Association between air pollution and emergency room admission for eye diseases in Rome, Italy: A time-series analysis

Eye diseases impose a significant burden on health services due to high case numbers. However, exposure to outdoor air pollution is seldom mentioned as potential harmful factor. We conducted a time-series analysis in Rome, Italy, to estimate the association between daily mean concentration of NO2, PM10 and PM2.5 and daily number of emergency room (ER) admissions for a selected cluster of eye diseases from 2006 to 2016. We used Poisson regression adjusted for time trend, population decrease during summer vacations and holidays, day of week, apparent temperature (hot and cold) and daily concentration of nine pollen species. We observed 581,868 ER admissions during the study period. 44.74% of cases were observed in subjects with less than 20 years, 19.50% in 51-65 age category and 13.4% among children (0-14 years). No differences between sexes were recorded. Mean values of pollutant concentrations were 54.75, 31.01 and 18.14 μg/m3 for NO2, PM10 and PM2.5 respectively. The air temperature ranged from -1 °C to 32.5 °C, with a mean value of 16 °C (SD = 6.88). The apparent temperature spaced from -3.58 °C to 34.08 °C (mean = 15.61 °C, SD = 8.5). The highest percent risk increases for 10 μg/m3 increases of the three pollutants were observed at lag0-1 day (1.3%, 0.63-1.98 for PM2.5; 1.03%, 0.56-1.51 for PM10 and 0.6%, 0.13-1.07 for NO2). Risk increased significantly also at lag0 and lag0-5 day for each pollutant. Secondary analyses showed higher effects in the elderly compared to younger subjects. No differences emerged between sexes. The dose response analysis suggested of possible effects on ER admission risk also at low-level concentrations of PM2.5. A strong confounding effect of pollen was not detected. RESULTS: of this study are coherent with previous analyses. Speculation can be done about the biological mechanisms that link air pollution to eye damage.

[BIGEPI project: environmental and health data]

OBJECTIVES

the BIGEPI project, co-funded by INAIL, has used big data to identify the health risks associated with short and long-term exposure to air pollution, extreme temperatures and occupational exposures.

DESIGN

the project consists of 5 specific work packages (WP) aimed at assessing: 1. the acute effects of environmental exposures over the national territory; 2. the acute effects of environmental exposures in contaminated areas, such as Sites of National Interest (SIN) and industrial sites; 3. the chronic effects of environmental exposures in 6 Italian longitudinal metropolitan studies; 4. the acute and chronic effects of environmental exposures in 7 epidemiological surveys on population samples; 5. the chronic effects of occupational exposures in the longitudinal metropolitan studies of Rome and Turin.

SETTING AND PARTICIPANTS

BIGEPI analyzed environmental and health data at different levels of detail: the whole Italian population (WP1); populations living in areas contaminated by pollutants of industrial origin (WP2); the entire longitudinal cohorts of the metropolitan areas of Bologna, Brindisi, Rome, Syracuse, Taranto and Turin (WP3 and WP5); population samples participating in the epidemiological surveys of Ancona, Palermo, Pavia, Pisa, Sassari, Turin and Verona (WP4).

MAIN OUTCOME MEASURES

environmental exposure: PM10, PM2,5, NO2 and O3 concentrations and air temperature at 1 Km2 resolution at national level. Occupational exposures: employment history of subjects working in at least one of 25 sectors with similar occupational exposures to chemicals/carcinogens; self-reported exposure to dust/fumes/gas in the workplace. Health data: cause-specific mortality/hospitalisation; symptoms/diagnosis of respiratory/allergic diseases; respiratory function and bronchial inflammation.

RESULTS

BIGEPI analyzed data at the level of the entire Italian population, data on 2.8 million adults (>=30 yrs) in longitudinal metropolitan studies and on about 14,500 individuals (>=18 yrs) in epidemiological surveys on population samples. The population investigated in the longitudinal metropolitan studies had an average age of approximately 55 years and that of the epidemiological surveys was about 48 years; in both cases, 53% of the population was female. As regards environmental exposure, in the period 2013-2015, at national level average values for PM10, PM2.5, NO2 and summer O3 were: 21.1±13.6, 15.1±10.9, 14.7±9.1 and 80.3±17.3 µg/m3, for the temperature the average value was 13.9±7.2 °C. Data were analyzed for a total of 1,769,660 deaths from non-accidental causes as well as 74,392 incident cases of acute coronary event and 45,513 of stroke. Epidemiological investigations showed a high prevalence of symptoms/diagnoses of rhinitis (range: 14.2-40.5%), COPD (range: 4.7-19.3%) and asthma (range: 3.2-13.2%). The availability of these large datasets has made it possible to implement advanced statistical models for estimating the health effects of short- and long-term exposures to pollutants. The details are reported in the BIGEPI papers already published in other international journals and in those published in this volume of E&P.

CONCLUSIONS

BIGEPI has confirmed the great potential of using big data in studies of the health effects of environmental and occupational factors, stimulating new directions of scientific research and confirming the need for preventive action on air quality and climate change for the health of the general population and the workers.

[Short-term effects of PM10 on cause-specific mortality and the role of long-term environmental pressures in the industrial areas of Brindisi and Civitavecchia]

OBJECTIVES

the health status of people living near industrial plants is often exposed to several environmental risk factors, including air pollution. The aim of this study is to assess the relationship between daily PM10 levels and cause-specific mortality in a selection of municipalities near two industrial plants from 2006 to 2015.

DESIGN

a time-series design with Poisson regression adjusted for a predefined set of confounders was used to quantify the association between exposure, calculated as daily PM10 levels extrapolated from machine-learning models using satellite data, and cause-specific mortality.

SETTING AND PARTICIPANTS

twenty municipalities near the thermal power plants in Civitavecchia and Brindisi were selected. The municipalities were then divided into three scenarios of chronic exposure derived from SPRAY simulation models of pollutant deposition.

MAIN OUTCOME MEASURES

daily cause-specific non-accidental, cardiovascular, and respiratory deaths defined according to the International Classification of Diseases code at the municipality level.

RESULTS

a total of 41,942 deaths were observed in the entire area (10,503 in the Civitavecchia area and 31,439 in the Brindisi area), of which approximately 41% were due to cardiovascular causes and 8% due to respiratory causes. The association showed an increase in shortterm effects in municipalities with higher chronic levels of pollution exposure. For example, risk estimates reported as percentage increases per 10-unit increase in PM10 were 6.7% (95% CI 0.9, 12.7%) in scenario 3 (highest exposure) compared to 4.2% (-1.2, 9.9%) and 2.7% (-4.2, 10.2%) in scenarios 2 and 1, respectively, in the area near the Civitavecchia plant. Similar effects were observed for the Brindisi area.

CONCLUSIONS

despite the well-documented relationship between short-term pollution and mortality, it appears that greater chronic exposure to industrial pollutants leads to increased short-term effects of PM10. The limited number of events suggests that this study could serve as a starting point for a larger investigation.

Long-term effects of air pollutants on respiratory and cardiovascular mortality in a port city along the Adriatic sea

BACKGROUND

Shipping and port-related air pollution has a significant health impact on a global scale. The present study aimed to assess the mortality burden attributable to long-term exposure to ambient particulate matter (PM2.5, PM10) and nitrogen dioxide (NO2) in the city of Ancona (Italy), with one of the leading national commercial harbours.

METHODS

Exposure to air pollutants was derived by dispersion models. The relationship between the long-term exposure of air pollution exposure and cause-specific mortality was evaluated by Poisson regression models, after adjustment for gender, age and socioeconomic status. Results are expressed as percent change of risk (and relative 95% confidence intervals) per 5 unit increases in the exposures. The health impact on the annual number of premature cause-specific deaths was also assessed.

RESULTS

PM2.5 and NO2 annual concentrations were higher in the area close to the harbour than in the rest of the city. Positive associations between each pollutant and most of the mortality outcomes were observed, with estimates of up to 7.6% (95%CI 0.1, 15.6%) for 10 µg/m3 increase in NO2 and cardiovascular mortality and 15.3% (95%CI-1.1, 37.2%) for 10 µg/m3 increase PM2.5 and lung cancer. In the subpopulation living close to the harbour, there were excess risks of up to 13.5%, 24.1% and 37.9% for natural, cardiovascular and respiratory mortality. The number of annual premature deaths due to the excess of PM2.5 and NO2 exposure (having as a reference the 2021 World Health Organization Air Quality Guidelines) was 82 and 25, respectively.

CONCLUSIONS

Our study confirms the long-term health effects of PM and NO2 on mortality and reveals a higher mortality burden in areas close to shipping and port-related emissions. Estimating the source-specific health burdens is key to achieve a deeper understanding of the role of different emission sources, as well as to support effective and targeted mitigation strategies.

Plasma heavy metal levels correlate with deregulated gene expression of detoxifying enzymes in osteoporotic patients

Heavy metal levels appear to be associated with low bone mineral density (BMD) and the consequent osteoporosis risk, but the relationship with the disease has not been clearly defined. The altered expression pattern of numerous genes, including detoxifying genes, seems to play a pivotal role in this context, leading to increased susceptibility to several diseases, including osteoporosis. The purpose of this study is to analyse circulating heavy metals levels and the expression of detoxifying genes in osteoporotic patients (OPs, n = 31), compared with healthy subjects (CTRs, n = 32). Heavy metals concentration in plasma samples was determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and the subsequent expression analysis of NAD(P)H quinone dehydrogenase 1 (NQO1), Catalase (CAT), and Metallothionein 1E (MT1E) genes in Peripheral Blood Mononuclear Cells (PBMCs) was assessed by real-time polymerase chain reaction (qRT-PCR). Copper (Cu), mercury (Hg), molybdenum (Mo) and lead (Pb) were found to be significantly higher in the plasma of OPs compared to CTRs. Analysis of the expression levels of detoxifying genes showed a significant decrease in CAT and MT1E in OP group. In addition, Cu correlated positively with the expression levels of both CAT and MT1E in CTRs group and MT1E in OPs. This study shows an increased circulating concentration of certain metals combined with an altered expression pattern of detoxifying genes in OPs, highlighting a novel aspect to be investigated in order to better characterize the role of metals in the pathogenesis of osteoporosis.

[A national overview of the topics covered by young people in epidemiology: a comparison before and after the Covid-19.]

INTRODUCTION

Epidemiology is increasingly involved on a wide variety of topics and to engage different professionals and disciplines in an increasingly active way. A fundamental role is played by young researchers active in Italian epidemiology who create opportunities for meeting and discussion, in the name of multidisciplinarity and integration of different skills.

