Health effects of air pollution : innovative approaches for spatio-temporal evaluations

Air pollution is one of the major risk factors to human health, causing both short- and long-term effects and the global burden on mortality is estimated in more than 4 million deaths every year. Most of the evidence on the short-term effects is based on studies conducted in major cities, because data or estimates of air pollutants exposures in non-urban settings have been historically lacking. This is a limitation, because a large fraction of the population lives outside the cities, where the vulnerability profile is different from that of urban populations. In the last decade, several attempts were made to estimate daily concentrations of particulate matter (PM) with high spatial resolution over large geographical domains. However, applications in Italy and Sweden, and on other pollutants as nitrogen dioxide (NO2) and ozone (O3), are almost lacking, leaving a gap in the knowledge of their health effects outside cities. This thesis has been designed to fill this gap, by providing daily estimates of multiple air pollutants at the national level, and exploring the spatial heterogeneity in their health effects. Italy represented a testing ground for the development of innovative mixed-effects regression models which combined PM measurements with satellite data, land-use parameters and meteorological fields, and produced daily estimates of PM10 (PM with diameter smaller than 10 m) for each squared kilometer of the country, and each day in 2006-2012 (Study I). More recently, machine learning methodologies have been tested in the U.S., therefore, we have updated estimates of PM10 till 2015 and produced new estimates of PM2.5 (PM < 2.5 m), using a random forest (RF) algorithm (Study II). We replicated the same approach in Sweden, to which we added models for NO2 and O3, and a few spatiotemporal predictors aimed at capturing sources of air pollutants’ variations missed in the previous studies (Study III). We collected national data on hospital discharges for all Italian public and private hospitals during 2013-2015. We created municipality-specific time-series of daily counts of acute admissions for multiple cardiovascular (CVD) endpoints, which we related to daily mean PM10 and PM2.5 concentrations. We found evidence of adverse effects of PM on total CVD admissions and on specific outcomes such as heart failure and atrial fibrillation. Also, we estimated highest effects at the lowest PM concentrations, also in non-urban municipalities (Study IV). Similarly, we collected daily mortality counts at small area level in the Stockholm county, that we analyzed in relation to daily mean exposure to PM10, PM2.5, NO2 and O3. We found evidence of an association between daily O3 and non-accidental mortality in the year-round analysis, and significant associations with PM and O3 in the warm (April-September) period only. Effects were slightly higher in more densely inhabited areas, but we found associations also in non- urban areas outside the Stockholm city (Study V). In conclusion, we developed novel spatiotemporal models to estimate air pollutant concentrations at fine spatial and temporal resolution in Italy and Sweden. These allowed us to document adverse short-term effects on mortality and morbidity at very low concentrations and in areas (and among populations) previously neglected by epidemiological investigations

Predictors of Lung Cancer Risk: An Ecological Study Using Mortality and Environmental Data by Municipalities in Italy

Lung cancer (LC) mortality remains a consistent part of the total deaths occurring world-wide. Its etiology is complex as it involves multifactorial components. This work aims in providing an epidemiological assessment on occupational and environmental factors associated to LC risk by means of an ecological study involving the 8092 Italian municipalities for the period 2006–2015. We consider mortality data from mesothelioma as proxy of asbestos exposure, as well as PM2.5 and radon levels as a proxy of environmental origin. The compensated cases for occupational respiratory diseases, urbanization and deprivation were included as predictors. We used a negative binomial distribution for the response, with analysis stratified by gender. We estimated that asbestos is responsible for about 1.1% (95% CI: 0.8, 1.4) and 0.5% (95% CI: 0.2, 0.8) of LC mortality in males and females, respectively. The corresponding figures are 14.0% (95% CI: 12.5, 15.7) and 16.3% (95% CI: 16.2, 16.3) for PM2.5 exposure, and 3.9% (95% CI: 3.5, 4.2) and 1.6% (95% CI: 1.4, 1.7) for radon expo-sure. The assessment of determinants contribution to observed LC deaths is crucial for improving awareness of its origin, leading to increase the equity of the welfare system

Multiannual assessment of the desert dust impact on air quality in Italy combining PM10 data with physics-based and geostatistical models

