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Background Epithelial sinonasal cancers (SNC) are rare tumours with recognized associations with known/suspected occupational carcinogens (wood/leather dust, nickel/chromium compounds and formaldehyde). In Italy, a national SNC registry organized as a network of regional registries was established by law in 2008. Aims To describe SNC time trends, occupational exposures and geographical distribution in Lombardy, North-West Italy, based on population registry data (2008–20). Methods The Lombardy SNC Registry records epithelial SNCs using various sources. Interviews to collect occupational history are performed using a standardized questionnaire. Using several standard populations, we calculated yearly crude and age-standardized rates (ASRs per 100,000 person-years). Standardized incidence ratios (SIR) at municipality level were calculated, and Bayesian models were fitted to produce smoothed SIR maps. Results We recorded 827 cases (553 men, 274 women). Crude (world standardized) ASRs were 0.9 (0.4) in men and 0.4 (0.2) in women, with no time trends. Interviews were obtained for 485 (88%) men and 223 (81%) women. Among men, 217 (45%) had been exposed to occupational carcinogens (wood/leather dust: 150/65 cases, 31%/13%), while only 36 women (16%) were exposed. Among 201 men with adenocarcinoma, exposure to wood/leather dust occurred in 103/50 cases (75%/50%). Areas with elevated SIRs associated with leather dust were found in the Western areas. Exposure to wood dust was more widespread. Conclusions This study found a high frequency of occupational exposures (wood and leather dust), particularly in men with SNC. Employment in shoe industries clustered in the Western part, while work in furniture industries was less spatially structured. [ABSTRACT FROM AUTHOR]

Chronic bee paralysis virus (CBPV) is a Apis mellifera viral infectious disease, exhibiting dark and hairless abdomen in workers with tremors and ataxita. Clinical signs are also typically linked to adverse weather conditions and overcrowding in the hive. The disease occurs in spring but recently it has been observed cases increase and seasonality loss of the disease incidence. This study analyses the evolution of CBPV in Italy, through data collected from 2009 to 2023 within three monitoring projects comprising nationwide extended detection networks, aimed to investigate the evolution of the CBPV spatial distribution, identifying high-risk areas for the virus spread. This study highlights an increased risk over years. Prevalence increased from 4.3% during 2009-2010 to 84.7% during 2021-2023 monitoring years. CBPV outbreaks were irregular between investigated seasons, highlighting Spring and Autumn as the most susceptible seasons. Risk of CBPV infection has increased, reaching high-risk in last years of monitoring. Sequence analysis showed a high similarity to other isolated Italian CBPVs. The study offers an epidemiological insight into the aetiology of this disease. CBPV distribution is a prerequisite to predict its future spread and factors involved in its propagation not only in honey bees but also in other pollinators and environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: We 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., Methods: This 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., Results: We 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., Conclusions: The 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., Competing Interests: Competing interests: CM and DCo served as consultants in litigations concerning asbestos-related diseases., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© 2024. The Author(s).)

Aim: Defining the epidemiology of systemic and cardiac amyloidosis (CA) is a contemporary challenge. The present study aimed to estimate incidence and time trends in amyloidosis-related hospitalizations (AH) in Veneto Region (5 million inhabitants, Northeastern Italy)., Methods: International Classification of Diseases (ICD-9) codes were used to identify AH in Veneto from 2010 to 2020. AH were defined as any hospitalization with a discharge summary reporting an ICD-9 code for systemic amyloidosis. Hospitalization for CA was defined as records with ICD-9 code for systemic amyloidosis and ICD-9 code for heart failure,cardiomyopathy or arrhythmia. Hospital/outpatient encounters for carpal tunnel syndrome (CTS) surgeries also were extracted. AH incidence was estimated using a buffer of 5 years., Results: In the time range 2015-2020, the incidence rate of AH was 23.5 cases per 10 6 (95% confidence interval, CI, 21.8; 25.3), mainly affecting patients>65 years (76.2%) and males (63.5%), with a progressively increasing trend (percent annual increase 17%, 95% CI 12; 22%). The 10 year prevalence of AH in 2020 was 124.5 per 10 6 (95% CI 114.9; 134.8). In 2020, annual hospitalized prevalent cases of CA were about 70% of all cases (159/228), mainly patients >65 years and males. Among patients with multiple CTS surgeries, a subsequent code for cardiac disease was found in 913 after a median of 3.9 years, more frequently in men than in women (463/6.526 7.1% versus 450/11.406 3.9%)., Conclusions: In Veneto, we recorded a significantly increasing trend in the incidence of AH, with concordant increasing prevalence estimates., Competing Interests: Declaration of competing interest Dr. De Michieli received honoraria from Pfizer Inc., Alnylam Pharmaceuticals, AstraZeneca. Dr. Cipriani received honoraria from Pfizer Inc., Alnylam Pharmaceuticals, AstraZeneca. The other authors have nothing to disclose., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: endometriosis is a chronic condition with a significant impact on women's health, featured by endometrial tissue outside the uterine cavity. A limited number of studies have been conducted in the general population, and the true prevalence of endometriosis is unknown for many areas of the country., Objectives: to better estimate the prevalence of endometriosis in three Italian regions (Friuli Venezia Giulia, Tuscany, Apulia) and to assess the relationship between endometriosis and environmental factors in three participating areas (Trieste, Barga, and Taranto), with a focus on Tuscany Region., Design: implementing a specific epidemiological registry for endometriosis, aimed at estimating the incidence and prevalence data. The registry collected information from hospital discharge records and anatomopathological reports of women residing in the three considered regions, aged 15 years or older. Additionally, the analysis includes the assessment of the spatial distribution of endometriosis at both regional and municipal levels in the three study areas. Further research investigations in these areas involve a multilevel screening of a sample of women of childbearing age. Women who test positive in the initial screening (through a self-administered questionnaire) will have the opportunity to undergo a second level of screening, consisting of a gynecological examination, transvaginal ultrasound, a swab for vaginal microbiome analysis, and the collection of blood and urine samples to assess the presence of polychlorinated biphenyls (PCBs) or heavy metals. The adopted scientific approach is based on post-normal science (PNS) concerning the extended peer community., Setting and Participants: women aged 15 years or older residing in the three regions., Main Outcomes Measures: estimating the incidence and prevalence of endometriosis based on data collected from the epidemiological registry. The analysis extends to assessing the spatial distribution of endometriosis at municipal levels in the three areas of interest., Results: the preliminary results of the study allowed for the estimation of the spatial distribution of endometriosis incidence in Tuscany. In particular, it was found that there is variability within the region, with some coastal and North-Western areas showing values 20% higher than the regional average. Cities such as Pisa, Lucca, Livorno, Grosseto, Orbetello, and the Serchio Valley with Barga had a probability of excess risk of more than 90% compared to the regional average., Conclusions: the study is ongoing and requires the active participation of women living in the region to ensure the completeness and accuracy of the collected data. This research effort represents an important contribution to understanding endometriosis in Tuscany and its possible environmental causes.

