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Summary: Background: High temperatures have been associated with increased mortality, with evidence reported predominately in large cities and for total cardiovascular or respiratory deaths. This case-crossover study examined heat-related cause-specific cardiopulmonary mortality and vulnerability factors using small-area data from Germany. Methods: We analyzed daily counts of cause-specific cardiopulmonary deaths from 380 German districts (2000–2016) and daily mean temperatures estimated by spatial–temporal models. We applied conditional quasi-Poisson regression using distributed lag nonlinear models to examine heat effects during May–September in each district and random-effects meta-analysis to pool the district-specific estimates. Potential individual- and district-level vulnerability factors were examined by subgroup analyses and meta-regressions, respectively. Findings: Heat was associated with increased mortality risks for all cardiopulmonary sub-causes. The relative risk (RR) of total cardiovascular and respiratory mortality for a temperature increment from the 75th to the 99th percentile was 1.24 (95% confidence interval: 1.23, 1.26) and 1.34 (1.30, 1.38), respectively. The RRs of cardiovascular sub-causes ranged from 1.16 (1.13, 1.19) for myocardial infarction to 1.32 (1.29, 1.36) for heart failure. For respiratory sub-causes, the RR was 1.27 (1.22, 1.31) for COPD and 1.49 (1.42, 1.57) for pneumonia. We observed greater susceptibility related to several individual- and district-level characteristics, e.g., among females or in highly urbanized districts. Heat vulnerability factors remained consistent between urban and rural areas. Interpretation: Our study highlights heat-related increases in cause-specific cardiopulmonary mortality across Germany and identifies key vulnerability factors, offering insights for improving public health practices to mitigate heat-related health impacts. Funding: European Union's Horizon 2020 research and innovation program; Helmholtz Associations Initiative and Networking Fund.

Introduction: A causal link between air pollution exposure and cardiovascular events has been suggested. However fewer studies have investigated the shape of the associations at low levels of air pollution and identified the most important temporal window of exposure. Here we assessed long-term associations between particulate matter 40) from the UK Biobank cohort, we investigated the associations between 1-year, 3-year and 5-year time-varying averages of PM2.5 and incidence of major adverse cardiovascular events (MACE), myocardial infarction (MI), heart failure, atrial fibrillation and flutter and cardiac arrest. We also investigated outcome subtypes for MI and stroke. Events were defined as hospital inpatient admissions. We fitted Cox proportional hazard regression models applying extensive control for confounding at both individual and area level. Finally, we assessed the shape of the exposure–response functions to assess effects at low levels of exposure. Results: We analysed data from 377,736 study participants after exclusion of prevalent subjects. The average follow-up (2006–2021) was 12.9 years. We detected 19,353 cases of MACE, 6,562 of acute MI, 6,278 of heart failure, 1,258 for atrial fibrillation and flutter, and 16,327 for cardiac arrest. Using a 5-year exposure window, we detected positive associations (for 5 μg/m3 increase in PM2.5) for 5-point MACE of [1.12 (95 %CI: 1.00–1.26)], heart failure [1.22 (1.00–1.50)] and cardiac arrest [1.16 (1.03–1.31)]. We did not find any association with acute MI, while non-ST-elevation MI was associated with the 1-year exposure window [1.52 (1.12–2.07)]. The assessment of the shape of the exposure–response relationships suggested that risk is approximately linear for most of the outcomes. Conclusions: We found positive associations between long-term exposure to PM2.5 and multiple cardiovascular outcomes for different exposure windows. The cardiovascular risk tends to rise even at exposure concentrations below 12–15 μg/m3, indicating high risk below UK national and international thresholds.

This study aimed to examine acute effects of exposure to ambient air pollution on COVID-19 hospital admissions and mortality in the Netherlands. We hypothesized that exposure to increased air pollution in the preceding week might trigger an exacerbation of health of infected individuals.Associations between daily concentrations of particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5) and ≤10 µm (PM10), nitrogen dioxide (NO2), ozone (O3) and risk of hospital admissions and mortality due to COVID-19 from February to December 2020 was analyzed across all 352 Dutch municipalities grouped into 12 provinces. Time-series models were used to fit province-specific estimates, followed by meta-analyses to produce national estimates. Analyses were based on daily averages of PM2.5, PM10, NO2, and maximum 8-hour running average of O3 on a 1×1 km grid and averaged on municipality level by population weight. Models were adjusted for spatiotemporal confounders, including government policies in response to the number of COVID-19 infections. Since there were only few COVID-19 cases during the summertime when O3 levels were highest, associations between O3 and COVID-19 health outcomes were not further explored.We found associations between exposure to air pollution in the preceding week (average of lag 0-7 days) and COVID-19 hospital admissions and mortality. On a national level, an interquartile range increase in PM2.5, PM10 and NO2 exposure was associated with 11-12% increased mortality risk; the risk for hospital admissions was higher: 19-25%. Observed associations were more robust for PM than NO2 in two-pollutant models.Our results suggest that short-term exposure to PM2.5 and PM10 may increase the risk of COVID-19 mortality and hospital admission. This indicates that, consistent with previous studies on air pollution and respiratory infections, the population at risk of being hospitalized or dying of COVID-19 is extra vulnerable to the adverse effects of short-term air pollution exposure.