Desert dust storms pose real threats to air quality and health of millions of people in source regions, with associated impacts extending to downwind areas. Europe (EU) is frequently affected by atmospheric transport of desert dust from the Northern Africa and Middle East drylands. This investigation aims at quantifying the role of desert dust transport events on air quality (AQ) over Italy, which is among the EU countries most impacted by this phenomenon. We focus on the particulate matter (PM) metrics regulated by the EU AQ Directive. In particular, we use multiannual (2006–2012) PM10 records collected in hundreds monitoring sites within the national AQ network to quantify daily and annual contributions of dust during transport episodes. The methodology followed was built on specific European Commission guidelines released to evaluate the natural contributions to the measured PM-levels, and was partially modified, tested and adapted to the Italian case in a previous study. Overall, we show that impact of dust on the yearly average PM10 has a clear latitudinal gradient (from less than 1 to greater than 10 µg/m3 going from north to south Italy), this feature being mainly driven by an increased number of dust episodes per year with decreasing latitude. Conversely, the daily-average dust-PM10 (≅12 µg/m3) is more homogenous over the country and shown to be mainly influenced by the site type, with enhanced values in more urbanized locations. This study also combines the PM10 measurements-approach with geostatistical modelling. In particular, exploiting the dust-PM10 dataset obtained at site- and daily-resolution over Italy, a geostatistical, random-forest model was set up to derive a daily, spatially-continuous field of desert-dust PM10 at high (1-km) resolution. This finely resolved information represent the basis for a follow up investigation of both acute and chronic health effects of desert dust over Italy, stemming from daily and annual exposures, respectively.This work was performed as an ‘After-LIFE’ activity of the EU LIFE+2010 DIAPASON project (LIFE+10 ENV/IT/391) and is a contributing activity to the COST Action InDust (CA16202) and to the EU ERA4CS project DustClim (Grant n. 690462). We thank the Barcelona Supercomputing Center (BSC, http://www.bsc.es/earth-sciences/mineral-dust-forecast-system/) for maintaining the BSC-DREAM8b daily dust forecasts used in this study. S. Basart acknowledges AXA Research Fund for supporting the long-term mineral dust research at the Earth Sciences Department at BSC. N. Alvan Romero carried out this research during an internship at ISAC-CNR under the supervision of F. Barnaba. Constructive comments by Jorge Pey and two other anonymous Reviewers are also gratefully acknowledged.Peer ReviewedPostprint (published version

A satellite-based spatio-temporal machine learning model to reconstruct daily PM2.5 concentrations across Great Britain

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5

P.Re.Val.E.: outcome research program for the evaluation of health care quality in Lazio, Italy

<p>Abstract</p> <p>Background</p> <p>P.Re.Val.E. is the most comprehensive comparative evaluation program of healthcare outcomes in Lazio, an Italian region, and the first Italian study to make health provider performance data available to the public.</p> <p>The aim of this study is to describe the P.Re.Val.E. and the impact of releasing performance data to the public.</p> <p>Methods</p> <p>P.Re.Val.E. included 54 outcome/process indicators encompassing many different clinical areas. Crude and adjusted rates were estimated for the 2006-2009 period. Multivariate regression models and direct standardization procedures were used to control for potential confounding due to individual characteristics. Variable life-adjusted display charts were developed, and 2008-2009 results were compared with those from 2006-2007.</p> <p>Results</p> <p>Results of 54 outcome indicators were published online at <url>http://www.epidemiologia.lazio.it/prevale10/index.php</url>.</p> <p>Public disclosure of the indicators' results caused mixed reactions but finally promoted discussion and refinement of some indicators.</p> <p>Based on the P.Re.Val.E. experience, the Italian National Agency for Regional Health Services has launched a National Outcome Program aimed at systematically comparing outcomes in hospitals and local health units in Italy.</p> <p>Conclusions</p> <p>P.Re.Val.E. highlighted aspects of patient care that merit further investigation and monitoring to improve healthcare services and equity.</p

Spatial variability of nitrogen dioxide and formaldehyde and residential exposure of children in the industrial area of Viadana, Northern Italy

Chipboard production is a source of ambient air pollution. We assessed the spatial variability of outdoor pollutants and residential exposure of children living in proximity to the largest chipboard industry in Italy, and evaluated the reliability of exposure estimates obtained from a number of available models. We obtained passive sampling data on NO2 and formaldehyde collected by the environmental protection agency of Lombardia region at 25 sites in the municipality of Viadana during 10 weeks (2017-18), and compared NO2 measurements with average weekly concentrations from continuous monitors. We compared interpolated NO2 and formaldehyde surfaces with previous maps for 2010. We assessed the relationship between residential proximity to the industry and pollutant exposures assigned using these maps, as well as other available countrywide/continental models based on routine data on NO2, PM10, and PM2.5. The correlation between NO2 concentrations from continuous and passive sampling was high (Pearson\u2019s r=0.89), although passive sampling underestimated NO2 especially during winter. For both 2010 and 2017-18, we observed higher NO2 and formaldehyde concentrations in the south of Viadana, with hot-spots in proximity to the industry. PM10 and PM2.5 exposures were higher for children at &lt;1km compared to the children living at &gt;3.5 km to the industry, whereas NO2 exposure was higher at 1-1.7 km to the industry. Road and population densities were also higher close to the industry. Findings from a variety of exposure models suggest that children living in proximity to the chipboard industry in Viadana are more exposed to air pollution, and that exposure gradients are relatively stable over time

Short-Term Effects of Heat on Mortality and Effect Modification by Air Pollution in 25 Italian Cities.

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