OBJECTIVE

The aim of this paper is to provide a detailed description of the topics most frequently studied in epidemiology by young people and to highlight any changes in these topics in the pre- and post-Covid-19 workplaces.

METHODS

All abstracts submitted in the years 2019 and 2022 by young participants in the Maccacaro Prize, an annual award aimed at Italian association of epidemiology (Aie) conference addressed to people under 35 years of age, were considered. In addition to the comparison of the topics, a comparison of the related work structures and their geographical location was carried out by grouping the research centres into three Italian geographical regions: north, centre and south/islands.

RESULTS

Between 2019 and 2022, the number of abstracts participating in the Maccacaro Prize increased. The interest in topics related to infectious diseases, vaccines, and pharmaco-epidemiology has sharply increased, while in environmental and maternal and child epidemiology it has moderately increased. Social epidemiology, health promotion and prevention, as well as clinical and evaluative epidemiology, have experienced a decrease in interest. Finally, after analysing the geographical distribution of reference centres, it was discovered that certain regions, such as Piedmont, Lombardy, Veneto, Emilia-Romagna, Tuscany and Latium, have a strong and consistent presence of young people in the field of epidemiology. Conversely, there is a small number of young professionals working in this field in other Italian regions, especially in Southern regions.

CONCLUSIONS

The pandemic has changed our personal and working habits, but it has also played a fundamental role in making epidemiology known. The increase in young people joining an association such as the Aie is a clear sign of the growing interest in this discipline.

Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study

BACKGROUND

Fine particulate matter (PM_2.5) is a well-recognized risk factor for premature death. However, evidence on which PM_2.5 components are most relevant is unclear.

METHODS

We evaluated the associations between mortality and long-term exposure to eight PM_2.5 elemental components [copper (Cu), iron (Fe), zinc (Zn), sulfur (S), nickel (Ni), vanadium (V), silicon (Si), and potassium (K)]. Studied outcomes included death from diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders as well as all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), and lung cancer. We followed all residents in Denmark (aged ≥30 years) from January 1, 2000 to December 31, 2017. We used European-wide land-use regression models at a 100 × 100 m scale to estimate the residential annual mean levels of exposure to PM_2.5 components. The models were developed with supervised linear regression (SLR) and random forest (RF). The associations were evaluated by Cox proportional hazard models adjusting for individual- and area-level socioeconomic factors and total PM_2.5 mass.

RESULTS

Of 3,081,244 individuals, we observed 803,373 death from natural causes during follow-up. We found significant positive associations between all-natural mortality with Si and K from both exposure modeling approaches (hazard ratios; 95% confidence intervals per interquartile range increase): SLR-Si (1.04; 1.03-1.05), RF-Si (1.01; 1.00-1.02), SLR-K (1.03; 1.02-1.04), and RF-K (1.06; 1.05-1.07). Strong associations of K and Si were detected with most causes of mortality except CKD and K, and diabetes and Si (the strongest associations for psychiatric disorders mortality). In addition, Fe was relevant for mortality from RD, lung cancer, CKD, and psychiatric disorders; Zn with mortality from CKD, RD, and lung cancer, and; Ni and V with lung cancer mortality.

CONCLUSIONS

We present novel results of the relevance of different PM_2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.

Association between short-term exposure to air pollutants and cause-specific daily mortality in Italy. A nationwide analysis

BACKGROUND/AIM

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.

METHODS

Natural, cardiovascular, cardiac, ischemic, cerebrovascular, respiratory, metabolic, diabetes, nervous and psychiatric causes of death occurred in Italy were extracted during 2013-2015. Daily ambient PM₁₀, PM_2.5 and NO₂ concentrations were estimated through machine learning algorithms. The associations between air pollutants and cause-specific mortality were estimated with a time-series approach using a two-stage analytic protocol where area-specific over-dispersed Poisson regression models where fit in the first stage, followed by a meta-analysis in the second. We tested for effect modification by sex, age class and the degree of urbanisation of the municipality.

RESULTS

We estimated a positive association between PM₁₀ and PM_2.5 and the mortality from natural, cardiovascular, cardiac, respiratory and nervous system causes, but not with metabolic or psychiatric causes of death. In particular, mortality from nervous diseases increased by 4.55% (95% CI: 2.51-6.63) and 9.64% (95% CI: 5.76-13.65) for increments of 10 μg/m³ in PM₁₀ and PM_2.5 (lag 0-5 days), respectively. NO₂ was positively associated with respiratory (6.68% (95% CI: 1.04-12.62)) and metabolic (7.30% (95% CI: 1.03-13.95)) mortality for increments of 10 μg/m³ (lag 0-5). Higher associations with natural mortality were found among the elderly, while there were no differential effects between sex or between rural and urban areas.

CONCLUSIONS

Short-term exposure to particulate matter was associated with mortality from nervous diseases. Mortality from metabolic diseases was associated with NO₂ exposure. Other associations are confirmed and updated, including the contribution of lowly urbanised areas. Health effects were also found in suburban and rural areas.

Short-term effects of ultrafine particles on heart rate variability: A systematic review and meta-analysis

An increasing number of epidemiological studies have examined the association between ultrafine particles (UFP) and imbalanced autonomic control of the heart, a potential mechanism linking particulate matter air pollution to cardiovascular disease. This study systematically reviews and meta-analyzes studies on short-term effects of UFP on autonomic function, as assessed by heart rate variability (HRV). We searched PubMed and Web of Science for articles published until June 30, 2022. We extracted quantitative measures of UFP effects on HRV with a maximum lag of 15 days from single-pollutant models. We assessed the risk of bias in the included studies regarding confounding, selection bias, exposure assessment, outcome measurement, missing data, and selective reporting. Random-effects models were applied to synthesize effect estimates on HRV of various time courses. Twelve studies with altogether 1,337 subjects were included in the meta-analysis. For an increase of 10,000 particles/cm³ in UFP assessed by central outdoor measurements, our meta-analysis showed immediate decreases in the standard deviation of the normal-to-normal intervals (SDNN) by 4.0% [95% confidence interval (CI): 7.1%, -0.9%] and root mean square of successive R-R interval differences (RMSSD) by 4.7% (95% CI: 9.1%, 0.0%) within 6 h after exposure. The immediate decreases in SDNN and RMSSD associated with UFP assessed by personal measurements were smaller and borderline significant. Elevated UFP were also associated with decreases in SDNN, low-frequency power, and the ratio of low-frequency to high-frequency power when pooling estimates of lags across hours to days. We did not find associations between HRV and concurrent-day UFP exposure (daily average of at least 18 h) or exposure at lags ≥ one day. Our study indicates that short-term exposure to ambient UFP is associated with decreased HRV, predominantly as an immediate response within hours. This finding highlights that UFP may contribute to the onset of cardiovascular events through autonomic dysregulation.

Exposure to surrounding greenness and natural-cause and cause-specific mortality in the ELAPSE pooled cohort

BACKGROUND

The majority of studies have shown higher greenness exposure associated with reduced mortality risks, but few controlled for spatially correlated air pollution and traffic noise exposures. We aim to address this research gap in the ELAPSE pooled cohort.

METHODS

Mean Normalized Difference Vegetation Index (NDVI) in a 300-m grid cell and 1-km radius were assigned to participants' baseline home addresses as a measure of surrounding greenness exposure. We used Cox proportional hazards models to estimate the association of NDVI exposure with natural-cause and cause-specific mortality, adjusting for a number of potential confounders including socioeconomic status and lifestyle factors at individual and area-levels. We further assessed the associations between greenness exposure and mortality after adjusting for fine particulate matter (PM2.5), nitrogen dioxide (NO2) and road traffic noise.

RESULTS

The pooled study population comprised 327,388 individuals who experienced 47,179 natural-cause deaths during 6,374,370 person-years of follow-up. The mean NDVI in the pooled cohort was 0.33 (SD 0.1) and 0.34 (SD 0.1) in the 300-m grid and 1-km buffer. In the main fully adjusted model, 0.1 unit increment of NDVI inside 300-m grid was associated with 5% lower risk of natural-cause mortality (Hazard Ratio (HR) 0.95 (95% CI: 0.94, 0.96)). The associations attenuated after adjustment for air pollution [HR (95% CI): 0.97 (0.96, 0.98) adjusted for PM2.5; 0.98 (0.96, 0.99) adjusted for NO2]. Additional adjustment for traffic noise hardly affected the associations. Consistent results were observed for NDVI within 1-km buffer. After adjustment for air pollution, NDVI was inversely associated with diabetes, respiratory and lung cancer mortality, yet with wider 95% confidence intervals. No association with cardiovascular mortality was found.

CONCLUSIONS

We found a significant inverse association between surrounding greenness and natural-cause mortality, which remained after adjusting for spatially correlated air pollution and traffic noise.

Long-term exposure to air pollution and mortality in a Danish nationwide administrative cohort study: Beyond mortality from cardiopulmonary disease and lung cancer

BACKGROUND

The association between long-term exposure to air pollution and mortality from cardiorespiratory diseases is well established, yet the evidence for other diseases remains limited.

OBJECTIVES

To examine the associations of long-term exposure to air pollution with mortality from diabetes, dementia, psychiatric disorders, chronic kidney disease (CKD), asthma, acute lower respiratory infection (ALRI), as well as mortality from all-natural and cardiorespiratory causes in the Danish nationwide administrative cohort.

METHODS

We followed all residents aged ≥ 30 years (3,083,227) in Denmark from 1 January 2000 until 31 December 2017. Annual mean concentrations of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), black carbon (BC), and ozone (warm season) were estimated using European-wide hybrid land-use regression models (100 m × 100 m) and assigned to baseline residential addresses. We used Cox proportional hazard models to evaluate the association between air pollution and mortality, accounting for demographic and socioeconomic factors. We additionally applied indirect adjustment for smoking and body mass index (BMI).

