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Administrative data can be precious in connecting information from different sectors. For the first time, we used data from the National Social Insurance Agency (INPS) to investigate the association between the occupational sectors and both non-accidental and accidental mortality. We retrieved information on occupational sectors from 1974 to 2011 for private sector workers included in the 2011 census cohort of Rome. We classified the occupational sectors into 25 categories and analyzed occupational exposure as ever/never have been employed in a sector or as the lifetime prevalent sector. We followed the subjects from the census reference day (9 October 2011) to 31 December 2019. We calculated age-standardized mortality rates for each occupational sector, separately in men and women. We used Cox regression to investigate the association between the occupational sectors and mortality, producing hazard ratios (HRs) and 95% confidence intervals (95%CI). We analyzed 910,559 30+-year-olds (53% males) followed for 7 million person-years. During the follow-up, 59,200 and 2560 died for non-accidental and accidental causes, respectively. Several occupational sectors showed high mortality risks in men in age-adjusted models: food and tobacco production with HR = 1.16 (95%CI: 1.09–8.22), metal processing (HR = 1.66, 95%CI: 1.21–11.8), footwear and wood (HR = 1.19, 95%CI: 1.11–1.28), construction (HR = 1.15, 95%CI: 1.12–1.18), hotels, camping, bars, and restaurants (HR = 1.16, 95%CI: 1.11–1.21) and cleaning (HR = 1.42, 95%CI: 1.33–1.52). In women, the sectors that showed higher mortality than the others were hotels, camping, bars, and restaurants (HR = 1.17, 95%CI: 1.10–1.25) and cleaning services (HR = 1.23, 95%CI: 1.17–1.30). Metal processing and construction sectors showed elevated accidental mortality risks in men. Social Insurance Agency data have the potential to characterize high-risk sectors and identify susceptible groups in the population.

Daily air pollution has been linked with mortality from urban studies. Associations in rural areas are still unclear and there is growing interest in testing the role that air pollution has on other causes of death. This study aims to evaluate the association between daily air pollution and cause-specific mortality in all 8092 Italian municipalities.Natural, cardiovascular, cardiac, ischemic, cerebrovascular, respiratory, metabolic, diabetes, nervous and psychiatric causes of death occurred in Italy were extracted during 2013-2015. Daily ambient PMWe estimated a positive association between PMShort-term exposure to particulate matter was associated with mortality from nervous diseases. Mortality from metabolic diseases was associated with NO

Aims We aimed at investigating the relationship between particulate matter (PM) and daily admissions for cardiovascular diseases (CVDs) at national level in Italy. Methods and results Daily numbers of cardiovascular hospitalizations were collected for all 8084 municipalities of Italy, in the period 2013–2015. A satellite-based spatiotemporal model was used to estimate daily PM10 (inhalable particles) and PM2.5 (fine particles) concentrations at 1-km2 resolution. Multivariate Poisson regression models were fit to estimate the association between daily PM and cardiovascular admissions. Flexible functions were estimated to explore the shape of the associations at low PM concentrations, also in non-urban areas. We analysed 2 154 810 acute hospitalizations for CVDs (25% stroke, 24% ischaemic heart diseases, 22% heart failure, and 5% atrial fibrillation). Relative increases of total cardiovascular admissions, per 10 µg/m3 variation in PM10 and PM2.5 at lag 0–5 (average of last 6 days since admission), were 0.55% (95% confidence intervals: 0.32%, 0.77%) and 0.97% (0.67%, 1.27%), respectively. The corresponding estimates for heart failure were 1.70% (1.28%, 2.13%) and 2.66% (2.09%, 3.23%). We estimated significant effects of PM10 and PM2.5 also on ischaemic heart diseases, myocardial infarction, atrial fibrillation, and ischaemic stroke. Associations were similar between less and more urbanized areas, and persisted even at low concentrations, e.g. below WHO guidelines. Conclusion PM was robustly associated with peaks in daily cardiovascular admissions, especially for heart failure, both in large cities and in less urbanized areas of Italy. Current WHO Air Quality Guidelines for PM10 and PM2.5 are not sufficient to protect public health.

