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Background: Short-term exposure to particulate matter (PM) has been related to mortality worldwide. Most evidence comes from studies conducted in major cities, while little is known on the effects of low concentrations of PM and in less urbanized areas. We aim to investigate the relationship between PM and all-cause mortality at national level in Italy., Methods: Daily numbers of all-cause mortality were collected for all 8092 municipalities of Italy, from 2006 to 2015. A satellite-based spatiotemporal model was developed to estimate daily PM 10 (inhalable particles) and PM 2.5 (fine particles) concentrations at 1-km resolution. Multivariate Poisson regression models were fit to estimate the association between daily PM and mortality at province level, and then, results were pooled with a random-effects meta-analysis. Associations were estimated by combination of age and sex and degree of urbanization of the municipalities. Flexible functions were estimated to explore the shape of the associations at low PM concentrations., Results: We analyzed 5,884,900 deaths (40% among subjects older than 85 years, 60% occurring outside the main urban areas). National daily mean (interquartile range) PM 10 and PM 2.5 concentrations were 23 (14) μg/m 3 and 15 (11) μg/m 3 , respectively. Relative increases of mortality per 10 μg/m 3 variation in lag 0-5 (average of last six days since death) PM 10 and PM 2.5 were 1.47% (95% Confidence Intervals (CI): 1.15%, 1.79%) and 1.96% (1.33%, 2.59%), respectively. Associations were highest among elderly and women for PM 10 only, similar between rural and urbanized areas, and were present even at low concentrations, e.g., below WHO guidelines., Conclusions: Air pollution was robustly associated with peaks in daily all-cause mortality in Italy, both in large cities and in less urbanized areas of Italy. Current WHO Air Quality Guidelines (2021) for PM 10 and PM 2.5 are not sufficient to protect public health.

Objectives: to evaluate the gap in life expectancy on the municipality of Rome, through the spatio-temporal changes in life expectancy at the birth and at 65 years old of residents in between 2006 and 2017, by gender, socioeconomic position, and health district., Design: epidemiological study based on the construction of mortality tables describing age-specific mortality rates and life expectancy at birth and at 65 years old., Setting and Participants: from the Register of the Causes of Death (ReNCaM), between 2006 to 2017, 329,426 deaths of residents in Rome were selected., Main Outcome Measures: the variation in life expectancy at birth was estimated by gender, by socioeconomic position, and by health district for four triennial observation periods from 2006 to 2017. Temporal and socioeconomic position changes, through an indicator of deprivation of a small area specific for the city of Rome, and possible geographical patterns, by observing the variation in the 15 health districts, between 2006 and 2017 were observed., Results: in Rome, between the first three-year period (2006-2008) and the last one (2015-2017), the life expectancy at birth increases, whit a gain of about 1.5 years among males (from 78.7 to 80.4 years) and just under 1 year among females (from 83.6 to 84.4 years). The increase in life expectancy, however, is not uniform in the population and the results show differences in socioeconomic position that increase in the most recent three years to 4 years for males and 2.2 years for females. The geographical analysis shows important differences in the life expectancy among health districts with differences up to about 3 years for males and 2.2 years for females at the beginning of the study period among districts in the historical centre and districts on the outskirts of the City of Rome, which are reduced to 2.7 years for males and 2.1 years for females in the most recent period., Conclusions: the analysis of geographical and socioeconomic differences of the life expectancy provides important information to identify the areas of the Municipality of Rome on which to strengthen prevention interventions and the actions to be taken for Objective 10 ("Reducing inequalities") of Agenda 2030 for sustainable development.

Background: Standardized mortality surveillance data, capable of detecting variations in total mortality at population level and not only among the infected, provide an unbiased insight into the impact of epidemics, like COVID-19 (Coronavirus disease). We analysed the temporal trend in total excess mortality and deaths among positive cases of SARS-CoV-2 by geographical area (north and centre-south), age and sex, taking into account the deficit in mortality in previous months., Methods: Data from the Italian rapid mortality surveillance system was used to quantify excess deaths during the epidemic, to estimate the mortality deficit during the previous months and to compare total excess mortality with deaths among positive cases of SARS-CoV-2. Data were stratified by geographical area (north vs centre and south), age and sex., Results: COVID-19 had a greater impact in northern Italian cities among subjects aged 75-84 and 85+ years. COVID-19 deaths accounted for half of total excess mortality in both areas, with differences by age: almost all excess deaths were from COVID-19 among adults, while among the elderly only one third of the excess was coded as COVID-19. When taking into account the mortality deficit in the pre-pandemic period, different trends were observed by area: all excess mortality during COVID-19 was explained by deficit mortality in the centre and south, while only a 16% overlap was estimated in northern cities, with quotas decreasing by age, from 67% in the 15-64 years old to 1% only among subjects 85+ years old., Conclusions: An underestimation of COVID-19 deaths is particularly evident among the elderly. When quantifying the burden in mortality related to COVID-19, it is important to consider seasonal dynamics in mortality. Surveillance data provides an impartial indicator for monitoring the following phases of the epidemic, and may help in the evaluation of mitigation measures adopted.