RESULTS

During 47,023,454 person-years of follow-up, 803,881 people died from natural causes. Long-term exposure to PM_2.5 (mean: 12.4 µg/m³), NO₂ (20.3 µg/m³), and/or BC (1.0 × 10^-5/m) was statistically significantly associated with all studied mortality outcomes except CKD. A 5 µg/m³ increase in PM_2.5 was associated with higher mortality from all-natural causes (hazard ratio 1.11; 95% confidence interval 1.09-1.13), cardiovascular disease (1.09; 1.07-1.12), respiratory disease (1.11; 1.07-1.15), lung cancer (1.19; 1.15-1.24), diabetes (1.10; 1.04-1.16), dementia (1.05; 1.00-1.10), psychiatric disorders (1.38; 1.27-1.50), asthma (1.13; 0.94-1.36), and ALRI (1.14; 1.09-1.20). Associations with long-term exposure to ozone (mean: 80.2 µg/m³) were generally negative but became significantly positive for several endpoints in two-pollutant models. Generally, associations were attenuated but remained significant after indirect adjustment for smoking and BMI.

CONCLUSION

Long-term exposure to PM_2.5, NO₂, and/or BC in Denmark were associated with mortality beyond cardiorespiratory diseases, including diabetes, dementia, psychiatric disorders, asthma, and ALRI.

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.

Long-term exposure to fine particle elemental components and mortality in Europe: Results from six European administrative cohorts within the ELAPSE project

Evidence for the association between long-term exposure to ambient particulate matter components and mortality from natural causes is sparse and inconsistent. We evaluated this association in six large administrative cohorts in the framework of the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) project. We analyzed data from country-wide administrative cohorts in Norway, Denmark, the Netherlands, Belgium, Switzerland and in Rome (Italy). Annual 2010 mean concentrations of copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V) and zinc (Zn) in fine particulate matter (PM_2.5) were estimated using 100 × 100 m Europe-wide hybrid land use regression models assigned to the participants' residential addresses. We applied cohort-specific Cox proportional hazard models controlling for area- and individual-level covariates to evaluate associations with natural mortality. Two pollutant models adjusting for PM_2.5 total mass or nitrogen dioxide (NO₂) were also applied. We pooled cohort-specific estimates using a random effects meta-analysis. We included almost 27 million participants contributing more than 240 million person-years. All components except Zn were significantly associated with natural mortality [pooled Hazard Ratios (HRs) (95% CI): 1.037 (1.014, 1.060) per 5 ng/m³ Cu; 1.069 (1.031, 1.108) per 100 ng/m³ Fe; 1.039 (1.018, 1.062) per 50 ng/m³ K; 1.024 (1.006, 1.043) per 1 ng/m³ Ni; 1.036 (1.016, 1.057) per 200 ng/m³ S; 1.152 (1.048, 1.266) per 100 ng/m³ Si; 1.020 (1.006, 1.034) per 2 ng/m³ V]. Only K and Si were robust to PM_2.5 or NO₂ adjustment [pooled HRs (95% CI) per 50 ng/m³ in K: 1.025 (1.008, 1.044), 1.020 (0.999, 1.042) and per 100 ng/m³ in Si: 1.121 (1.039, 1.209), 1.068 (1.022, 1.117) adjusted for PM_2.5 and NO₂ correspondingly]. Our findings indicate an association of natural mortality with most components, which was reduced after adjustment for PM_2.5 and especially NO₂.

A nationwide study of air pollution from particulate matter and daily hospitalizations for respiratory diseases in Italy

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 PM_2.5 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 PM_2.5 concentrations over the study period were 23.3 and 17 μg/m³, respectively. For each 10 μg/m³ increases in PM₁₀ and PM_2.5 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.

Association between air temperature, air pollution and hospital admissions for pulmonary embolism and venous thrombosis in Italy

BACKGROUND

Previous studies reported a link between short-term exposure to environmental stressors (air pollution and air temperature) and atherothrombotic cardiovascular diseases. However, only few of them reported consistent associations with venous thromboembolism (VTE). Our aim was to estimate the association between daily air temperature and particulate matter (PM) air pollution with hospital admissions for pulmonary embolism (PE) and venous thrombosis (VT) at national level in Italy.

METHODS

We collected daily hospital PE and VT admissions from the Italian Ministry of Health during 2006-2015 in all the 8,084 municipalities of Italy, and we merged them with air temperature and daily PM10 concentrations estimated by satellite-based spatiotemporal models. First, we applied multivariate Poisson regression models at province level. Then, we obtained national overall effects by random-effects meta-analysis.

RESULTS

This analysis was conducted on 219,952 PE and 275,506 VT hospitalizations. Meta-analytical results showed weak associations between the two exposures and the study outcomes in the full year analysis. During autumn and winter, PE hospital admissions increased by 1.07% (95% confidence intervals [CI]: 0.21%; 1.92%) and 0.96% (95% CI: 0.07%; 1.83%) respectively, per 1 °C decrement of air temperature in the previous 10 days (lag 0-10). In summer we observed adverse effects at high temperatures, with a 1% (95% CI: 0.10%; 1.91%) increasing risk per 1 °C increment. We found no association between VT and cold temperatures.

CONCLUSION

Results show a significant effect of air temperature on PE hospitalizations in the cold seasons and summer. No effect of particulate matter was detected.

Long-term exposure to air pollution and risk of venous thromboembolism in a large administrative cohort

BACKGROUND

Venous thromboembolisms (VTE) are one of the most frequent cause among the cardiovascular diseases. Despite the association between long-term exposure to air pollution and cardiovascular outcomes have been widely explored in epidemiological literature, little is known about the air pollution related effects on VTE. We aimed to evaluate this association in a large administrative cohort in 15 years of follow-up.

METHODS

Air pollution exposure (NO₂, PM₁₀ and PM_2.5) was derived by land use regression models obtained by the ESCAPE framework. Administrative health databases were used to identify VTE cases. To estimate the association between air pollutant exposures and risk of hospitalizations for VTE (in total and divided in deep vein thrombosis (DVT) and pulmonary embolism (PE)), we used Cox regression models, considering individual, environmental (noise and green areas), and contextual characteristics. Finally, we considered potential effect modification for individual covariates and previous comorbidities.

RESULTS

We identified 1,954 prevalent cases at baseline and 20,304 cases during the follow-up period. We found positive associations between PM_2.5 exposures and DVT, PE and VTE with hazard ratios (HRs) up to 1.082 (95% confidence intervals: 0.992, 1.181), 1.136 (0.994, 1.298) and 1.074 (0.996, 1.158) respectively for 10 μg/m³ increases. The association was stronger in younger subjects (< 70 years old compared to > 70 years old) and among those who had cancer.

CONCLUSION

The effect of pollutants on PE and VTE hospitalizations, although marginally non-significant, should be interpreted as suggestive of a health effect that deserves attention in future studies.

Variability in the association between long-term exposure to ambient air pollution and mortality by exposure assessment method and covariate adjustment: A census-based country-wide cohort study

BACKGROUND

Ambient air pollution exposure has been associated with higher mortality risk in numerous studies. We assessed potential variability in the magnitude of this association for non-accidental, cardiovascular disease, respiratory disease, and lung cancer mortality in a country-wide administrative cohort by exposure assessment method and by adjustment for geographic subdivisions.

METHODS

We used the Belgian 2001 census linked to population and mortality register including nearly 5.5 million adults aged ≥30 (mean follow-up: 9.97 years). Annual mean concentrations for fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), black carbon (BC) and ozone (O₃) were assessed at baseline residential address using two exposure methods; Europe-wide hybrid land use regression (LUR) models [100x100m], and Belgium-wide interpolation-dispersion (RIO-IFDM) models [25x25m]. We used Cox proportional hazards models with age as the underlying time scale and adjusted for various individual and area-level covariates. We further adjusted main models for two different area-levels following the European Nomenclature of Territorial Units for Statistics (NUTS); NUTS-1 (n = 3), or NUTS-3 (n = 43).

RESULTS

We found no consistent differences between both exposure methods. We observed most robust associations with lung cancer mortality. Hazard Ratios (HRs) per 10 μg/m³ increase for NO₂ were 1.060 (95%CI 1.042-1.078) [hybrid LUR] and 1.040 (95%CI 1.022-1.058) [RIO-IFDM]. Associations with non-accidental, respiratory disease and cardiovascular disease mortality were generally null in main models but were enhanced after further adjustment for NUTS-1 or NUTS-3. HRs for non-accidental mortality per 5 μg/m³ increase for PM_2.5 for the main model using hybrid LUR exposure were 1.023 (95%CI 1.011-1.035). After including random effects HRs were 1.044 (95%CI 1.033-1.057) [NUTS-1] and 1.076 (95%CI 1.060-1.092) [NUTS-3].

CONCLUSION

Long-term air pollution exposure was associated with higher lung cancer mortality risk but not consistently with the other studied causes. Magnitude of associations varied by adjustment for geographic subdivisions, area-level socio-economic covariates and less by exposure assessment method.

Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project

BACKGROUND

Long-term exposure to ambient air pollution has been associated with premature mortality, but associations at concentrations lower than current annual limit values are uncertain. We analysed associations between low-level air pollution and mortality within the multicentre study Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE).

METHODS

In this multicentre longitudinal study, we analysed seven population-based cohorts of adults (age ≥30 years) within ELAPSE, from Belgium, Denmark, England, the Netherlands, Norway, Rome (Italy), and Switzerland (enrolled in 2000-11; follow-up until 2011-17). Mortality registries were used to extract the underlying cause of death for deceased individuals. Annual average concentrations of fine particulate matter (PM_2·5), nitrogen dioxide (NO₂), black carbon, and tropospheric warm-season ozone (O₃) from Europe-wide land use regression models at 100 m spatial resolution were assigned to baseline residential addresses. We applied cohort-specific Cox proportional hazard models with adjustment for area-level and individual-level covariates to evaluate associations with non-accidental mortality, as the main outcome, and with cardiovascular, non-malignant respiratory, and lung cancer mortality. Subset analyses of participants living at low pollutant concentrations (as per predefined values) and natural splines were used to investigate the concentration-response function. Cohort-specific effect estimates were pooled in a random-effects meta-analysis.

FINDINGS

We analysed 28 153 138 participants contributing 257 859 621 person-years of observation, during which 3 593 741 deaths from non-accidental causes occurred. We found significant positive associations between non-accidental mortality and PM_2·5, NO₂, and black carbon, with a hazard ratio (HR) of 1·053 (95% CI 1·021-1·085) per 5 μg/m³ increment in PM_2·5, 1·044 (1·019-1·069) per 10 μg/m³ NO₂, and 1·039 (1·018-1·059) per 0·5 × 10^-5/m black carbon. Associations with PM_2·5, NO₂, and black carbon were slightly weaker for cardiovascular mortality, similar for non-malignant respiratory mortality, and stronger for lung cancer mortality. Warm-season O₃ was negatively associated with both non-accidental and cause-specific mortality. Associations were stronger at low concentrations: HRs for non-accidental mortality at concentrations lower than the WHO 2005 air quality guideline values for PM_2·5 (10 μg/m³) and NO₂ (40 μg/m³) were 1·078 (1·046-1·111) per 5 μg/m³ PM_2·5 and 1·049 (1·024-1·075) per 10 μg/m³ NO₂. Similarly, the association between black carbon and non-accidental mortality was highest at low concentrations, with a HR of 1·061 (1·032-1·092) for exposure lower than 1·5× 10^-5/m, and 1·081 (0·966-1·210) for exposure lower than 1·0× 10^-5/m.