Background: Allergic rhinitis (AR) is a major non-communicable disease that affects the health-related quality of life (HRQoL) of patients. AR is significantly related to asthma also affecting HRQoL. However, data on HRQoL and symptom control in AR patients with comorbid asthma are lacking. Objective: To assess the differences of symptom control and HRQoL in AR patients with and without comorbid asthma. Methods: In this multicentre, cross-sectional study, patients with AR were screened and administered questionnaires of demographic characteristics and health conditions (symptoms/diagnosis of AR and asthma, disease severity level, and allergic conditions). HRQoL was assessed using a modified version of the RHINASTHMA questionnaire and symptom control was evaluated by a modified version of the Control of Allergic Rhinitis/Asthma Test (CARAT). Results: Out of 643 patients with AR, 500 (78%) had asthma as a comorbidity, and 54% had moderate-severe intermittent AR, followed by moderate-severe persistent AR (34%). Patients with both AR and asthma had significantly higher RHINASTHMA scores than the patients with AR alone (e.g., median RHINASTHMA-total score 84 vs. 48.5, respectively). Conversely, CARAT scores were significantly lower in AR with comorbid asthma than in the patients with AR alone (median CARAT-total score 16.5 vs. 23, respectively). Upon stratifying asthma based on severity, AR patients with severe persistent asthma had worse HRQoL and control than AR patients with mild persistent asthma. Conclusions: Our observation of poorer HRQoL and symptoms control in AR patients with comorbid asthma supports the importance of a comprehensive approach for the management of AR in case of a comorbid allergic condition.

Indoor environment comprises ordinary living confined spaces as well as non-industrial work-places. Indeed, for industrial working environment there is a specific regulation assessed by hygienists and occupational physicians. Indoor exposure mainly occurs in public and private buildings, offices, community structures (hospitals, schools, barracks, hotels, banks, etc.), premises for recreational and/or social activities (cinemas, bars, restaurants, shops, sports facilities, etc.), or in microenvironments, such as public or private transport (cars, buses, trains, subways, airplanes.). Indoor environment contributes significantly to human exposure to air pollutants in industrialized countries, which can be defined as countries that have a developed economy and advanced technological infrastructure relative to other less industrialized nations. Most commonly, the criteria for evaluating the degree of economic development are gross domestic product, gross national product, the per capita income, level of industrialization, amount of widespread infrastructure and general standard of living (Investopedia website). The 31 industrialized countries, listed in the world population review website, have a per capita gross domestic product that exceeds $15.000, although there are a few exceptions (Worldpopulationreview website). In industrialized countries, people spend up to 90% of their time indoors, especially at home, at school, or at work. There is consistent evidence that exposure to indoor pollutants increases the risk of several respiratory/allergic symptoms and diseases (Viegi et al., 2004; Baldacci et al., 2015). Furthermore, a relevant quota of indoor pollution derives from tobacco smoking, which is the major source of indoor particulate matter (PM). Conversely, in developing countries, elevated concentrations of indoor PM are mainly associated with biomass combustion for heating and cooking. However, biomass combustion is increasing, especially in winter, also in the developed world.

Background: Noncommunicable diseases (NCDs) and chronic respiratory diseases (CRDs) are the main causes of mortality and morbidity worldwide. Methods: The main evidences about the NCDs and CRDs burden and related risk factors, from updated international reports and results of original researches, were collected and described in this review. Results: Most recent evidence is available from the Global Burden of Diseases Study (GBD) 2017 reports. There were 3.2 million deaths due to chronic obstructive pulmonary disease (COPD) and 495,000 deaths due to asthma. COPD was the seventh leading cause of years of life lost (YLLs). Overall, prevalent cases of CRDs were 545 million: about 50% for COPD and 50% for asthma. Incident cases of CRDs were 62 million, mostly due to asthma (69%) and COPD (29%). COPD accounted for 81.6 million disability-adjusted life years, asthma for 22.8 million. COPD prevalence of 9.1% has been found in a recent general population sample of North- Eastern Italy, while in Central Italy a 25-year follow-up of a general population sample has shown an increased prevalence of COPD and asthma up to 6.8% and 7.8%, respectively. In Central Italy, a COPD incidence of 8% and an asthma incidence of 3.2% have been found in adult subjects at an 18-year follow-up. Among the risk factors, a relevant role is played by smoking and high body mass index for asthma, while smoking, particulate matter pollution, ambient ozone pollution, occupational exposure to particulate matter, gases and fumes, as well as second-hand smoke, play an important role for COPD. Forecasting the YLLs by 2040 indicates a rising toll from several NCDs due to population growth and aging, with COPD expected to reach the fourth leading cause. Conclusions: Several recent studies and international reports highlighted the huge global health burden of CRDs and other major NCDs, pointing out the need for implementing international collaborations to fight this epidemic trend.