In Italy, few multicentre population-based studies on pregnancy outcomes are available. Therefore, we established a network of population-based birth cohorts in the cities of Turin, Reggio Emilia, Modena, Bologna, and Rome (northern and central Italy), to study the role of socioeconomic factors and air pollution exposure on term low birthweight, preterm births and the prevalence of small for gestational age. In this article, we will report the full methodology of the study and the first descriptive results. We linked 2007-2013 delivery certificates with municipal registry data and hospital records, and selected singleton livebirths from women who lived in the cities for the entire pregnancy, resulting in 211,853 births (63% from Rome, 21% from Turin and the remaining 16% from the three cities in Emilia-Romagna Region). We have observed that the association between socioeconomic characteristics and air pollution exposure varies by city and pollutant, suggesting a possible effect modification of both the city and the socioeconomic position on the impact of air pollution on pregnancy outcomes. This is the largest Italian population-based birth cohort, not distorted by selection mechanisms, which has also the advantage of being sustainable over time and easily transferable to other areas. Results from the ongoing multivariable analyses will provide more insight on the relative impact of different strands of risk factors and on their interaction, as well as on the modifying effect of the contextual characteristics. Useful recommendations for strategies to prevent adverse pregnancy outcomes may eventually derive from this study.

Background: Increased PM 10 concentrations are commonly observed during Saharan dust advections. Limited epidemiological evidence suggests that PM 10 from anthropogenic and desert sources increase mortality. We aimed to evaluate the association between source-specific PM 10 (non-desert and desert) and cause-specific mortality in Sicily during 2006-2012 period., Methods: Daily PM 10 concentrations at 1-km 2 were estimated in Sicily using satellite-based data, fixed monitors and land use variables. We identified Saharan dust episodes using meteorological models, back-trajectories, aerosol maps, and satellite images. For each dust day, we estimated desert and non-desert PM 10 concentrations. We applied a time-series approach on 390 municipalities of Sicily to estimate the association between PM 10 (non-desert and desert) and daily cause-specific mortality., Results: 33% of all days were affected by Saharan dust advections. PM 10 concentrations were 8 μg/m 3 higher during dust days compared to other days. We found positive associations of both non-desert and desert PM 10 with cause-specific mortality. We estimated percent increases of risk (IR%) of non-accidental mortality equal to 2.3% (95% Confidence Interval [CI]: 1.4, 3.1) and 3.8% (3.2, 4.4), per 10 μg/m 3 increases in non-desert and desert PM 10 at lag 0-5, respectively. We also observed significant associations with cardiovascular (2.4% [1.3, 3.4] and 4.5% [3.8, 5.3]) and respiratory mortality (8.1% [6.8, 9.5], and 6.3% [5.4, 7.2]). We estimated higher effects during April-September, with IR% = 4.4% (3.2, 5.7) and 6.3% (5.4, 7.2) for non-desert and desert PM 10 , respectively., Conclusions: Our results confirm previous evidence of harmful effects of desert PM 10 on non-accidental and cardio-respiratory mortality, especially during the warm season., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Evidence on the health effects of extreme temperatures and air pollution is copious. However few studies focused on their interaction. The aim of this study is to evaluate daily PM10 and ozone as potential effect modifiers of the relationship between temperature and natural mortality in 25 Italian cities. Time-series analysis was run for each city. To evaluate interaction, a tensor product between mean air temperature (lag 0⁻3) and either PM10 or ozone (both lag 0⁻5) was defined and temperature estimates were extrapolated at low, medium, and high levels of pollutants. Heat effects were estimated as percent change in mortality for increases in temperature between 75th and 99th percentiles. Results were pooled by geographical area. Differential temperature-mortality risks by air pollutants were found. For PM10, estimates ranged from 3.9% (low PM10) to 14.1% (high PM10) in the North, from 3.6% to 24.4% in the Center, and from 7.5% to 21.6% in the South. Temperature-related mortality was similarly modified by ozone in northern and central Italy, while no effect modification was observed in the South. This study underlines the synergistic effects of heat and air pollution on mortality. Considering the predicted increase in heat waves and stagnation events in the Mediterranean countries such as Italy, it is time to enclose air pollution within public health heat prevention plans.

Objectives: to define a national geographic domain, with high spatial (1 km²) and temporal (daily) resolution, and to build a list of georeferenced environmental and temporal indicators useful for environmental epidemiology applications at national level., Design: geographic study., Setting and Participants: study domain: Italian territory divided into 307,635 1-km² grid cells; study period: 2006-2012, divided into 2,557 daily time windows., Main Outcome Measures: for each grid cell and day, an extensive number of indicators has been computed. These indicators include spatial (administrative layers, resident population, presence of water bodies, climatic zones, land use variables, impervious surfaces, orography, viability, point and areal emissions of air pollutants) and spatio-temporal predictors (particulate matter data from monitoring stations, meteorological parameters, desert dust advection episodes, aerosol optical depth, normalized difference vegetation index, planetary boundary layer) potentially useful to characterize population environmental exposures and to estimate their health effects, at national level., Results and Conclusions: this study represents the first example of relational big data in environmental epidemiology at national level, where multiple sources of data (satellite, environmental, meteorology, land use, population) have been linked on a common spatial and temporal domain, aimed at promoting environmental epidemiology applications at national and local level.