INTERPRETATION

Long-term exposure to concentrations of PM_2·5 and NO₂ lower than current annual limit values was associated with non-accidental, cardiovascular, non-malignant respiratory, and lung cancer mortality in seven large European cohorts. Continuing research on the effects of low concentrations of air pollutants is expected to further inform the process of setting air quality standards in Europe and other global regions.

FUNDING

Health Effects Institute.

Association of long-term exposure to PM2.5 with hypertension prevalence and blood pressure in China: a cross-sectional study

OBJECTIVE

Evidence of the effects of long-term fine particulate matter (PM_2.5) exposure on hypertension and blood pressure is limited for populations exposed to high levels of PM_2.5. We aim to assess associations of long-term exposure to PM_2.5 with hypertension prevalence and blood pressure, and further explore the subpopulation differences and effect modification by participant characteristics in these associations in China.

METHODS

We analysed cross-sectional data from 883 827 participants aged 35-75 years in the China Patient-Centred Evaluative Assessment of Cardiac Events Million Persons Project. Data from the monitoring station were used to estimate the 1-year average concentration of PM_2.5. The associations of PM_2.5 exposure with hypertension prevalence and blood pressure were investigated by generalised linear models, with PM_2.5 included as either linear or spline functions.

RESULTS

The 1-year PM_2.5 exposure of the study population ranged from 8.8 to 93.8 µg/m³ (mean 49.2 µg/m³). The adjusted OR of hypertension prevalence related to a 10 μg/m³ increase in 1-year PM_2.5 exposure was 1.04 (95% CI, 1.02 to 1.05). Each 10 μg/m³ increment in PM_2.5 exposure was associated with increases of 0.19 mm Hg (95% CI, 0.10 to 0.28) and 0.13 mm Hg (95% CI, 0.08 to 0.18) in systolic blood pressure and diastolic blood pressure, respectively. The concentration-response curves for hypertension prevalence and systolic blood pressure showed steeper slopes at higher PM_2.5 levels; while the curve for diastolic blood pressure was U-shaped. The elderly, men, non-current smokers and obese participants were more susceptible to the exposure of PM_2.5.

CONCLUSIONS

Long-term exposure to PM_2.5 is associated with higher blood pressure and increased risk of hypertension prevalence. The effects of PM_2.5 on hypertension prevalence become more pronounced at higher PM_2.5 levels. These findings emphasise the need to reduce air pollution, especially in areas with severe air pollution.

Long term exposure to low level air pollution and mortality in eight European cohorts within the ELAPSE project: pooled analysis

OBJECTIVE

To investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines.

DESIGN

Pooled analysis of eight cohorts.

SETTING

Multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries.

PARTICIPANTS

325 367 adults from the general population recruited mostly in the 1990s or 2000s with detailed lifestyle data. Stratified Cox proportional hazard models were used to analyse the associations between air pollution and mortality. Western Europe-wide land use regression models were used to characterise residential air pollution concentrations of ambient fine particulate matter (PM_2.5), nitrogen dioxide, ozone, and black carbon.

MAIN OUTCOME MEASURES

Deaths due to natural causes and cause specific mortality.

RESULTS

Of 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM_2.5, nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m³ in PM_2.5 was associated with 13% (95% confidence interval 10.6% to 15.5%) increase in natural deaths; the corresponding figure for a 10 µg/m³ increase in nitrogen dioxide was 8.6% (7% to 10.2%). Associations with PM_2.5, nitrogen dioxide, and black carbon remained significant at low concentrations. For participants with exposures below the US standard of 12 µg/m³ an increase of 5 µg/m³ in PM_2.5 was associated with 29.6% (14% to 47.4%) increase in natural deaths.

CONCLUSIONS

Our study contributes to the evidence that outdoor air pollution is associated with mortality even at low pollution levels below the current European and North American standards and WHO guideline values. These findings are therefore an important contribution to the debate about revision of air quality limits, guidelines, and standards, and future assessments by the Global Burden of Disease.

Mortality and Morbidity Effects of Long-Term Exposure to Low-Level PM2.5, BC, NO2, and O3: An Analysis of European Cohorts in the ELAPSE Project

INTRODUCTION

Epidemiological cohort studies have consistently found associations between long-term exposure to outdoor air pollution and a range of morbidity and mortality endpoints. Recent evaluations by the World Health Organization and the Global Burden of Disease study have suggested that these associations may be nonlinear and may persist at very low concentrations. Studies conducted in North America in particular have suggested that associations with mortality persisted at concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) well below current air quality standards and guidelines. The uncertainty about the shape of the concentration-response function at the low end of the concentration distribution, related to the scarcity of observations in the lowest range, was the basis of the current project. Previous studies have focused on PM2.5, but increasingly associations with nitrogen dioxide (NO2) are being reported, particularly in studies that accounted for the fine spatial scale variation of NO2. Very few studies have evaluated the effects of long-term exposure to low concentrations of ozone (O3). Health effects of black carbon (BC), representing primary combustion particles, have not been studied in most large cohort studies of PM2.5. Cohort studies assessing health effects of particle composition, including elements from nontailpipe traffic emissions (iron, copper, and zinc) and secondary aerosol (sulfur) have been few in number and reported inconsistent results. The overall objective of our study was to investigate the shape of the relationship between long-term exposure to four pollutants (PM2.5, NO2, BC, and O3) and four broad health effect categories using a number of different methods to characterize the concentration-response function (i.e., linear, nonlinear, or threshold). The four health effect categories were (1) natural- and cause-specific mortality including cardiovascular and nonmalignant as well as malignant respiratory and diabetes mortality; and morbidity measured as (2) coronary and cerebrovascular events; (3) lung cancer incidence; and (4) asthma and chronic obstructive pulmonary disease (COPD) incidence. We additionally assessed health effects of PM2.5 composition, specifically the copper, iron, zinc, and sulfur content of PM2,5.

METHODS

We focused on analyses of health effects of air pollutants at low concentrations, defined as less than current European Union (EU) Limit Values, U.S. Environmental Protection Agency (U.S. EPA), National Ambient Air Quality Standards (NAAQS), and/or World Health Organization (WHO) Air Quality Guideline values for PM2.5, NO2, and O3. We address the health effects at low air pollution levels by performing new analyses within selected cohorts of the ESCAPE study (European Study of Cohorts for Air Pollution Effects; Beelen et al. 2014a) and within seven very large European administrative cohorts. By combining well-characterized ESCAPE cohorts and large administrative cohorts in one study the strengths and weaknesses of each approach can be addressed. The large administrative cohorts are more representative of national or citywide populations, have higher statistical power, and can efficiently control for area-level confounders, but have fewer possibilities to control for individual-level confounders. The ESCAPE cohorts have detailed information on individual confounders, as well as country-specific information on area-level confounding. The data from the seven included ESCAPE cohorts and one additional non-ESCAPE cohort have been pooled and analyzed centrally. More than 300,000 adults were included in the pooled cohort from existing cohorts in Sweden, Denmark, Germany, the Netherlands, Austria, France, and Italy. Data from the administrative cohorts have been analyzed locally, without transfer to a central database. Privacy regulations prevented transfer of data from administrative cohorts to a central database. More than 28 million adults were included from national administrative cohorts in Belgium, Denmark, England, the Netherlands, Norway, and Switzerland as well as an administrative cohort in Rome, Italy. We developed central exposure assessment using Europewide hybrid land use regression (LUR) models, which incorporated European routine monitoring data for PM2.5, NO2, and O3, and ESCAPE monitoring data for BC and PM2.5 composition, land use, and traffic data supplemented with satellite observations and chemical transport model estimates. For all pollutants, we assessed exposure at a fine spatial scale, 100 × 100 m grids. These models have been applied to individual addresses of all cohorts including the administrative cohorts. In sensitivity analyses, we applied the PM2.5 models developed within the companion HEI-funded Canadian MAPLE study (Brauer et al. 2019) and O3 exposures on a larger spatial scale for comparison with previous studies. Identification of outcomes included linkage with mortality, cancer incidence, hospital discharge registries, and physician-based adjudication of cases. We analyzed natural-cause, cardiovascular, ischemic heart disease, stroke, diabetes, cardiometabolic, respiratory, and COPD mortality. We also analyzed lung cancer incidence, incidence of coronary and cerebrovascular events, and incidence of asthma and COPD (pooled cohort only). We applied the Cox proportional hazard model with increasing control for individual- and area-level covariates to analyze the associations between air pollution and mortality and/or morbidity for both the pooled cohort and the individual administrative cohorts. Age was used as the timescale because of evidence that this results in better adjustment for potential confounding by age. Censoring occurred at the time of the event of interest, death from other causes, emigration, loss to follow-up for other reasons, or at the end of follow-up, whichever came first. A priori we specified three confounder models, following the modeling methods of the ESCAPE study. Model 1 included only age (time axis), sex (as strata), and calendar year of enrollment. Model 2 added individual-level variables that were consistently available in the cohorts contributing to the pooled cohort or all variables available in the administrative cohorts, respectively. Model 3 further added area-level socioeconomic status (SES) variables. A priori model 3 was selected as the main model. All analyses in the pooled cohort were stratified by subcohort. All analyses in the administrative cohorts accounted for clustering of the data in neighborhoods by adjusting the variance of the effect estimates. The main exposure variable we analyzed was derived from the Europewide hybrid models based on 2010 monitoring data. Sensitivity analyses were conducted using earlier time periods, time-varying exposure analyses, local exposure models, and the PM2.5 models from the Canadian MAPLE project. We first specified linear single-pollutant models. Two-pollutant models were specified for all combinations of the four main pollutants. Two-pollutant models for particle composition were analyzed with PM2.5 and NO2 as the second pollutant. We then investigated the shape of the concentration-response function using natural splines with two, three, and four degrees of freedom; penalized splines with the degrees of freedom determined by the algorithm and shape-constrained health impact functions (SCHIF) using confounder model 3. Additionally, we specified linear models in subsets of the concentration range, defined by removing concentrations above a certain value from the analysis, such as for PM2.5 25 μg/m3 (EU limit value), 20, 15, 12 μg/m3 (U.S. EPA National Ambient Air Quality Standard), and 10 μg/m3 (WHO Air Quality Guideline value). Finally, threshold models were evaluated to investigate whether the associations persisted below specific concentration values. For PM2.5, we evaluated 10, 7.5, and 5 μg/m3 as potential thresholds. Performance of threshold models versus the corresponding no-threshold linear model were evaluated using the Akaike information criterion (AIC).