Background Few population-based studies on the effects of environmental exposure variation exist. Aim Assessing respiratory symptom/disease incidence related to risk factor exposure changes. Methods A longitudinal general population sample from two surveys (PISA2:1991–1993; PISA3:2009–2011; no. = 970), aged ≥20 years at baseline, completed a questionnaire on respiratory symptoms/diseases, risk factor exposure and performed spirometry. 18-year follow-up cumulative incidence of respiratory symptoms/diseases and longitudinal changes (persistence, incidence, remittance) in risk factor exposure were computed. Results Cumulative incidence values were: 3.2% (corresponding to a 1.8‰/year incidence rate), asthma; 6.6% (3.8‰/year), asthma attacks; 4.5% (2.6‰/year), wheeze; 31.7% (21.0‰/year), allergic rhinitis-AR; 7.6% (4.4‰/year), chronic obstructive pulmonary disease-COPD; 16.1% (9.7‰/year), usual cough; 18.5% (11.3‰/year), usual phlegm; 30.7% (20.1‰/year), dyspnoea 1+; 13.9% (8.3‰/year), airway obstruction. The following associations emerged among respiratory symptom/disease cumulative incidence and risk factor exposure changes: a two-to-five fold higher risk for COPD, phlegm, cough, dyspnoea, asthma attacks, airway obstruction in persistent smokers; a two-to-three fold higher risk for COPD in remittent smokers; a two-fold higher risk for AR, phlegm and a four-fold higher risk for asthma in subjects with persistent occupational exposure; a two-fold higher risk for cough, phlegm, dyspnoea, AR in subjects with incident occupational exposure; a two-fold higher risk for AR, asthma attacks, COPD in subjects with incident traffic exposure. Conclusions Our study showed noteworthy respiratory symptom/disease incidence values and indicated that life-style and environmental exposure changes can differently influence onset. This information could be useful for primary prevention strategies in order to reduce the chronic disease burden in the general population.

follow-up studies on registries of severe/uncontrolled asthma (SUA) patients are scanty.to analyze baseline and follow-up characteristics of SUA patients and their longitudinal patterns.180 adult patients (age ≥15 yrs) were investigated at baseline and 12-month follow-up through the Italian SUA registry (RItA). Latent transition analysis (LTA) was performed to detect cross-sectional SUA phenotypes and longitudinal patterns. Risk factors for longitudinal patterns were assessed through logistic regression.a significant/borderline improvement of asthma control outcomes in the last 2-4 weeks emerged at follow-up with respect to baseline for: daily activities limitations (Δ -16%), frequent diurnal symptoms (Δ -25%), uncontrolled asthma symptoms according to ACT (Δ -26%). Last 12-month use of oral corticosteroids was less frequent at follow-up than at baseline (Δ -25%). Health status improvement was confirmed by lung function test results. Through LTA, two longitudinal patterns were detected considering last 12-month control outcomes: "persistence/worsening" (53.9%), "under control/improvement" (46.1%). A lower likelihood of having "persistence/worsening" SUA was exhibited by patients under anti-IgE (OR 0.38, 95% CI 0.17-0.84) and inhaled corticosteroids-bronchodilator association treatment (OR 0.13, 95% CI 0.01-1.26, borderline value), while a higher likelihood was shown by older age at first asthma diagnosis (OR 1.04, 95% CI 1.01-1.07).the implementation of a SUA registry, the availability of patient-level data and the application of an innovative longitudinal analysis allowed to observe a general improvement in asthma control, one year after baseline, and a lower risk of SUA "persistence/worsening" in patients under anti-IgE and regular ICS-bronchodilator association use.