Space-time analysis of mortality risk is useful to evaluate the epidemiologic transitions at the subnational level. In our study, we analysed the death certificate records for lung cancer in Italy in 1995-2016, obtained from the Italian National Institute of Statistics. Our objective was to investigate the spatial-temporal evolution of lung cancer mortality by sex and province of residence (n = 107) using the birth cohort as relevant time axis. We built Bayesian space-time models with space-time interactions. Among males (n = 554 829), mortality peaked in the 1920-1929 cohort, followed by a generalised decline. Among females (n = 158 619), we found novel original evidence for a peak in the 1955-1964 cohort, equivalent to a 35-year delay, with a downward trend being observed thereafter. Over time, the documented north-south decreasing mortality gradient has been replaced by a west-east decreasing gradient. Naples has become the province at highest risk in Italy, both among males and females. This pattern is consistent with an epidemiologic transition of risk factors for lung cancer to the south-west of the country and raises concern, because 5-year age-standardised net survival from the disease in this geographic area is lower than in northern and central Italy. The variability of mortality rates among provinces has changed over time, with an increasing homogeneity for males and an opposite trend for females in the more recent birth cohorts. These unprecedented observations provide evidence for a profound spatio-temporal transition of lung cancer mortality in Italy., (© 2023 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

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.

The p-value has been widely criticized in the scientific literature for its naive use in classifying results as 'significant' and 'non significant'. Much has been written about it; for example, see the American Statistical Association position statement of march 2016. To date, few alternative measures have been suggested and few changes were observed in the scientific practice regarding the use of p-value despite general agreement on the critics raised on it. In this paper, we use an alternative measure to p-value. It consists in the probability of the direction of the effect, that is the strength of empirical evidence in favour of the alternative directional hypothesis. In the context of scientific research, reporting the probability of the direction of the effect is easier to understand. Moreover, it focuses on the effect in the study rather than on the value under the null hypothesis, which sometimes has little meaning or has been used opportunistically. The proposal is not intended as an alternative to using the confidence interval, but as a probabilistic metric to be used instead of the p-value when we refer to particular hypotheses to be tested.

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.

Currently, there are more and more requests for a characterization of the health profile by populations living in areas affected by several types of environmental contaminations, notably for the presence of previously unknown and accidentally discovered landfills. The aim of the present paper is to describe the mortality profile of the residents of Piazzola sul Brenta (Veneto Region, Northern Italy). In this area, it was discovered an environmental contamination of the soil in the nearby of a large school building, where was formerly located a phosphate fertiliser plant. Using cause of death data provided by the Italian National Institute of Health, 10th International Classification 2013-2018, standardised mortality ratios (SMRs) and 90% confidence intervals (CIs) were calculated by gender and specific causes of death. Compared to the provincial reference population, no excess was found, for both genders, for all-causes mortality and for circulatory diseases. However, an excess for malignant tumours was found, 523 observed deaths over 498 expected deaths, an SMR of 105 (IC90% 98-113), and 85% probability of excess cancer mortality; lung cancer (SMR 117; IC90% 100-135) and lympho-hematopoietic tumours (in men only, SMR 134; IC90% 101-178).In light of these results and of the potential human exposure to carcinogenic substances, present results support further epidemiological investigations and environmental remediation.

In a wide area among the provinces of Vicenza, Verona, and Padua (Veneto Region, Northern Italy), one of the larger known massive contamination of the environment by per- and polyfluoroalkyl substances (PFASs) occurred since the year 1965. The most important source of human exposure was through aquiferous and contaminated water supplies (the regional authorities distinguished red zone A - aquiferous - and red zone B - water supply) for a total of slightly less than two hundred thousand exposed people. Food contamination and food contribution to total human exposure were assessed by a food monitoring campaign on the years 2016-2017 by the National Health Institute sponsored by Veneto Region.Thanks to the availability of the individual records of the monitoring campaign, we evaluated the spatial distribution of PFAS contamination by food matrix. Generally speaking, PFAS contamination was widespread over the entire red zone (either A or B). Some zones appeared to be higher contaminated consistently with the water plume. Other higher contaminated zones were less consistent and such result raises questions about routes of environmental pollution.

Background: Diagnosis of endometriosis and evaluation of incidence data are complex tasks because the disease is identified laparoscopically and confirmed histologically. Incidence estimates reported in literature are widely inconsistent, presumably reflecting geographical variability of risk and the difficulty of obtaining reliable data., Methods: We retrieved incident cases of endometriosis in women aged 15-50 years using hospital discharge records and pathology databases of the Friuli Venezia Giulia region in the calendar period 2004-2017. We studied the spatial pattern of endometriosis incidence applying Bayesian approaches to Disease Mapping, and profiled municipalities at higher risk controlling for multiple comparisons using both q-values and a fully Bayesian approach., Results: 4125 new cases of endometriosis were identified in the age range 15 to 50 years in the period 2004-2017. The incidence rate (x100 000) is 111 (95% CI 110-112), with a maximum of 160 in the age group 31-35 years. The geographical distribution of endometriosis incidence showed a very strong north-south spatial gradient. We consistently identified a group of five neighboring municipalities at higher risk (RR 1.31 95% CI 1.13; 1.52), even accounting for ascertainment bias., Conclusions: The cluster of 5 municipalities in the industrialized and polluted south-east part of the region is suggestive. However, due to the ecologic nature of the present study, information on the patients' characteristics and exposure histories are limited. Individual studies, including biomonitoring, and life-course studies are necessary to better evaluate our findings.

The confinement and lockdown imposed by the COVID-19 pandemic have produced restrictions in the lifestyle of Italian citizens with variations in their psychological well-being. The aim of the study was to identify changes and relationship with socio-demographic parameters. An online survey was administered to 1383 subjects (1007 females and 307 males) working in the University of Florence, Italy. Three validated questionnaires were used for the survey: the Global Physical Activity Questionnaire, the Med Diet Score and the Psychological General Well-Being Index-A. All the subjects were asked to complete the questionnaires twice, in order to attain a picture of the habits before and a later time point during confinement. Our results show that work-related physical activity was decreased, along with an increase in sedentary behaviour (from 07:22±03:20 to 08:49±03:41 h:min; p<0.001, ES = 0.38), whereas recreational physical activity was increased (vigorous exercise varied from 568.5 ± 838.6 to 833.7 ± 1263.0 METs; p<0.002, ES = 0.25). Eating habits changed according to the place where meals were eaten, with an increased habit for breakfast and snacks and a slight increase in alcohol consumption. Psychological well-being decreased (Index from 21.4±3.9 to 18.0±5.3; p<0.001, ES = 0.723), especially in terms of vitality and positive thinking. The socio-demographic variables affecting these variations were mostly represented by age, gender and working conditions: young age and self-employment conditions can be considered factors for the changes in daily habits induced by confinement that may affect psychological well-being., Competing Interests: The authors have declared that no competing interests exist.

