (V), and manganese (Mn). The associations between socio-demographics, lifestyle, diet, environmental exposure, and metal concentrations were evaluated using multiple log-linear regression models, adjusted for urinary creatinine. A total of 2034 urine and blood samples were collected. Adjusted geometric averages were higher in women (except for blood Hg) and younger subjects (except for Tl and Cd). Smoking was associated with Cd, As, and V. Some dietary habits (rice, fish, and wine consumption) were associated with As, Hg, Co, and Ni. Amalgam dental fillings and contact lenses were associated with Hg levels, piercing with As, Co, and Ni. Among environmental determinants, urinary As levels were higher in subjects using the aqueduct water for drinking/cooking. The consumption of locally grown fruits and vegetables was associated with Hg, Tl, and Co. Exposure to geothermal plant emissions was associated only with Tl.

Background: Asbestos exposure is a recognized risk factor for ovarian cancer and malignant mesothelioma. There are reports in the literature of geographical ecological associations between the occurrence of these two diseases. Our aim was to further explore this association by applying advanced Bayesian techniques to a large population (10 million people). Methods: We specified a series of Bayesian hierarchical shared models to the bivariate spatial distribution of ovarian and pleural cancer mortality by municipality in the Lombardy Region (Italy) in 2000–2018. Results: Pleural cancer showed a strongly clustered spatial distribution, while ovarian cancer showed a less structured spatial pattern. The most supported Bayesian models by predictive accuracy (widely applicable or Watanabe–Akaike information criterion, WAIC) provided evidence of a shared component between the two diseases. Among five municipalities with significant high standardized mortality ratios of ovarian cancer, three also had high pleural cancer rates. Wide uncertainty was present when addressing the risk of ovarian cancer associated with pleural cancer in areas at low background risk of ovarian cancer. Conclusions: We found evidence of a shared risk factor between ovarian and pleural cancer at the small geographical level. The impact of the shared risk factor can be relevant and can go unnoticed when the prevalence of other risk factors for ovarian cancer is low. Bayesian modelling provides useful information to tailor epidemiological surveillance.

Geothermal fluids for electricity and heat production have long been exploited in the Mt. Amiata area (Tuscany, Italy). Public concern about the health impact of geothermal plants has been present from the outset. Several factors influence the way people perceive risk; therefore, the objective of the present research is to develop indicators of risk perception and assess indices differences in relation to some questionnaire variables. A cross-sectional survey was conducted in the Amiata area on 2029 subjects aged 18–77. From the questionnaire section about risk perception from environmental hazards, four indicators were developed and analysed. A total of 64% of the subjects considered the environmental situation to be acceptable or excellent, 32% serious but reversible, and 4% serious and irreversible; as the values of the various perception indicators increased, an upward trend was observed in the averages. Risk perception was higher among women and young people, and was associated with higher education. Those who smelled bad odours in their surroundings reported higher risk perception. Furthermore, risk perception was higher in four municipalities. The results represent the basis for further investigations to analyse the link among risk perception indicators, exposure parameters, and health status.