Background/aim The relationship between air pollution and respiratory morbidity has been widely addressed in urban and metropolitan areas but little is known about the effects in non-urban settings. Our aim was to assess the short-term effects of PM10 and PM2.5 on respiratory admissions in the whole country of Italy during 2006–2015. Methods We estimated daily PM concentrations at the municipality level using satellite data and spatiotemporal predictors. We collected daily counts of respiratory hospital admissions for each Italian municipality. We considered five different outcomes: all respiratory diseases, asthma, chronic obstructive pulmonary disease (COPD), lower and upper respiratory tract infections (LRTI and URTI). Meta-analysis of province-specific estimates obtained by time-series models, adjusting for temperature, humidity and other confounders, was applied to extrapolate national estimates for each outcome. At last, we tested for effect modification by sex, age, period, and urbanization score. Analyses for PM2.5 were restricted to 2013–2015 cause the goodness of fit of exposure estimation. Results A total of 4,154,887 respiratory admission were registered during 2006–2015, of which 29% for LRTI, 12% for COPD, 6% for URTI, and 3% for asthma. Daily mean PM10 and PM2.5 concentrations over the study period were 23.3 and 17 μg/m3, respectively. For each 10 μg/m3 increases in PM10 and PM2.5 at lag 0–5 days, we found excess risks of total respiratory diseases equal to 1.20% (95% confidence intervals, 0.92, 1.49) and 1.22% (0.76, 1.68), respectively. The effects for the specific diseases were similar, with the strongest ones for asthma and COPD. Higher effects were found in the elderly and in less urbanized areas. Conclusions Short-term exposure to PM is harmful for the respiratory system throughout an entire country, especially in elderly patients. Strong effects can be found also in less urbanized areas.

Background: the built environment in urban areas may have side effects on children's respiratory health, whilst less is known for adulthood.Aim: to assess the association between increasing exposure to grey spaces and allergic status in an adult general population sample.Methods: 2070 subjects (age range 15–84 yrs), living in Pisa/Cascina, Italy, were investigated in 1991–93 through a questionnaire on health status and risk factors, skin prick test (SPT), serum Immunoglobulins E (IgE), and serum antibodies to benzo(a)pyrene diol epoxide (BPDE)-DNA adducts. Land-cover exposure within a 1000 m buffer from each subject's home address was assessed through the CORINE Land Cover program (CLC 1990) within the FP7/HEALS project (2013–2018). Participants' residential addresses were geocoded and the proportion of surrounding grey spaces was calculated. Through logistic regression models, adjusting for potential confounding factors, the effect of a 10% increase in grey spaces exposure on allergic biomarkers/conditions was assessed; the relationship with serum antibodies to BPDE-DNA adducts positivity was also analyzed.Results: A 10% increase in grey spaces coverage was associated with a higher probability of having SPT positivity (OR 1.07, 95% CI 1.02-1.13), seasonal SPT positivity (OR 1.12, 1.05-1.19), polysensitization (OR 1.11, 1.04-1.19), allergic rhinitis (OR 1.10, 1.04-1.17), co-presence of SPT positivity and asthma/allergic rhinitis (OR 1.16, 1.08-1.25), asthma/allergic rhinitis (OR 1.06, 1.00-1.12), presence of serum antibodies to BPDE-DNA adducts positivity (OR 1.07, 1.01-1.14).Conclusions: grey spaces have adverse effects on allergic status and are related to a biomarker of polycyclic aromatic hydrocarbons exposure in adulthood. Thus, they may be used as a proxy of urban environmental exposure.