Background: In the context of the COVID-19 pandemic, there is interest in assessing if per- and polyfluoroalkyl substances (PFAS) exposures are associated with any increased risk of COVID-19 or its severity, given the evidence of immunosuppression by some PFAS. The objective of this paper is to evaluate at the ecological level if a large area (Red Zone) of the Veneto Region, where residents were exposed for decades to drinking water contaminated by PFAS, showed higher mortality for COVID-19 than the rest of the region., Methods: We fitted a Bayesian ecological regression model with spatially and not spatially structured random components on COVID-19 mortality at the municipality level (period between 21 February and 15 April 2020). The model included education score, background all-cause mortality (for the years 2015-2019), and an indicator for the Red Zone. The two random components are intended to adjust for potential hidden confounders., Results: The COVID-19 crude mortality rate ratio for the Red Zone was 1.55 (90% Confidence Interval 1.25; 1.92). From the Bayesian ecological regression model adjusted for education level and baseline all-cause mortality, the rate ratio for the Red Zone was 1.60 (90% Credibility Interval 0.94; 2.51)., Conclusion: In conclusion, we observed a higher mortality risk for COVID-19 in a population heavily exposed to PFAS, which was possibly explained by PFAS immunosuppression, bioaccumulation in lung tissue, or pre-existing disease being related to PFAS.

Background: the first confirmed cases of COVID-19 in WHO European Region was reported at the end of January 2020 and, from that moment, the epidemic has been speeding up and rapidly spreading across Europe. The health, social, and economic consequences of the pandemic are difficult to evaluate, since there are many scientific uncertainties and unknowns., Objectives: the main focus of this paper is on statistical methods for profiling municipalities by excess mortality, directly or indirectly caused by COVID-19., Methods: the use of excess mortality for all causes has been advocated as a measure of impact less vulnerable to biases. In this paper, observed mortality for all causes at municipality level in Italy in the period January-April 2020 was compared to the mortality observed in the corresponding period in the previous 5 years (2015-2019). Mortality data were made available by the Ministry of Internal Affairs Italian National Resident Population Demographic Archive and the Italian National Institute of Statistics (Istat). For each municipality, the posterior predictive distribution under a hierarchical null model was obtained. From the posterior predictive distribution, we obtained excess death counts, attributable community rates and q-values. Full Bayesian models implemented via MCMC simulations were used., Results: absolute number of excess deaths highlights the burden paid by major cities to the pandemic. The Attributable Community Rate provides a detailed picture of the spread of the pandemic among the municipalities of Lombardy, Piedmont, and Emilia-Romagna Regions. Using Q-values, it is clearly recognizable evidence of an excess of mortality from late February to April 2020 in a very geographically scattered number of municipalities. A trade-off between false discoveries and false non-discoveries shows the different values of public health actions., Conclusions: despite the variety of approaches to calculate excess mortality, this study provides an original methodological approach to profile municipalities with excess deaths accounting for spatial and temporal uncertainty.

Introduction: Measuring and mapping the occurrence of malignant mesothelioma (MM) is a useful means to monitor the impact of past asbestos exposure and possibly identify previously unknown sources of asbestos exposure., Objective: Our goal is to decompose the observed spatial pattern of incidence of MM in the Lombardy region (Italy) in gender-specific components linked to occupational exposure and a shared component linked to environmental exposure., Materials and Methods: We selected from the Lombardy Region Mesothelioma Registry (RML) all incident cases of MM (pleura, peritoneum, pericardium, and tunica vaginalis testis) with first diagnosis in the period 2000-2016. We mapped at municipality level crude incidence rates and smoothed rates using the Besag York and Mollié model separately for men and women. We then decomposed the spatial pattern of MM in gender-specific occupational components and a shared environmental component using a multivariate hierarchical Bayesian model., Results: We globally analyzed 6226 MM cases, 4048 (2897 classified as occupational asbestos exposure at interview) in men and 2178 (780 classified as occupational asbestos exposure at interview) in women. The geographical analysis showed a strong spatial pattern in the distribution of incidence rates in both genders. The multivariate hierarchical Bayesian model decomposed the spatial pattern in occupational and environmental components and consistently identified some known occupational and environmental hot spots. Other areas at high risk for MM occurrence were highlighted, contributing to better characterize environmental exposures from industrial sources and suggesting a role of natural sources in the Alpine region., Conclusion: The spatial pattern highlights areas at higher risk which are characterized by the presence of industrial sources - asbestos-cement, metallurgic, engineering, textile industries - and of natural sources in the Alpine region. The multivariate hierarchical Bayesian model was able to disentangle the geographical distribution of MM cases in two components interpreted as occupational and environmental., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Sporadic Creutzfeldt-Jakob disease (CJD) is a rare neurodegenerative disease caused by prions that is randomly distributed in all countries, with an overall yearly mortality rate of about 1-2 cases per million people. On a few occasions, however, sporadic CJD occurred with higher than expected rates, but further investigations failed to recognize any convincing causal link. In Italy, cluster analyses of sporadic CJD cases have not been performed previously., Objective: To investigate the geographical distribution of sporadic CJD using municipality geographical data of Apulia with the aim of detecting spatial clusters of disease., Patients and Methods: Patients included in this study were diagnosed as probable or definite sporadic CJD and were residents of the Apulia Region (Italy). Bayesian hierarchical models with spatially structured and unstructured random components were used to describe the spatial pattern of the disease and to assess the extent of heterogeneity among municipalities. The Kulldorff-Nagarwalla scan test and the flexible spatial scan statistic were used for detecting spatial clusters., Results: Smoothed Bayesian relative risks above the null value were observed in a few adjacent municipalities in the north and middle areas of Apulia. However, both the circular scanning method and the flexible spatial scan statistic identified only a single cluster in the central part of the region., Conclusion: Geographical analyses and tests for spatial randomness identified a restricted area with an unusually high number of sporadic CJD cases in the Apulia region of Italy. Environmental and genetic risk factors other than mutations in the prion protein gene however, need to be investigated., (© 2019 S. Karger AG, Basel.)