RESULTS

In the pooled cohort, virtually all subjects in 2010 had PM2.5 and NO2 annual average exposures below the EU limit values (25 μg/m3 and 40 μg/m3, respectively). More than 50,000 had a residential PM2.5 exposure below the U.S. EPA NAAQS (12 μg/m3). More than 25,000 subjects had a residential PM2.5 exposure below the WHO guideline (10 μg/m3). We found significant positive associations between PM2.5, NO2, and BC and natural-cause, respiratory, cardiovascular, and diabetes mortality. In our main model, the hazard ratios (HRs) (95% [confidence interval] CI) were 1.13 (CI = 1.11, 1.16) for an increase of 5 μg/m3 PM2.5, 1.09 (CI = 1.07, 1.10) for an increase of 10 μg/m3 NO2, and 1.08 (CI = 1.06, 1.10) for an increase of 0.5 × 10-5/m BC for natural-cause mortality. The highest HRs were found for diabetes mortality. Associations with O3 were negative, both in the fine spatial scale of the main ELAPSE model and in large spatial scale exposure models. For PM2.5, NO2, and BC, we generally observed a supralinear association with steeper slopes at low exposures and no evidence of a concentration below which no association was found. Subset analyses further confirmed that these associations remained at low levels: below 10 μg/m3 for PM2.5 and 20 μg/m3 for NO2. HRs were similar to the full cohort HRs for subjects with exposures below the EU limit values for PM2.5 and NO2, the U.S. NAAQS values for PM2.5, and the WHO guidelines for PM2.5 and NO2. The mortality associations were robust to alternative specifications of exposure, including different time periods, PM2.5 from the MAPLE project, and estimates from the local ESCAPE model. Time-varying exposure natural spline analyses confirmed associations at low pollution levels. HRs in two-pollutant models were attenuated but remained elevated and statistically significant for PM2.5 and NO2. In two-pollutant models of PM2.5 and NO2 HRs for natural-cause mortality were 1.08 (CI = 1.05, 1.11) for PM2.5 and 1.05 (CI = 1.03, 1.07) for NO2. Associations with O3 were attenuated but remained negative in two-pollutant models with NO2, BC, and PM2.5. We found significant positive associations between PM2.5, NO2, and BC and incidence of stroke and asthma and COPD hospital admissions. Furthermore, NO2 was significantly related to acute coronary heart disease and PM2.5 was significantly related to lung cancer incidence. We generally observed linear to supralinear associations with no evidence of a threshold, with the exception of the association between NO2 and acute coronary heart disease, which was sublinear. Subset analyses documented that associations remained even with PM2.5 below 20 μg/m3 and possibly 12 μg/m3. Associations remained even when NO2 was below 30 μg/m3 and in some cases 20 μg/m3. In two-pollutant models, NO2 was most consistently associated with acute coronary heart disease, stroke, asthma, and COPD hospital admissions. PM2.5 was not associated with these outcomes in two-pollutant models with NO2. PM2.5 was the only pollutant that was associated with lung cancer incidence in two-pollutant models. Associations with O3 were negative though generally not statistically significant. In the administrative cohorts, virtually all subjects in 2010 had PM2.5 and NO2 annual average exposures below the EU limit values. More than 3.9 million subjects had a residential PM2.5 exposure below the U.S. EPA NAAQS (12 μg/m3) and more than 1.9 million had residential PM2.5 exposures below the WHO guideline (10 μg/m3). We found significant positive associations between PM2.5, NO2, and BC and natural-cause, respiratory, cardiovascular, and lung cancer mortality, with moderate to high heterogeneity between cohorts. We found positive but statistically nonsignificant associations with diabetes mortality. In our main model meta-analysis, the HRs (95% CI) for natural-cause mortality were 1.05 (CI = 1.02, 1.09) for an increase of 5 μg/m3 PM2.5, 1.04 (CI = 1.02, 1.07) for an increase of 10 μg/m3 NO2, and 1.04 (CI = 1.02, 1.06) for an increase of 0.5 × 10-5/m BC, and 0.95 (CI = 0.93, 0.98) for an increase of 10 μg/m3 O3. The shape of the concentration-response functions differed between cohorts, though the associations were generally linear to supralinear, with no indication of a level below which no associations were found. Subset analyses documented that these associations remained at low levels: below 10 μg/m3 for PM2.5 and 20 μg/m3 for NO2. BC and NO2 remained significantly associated with mortality in two-pollutant models with PM2.5 and O3. The PM2.5 HR attenuated to unity in a two-pollutant model with NO2. The negative O3 association was attenuated to unity and became nonsignificant. The mortality associations were robust to alternative specifications of exposure, including time-varying exposure analyses. Time-varying exposure natural spline analyses confirmed associations at low pollution levels. Effect estimates in the youngest participants (<65 years at baseline) were much larger than in the elderly (>65 years at baseline). Effect estimates obtained with the ELAPSE PM2.5 model did not differ from the MAPLE PM2.5 model on average, but in individual cohorts, substantial differences were found.

CONCLUSIONS

Long-term exposure to PM2.5, NO2, and BC was positively associated with natural-cause and cause-specific mortality in the pooled cohort and the administrative cohorts. Associations were found well below current limit values and guidelines for PM2.5 and NO2. Associations tended to be supralinear, with steeper slopes at low exposures with no indication of a threshold. Two-pollutant models documented the importance of characterizing the ambient mixture with both NO2 and PM2.5. We mostly found negative associations with O3. In two-pollutant models with NO2, the negative associations with O3 were attenuated to essentially unity in the mortality analysis of the administrative cohorts and the incidence analyses in the pooled cohort. In the mortality analysis of the pooled cohort, significant negative associations with O3 remained in two-pollutant models. Long-term exposure to PM2.5, NO2, and BC was also positively associated with morbidity outcomes in the pooled cohort. For stroke, asthma, and COPD, positive associations were found for PM2.5, NO2, and BC. For acute coronary heart disease, an increased HR was observed for NO2. For lung cancer, an increased HR was found only for PM2.5. Associations mostly showed steeper slopes at low exposures with no indication of a threshold.

Long-term exposure to low-level ambient air pollution and incidence of stroke and coronary heart disease: a pooled analysis of six European cohorts within the ELAPSE project

BACKGROUND

Long-term exposure to outdoor air pollution increases the risk of cardiovascular disease, but evidence is unclear on the health effects of exposure to pollutant concentrations lower than current EU and US standards and WHO guideline limits. Within the multicentre study Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), we investigated the associations of long-term exposures to fine particulate matter (PM2·5), nitrogen dioxide (NO2), black carbon, and warm-season ozone (O3) with the incidence of stroke and acute coronary heart disease.

METHODS

We did a pooled analysis of individual data from six population-based cohort studies within ELAPSE, from Sweden, Denmark, the Netherlands, and Germany (recruited 1992-2004), and harmonised individual and area-level variables between cohorts. Participants (all adults) were followed up until migration from the study area, death, or incident stroke or coronary heart disease, or end of follow-up (2011-15). Mean 2010 air pollution concentrations from centrally developed European-wide land use regression models were assigned to participants' baseline residential addresses. We used Cox proportional hazards models with increasing levels of covariate adjustment to investigate the association of air pollution exposure with incidence of stroke and coronary heart disease. We assessed the shape of the concentration-response function and did subset analyses of participants living at pollutant concentrations lower than predefined values.

FINDINGS

From the pooled ELAPSE cohorts, data on 137 148 participants were analysed in our fully adjusted model. During a median follow-up of 17·2 years (IQR 13·8-19·5), we observed 6950 incident events of stroke and 10 071 incident events of coronary heart disease. Incidence of stroke was associated with PM2·5 (hazard ratio 1·10 [95% CI 1·01-1·21] per 5 μg/m3 increase), NO2 (1·08 [1·04-1·12] per 10 μg/m3 increase), and black carbon (1·06 [1·02-1·10] per 0·5 10-5/m increase), whereas coronary heart disease incidence was only associated with NO2 (1·04 [1·01-1·07]). Warm-season O3 was not associated with an increase in either outcome. Concentration-response curves indicated no evidence of a threshold below which air pollutant concentrations are not harmful for cardiovascular health. Effect estimates for PM2·5 and NO2 remained elevated even when restricting analyses to participants exposed to pollutant concentrations lower than the EU limit values of 25 μg/m3 for PM2·5 and 40 μg/m3 for NO2.

INTERPRETATION

Long-term air pollution exposure was associated with incidence of stroke and coronary heart disease, even at pollutant concentrations lower than current limit values.

FUNDING

Health Effects Institute.

Short-term health effects from outdoor exposure to biomass burning emissions: A review

Biomass burning (BB) including forest, bush, prescribed fires, agricultural fires, residential wood combustion, and power generation has long been known to affect climate, air quality and human health. With this work we supply a systematic review on the health effects of BB emissions in the framework of the WHO activities on air pollution. We performed a literature search of online databases (PubMed, ISI, and Scopus) from year 1980 up to 2020. A total of 81 papers were considered as relevant for mortality and morbidity effects. High risk of bias was related with poor estimation of BB exposure and lack of adjustment for important confounders. PM10 and PM2.5 concentrations originating from BB were associated with all-cause mortality: the meta-analytical estimate was equal to 1.31% (95% CI 0.71, 1.71) and 1.92% (95% CI -1.19, 5.03) increased mortality per each 10 μg m^-3 increase of PM10 and PM2.5, respectively. Regarding cardiovascular mortality 8 studies reported quantitative estimates. For smoky days and for each 10 μg m^-3 increase in PM2.5 concentrations, the risk of cardiovascular mortality increased by 4.45% (95% CI 0.96, 7.95) and by 3.30% (95% CI -1.97, 8.57), respectively. Fourteen studies evaluated whether respiratory morbidity was adversely related to PM2.5 (9 studies) or PM10 (5 studies) originating from BB. All found positive associations. The pooled effect estimates were 4.10% (95% CI 2.86, 5.34) and 4.83% (95% CI 0.06, 9.60) increased risk of total respiratory admissions/emergency visits, per 10 μg m^-3 increases in PM2.5 and PM10, respectively. Regarding cardiovascular morbidity, sixteen studies evaluated whether this was adversely related to PM2.5 (10 studies) or PM10 (6 studies) originating from BB. They found both positive and negative results, with summary estimates equal to 3.68% (95% CI -1.73, 9.09) and 0.93% (95% CI -0.18, 2.05) increased risk of total cardiovascular admissions/emergency visits, per 10 μg m^-3 increases in PM2.5 and PM10, respectively. To conclude, a significant number of studies indicate that BB exposure is associated with all-cause and cardiovascular mortality and respiratory morbidity.