The role of atmospheric dispersion models is becoming increasingly relevant to assess air pollution urban population exposure for epidemiological studies. Estimating urban air quality is challenging, because of the intrinsic characteristics of cities atmospheric structure, such as high density of primary emissions and presence of local dispersion processes, that produce strong concentration gradients. Therefore, very high spatial resolution simulations may often be required to improve the accuracy of estimations. The objective of this study is developing a microscale hybrid modelling system (HMS) to carry out, in a reasonable computational time, long-term simulations providing hourly concentration fields at building-resolving scale in extended urban areas in order to calculate annual indicators to evaluate exposure. The proposed system couples two atmospheric dispersion models suited for different scales: a Eulerian chemical transport model, FARM (Flexible Air quality Regional Model), accounting for dispersion phenomena due to regional and local emission sources, and a Lagrangian particle micro-scale dispersion model, PMSS (Parallel Micro Swift Spray), used to compute concentrations induced by vehicular traffic inside the city. The HMS has been applied on 12 × 12 km2 domain in Rome with a horizontal resolution of 4 m for calculating NO2 and PM10 concentrations for all year 2015. This study has been carried out in the frame of project BEEP (Big data in Environmental and occupational Epidemiology), that is an Italian research project in epidemiological field. Results show that the combined use of the two models reproduces the spatial and temporal variability of the observed atmospheric pollutants with a good agreement. The statistical analysis performed on daily average concentrations proves that the HMS suits the standard acceptance criteria for urban dispersion model evaluation, with a FAC2 of 0.92 and 0.80 and a Fractional Bias of −0.03 and −0.2 for NO2 and PM10 respectively. Furthermore, the implementation of an innovative kernel method to calculate concentrations in PMSS has made possible to reduce the computational time by 80%, leading to an average computational time of 3 h per simulated day on an HPC (High Performance Computing) system with 180 cores.

Air pollution effects on cardiovascular hospitalizations in small urban/suburban areas have been scantly investigated. Such effects were assessed among the participants in the analytical epidemiological survey carried out in Pisa and Cascina, Tuscany, Italy (2009–2011). Cardiovascular hospitalizations from 1585 subjects were followed up (2011–2015). Daily mean pollutant concentrations were estimated through random forests at 1 km (particulate matter: PM10, 2011–2015, PM2.5, 2013–2015) and 200 m (PM10, PM2.5, NO2, O3, 2013–2015) resolutions. Exposure effects were estimated using the case-crossover design and conditional logistic regression (odds ratio—OR—and 95% confidence interval—CI—for 10 μg/m3 increase, lag 0–6). During the period 2011–2015 (137 hospitalizations), a significant effect at lag 0 was observed for PM10 (OR = 1.137, CI: 1.023–1.264) at 1 km resolution. During the period 2013–2015 (69 hospitalizations), significant effects at lag 0 were observed for PM10 (OR = 1.268, CI: 1.085–1.483) and PM2.5 (OR = 1.273, CI: 1.053–1.540) at 1 km resolution, as well as for PM10 (OR = 1.365, CI: 1.103–1.690), PM2.5 (OR = 1.264, CI: 1.006–1.589) and NO2 (OR = 1.477, CI: 1.058–2.061) at 200 m resolution, significant effects were observed up to lag 2. Larger ORs were observed in males and in subjects reporting pre-existent cardiovascular/respiratory diseases. Combining analytical and routine epidemiological data with high-resolution pollutant estimates provides new insights on acute cardiovascular effects in the general population and in potentially susceptible subgroups living in small urban/suburban areas.

Background: A link between residential land cover and allergic symptoms has been suggested. Aim: To assess the influence of residential land cover on objective biomarkers of exposure and effect in a general population sample.Methods: 2070 subjects (age range: 15-84) living in Pisa, Italy, were investigated in 1991-93 through a questionnaire on health status and risk factors, blood analyses, skin prick test (SPT). Land-cover exposure within a 1000 m buffer from each subjects' home address was computed using data from the CORINE program (CLC 1990). The land coverage effects on positivity to SPT and to serum antibodies anti-benzo(a)pyrene diol epoxide (BPDE)-DNA adducts, a markers of polycyclic aromatic hydrocarbons exposure, were assessed using logistic regression models, adjusted for sex, age, BMI, education, smoking habits, occupational exposure, family history of allergic/respiratory diseases. Results: A 10% increase in agricultural space coverage was associated with lower risk of having BPDE-DNA (OR [95% CI]: 0.91 [CI 0.87-0.97]), overall SPT positivity (OR 0.93 [0.88-0.98]), seasonal SPT positivity (OR 0.89 [0.84-0.95]) and polisensitization (OR 0.87 [0.81-0.94]). A 10% increase in grey space coverage was associated with higher risk of having BPDE-DNA (OR 1.09 [1.03-1.15]), overall SPT positivity (OR 1.07 [1.02-1.13]), seasonal SPT positivity (OR 1.12 [1.05-1.19]) and polisensitization (OR 1.15 [1.06-1.23]). Conclusions: Our results provide evidence for a protective effect of agricultural space and an adverse effect of grey space on allergic status using biomarkers. Further, the validity of residential land cover as a proxy of air pollution exposure is suggested.