To determine the acute effects on respiratory function of children exposed to sulphur dioxide (SO₂), we conducted two population-based longitudinal investigations near a major oil refinery. We enrolled 233 children, age 8⁻14, in Sarroch (Italy). The first study entailed five monthly spirometric visits (Panel 5). In a subgroup, children positive for history of respiratory symptoms were tested weekly (20 times) with spirometry and fractional exhaled nitric oxide (FeNO) measurement (Panel 20). Baseline questionnaires and daily diaries were recorded. SO₂, NO₂, PM 10 and O₃ were measured by monitoring stations. Multiple regression models were fitted. Using a multipollutant model, we found that a 10 µg/m³ SO₂ increase at lag0⁻2 days determined a percent variation (PV) of -3.37 (90% confidence interval, CI: -5.39; -1.30) for forced expiratory volume after one second (FEV1) in Panel 5 and a PV = -3.51 (90% CI: -4.77; -2.23) in Panel 20. We found a strong dose-response relation: 1-h SO₂ peaks >200 µg/m³ at lag2 days = FEV1 PV -2.49. For FeNO, we found a PV = 38.12 (90% CI: 12.88; 69.01) for each 10 µg/m³ SO₂ increase at 8-h time lag and a strong dose-response relation. Exposure to SO₂ is strongly associated with reduction of lung function and an increase in airway inflammation. This new evidence of harmful effects of SO₂ peaks should induce regulatory intervention.

Objectives This study aimed to (i) describe mesothelioma incidence in the Italian national priority contaminated sites (NPCS) on the basis of data available from the Italian National Mesothelioma Registry (ReNaM) and (ii) profile NPCS using Bayesian rank analysis. Methods Incident cases of mesothelioma and standardized incidence ratios (SIR) were estimated for both genders in each of the 39 selected NPCS in the period 2000-2011. Age-standardized rates of Italian geographical macro areas were used to estimate expected cases. Rankings of areas were produced by a hierarchical Bayesian model. Asbestos exposure modalities were discussed for each site. Results In the study period, 2683 incident cases of mesothelioma (1998 men, 685 women) were recorded. An excess of mesothelioma incidence was confirmed in sites with a known past history of direct use of asbestos (among men) such as Balangero (SIR 197.1, 95% CI 82.0-473.6), Casale Monferrato (SIR 910.7, 95% CI 816.5-1012.8), and Broni (SIR 1288.5, 95% CI 981.9-1691.0), in sites with shipyards and harbors (eg, Trieste, La Spezia, Venice, and Leghorn), and in settings without documented direct use of asbestos. The analysis ranked the sites of Broni and Casale Monferrato (both genders) and Biancavilla (only for women) the highest. Conclusions The present study confirms that asbestos pollution is a risk for people living in polluted areas, due to not only occupational exposure in industrial settings with direct use of asbestos but also the presence of asbestos in the environment. Epidemiological surveillance of asbestos-related diseases is a fundamental tool for monitoring the health profile in NPCS.

Background: Measuring malignant mesothelioma (MM) occurrence is a useful means to monitor the impact of past asbestos exposure and possibly identify new sources of asbestos exposure., Objectives: Aim of this study is to describe the results of the MM registry of the Lombardy Region, North-West Italy, the most populated (currently, 10 million inhabitants) and industrialised Italian region., Methods: We extracted from the Lombardy Region Mesothelioma Registry (Registro Mesoteliomi Lombardia, RML) database all incident cases of MM (pleura, peritoneum, pericardium, and tunica vaginalis testis) with first diagnosis in 2000 through 2012. For each Province, we calculated crude and standardised incidence rates using Italy 2001, European, and world (Segi's) standard populations. To examine spatial patterns of MM occurrence across municipalities we drew maps of crude rates smoothed according to the Besag, York and Mollié (BYM) method., Results: We recorded 4442 MM cases (2850 in men and 1592 in women), representing about one fourth of MM cases occurring in Italy. Occupational exposure was more frequent in men (73.6%) than in women (38.2%). The crude regional rates were 4.7 per 100,000 person-years in men and 2.5 per 100,000 person-years in women. The highest rates were observed in the Pavia Province (crude rates: 8.7 per 100,000 in men and 5.3 and per 100,000 person-years in women)., Conclusions: This study documented high MM occurrence in both genders, attributable to extensive asbestos exposure in the past.

Background: The Lombardy region in northern Italy ranks among the most air polluted areas of Europe. Previous studies showed air pollution short-term effects on all-cause mortality. We examine here the effects of particulate matter with aerodynamic diameter ≤10µm (PM10) and nitrogen dioxide (NO2) exposure on deaths and hospitalizations from specific causes, including cardiac, cerebrovascular and respiratory diseases., Methods: We considered air pollution, mortality and hospitalization data for a non-opportunistic sample of 18 highly polluted and most densely populated areas of the region in the years 2003-2006. We obtained area-specific effect estimates for PM10 and NO2 from a Poisson regression model on the daily number of total deaths or cause-specific hospitalizations and then combined them in a Bayesian random-effects meta-analysis. For cause-specific mortality, we applied a case-crossover analysis. Age- and season-specific analyses were also performed. Effect estimates were expressed as percent variation in mortality or hospitalizations associated with a 10µg/m(3) increase in PM10 or NO2 concentration., Results: Natural mortality was positively associated with both pollutants (0.30%, 90% Credibility Interval [CrI]: -0.31; 0.78 for PM10; 0.70%, 90%CrI: 0.10; 1.27 for NO2). Cardiovascular deaths showed a higher percent variation in association with NO2 (1.12%, 90% Confidence Interval [CI]: 0.14; 2.11), while the percent variation for respiratory mortality was highest in association with PM10 (1.64%, 90%CI: 0.35; 2.93). The effect of both pollutants was more evident in the summer season. Air pollution was also associated to hospitalizations, the highest variations being 0.77% (90%CrI: 0.22; 1.43) for PM10 and respiratory diseases, and 1.70% (90%CrI: 0.39; 2.84) for NO2 and cerebrovascular diseases. The effect of PM10 on respiratory hospital admissions appeared to increase with age. For both pollutants, effects on cerebrovascular hospitalizations were more evident in subjects aged less than 75 years., Conclusions: Our study provided a sound characterization of air pollution exposure and its potential effects on human health in the most polluted, and also most populated and productive, Italian region, further documenting the need for effective public health policies., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Exposure to air pollution is associated with a short-term increase in mortality, and this field has begun to focus on health impact assessment., Objectives: Our aim was to estimate the impact of PM10 on mortality within 2 days from the exposure in the Italian region of Lombardy for the year 2007, at the municipality level, examining exposure entailed by daily intermunicipality commuting and accounting for uncertainty propagation., Methods: We combined data from different sources to derive probabilistic distributions for all input quantities used to calculate attributable deaths (mortality rates, PM10 concentrations, estimated PM10 effects, and commuting flows) and applied a Monte Carlo procedure to propagate uncertainty and sample the distribution of attributable deaths for each municipality., Results: We estimated that annual average PM10 concentrations above the World Health Organization-recommended threshold of 20 μg/m3 were responsible for 865 short-term deaths (80% credibility interval: 475, 1,401), 26% of which were attributable to PM10 above the European Union limit of 40 μg/m3. Reducing annual average PM10 concentrations > 20 μg/m3 by 20% would have reduced the number of attributable deaths by 36%. The largest estimated impacts were along the basin of the Po River and in the largest cities. Commuting contributed to the spatial distribution of the estimated impact., Conclusions: Our estimates, which incorporated uncertainty quantification, indicate that the short-term impact of PM10 on mortality in Lombardy in 2007 was notable, and that reduction in air pollution would have had a substantial beneficial effect on population health. Using commuting data helped to identify critical areas for prioritizing intervention.