Long-term exposure to air pollution and liver cancer incidence in six European cohorts

Particulate matter air pollution and diesel engine exhaust have been classified as carcinogenic for lung cancer, yet few studies have explored associations with liver cancer. We used six European adult cohorts which were recruited between 1985 and 2005, pooled within the "Effects of low-level air pollution: A study in Europe" (ELAPSE) project, and followed for the incidence of liver cancer until 2011 to 2015. The annual average exposure to nitrogen dioxide (NO₂ ), particulate matter with diameter <2.5 μm (PM_2.5 ), black carbon (BC), warm-season ozone (O₃ ), and eight elemental components of PM_2.5 (copper, iron, zinc, sulfur, nickel, vanadium, silicon, and potassium) were estimated by European-wide hybrid land-use regression models at participants' residential addresses. We analyzed the association between air pollution and liver cancer incidence by Cox proportional hazards models adjusting for potential confounders. Of 330 064 cancer-free adults at baseline, 512 developed liver cancer during a mean follow-up of 18.1 years. We observed positive linear associations between NO₂ (hazard ratio, 95% confidence interval: 1.17, 1.02-1.35 per 10 μg/m³ ), PM_2.5 (1.12, 0.92-1.36 per 5 μg/m³ ), and BC (1.15, 1.00-1.33 per 0.5 10^-5 /m) and liver cancer incidence. Associations with NO₂ and BC persisted in two-pollutant models with PM_2.5 . Most components of PM_2.5 were associated with the risk of liver cancer, with the strongest associations for sulfur and vanadium, which were robust to adjustment for PM_2.5 or NO₂ . Our study suggests that ambient air pollution may increase the risk of liver cancer, even at concentrations below current EU standards.

Long-Term Exposure to Fine Particle Elemental Components and Natural and Cause-Specific Mortality-a Pooled Analysis of Eight European Cohorts within the ELAPSE Project

BACKGROUND

Inconsistent associations between long-term exposure to particles with an aerodynamic diameter ≤ 2.5   μ m [fine particulate matter (PM 2.5 )] components and mortality have been reported, partly related to challenges in exposure assessment.

OBJECTIVES

We investigated the associations between long-term exposure to PM 2.5 elemental components and mortality in a large pooled European cohort; to compare health effects of PM 2.5 components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms.

METHODS

We pooled data from eight European cohorts with 323,782 participants, average age 49 y at baseline (1985-2005). Residential exposure to 2010 annual average concentration of eight PM 2.5 components [copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)] was estimated with Europe-wide SLR and RF models at a 100 × 100   m scale. We applied Cox proportional hazards models to investigate the associations between components and natural and cause-specific mortality. In addition, two-pollutant analyses were conducted by adjusting each component for PM 2.5 mass and nitrogen dioxide (NO 2 ) separately.

RESULTS

We observed 46,640 natural-cause deaths with 6,317,235 person-years and an average follow-up of 19.5 y. All SLR-modeled components were statistically significantly associated with natural-cause mortality in single-pollutant models with hazard ratios (HRs) from 1.05 to 1.27. Similar HRs were observed for RF-modeled Cu, Fe, K, S, V, and Zn with wider confidence intervals (CIs). HRs for SLR-modeled Ni, S, Si, V, and Zn remained above unity and (almost) significant after adjustment for both PM 2.5 and NO 2 . HRs only remained (almost) significant for RF-modeled K and V in two-pollutant models. The HRs for V were 1.03 (95% CI: 1.02, 1.05) and 1.06 (95% CI: 1.02, 1.10) for SLR- and RF-modeled exposures, respectively, per 2   ng / m 3 , adjusting for PM 2.5 mass. Associations with cause-specific mortality were less consistent in two-pollutant models.

CONCLUSION

Long-term exposure to V in PM 2.5 was most consistently associated with increased mortality. Associations for the other components were weaker for exposure modeled with RF than SLR in two-pollutant models. https://doi.org/10.1289/EHP8368.

Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project

BACKGROUND

We evaluated methods for the analysis of multi-level survival data using a pooled dataset of 14 cohorts participating in the ELAPSE project investigating associations between residential exposure to low levels of air pollution (PM2.5 and NO2) and health (natural-cause mortality and cerebrovascular, coronary and lung cancer incidence).

METHODS

We applied five approaches in a multivariable Cox model to account for the first level of clustering corresponding to cohort specification: (1) not accounting for the cohort or using (2) indicator variables, (3) strata, (4) a frailty term in frailty Cox models, (5) a random intercept under a mixed Cox, for cohort identification. We accounted for the second level of clustering due to common characteristics in the residential area by (1) a random intercept per small area or (2) applying variance correction. We assessed the stratified, frailty and mixed Cox approach through simulations under different scenarios for heterogeneity in the underlying hazards and the air pollution effects.

RESULTS

Effect estimates were stable under approaches used to adjust for cohort but substantially differed when no adjustment was applied. Further adjustment for the small area grouping increased the effect estimates' standard errors. Simulations confirmed identical results between the stratified and frailty models. In ELAPSE we selected a stratified multivariable Cox model to account for between-cohort heterogeneity without adjustment for small area level, due to the small number of subjects and events in the latter.

CONCLUSIONS

Our study supports the need to account for between-cohort heterogeneity in multi-center collaborations using pooled individual level data.

Long-term exposure to fine particle elemental components and lung cancer incidence in the ELAPSE pooled cohort

BACKGROUND

An association between long-term exposure to fine particulate matter (PM_2.5) and lung cancer has been established in previous studies. PM_2.5 is a complex mixture of chemical components from various sources and little is known about whether certain components contribute specifically to the associated lung cancer risk. The present study builds on recent findings from the "Effects of Low-level Air Pollution: A Study in Europe" (ELAPSE) collaboration and addresses the potential association between specific elemental components of PM_2.5 and lung cancer incidence.

METHODS

We pooled seven cohorts from across Europe and assigned exposure estimates for eight components of PM_2.5 representing non-tail pipe emissions (copper (Cu), iron (Fe), and zinc (Zn)), long-range transport (sulfur (S)), oil burning/industry emissions (nickel (Ni), vanadium (V)), crustal material (silicon (Si)), and biomass burning (potassium (K)) to cohort participants' baseline residential address based on 100 m by 100 m grids from newly developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status).

RESULTS

The pooled study population comprised 306,550 individuals with 3916 incident lung cancer events during 5,541,672 person-years of follow-up. We observed a positive association between exposure to all eight components and lung cancer incidence, with adjusted HRs of 1.10 (95% CI 1.05, 1.16) per 50 ng/m³ PM_2.5 K, 1.09 (95% CI 1.02, 1.15) per 1 ng/m³ PM_2.5 Ni, 1.22 (95% CI 1.11, 1.35) per 200 ng/m³ PM_2.5 S, and 1.07 (95% CI 1.02, 1.12) per 200 ng/m³ PM_2.5 V. Effect estimates were largely unaffected by adjustment for nitrogen dioxide (NO₂). After adjustment for PM_2.5 mass, effect estimates of K, Ni, S, and V were slightly attenuated, whereas effect estimates of Cu, Si, Fe, and Zn became null or negative.

CONCLUSIONS

Our results point towards an increased risk of lung cancer in connection with sources of combustion particles from oil and biomass burning and secondary inorganic aerosols rather than non-exhaust traffic emissions. Specific limit values or guidelines targeting these specific PM_2.5 components may prove helpful in future lung cancer prevention strategies.

[Interrupting the chains of transmission of COVID-19 in Italy: survey among the Prevention Departments]

BACKGROUND

the ability to implement effective preventive and control measures is rooted in public health surveillance to promptly identify and isolate contagious patients.

OBJECTIVES

to describe some organizational aspects and resources involved in the control of COVID-19 pandemic.

DESIGN

observational cross sectional study.

SETTING AND PARTICIPANTS

a survey of methods and tools adopted by the competent service (Prevention department) in the Local public health units (LHU) of the regional Health services has been performed in May 2020. The survey collected data related to activities carried out during the month of April 2020 on the surveillance system for collection of suspected cases, their virological ascertainment, the isolation procedures and contact-tracing activities by means of an online questionnaire filled in by the public health structure of the regional health system. A convenience sample of Prevention departments was recruited.

RESULTS

in 44 Prevention departments of 14 Regions/Autonomous Provinces (caring for 40% of the population residing in Italy), different services were swiftly engaged in pandemic response. Reports of suspected cases were about 3 times the number of confirmed cases in the same month. Local reporting form was used in 46% of the LHUs while a regional form was available in 42% of the Departments (in 9/14 Regions). In one fourth the forms were not always used and 2% had no forms for the reporting of suspected cases. Data were recorded in 52% of LHUs on local databases, while in 20% a regional database (in 7 Regions) had been created. A proportion of 11% did not record the data for further elaboration. The virological assessment with nasopharyngeal swabs out of the hospital setting was carried out on the average in 7 points in each LHU (median 5) and the average daily capacity was 350 (71 per 100,000) swabs. The rate of subjects newly tested during the month of April was of 893 per 100,000 new people. Data collected at the swabbing were recorded on a regional platform in 17 LHUs (39%) of 8 Regions. In 7% LHUs only positive specimens were recorded electronically. Local files were used in 27% LHUs. The interview with confirmed cases was carried out with a local questionnaire in 52% LHUs, while 14% stated that a standardized form was not used. The data collected about cases were recorded on a regional IT platform in 30% Departments (in 8 Regions) and in 41% data were registered only locally. For each confirmed case in April, a median of 4 contacts were identified. Only 13 (30%) Departments in 9 Regions have registered contact data on a regional database. Ten Departments (23%) have only hard copies, while 56% recorded data on local databases. About 5 health professionals for 100,000 resident population were involved in each LHU in each of the following activities as receiving reports of suspected cases, swabs collection, interviews of cases and contact identifications.