Background: A recent research has shown that residential land cover may affect health outcomes in children (Parmes, Envir Res 2019); the influence of residential land cover on respiratory and allergic health in adults, however, still needs to be clarified. Aim: To assess influence of residential land cover on allergic/respiratory outcomes in a general population sample. Methods: 2706 subjects (females: 55.4%; age range: 15-97) living in Pisa, Italy, were investigated in 1991-93 through a questionnaire on health status and risk factors. Land-cover exposures within a 1000 m buffer from each subjects' home address were computed using data from the CORINE program (CLC 1990). The associations of allergic rhinitis, asthma and chronic obstructive pulmonary disease (COPD) with land coverage were estimated using logistic regression models, adjusted for sex, age, body mass index, education, smoking habits, occupational exposure and family history of allergic/respiratory diseases. Results: A 10% increase in agricultural space coverage was associated with lower risk of having allergic rhinitis (OR [95% CI]: 0.95 [0.91-0.99]), asthma (OR 0.95 [0.90-0.99]) and COPD (OR 0.93 [0.89-0.97]) symptoms/diagnoses; vice versa, a 10% increase in grey space coverage was associated with higher risk of having allergic rhinitis (OR 1.04 [1.01-1.09], asthma (OR 1.05 [1.00-1.10]) and COPD (OR 1.08 [1.03-1.12] symptoms/diagnoses.Conclusions: Our results provide further evidence that living close to agricultural space may provide protective effects on allergic/respiratory health outcomes, while exposure to grey spaces may have adverse effects.

Substantial knowledge is available on the association of the indoor school environment and its effect among schoolchildren. In the same context, the SINPHONIE (School indoor pollution and health: Observatory network in Europe) conducted a study to collect data and determine the distribution of several indoor air pollutants (IAPs), physical and thermal parameters and their association with eye, skin, upper-, lower respiratory and systemic disorder symptoms during the previous three months. Finally, data from 115 schools in 54 European cities from 23 countries were collected and included 5175 schoolchildren using a harmonized and standardized protocol. The association between exposures and the health outcomes were examined using logistic regression models on the environmental stressors assessed in classroom while adjusting for several confounding factors; a VOC (volatile organic compound) score defined as the sum of the number of pollutants to which the children were highly exposed (concentration > median of the distribution) in classroom was also introduced to evaluate the mul tiexposu re - outcome association. Schoolchildren while adjusting for several confounding factors. Schoolchildren exposed to above or equal median concentration of PM2.5, benzene, limonene, ozone and radon were at significantly higher odds of suffering from upper, lower airways, eye and systemic disorders. Increased odds were also observed for any symptom (sick school syndrome) among schoolchildren exposed to concentrations of limonene and ozone above median values. Furthermore, the risks for upper and lower airways and systemic disorders significantly increased with the VOCs score. Results also showed that increased ventilation rate was significantly associated with decreased odds of suffering from eye and skin disorders whereas similar association was observed between temperature and upper airways symptoms. The present study provides evidence that exposure to IAPs in schools is associated with various health problems in children. Further investigations are needed to confirm our findings. (C) 2020 Elsevier B.V. All rights reserved.

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Asthma is a heterogeneous disease, generally characterized by chronic inflammation. Severe asthma (SA) affects 5-10% of the asthma population, with the highest values in adolescents.