Model-based geostatistics and Bayesian approaches are appropriate in the context of Veterinary Epidemiology when point data have been collected by valid study designs. The aim is to predict a continuous infection risk surface. Little work has been done on the use of predictive infection probabilities at farm unit level. In this paper we show how to use predictive infection probability and related uncertainty from a Bayesian kriging model to draw a informative samples from the 8794 geo-referenced sheep farms of the Campania region (southern Italy). Parasitological data come from a first cross-sectional survey carried out to study the spatial distribution of selected helminths in sheep farms. A grid sampling was performed to select the farms for coprological examinations. Faecal samples were collected for 121 sheep farms and the presence of 21 different helminths were investigated using the FLOTAC technique. The 21 responses are very different in terms of geographical distribution and prevalence of infection. The observed prevalence range is from 0.83% to 96.69%. The distributions of the posterior predictive probabilities for all the 21 parasites are very heterogeneous. We show how the results of the Bayesian kriging model can be used to plan a second wave survey. Several alternatives can be chosen depending on the purposes of the second survey: weight by posterior predictive probabilities, their uncertainty or combining both information. The proposed Bayesian kriging model is simple, and the proposed samping strategy represents a useful tool to address targeted infection control treatments and surbveillance campaigns. It is easily extendable to other fields of research., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Epidemiological surveillance on high risk environmental areas or areas covered by cancer registration yields long inventories of relative risks. Summaries of the results' tables must be produced to identify priorities and tailor public health actions. The aim is, therefore, to draw conclusions from each area's disease profile, or from the area signature of each disease.With this inmind, we used data on cancer incidence from 17 Cancer Registries that participated in the ISS-AIRTUM (National Institute of Health-Italian Network of Cancer Registries) study, and we produced conditional and marginal rankings of areas/diseases using a multivariate hierarchical Bayesian model. In this context, it is important to obtain an uncertainty evaluation by calculating the credibility intervals of ranks. The areas marginal ranking shows a large overlapping of credibility intervals, such that it is not possible to speak of a limited number of ISS-AIRTUM areas as being particularly affected. Every ISS-AIRTUMarea, therefore,must be considered individually and ordering themby ranking of cancer incidence wouldn't be appropriate. Instead,marginal ranking of diseases highlights the impact of asbestos exposure in all the analyzed areas.

A core challenge in epidemiological analysis of the impact of exposure to air pollution on health is assessment of the individual exposure for subjects at risk. Geographical information systems (GIS)-based pollution mapping, such as kriging, has become one of the main tools for evaluating individual exposure to ambient pollutants. We applied universal Bayesian kriging to estimate the residential exposure to gaseous air pollutants for children living in a high-risk area (Milazzo- Valle del Mela in Sicily, Italy). Ad hoc air quality monitoring campaigns were carried out: 12 weekly measurements for sulphur dioxide (SO2) and nitrogen dioxide (NO2) were obtained from 21 passive dosimeters located at each school yard of the study area from November 2007 to April 2008. Universal Bayesian kriging was performed to predict individual exposure levels at each residential address for all 6- to 12-years-old children attending primary school at various locations in the study area. Land use, altitude, distance to main roads and population density were included as covariates in the models. A large geographical heterogeneity in air quality was recorded suggesting complex exposure patterns. We obtained a predicted mean level of 25.78 (± 10.61) µg/m(3) of NO2 and 4.10 (± 2.71) µg/m(3) of SO2 at 1,682 children's residential addresses, with a normalised root mean squared error of 28% and 25%, respectively. We conclude that universal Bayesian kriging approach is a useful tool for the assessment of realistic exposure estimates with regard to ambient pollutants at home addresses. Its prediction uncertainty is highly informative and can be used for both designing subsequent campaigns and for improved modelling of epidemiological associations.

Air quality is a primary environmental concern in highly industrialised areas, with potential health effects in children residing nearby. The Sarroch industrial estate in Cagliari province, Sardinia Island, Italy, hosts the world's largest power plant and the second largest European oil refinery and petrochemical park. This industrial estate produces a complex mixture of air pollutants, including benzene, heavy metals and polycyclic aromatic hydrocarbons. Thus, we conducted a cross-sectional study to evaluate the prevalence of malondialdehyde-deoxyguanosine adducts in the nasal epithelium of 75 representative children, aged 6-14 years, attending primary and secondary schools in Sarroch in comparison with 73 rural controls. Additionally, the levels of bulky DNA adducts were analysed in a subset of 62 study children. DNA damage was measured by (32)P-postlabelling methodologies. The air concentrations of benzene and ethyl benzene were measured in the school gardens of Sarroch and a rural village by diffusive samplers. Outdoor measurements were also performed in other Sarroch areas and in the proximity of the industrial estate. The outdoor levels of benzene and ethyl benzene were significantly higher in the school gardens of Sarroch than in the rural village. Higher concentrations were also found in other Sarroch areas and in the vicinity of the industrial park. The mean levels of malondialdehyde-deoxyguanosine adducts per 10(8) normal nucleotides ± standard error (SE) were 74.6±9.1 and 34.1±4.4 in the children from Sarroch and the rural village, respectively. The mean ratio was 2.53, 95% confidence interval (CI): 1.71-2.89, P < 0.001, versus rural controls. Similarly, the levels of bulky DNA adducts per 10(8) normal nucleotides ± SE were 2.9±0.4 and 1.6±0.2 in the schoolchildren from Sarroch and the rural village, respectively. The means ratio was 1.90, 95% CI: 1.25-2.89, P = 0.003 versus rural controls. Our study indicates that children residing near the industrial estate have a significant increment of DNA damage.

We address the problem of farm and parasitic risk profiling in the context of Veterinary Epidemiology. We take advantage of a cross-sectional study carried out in the Campania Region in order to study the spatial distribution of 16 parasites in 121 ovine farms. We propose a tri-level hierarchical Bayesian model, which account for multivariate spatially structured overdispersion, to obtain estimate of posterior classification probabilities, that is for each parasite and farm the probability to belong to the set of the null hypothesis. We explore four decision rules based on either posterior probabilities or posterior means and compare the results in terms of the number of false discoveries/non-discoveries or the rate of false discovery/non-discovery. Our approach proved useful for parasitological risk profiling and we show that decision rules can be easily handled.