CONCLUSIONS

the pandemic required rapidly a great organizational effort and great flexibility to increase response capacity, which now must be strengthened and maintained. Several different tools (forms and electronic files) have been developed in each LHU and used for the same surveillance operational processes with a loss in local efficiency. The inhomogeneous data collection and recording is an obstacle for further analyses and risk identifications and is a missed opportunity for the advancement of our knowledge on pandemic epidemiology analysis. In Italy, updating the pandemic response plans is the priority, at national, regional and local level, and the occasion to fill the gaps and to improve surveillance systems to the interruption of COVID-19 transmission.

Impact of different exposure models and spatial resolution on the long-term effects of air pollution

Long-term exposure to air pollution has been related to mortality in several epidemiological studies. The investigations have assessed exposure using various methods achieving different accuracy in predicting air pollutants concentrations. The comparison of the health effects estimates are therefore challenging. This paper aims to compare the effect estimates of the long-term effects of air pollutants (particulate matter with aerodynamic diameter less than 10 μm, PM₁₀, and nitrogen dioxide, NO₂) on cause-specific mortality in the Rome Longitudinal Study, using exposure estimates obtained with different models and spatial resolutions. Annual averages of NO₂ and PM₁₀ were estimated for the year 2015 in a large portion of the Rome urban area (12 × 12 km²) applying three modelling techniques available at increasing spatial resolution: 1) a chemical transport model (CTM) at 1km resolution; 2) a land-use random forest (LURF) approach at 200m resolution; 3) a micro-scale Lagrangian particle dispersion model (PMSS) taking into account the effect of buildings structure at 4 m resolution with results post processed at different buffer sizes (12, 24, 52, 100 and 200 m). All the exposures were assigned at the residential addresses of 482,259 citizens of Rome 30+ years of age who were enrolled on 2001 and followed-up till 2015. The association between annual exposures and natural-cause, cardiovascular (CVD) and respiratory (RESP) mortality were estimated using Cox proportional hazards models adjusted for individual and area-level confounders. We found different distributions of both NO₂ and PM₁₀ concentrations, across models and spatial resolutions. Natural cause and CVD mortality outcomes were all positively associated with NO₂ and PM₁₀ regardless of the model and spatial resolution when using a relative scale of the exposure such as the interquartile range (IQR): adjusted Hazard Ratios (HR), and 95% confidence intervals (CI), of natural cause mortality, per IQR increments in the two pollutants, ranged between 1.012 (1.004, 1.021) and 1.018 (1.007, 1.028) for the different NO₂ estimates, and between 1.010 (1.000, 1.020) and 1.020 (1.008, 1.031) for PM₁₀, with a tendency of larger effect for lower resolution exposures. The latter was even stronger when a fixed value of 10 μg/m³ is used to calculate HRs. Long-term effects of air pollution on mortality in Rome were consistent across different models for exposure assessment, and different spatial resolutions.

Long-term low-level ambient air pollution exposure and risk of lung cancer - A pooled analysis of 7 European cohorts

BACKGROUND/AIM

Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence.

METHODS

The "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), black carbon (BC), and ozone (O₃) to assign exposure to cohort participants' residential addresses in 100 m by 100 m grids. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines.

RESULTS

The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO₂, PM_2.5, BC and O₃ (warm season) were 24.2 µg/m³ (19.5, 29.7), 15.4 µg/m³ (12.8, 17.3), 1.6 10^-5m^-1 (1.3, 1.8), and 86.6 µg/m³ (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM_2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m³). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exposure below the EU limit value of 25 µg/m³. We did not observe associations between NO₂, BC or O₃ and lung cancer incidence.

CONCLUSIONS

Long-term ambient PM_2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines.

Short-term effects of particulate matter on cardiovascular morbidity in Italy: a national analysis

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.

Meta-analysis on short-term exposure to ambient ultrafine particles and respiratory morbidity

Aim

There is growing interest in the health effects following exposure to ambient particles with a diameter <100 nm defined as ultrafine particles (UFPs), although studies so far have reported inconsistent results. We have undertaken a systematic review and meta-analysis for respiratory hospital admissions and emergency room visits following short-term exposure to UFPs.

Methods

We searched PubMed and the Web of Science for studies published up to March 2019 to update previous reviews. We applied fixed- and random-effects models, assessed heterogeneity between cities and explored possible effect modifiers.

Results

We identified nine publications, reporting effects from 15 cities, 11 of which were European. There was great variability in exposure assessment, outcome measures and the exposure lags considered. Our meta-analyses did not support UFP effects on respiratory morbidity across all ages. We found consistent statistically significant associations following lag 2 exposure during the warm period and in cities with mean daily UFP concentrations <6000 particles·cm^‒3, which was approximately the median of the city-specific mean levels. Among children aged 0–14 years, a 10 000 particle·cm^‒3 increase in UFPs 2 or 3 days before was associated with a relative risk of 1.01 (95% CI 1.00–1.02) in respiratory hospital admissions.

Conclusions

Our study indicates UFP effects on respiratory health among children, and during the warm season across all ages at longer lags. The limited evidence and the large heterogeneity of previous reports call for future exposure assessment harmonisation and expanded research.

Studies on short-term exposure to ultrafine particles and respiratory admissions show large variability in the exposure assessment methodology. We found indications of effects in lower concentrations, children and during the warm period of the year.https://bit.ly/2zynMza

A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data

Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO₂, O₃, PM_2.5 and PM₁₀ concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013-2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R² of 0.59, 0.72, 0.76 and 0.75 for NO₂, PM₁₀, PM_2.5 and O₃, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO₂. 50% of the population was estimated to be exposed to NO₂ concentrations between 12 and 38 μg/m³ and PM₁₀ between 20 and 35 μg/m³. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions.

Short-term exposure to PM2.5 and risk of venous thromboembolism: A case-crossover study

BACKGROUND

Short-term exposure to air pollution increases the risk of cardiovascular mortality and morbidity but little evidence is available on pollution effects on venous thromboembolism (VTE), a common vascular disease.

METHODS

We conducted a case-crossover analysis of all urgent hospitalizations for deep vein thrombosis (DVT) or pulmonary embolism (PE) among patients >35 years during the period 2006 to 2017 in Rome (Italy). We examined whether 1) short-term exposure to particulate matter with aerodynamic diameter <2.5 μg (PM_2.5) increases the risk of hospitalization for DVT or PE, and 2) if the associations are modified by the period of the year (warm and cold seasons), sex, age and comorbidity.

RESULTS

We found that short-term exposure to PM_2.5 was associated with an increase of PE hospitalization risk of during the warm season (April to September) of 19.6% (95% confidence intervals: 8.3, 31%) per 10 μg/m³, while no statistically significant effects were displayed during the cold season or the whole year or for DVT hospitalizations. The effect of PM_2.5 remained significant (%change: 21.3; 95%CI: 5.4, 39.5) after adjustment for nitrogen dioxide (NO₂) co-exposure (a marker of traffic sources) and when limiting to primary diagnosis of PE (%change: 19.1; 95%CI: 4.2, 36.1). Age, sex and comorbid conditions did not modify the association.

CONCLUSIONS

Our results suggested a positive association between short-term exposure to PM_2.5 and pulmonary embolism during the warm period of the year while no evidence emerged for deep vein thrombosis.

Long-term exposure to air pollution and hospitalization for dementia in the Rome longitudinal study

BACKGROUND

Few studies have explored the role of air pollution in neurodegenerative processes, especially various types of dementia. Our aim was to evaluate the association between long-term exposure to air pollution and first hospitalization for dementia subtypes in a large administrative cohort.

METHODS

We selected 350,844 subjects (free of dementia) aged 65-100 years at inclusion (21/10/2001) and followed them until 31/12/2013. We selected all subjects hospitalized for the first time with primary or secondary diagnoses of various forms of dementia. We estimated the exposure at residence using land use regression models for nitrogen oxides (NOx, NO₂) and particulate matter (PM) and a chemical transport model for ozone (O₃). We used Cox models to estimate the association between exposure and first hospitalization for dementia and its subtypes: vascular dementia (Vd), Alzheimer's disease (Ad) and senile dementia (Sd).

RESULTS

We selected 21,548 first hospitalizations for dementia (7497 for Vd, 7669 for Ad and 7833 for Sd). Overall, we observed a negative association between exposure to NO₂ (10 μg/m³) and dementia hospitalizations (HR = 0.97; 95% CI: 0.96-0.99) and a positive association between exposure to O₃, NOx and dementia hospitalizations, (O₃: HR = 1.06; 95% CI: 1.04-1.09 per 10 μg/m³; NOx: HR = 1.01; 95% CI: 1.00-1.02 per 20 μg/m³).H. Exposure to NOx, NO₂, PM_2.5, and PM₁₀ was positively associated with Vd and negatively associated with Ad. Hospitalization for Sd was positively associated with exposure to O₃ (HR = 1.20; 95% CI: 1.15-1.24 per 10 μg/m³).

CONCLUSIONS

Our results showed a positive association between exposure to NOx and O₃ and hospitalization for dementia and a negative association between NO₂ exposure and hospitalization for dementia. In the analysis by subtype, exposure to each pollutants (except O₃) demonstrated a positive association with vascular dementia, while O₃ exposure was associated with senile dementia. The results regarding vascular dementia are a clear indication that the brain effects of air pollution are linked with vascular damage.

Long-Term PM10 Exposure and Cause-Specific Mortality in the Latium Region (Italy): A Difference-in-Differences Approach

BACKGROUND

The link between particulate matter (PM) exposure and adverse health outcomes has been widely evaluated using large cohort studies. However, the possibility of residual confounding and lack of information about the health effects of PM in rural and suburban areas are unsolved issues.

OBJECTIVE

Our aim was to estimate the effect of annual PM≤10µg (PM₁₀) exposure on cause-specific mortality in the Latium region (central Italy, of which Rome is the main city) during 2006-2012 using a difference-in-differences approach.

METHODS

We estimated daily PM₁₀ concentrations for each 1 km² of the region from 2006 to 2012 by use of satellite data, land-use predictors, and meteorological parameters. For each of the 378 regional municipalities and each year, we averaged daily PM₁₀ values to obtain annual mean PM₁₀ exposures. We applied a variant of the difference-in-differences approach to estimate the association between PM₁₀ and cause-specific mortality by focusing on within-municipality fluctuations of mortality rates and annual PM exposures around municipality means, therefore controlling by design for confounding from all spatial and temporal potential confounders. Analyses were also stratified by population size of the municipalities to obtain effect estimates in rural and suburban areas of the region.