Background: A former cross-sectional study on health effects of air pollution in Pisa was based on a proxy of traffic-related exposure. PM10 exposure estimates available from the BEEP project allow a better assessment of health effects. Aim: To assess health effects of self-reported risk factors and PM10 estimates in a sub-sample of long-term residents. Methods: 305 Pisan subjects (mean age 64 years) living at the same address from 1991-1993 to 2011 were selected. Questionnaire information on occurrence of respiratory diseases/symptoms and risk factors was available. Mean PM10 concentrations (µg/m3, year 2011) were estimated at residential address (1-km2 resolution) using a Random Forest Machine Learning Approach (RFMLA). Data were analysed through multivariable logistic regression models. Results: Chronic obstructive pulmonary disease (COPD) was associated with increasing age (years) (OR=1.06 [95% CI: 1.03, 1.11] per unit increase [p.u.i.]), and with increasing PM10 concentrations (OR=2.19 [1.27, 4.27] p.u.i.). Chronic cough was linked to increasing PM10 concentrations (OR=2.27 [1.09, 5.99] p.u.i.) and current active smoking (OR=5.29 [1.47, 22.81] vs never smoking). Chronic phlegm was related to increasing age (OR=1.04 [1.01, 1.07] p.u.i.), current active smoking (OR=4.33 [1.44, 14.74] vs never smoking) and occupational exposure (OR=2.34 [1.09, 5.5] vs not exposed). No significant association was found for current asthma. Gender and socio-economic status were not associated with disease/symptom occurrence. Conclusions: Estimating PM10 concentrations through RFMLA may provide new insights about respiratory health effects of long-term air pollution exposure in epidemiological studies.

Introduction: The relationship between air pollution and respiratory morbidity has been widely addressed in urban and metropolitan areas but little is known about the effects in non-urban settings. Aims: To assess the short-term effects of PM10 and PM2.5 on respiratory admissions in whole country of Italy during 2006-2015. Methods: We estimated daily PM concentrations at the municipality level (n 8,092) using a machine learning method based on satellite data and spatiotemporal predictors. We collected daily counts of respiratory hospital admissions for each Italian municipality. We considered five different outcomes: all respiratory, asthma, chronic obstructive pulmonary disease (COPD), lower and upper respiratory tract infections (LRTI and URTI). We ran municipality-specific time-series models and we meta-analyzed individual effects to obtain national estimates for the above outcomes. Finally, we tested for effect modification by sex, age classes and degree of urbanization. Results: A total of 4,154,887 non-scheduled acute respiratory admission were registered during 2006-2015, of which 29%, 12%, 6%, and 3% were for LRTI, COPD, URTI and asthma, respectively. Daily mean PM10 and PM2.5 concentrations over the study period were 23.3 and 17 mg/m3. For each 10 mg/m3 increases in PM10 and PM2.5 at lag 0-5 days, we found excess risks of total respiratory diseases equal to 1.10% (95% confidence intervals: 0.82, 1.39) and 0.82 (0.35, 1.30), respectively. Higher effects were found in the elderly and in areas less urbanized. Conclusions: Short-term exposure to PM is harmful for the respiratory system, especially in elderly patients. Strong effects were found also in rural areas.

Background Although general practitioners (GPs) are frequently the first healthcare professionals whom asthma patients refer to for their symptoms, few studies have explored the extent of adherence to guidelines for asthma management based on data provided directly by GPs. Aims of the present study were to assess drug prescriptions for asthma by GPs and to evaluate prescriptive adherence to GINA guidelines (GL) and its relationship with disease control in real life. Methods 995 asthmatic patients (45% males, mean age 43.3 ± 17.7 yrs) were enrolled by 107 Italian GPs distributed throughout the country. Data on diagnosis, disease severity, prescribed anti-asthmatic drugs and control were collected through questionnaires filled out by GPs taking into consideration the 2009 GINA Guidelines. Data on drug use and chronic sinusitis, nasal polyposis, chronic bronchitis, emphysema were reported by patients through a self-administered questionnaire. Results The large majority of patients were classified by GPs as having intermittent (48.4%) or mild persistent asthma (25.3%); 61% had co-morbid allergic rhinitis (AR). The prevalent therapeutic regimen used by patients was a combination of inhaled corticosteroids (ICS) plus long-acting β2-agonists (LABA) (54.1%), even in the intermittent/mild persistent group. ICS as mono-therapy or in combination with other drugs but LABA, was the second most frequently adopted treatment (14.4%). In general, the GPs adherence to GL treatment indications was 28.8%, with a significant association with a good asthma control (OR 1.85, 95% CI 1.18–2.92). On the other hand, comorbidity (OR 0.52, 95% CI 0.32–0.84), moderate (0.44, 0.28–0.69) and severe (0.06, 0.02–0.20) persistent asthma showed significant negative effects on asthma control. Conclusions Our results show that over-treatment of intermittent/mild persistent asthma is frequent in the GPs setting while therapeutic regimens are more appropriately applied for moderate/severe asthma. In general, we found low adherence to GINA GL treatment recommendations even if its relevance in asthma control was confirmed.