The problem of multiple testing is rarely addressed in disease mapping or descriptive epidemiology. This issue is relevant when a large number of small areas or diseases are analysed. Control of the family wise error rate (FWER), for example via the Bonferroni correction, is avoided because it leads to loss of statistical power. To overcome such difficulties, control of the false discovery rate (FDR), the expected proportion of false rejections among all rejected hypotheses, was proposed in the context of clinical trials and genomic data analysis. FDR has a Bayesian interpretation and it is the basis of the so called q-value, the Bayesian counterpart of the p-value. In the present work, we address the multiplicity problem in disease mapping and show the performance of the FDR approach with two real examples and a small simulation study. The examples consider testing multiple diseases for a given area or multiple areas for a given disease. Using unadjusted p-values for multiple testing, an inappropriately large number of areas or diseases at altered risk are identified, whilst FDR procedures are appropriate and more powerful than the control of the FWER with the Bonferroni correction. We conclude that the FDR approach is adequate to screen for high/low risk areas or for disease excess/deficit and useful as a complementary procedure to point estimates and confidence intervals.

Lung cancer epidemic among males and females was studied at small geographical level in Tuscany Region (Italy), about 3.5 million inhabitants over almost 30 years (1971-1999). The joint analysis of the space-time pattern of relative risk for a given cause on males and females was conducted specifying a series of Hierarchical Bayesian models. Goodness-of-fit, parsimony and robustness under misspecification were used to identify candidate models. We chose birth cohort as relevant time axis and restricted our attention to birth cohorts born between 1905 and 1940. We found very different pattern by gender: the epidemic curve among males had a peak for the birth cohort born around 1930, the younger cohorts being at lower risk. Among females the relative risk was rising almost linearly on the log scale, the younger birth cohorts being at higher risk. Both curves showed two accelerations corresponding to the post-war periods (1915-1929 and 1945-1959), assuming an average age at first exposure of 20 years old. The spatial distribution among the 287 municipalities investigated was characterized by high risks in all industrial areas in males, and limited to large towns in females. The shared spatial clustering component highlighted the historically developed part of the Tuscany Region.

In environmental epidemiology, long lists of relative risk estimates from exposed populations are compared to a reference to scrutinize the dataset for extremes. Here, inference on disease profiles for given areas, or for fixed disease population signatures, are of interest and summaries can be obtained averaging over areas or diseases. We have developed a multivariate hierarchical Bayesian approach to estimate posterior rank distributions and we show how to produce league tables of ranks with credibility intervals useful to address the above mentioned inferential problems. Applying the procedure to a real dataset from the report "Environment and Health in Sardinia (Italy)" we selected 18 areas characterized by high environmental pressure for industrial, mining or military activities investigated for 29 causes of deaths among male residents. Ranking diseases highlighted the increased burdens of neoplastic (cancerous), and non-neoplastic respiratory diseases in the heavily polluted area of Portoscuso. The averaged ranks by disease over areas showed lung cancer among the three highest positions.

A cross-sectional survey was carried out on 2004-2005 in the Campania region, southern Italy, to study the multivariate geographical distribution of four different sheep helminths, i.e. Fasciola hepatica (liver fluke), Calicophoron (Paramphistomum) daubneyi (rumen fluke), Dicrocoelium dendriticum (lancet fluke), and the gastrointestinal strongyle Haemonchus contortus. A series of multivariate Bayesian hierarchical models based on square root transformation of faecal egg counts were performed. The results were consistent with theoretical knowledge of the biology and epidemiology of the four studied helminths. In particular, the impact of common intermediate hosts (F. hepatica and C. daubneyi share the same intermediate host species) was quantified and evidence of previously unknown ecological components was given. D. dendriticum was correlated to F. hepatica and H. contortus was found not to be spatially associated with the previously mentioned helminths.

Background: Dogs are associated with more than 60 zoonotic diseases among which, parasitosis and, in particular, helminthosis, can pose serious public-health concerns worldwide. Many canine gastrointestinal parasites eliminate their dispersion elements (eggs, larvae, oocysts) by the faecal route. The quantity of canine faeces deposited on public and private property in cities worldwide is both a perennial nuisance and an important health issue. Public sites such as playgrounds, parks, gardens, public squares and sandpits may be an important source of human infection. The aim of this study was to evaluate the extent of both canine faecal contamination in the city of Naples (southern Italy), and presence of canine parasitic elements, with particular regard to those which are potential agents of zoonosis. A regular grid of sub-areas (1 km x 700 m) was overlaid on the city map using a Geographical Information System (GIS). In each sub-area the straightest 1 km transect was drawn and digitalized on-screen in the GIS. Between February and May 2005 canine faeces were counted along the 1 km transects in 143 sub-areas, and 415 canine faecal samples were collected and submitted to coprological examinations. Negative binomial regression models and Gaussian random effects models were used to analyze the association between faeces count and human population density taking into account for extraPoisson variability. Logistic regression model was used to evaluate the association between positivity to parasitic elements and number of canine faeces., Results: Out of the 143 studied sub-areas, 141 (98.6%) contained canine faeces. There was a strong spatial gradient with 48% of the total variability accounted by between neighbourhood variability; a positive association between the number of faeces and the human population density was found. Seventy (over 415, 16.9%) canine faecal samples were positive for parasitic elements. There was no association between positivity to parasitic elements and the number of canine faeces. Eggs of Toxocara canis, Toxascaris leonina, Ancylostoma caninum and Trichuris vulpis were found, as well as oocysts of Isospora canis., Conclusion: In conclusion, the results of the present study, conducted using GIS both for planning and sampling and for evaluation and presentation of findings, showed the presence of canine faecal contamination in the city of Naples, and the presence of canine parasitic elements, some of which are potential agents of zoonosis.

Model-based geostatistics and Bayesian approaches are useful in the context of veterinary epidemiology when point data have been collected by appropriate study design. We take advantage of an example of Epidemiological Surveillance on urban settings where a two-stage sampling design with first stage transects is applied to study the risk of dog parasite infection in the city of Naples, 2004-2005. We specified Bayesian Gaussian spatial exponential models and Bayesian kriging were performed to predict the continuous risk surface of parasite infection on the study region. We compared the results with those obtained by the application of hierarchical Bayesian models on areal data (proportion of positive specimens by transect). The models results were consistent with each other and the Bayesian geostatistical approach proved to be more accurate in identifying areas at risk of zoonotic parasitic diseases. In general, larger risk areas were identified at the city border where wild dogs mixed with domestic dogs and human or urban barriers were less present.