RESULTS

In the period 2006-2012, we observed deaths due to three causes: 347,699 nonaccidental; 92,787 cardiovascular; and 16,509 respiratory causes. The annual average (standard deviation, SD) PM₁₀ concentration was 21.9 (±4.9) µg/km³ in Latium. For each 1-µg/m³ increase in annual PM₁₀ we estimated increases of 0.8% (95% confidence intervals (CIs): 0.2%, 1.3%), 0.9% (0.0%, 1.8%), and 1.4% (-0.4%, 3.3%) in nonaccidental, cardiovascular, and respiratory mortality, respectively. Similar results were found when we excluded the metropolitan area of Rome from the analysis. Higher effects were estimated in the smaller municipalities, e.g., those with population < 5,000 inhabitants.

CONCLUSION

Our study suggests a significant association of annual PM₁₀ exposure with nonaccidental and cardiorespiratory mortality in the Latium region, even outside Rome and in suburban and rural areas. https://doi.org/10.1289/EHP3759.

Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM₁₀ (PM < 10 μm), fine (PM < 2.5 μm, PM_2.5) and coarse particles (PM between 2.5 and 10 μm, PM_2.5-10) at 1-km² grid for 2013-2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM_2.5 and PM_2.5-10 concentrations in monitors where only PM₁₀ data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km² grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R² of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM₁₀ and PM_2.5, respectively. Model fitting was less optimal for PM_2.5-10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects.

Short-term effects of desert and non-desert PM10 on mortality in Sicily, Italy

BACKGROUND

Increased PM₁₀ concentrations are commonly observed during Saharan dust advections. Limited epidemiological evidence suggests that PM₁₀ from anthropogenic and desert sources increase mortality. We aimed to evaluate the association between source-specific PM₁₀ (non-desert and desert) and cause-specific mortality in Sicily during 2006-2012 period.

METHODS

Daily PM₁₀ concentrations at 1-km² 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 PM₁₀ concentrations. We applied a time-series approach on 390 municipalities of Sicily to estimate the association between PM₁₀ (non-desert and desert) and daily cause-specific mortality.

RESULTS

33% of all days were affected by Saharan dust advections. PM₁₀ concentrations were 8 μg/m³ higher during dust days compared to other days. We found positive associations of both non-desert and desert PM₁₀ 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/m³ increases in non-desert and desert PM₁₀ 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 PM₁₀, respectively.

CONCLUSIONS

Our results confirm previous evidence of harmful effects of desert PM₁₀ on non-accidental and cardio-respiratory mortality, especially during the warm season.

[Validation of algorithms for the identification of subjects with chronic disease using health information systems]

OBJECTIVES

to test the validity of algorithms to identify diabetes, chronic obstructive pulmonary disease (COPD), hypertension, and hypothyroidism from routinely collected health data using information from self-reported diagnosis and laboratory or functional test.

SETTING AND PARTICIPANTS

clinical or self-reported diagnosis from three surveys conducted in Lazio Region (Central Italy) between year 2010 and 2014 were assumed as gold standard and compared to the results of the algorithms application to administrative data.

MAIN OUTCOME MEASURES

prevalence resulted from administrative data and from information available in the surveys were compared. Sensitivity, specificity, positive predictive value, and positive likelihood ratio of algorithms with respect to self-reported diagnosis, laboratory or functional test, assumed as gold standards, were calculated.

RESULTS

we analyzed data of 7,318 subjects (1,545 for diabetes, 1,783 for COPD, 2,448 for hypertension, and 1,542 for hypothyroidism). For hypertension and hypothyroidism, we observed a higher prevalence from laboratory or functional test compared to self-reported diagnosis (54.5% vs. 44.9% and 7.5% vs. 1.5%). Sensitivity of administrative data with respect to self-reported diagnosis resulted 90.9%, 38.5%, 88.3%, and 47.8%, respectively, for diabetes, COPD, hypertension, and hypothyroidism. Respectively, specificity was 97.4%, 91.7%, 84.8% and 91.8%; positive predictive value was 70,9%, 38.1%, 82.6% and 8.1%. All values of positive likelihood ratio resulted moderate (about 5), with exception of the diabetes algorithm and the disease-specific payment exemptions register for hypertension (respectively 35.5 and 17.4).

CONCLUSION

hypertension and hypothyroidism resulted markedly underdiagnosed from self-reported data. Case identification algorithms are highly specific, allowing their utilization for selection of cohort of subject affected by chronic diseases. The sub-optimal sensitivity observed for COPD and hypothyroidism could limit the utilization of the algorithms for prevalence estimation.

Air pollution and occurrence of type 2 diabetes in a large cohort study

The few cohort studies that have investigated the association between exposure to air pollution and occurrence of diabetes have reported conflicting results. We aimed to evaluate the association of long-term exposure to particulate matter (PM), nitrogen oxides (NO_x) and ozone (O₃), with baseline prevalence and incidence of type 2 diabetes in a large administrative cohort in Rome, Italy. A total of 1,425,580 subjects aged 35+years (January 1st, 2008) were assessed and followed for six years. We estimated PM₁₀, PM_2.5-10, PM_2.5, NO₂, and NO_x exposures at residence using land use regression models, and summer O₃ exposure using dispersion modeling. To estimate the association between air pollutant exposures and prevalence and incidence of diabetes, we used logistic and Cox regression models, considering individual, environmental (noise and green areas), and contextual characteristics. We identified 106,387 prevalent cases at baseline and 65,955 incident cases during the follow-up period. We found positive associations between nitrogen oxides exposures and prevalence of diabetes with odds ratios (ORs) up to 1.010 (95% CI: 1.002, 1.017) and 1.015 (1.009, 1.021) for NO₂ and NOx, respectively, per fixed increases (per 10μg/m³ and 20μg/m³, respectively). We also found some evidence of an association between NOx and O₃ and incidence of diabetes, with hazard ratios (HRs) of 1.011 (95%CI: 1.003-1.019) and 1.015 (1.002-1.027) per 20 and 10μg/m³ increases, respectively. The association with O₃ with incident diabetes was stronger in women than in men and among those aged <50years. In sum, long-term exposure to nitrogen oxides was associated with prevalent diabetes while NOx and O₃ exposures were associated with incident diabetes.

Analysis of Temporal Variability in the Short-term Effects of Ambient Air Pollutants on Nonaccidental Mortality in Rome, Italy (1998-2014)

OBJECTIVES

The association between short-term air pollution exposure and daily mortality has been widely investigated, but little is known about the temporal variability of the effect estimates. We examined the temporal relationship between exposure to particulate matter (PM) (PM₁₀, PM_2.5) and gases (NO₂, SO₂, and CO) with mortality in a large metropolitan area over the last 17 y.

METHODS

Our analysis included 359,447 nonaccidental deaths among ≥35-y-old individuals in Rome, Italy, over the study period 1998–2014. We related daily concentrations to mortality counts with a time-series Poisson regression analysis adjusted for long-term trends, meteorology, and population dynamics.

RESULTS

Annual average concentrations decreased over the study period for all pollutants (e.g., from 42.9 to 26.6 μg/m³ for PM₁₀). Each pollutant was positively associated with mortality, with estimated percentage increases over the entire study period ranging from 0.19% (95% CI: 0.13, 0.26) for a 1-Mg/m³ increase in CO (0–1 d lag) to 3.03% (95% CI: 2.44, 3.63) for a 10-μg/m³ increase in NO₂ (0–5 d lag). We did not observe clear temporal patterns in year- or period-specific effect estimates for any pollutant. For example, we estimated that a 10-μg/m³ increase in PM₁₀ was associated with 1.16% (95% CI: 0.53, 1.79), 0.99% (95% CI: 0.23, 1.77), and 1.87% (95% CI: 1.00, 2.74) increases in mortality for the periods 2001–2005, 2006–2010, and 2011–2014, respectively, and corresponding estimates for a 10-μg/m³ increase in NO₂ were 4.20% (95% CI: 3.15, 5.25), 1.78% (95% CI: 0.73, 2.85), and 3.32% (95% CI: 2.03, 4.63).

CONCLUSIONS

Mean concentrations of air pollutants have decreased over the last two decades in Rome, but effect estimates for a fixed increment in each exposure were generally consistent. These findings suggest that there has been little or no change in the overall toxicity of the air pollution mixture over time. https://doi.org/10.1289/EHP19.

[Health effects of Saharan dust in Sicily Region (Southern Italy)]

"OBJECTIVES: to investigate the increase of PM10 during Saharan dust outbreaks with adverse health effects in Sicily (Southern Italy), the largest Mediterranean Island.

DESIGN

pooled analyses of time series with Poisson regression models to estimate the association between PM10 from different sources (desert and non-desert) and different outcomes.

SETTING AND PARTICIPANTS

the four largest cities of Sicily (Palermo, Catania, Syracuse, and Messina) and three macroareas (North- East, South, and West) Sicily was divided into.

MAIN OUTCOME MEASURES

daily count of cause-specific (ICD-9 codes) mortality and hospital admissions: natural (0-799), cardiovascular (390-459), and respiratory causes (460-519).

RESULTS

962 days affected by Saharan dust (30% of all days: 2,257) were identified. Significant associations between desert PM10 and natural mortality both in the cities and in the macro-areas were found, with increases of risk and 95% confidence intervals equal to 1.1% (95%CI 0.1-2.1) and 1.1% (95%CI 0.8-1.5) per 10 μg/m3 increase in lag 0-1 PM10, respectively. Weaker estimates were found for cardiorespiratory mortality. Desert PM10 displayed an association with respiratory hospitalizations, especially in the three macroareas (0.5%; 95%CI 0.1-1.0). In contrast, cardiovascular hospitalizations were associated only with non-desert PM10 in the four cities (1.3%; 95%CI 0.4- 2.1%). Higher desert PM10-related mortality was found during the warmer months (period: April-September): 2.7% (95%CI 0.8-4.5) in the four cities and 2.5% (95%CI 1.8%-3.2%) in the three macroareas. CONCLUSIONS PM10 originating from desert was positively associated with mortality and hospitalizations in Sicily. Policies should aim to reduce anthropogenic emissions even in areas with large contribution from desert sources."