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One of the main aims of the HEALS project is to unravel, through an exposomic approach, the development of metabolic diseases. The objective of the present study was to investigate the relationship between body burden from endocrine disrupting chemicals (EDCs) exposure (Holtcamp, 2012 ; La Merrill and Birnbaum, 2011) and the onset of overweight, obesity and diabetes, in view to promote preventive actions. Data was obtained from several pre-existing European cohorts (REPRO-PL, PI1, PI2, IMCA, SEASD, CCM, PHIME-CRO, PHIME-SI, G21, EPITeen, EDEN, CHISQ200 and MAAS). They were harmonised and cleaned to obtain a final dataset of 28, 608 individuals. To replace missing values, a multiple imputation methodology was used. The EWAS analysis employed a survey-weighted logistic regression method with a Bonferroni correction to adjust the probability values and counteract the problem of multiple comparisons. As dependent variables, the health outcomes considered were overweight (BMI>=25), obesity (BMI>=30) and type-2 diabetes mellitus. As independent variables, 70 environmental assays and other demographic factors, at different periods of life were included. Obesity was found to be positively and significantly associated with PM10 (OR=1.174, p-value=0.005), NO2 (1.276, 0.030), PCB118 (2.687, 0.026)), zinc (1.098, 0.021) and several class of phthalates like MEHP (1.095, 0.026). A negative association was observed for some stressors like brominated diphenyl ether BDE153 (0.980, 0.001) and cadmium (0.868, 0.001). Comparable results were obtained for overweight and diabetes. Our EWAS analyses confirm potential association between multiple environmental factors and overweight, obesity and diabetes. Whether mechanisms underlying metabolic changes depend on adipocyte proliferation and differentiation, glucose homeostasis, insulin release or behaviour (traffic pollution may also lead to less physical activities and sedentary lifestyle) needs to be further investigated.

Background: Few data are available on the temporal pattern of respiratory disease phenotypes in general population. Aim: To detect longitudinal patterns of disease phenotypes related to risk factors and physician visits. Methods: Pisan general population sample from 2 cross-sectional studies (PI1: 1991-93; PI2: 2009-11; n=1107), questionnaire-based data. Latent transition analysis (LTA) was performed to assess respiratory disease phenotypes at PI1 and PI2, labelled according to disease/symptom occurrence. Possible patterns were persistence, worsening and improvement of the phenotype. Multiple logistic regression models were estimated to assess the association among phenotype patterns, risk factors (smoking habits-SH and occupational exposure-OE) and physician visits, adjusting for age, sex and educational level. Results: 4 cross-sectional phenotypes were ranked by severity in PI1 and in PI2: healthy (H) (59 and 55%), allergic rhinitis (AR) (28 and 27%), cough/phlegm (CP) (11 and 15%), asthma (A) (3 and 4%). The longitudinal patterns were: persistent H (53%), persistent AR (27%), persistent CP (9%), improvement (2%), worsening (10%). Significant Associations: persistent CP with persistent SH (OR 5.9), persistent OE (OR 2.9) and incident OE (OR 2.2); persistent CP and worsening of phenotype with current visits of family physician (OR 5.4 and OR 4.7, respectively) and of specialist (OR 2.7 and OR 3.9, respectively). Conclusions: LTA allowed to identify four different phenotypes based on respiratory symptoms/diseases and their longitudinal patterns over 18 years. Such analysis brings new perspectives in the analyses of population-based data.