Spatial clustering and cluster detection are statistical analysis developed to address relevant scientific hypothesis. The difficulty stays in the large number of alternative hypothesis due to the different mechanisms that could generate the anomalous cases aggregation. We review methods for marked point data (case/control) aimed to describe spatial intensity of disease risk, to test for randomness and to locate significant excesses. Bayesian Gaussian Spatial Exponential models are used to illustrate probabilistic aspects and the link with simpler non parametric tools are shown. We develop an informal guideline to the analysis and used data on faecal contamination and dog parasitic diseases in the city of Naples, Italy. Kernel density estimation resulted very sensitive to bandwidth choice and overemphasized localized excess, Ripley'K function and Cuzick-Edwards test were very consistent each other while the SatScan failed to detect excesses. The spatial range was around 600 meters and justifies several small clusters. Bayesian models were very powerful in reconstructing the phenomenon and allow inference on model parameters in good agreement with the non parametric analysis.

The work described in the present report has been requested by the Secretary of Hygiene, Health and Social Welfare of the Sardinia Region (Italy). It has been carried out by the Regional Epidemiological Observatory within the domain of ESA (Epidemiology Development and Environment) and with the support of the European Union. Eighteen areas (for a total of 73 municipalities) were identified a priori as "potentially polluted", accounting for a population of 917,977 in 2001 census (56% of the total population of Sardinia). The areas have been named after the most important town, as listed below (in brackets rounded 2001 population), major activities in industrial areas are briefly described., Industrial Areas: Portoscuso (59,000). Processing of aluminium and other metals. Foundry. Power plants. Dismissed mines (mainly coal mining, lead, zinc). Plants for storing and treating special wastes. Italian Law 349/1986 classified this area as "at high risk of environmental crisis" and classified some plants as being "at high technological risk" (Norma Seveso Decree 334/1999). The area is part of the Sulcis National Restoration site. San Gavino (24,000). Industrial and commercial activities. Lead and zinc foundry. Dairy factories. Food industry. Sarroch (52,000). Petrochemical and refinery industry. Power plants. Mining. Incinerator. Plants for storing and treating special wastes. Gas and mineral oil deposits. Ottana (15,000). Chemical industry. Production of plastics and synthetic fibres. Denim production. Porto Torres (168,000). Chemical industry: production of basic chemicals (benzene, toluene, ethylene, propylene and others), polyethylene, elastomers and vinyl chloride. Textile industry. First and second category landfills. Some plants have been classified "at high technological risk" (Norma Seveso Decree 334/1999). The area is a National Restoration site. The town of Sassari is included. Tortolì (23,000). Construction of steel structures for offshore facilities of the oil and gas industry. Paper industry. Tempio Pausania (21,000). Cork production. Stone quarries. Macomer (17,000). Textile industry (velvet). First and second category landfills. Incinerator., Mining Areas: Arbus (30,000). Extraction of zinc, lead and silver. Iglesias (39,000). Extraction of zinc, lead and silver., Military Sites: Teulada (16,000). La Maddalena (11,000). Naval army shipyards. Salto di Quirra (31,000). Mining area., Urban Areas: Cagliari (299,000). Petrochemical plants, port, airport. Nuoro (37,000). Olbia (47,000). Port and airport. Oristano (31,000). Sassari (121,000)., Results: THE COMPARISON SARDINIA-ITALY: In 1997-2001, the age-standardized mortality rate (x1,000 person-years) among males was higher than in Italy (84.4 vs 80.8) while the reverse occurred in females (50.9 vs 52.0). Ill defined causes of death were 1.4% in males and 2.5% in females (vs corresponding estimates of 1.1% and 1.4% in Italy). Compared to Italian national data, regional age-standardized estimates were higher in Sardinia for infectious diseases (23% in males and 12% in females), respiratory diseases (22% and 14%: pneumoconiosis was more than 6 times more frequent in Sardinia than in Italy), diseases of the digestive system (26% and 9%: for liver cirrhosis, the excess was 33% in males and 9% in females; corresponding figures for liver cancer were 13% and 16%), breast cancer in females (5%). On the other hand, regional mortality rates were lower than the national rates for cardiovascular diseases (-1.3% and -7.4% in males and females respectively), all cancers considered as a whole (-9% and -7%) and lung cancer (-5% and -32%). Regional and national death rates for non Hodgkin lymphoma in both sexes and for leukaemia in females were almost identical, whereas the latter rate in males was slightly higher in Sardinia than in Italy (9.4 vs 8.4 x100,000 person-years). Particularly in men, the differences in mortality rates from all causes and from cardiovascular, respiratory diseases and lung cancer among the four traditional Provinces (Cagliari, Nuoro, Oristano and Sassari) were greater than the difference between Sardinia and Italy. Remarkably enough, also death rates from lymphohaemopoietic tumours were more heterogeneous within Sardinia., Results in the Investigated Areas: Rates of hospital discharges in Sardinia showed a high variability, which is partly attributable to differences in the availability of both hospital beds and alternative forms of care. This heterogeneity must be taken into account in the interpretation of rates of hospital discharge. These were relatively high in some areas (Cagliari, Iglesias, Portoscuso, Tortolì) and low in others (Olbia, Porto Torres, Sassari). All the reported observed/expected ratios were based on material deprivation adjusted figures. All the estimated statistics were reported with 90% Confidence Interval., Industrial Areas: In 1997-2001, deaths from respiratory diseases were significantly in excess in males in Portoscuso (obs/exp 205/124.77) and in San Gavino (69/46.77). Deaths from pneumoconiosis were recorded sporadically, with the exception of Portoscuso, where the excess was impressive (obs/exp 112/30.46). SMRs for lung cancer in males ranged between 0.62 in Ottana and 1.22 in San Gavino, with statistically significant departure from expected values in Portoscuso and Sarroch (both with SMR significantly in excess in males: 1.24). In Porto Torres mortality from all causes was in significant excess in both sexes (SMRs 1.04 in males and 1.09 in females), for respiratory diseases (1.08 and 1.28), for diseases of the digestive system (1.13 and 1.21), for all cancers (1.04 and 1.09). Liver cancer deaths were also in excess in both sexes (SMRs 1.18 and 1.21). The latter finding is confirmed by incidence rates from the local cancer registry. Among industrial areas, Porto Torres was also the one with a stronger evidence of an excess of deaths from lymphohaemopoietic cancer in males (obs/exp 99/83.60) and females (73/68.20)., Mining Areas: These areas are characterized by statistically significant excesses of mortality in males, largely caused by non neoplastic respiratory conditions (obs/exp 119/86.41 in Iglesias and 156/62.55 in Arbus). In recent years, deaths from pneumoconiosis averaged 20 per year in Arbus and 10 per year in Iglesias. Lung cancer in males was also significantly in excess in both areas (obs/exp 72/56.38 in Arbus and 108/72.14 in Iglesias). There is a time trend (1981-2001) towards a decrease of mortality from respiratory conditions, which nevertheless remains largely in excess over the regional average also in the most recent period., Military Areas: Statistically significant excesses of deaths and hospital discharges for non Hodgkin lymphoma were detected in La Maddalena (mortality, 1981-2001, in males 17 observed cases vs 6.13 expected, in females 8/5.64). In Salto di Quirra in 1997-2001 deaths from myeloma (in males 5/2.3) and leukaemias were increased in both sexes (total obs/exp 20/13.3, statistically non significant)., Urban Areas: Urban areas in Sardinia are relatively well developed with high values of socioeconomic indicators. The health profile in Cagliari and Sassari is typical of towns of the Western world. In Cagliari there is a higher mortality for colorectal, breast, cervical and lung cancer., Conclusions: Environmental (non occupational) pollution might explain some of the observed excesses of disease in the investigated industrial areas of Sardinia, particularly in women, less likely to be exposed to hazards in the work environment, whereas in the mining areas studied the disease pattern suggests a major role of occupational exposures. On the other hand, the causal links between disease occurrence and exposures in the screened military areas remain uncertain. The disease patterns in the cities of Sardinia are likely to be associated with lifestyle and urban pollution. Historically, southern Italian Regions have been characterized by an advantage over the rest of the country in terms of health, but during the last decade such advantage tended to vanish. Sardinia confirms this secular trend. However in the most recent years studied, overall age-standardized mortality rate in Sardinian females still remains lower than Italian average, but this is not the case for males any more. Differences in the health profile between residents in different areas of Sardinia have been found to be far greater than the difference between Sardinia as a whole and Italy. A major contribution to intraregional differences is given by the 18 investigated areas where excesses were registered for: respiratory diseases (including cancer) in the industrial areas of Portoscuso, Sarroch and Porto Torres, and in the mining areas; diseases of the digestive tract, liver cancer and lymphohaemopoietic cancer in the area of Porto Torres; cancer of the lymphohaemopoietic system in some military areas; cancers of the colon and rectum, lung, breast and uterus in some of the major cities of the Region.

The relationship between socioeconomic factors and mortality for lung cancer is investigated. To identify the proper lag time between socioeconomic factors and lung cancer mortality, a space-time hierarchical Bayesian model with time-dependent covariates is adopted. A real example on lung cancer mortality, males, in Tuscany (Italy) during the period 1971-1999, is provided. Results confirm the presence of an association between mortality for lung cancer and socioeconomic factors with a temporal lag (latency time) of at least 10 years., (Copyright 2005 John Wiley & Sons, Ltd.)

Aim: Aim of the study is to investigate mortality for lymphoematopoietic tumours and soft tissue sarcoma of people resident near an urban waste incinerator., Materials and Methods: We have considered death certificates recorded by National Statistical Institute (ISTAT) for the period 1981-2001 of the residents in 277 municipalities included in a circle of 80 km of radius centered at Campi Bisenzio municipality. Mortality data have been analyzed by calendar period (1981-1985, 1986-1992, 1993-2001) and by specific causes of death. In particular we have considered mortality for soft tissue sarcoma and lymphoematopoietic tumours. Standardized mortality ratios (SMR) have been evaluated for each calendar period using the population of the circle as standard population., Results: Our findings point out that males resident in Campi Bisenzio municipality had a higher risk of non-Hodgkin lymphoma in the period 1986-1992, with 14 observed cases versus 7.6 expected (SMR 184.2 CI 90% 111-273). The excess risk is present also for all Lymphomas with 18 cases versus 9.07 expected. In the same period, in Campi Bisenzio, we observed 2 deaths for soft tissue Sarcoma., Conclusion: The study shows the presence of a localized cluster of deaths for non-Hodgkin lymphoma, for males, in the calendar period 1986-1992. In the same municipality an urban waste incinerator was operating from 1973 to 1986, when it was closed following evidence of dioxin soil contamination.

Statistical modelling for Disease Mapping and Ecological Analysis is of particular importance in veterinary parasitology because environmental characteristics can affect parasite distribution. However, the main difficulties relate to the concentration of animal populations within farms, which contrasts to the study of wild animal populations. In the present paper we report the results of a cross-sectional coprological survey designed to study the presence and distribution of the rumen fluke Calicophoron daubneyi--which causes paramphistomosis, a snail borne disease--in pastured sheep living in the Latina province of central Italy. We show how techniques derived from human epidemiology can be used to study the spatial distribution of parasite infection in animals. We proposed a hierarchical Bayesian model with random terms for unstructured variability (heterogeneity) to account for local farm characteristics and spatially structure terms (clustering) to cope with medium-large scale environmental characteristics.

The simplest way to study the spatial pattern of a disease is the geographical representation of its cases (or some indicators of them) over a map. Maps based on raw data are generally "wrong" since they do not take into consideration for sampling errors. Indeed, the observed differences between areas (or points in the map) are not directly interpretable, as they derive from the composition of true, structural differences and of the noise deriving from the sampling process. This problem is well known in human epidemiology, and several solutions have been proposed to filter the signal from the noise. These statistical methods are usually referred to as Disease Mapping. In geographical analysis a first goal is to evaluate the statistical significance of the heterogeneity between areas (or points). If the test indicates rejection of the hypothesis of homogeneity the following task is to study the spatial pattern of the disease. The spatial variability of risk is usually decomposed into two terms: a spatially structured (clustering) and a non spatially structured (heterogeneity) one. The heterogeneity term reflects spatial variability due to intrinsic characteristics of the sampling units (e.g. igienic conditions of farms), while the clustering term models the association due to proximity between sampling units, that usually depends on ecological conditions that vary over the study area and that affect in similar way breedings that are close to each other. Hierarchical bayesian models are the main tool to make inference over the clustering and heterogeneity components. The results are based on the marginal posterior distributions of the parameters of the model, that are approximated by Monte Carlo Markov Chain methods. Different models can be defined depending on the terms that are considered, namely a model with only the clustering term, a model with only the heterogeneity term and a model where both are included. Model selection criteria based on a compromise between degree of complexity and goodness of fit are then needed to discriminate among them, because each specification has a different biological meaning. Our aim is to demonstrate that these techniques can be used to study the geographical distribution of a parasite infection. Our analyses are based on data collected in 142 farms of the province of Latina. In each breeding a fixed number of sheeps has been sampled (20) and checked for the presence of C. daubneyi. We have specified a Binomial model for the proportion of infected animals in each breeding. The heterogeneity component is modelled in a standard way, while we have used different prior specifications for the clustering term to show how they affect the results. When we use the usual specification also for clustering, the two models show a completely different spatial pattern of infection, probably because the intrinsic spatial structure of the clustering term tend to bias our inferences. The selection criterion indicates in this case the heterogeneity model as the "best" one. However, if we modify the prior so that a lower degree of spatial interaction is assumed, the clustering model is less complex and its goodness of fit better and it should be preferred.