Introduction Traffic-related air and noise pollution are important public health issues. The aim of this study was to estimate their effects on allergic/respiratory outcomes in adult and elderly subjects.Materials and methods Six hundred and forty-five subjects living in Pisa (Tuscany, Italy) were investigated through a questionnaire on allergic/respiratory symptoms and diseases. Traffic-related air pollution and noise exposures were assessed at residential address by questionnaire, modelled annual mean NO2 concentrations (1 km and 200 m resolution), and noise level over a 24-h period (Lden). Exposure effects were assessed through logistic regression models stratified by age group (18–64 years, ≥65 years), and adjusted for sex, educational level, occupational exposure, and smoking habits.Results 63.6% of the subjects reported traffic exposure near home. Mean exposure levels were: 28.24 (±3.26 SD) and 27.23 (±3.16 SD) µg/m3 for NO2 at 200 m and 1 km of resolution, respectively; 57.79 dB(A) (±6.12 SD) for Lden. Exposure to vehicular traffic (by questionnaire) and to high noise levels [Lden ≥ 60 dB(A)] were significantly associated with higher odds of allergic rhinitis (OR 2.01, 95%CI 1.09–3.70, and OR 1.99, 95%CI 1.18–3.36, respectively) and borderline with rhino-conjunctivitis (OR 2.20, 95%CI 0.95–5.10, and OR 1.76, 95%CI 0.91–3.42, respectively) only in the elderly. No significant result emerged for NO2.Conclusions Our findings highlighted the need to better assess the effect of traffic-related exposure in the elderly, considering the increasing trend in the future global population’s ageing.

Over the last decades, the environmental burden borne by natural ecosystems to sustain life in urban areas has considerably increased. For this reason, the network of interconnected green areas and features, referred to as Urban Green Infrastructure, is increasingly being promoted as a valuable means to enhance the life quality of city dwellers by providing Ecosystem Services.We present a spatial analysis of Ecosystem Services provision and synergies/trade-offs along the urban–rural gradient for the following Italian coastal Metropolitan cities: Genoa, Rome, Bari, Naples, Cagliari, Reggio Calabria and Palermo. We focused our attention on three macro classes of human-induced environmental pressures that are particularly critical to the quality of life in urban contexts, namely air pollution, Urban Heat Island effect, and alteration of the water cycle.We found that all of the selected Ecosystem Services, besides a few exceptions, are provided as a bundle, which means they appear together repeatedly, forming an association. On average, the Ecosystem Services provision grows at comparable paces moving from the inner urban core towards the peri-urban and rural areas. As a consequence, most densely urbanized areas were found to be Ecosystem Services coldspots compared to peri-urban and rural areas.

Abstract Background The independent effects of short-term exposure to increased air temperature and air pollution on mortality are well-documented. There is some evidence indicating that elevated concentrations of air pollutants may lead to increased heat-related mortality, but this evidence is not consistent. Most of these effects have been documented through time-series studies using city-wide data, rather than at a finer spatial level. In our study, we examined the possible modification of the heat effects on total and cause-specific mortality by air pollution at municipality level in the Attica region, Greece, during the warm period of the years 2000 to 2016. Methods A municipality-specific over-dispersed Poisson regression model during the warm season (May–September) was used to investigate the heat effects on mortality and their modification by air pollution. We used the two-day average of the daily mean temperature and daily mean PM10, NO2 and 8 hour-max ozone (O3), derived from models, in each municipality as exposures. A bivariate tensor smoother was applied for temperature and each pollutant alternatively, by municipality. Α random-effects meta-analysis was used to obtain pooled estimates of the heat effects at different pollution levels. Heterogeneity of the between-levels differences of the heat effects was evaluated with a Q-test. Results A rise in mean temperature from the 75th to the 99th percentile of the municipality-specific temperature distribution resulted in an increase in total mortality of 12.4% (95% Confidence Interval (CI):7.76–17.24) on low PM10 days, and 21.25% (95% CI: 17.83–24.76) on high PM10 days. The increase on mortality was 10.09% (95% CI: − 5.62- 28.41) on low ozone days, and 14.95% (95% CI: 10.79–19.27) on high ozone days. For cause-specific mortality an increasing trend of the heat effects with increasing PM10 and ozone levels was also observed. An inconsistent pattern was observed for the modification of the heat effects by NO2, with higher heat effects estimated in the lower level of the pollutant. Conclusions Our results support the evidence of elevated heat effects on mortality at higher levels of PM10 and 8 h max O3. Under climate change, any policy targeted at lowering air pollution levels will yield significant public health benefits.

BackgroundMany studies reported associations between long-term exposure to environmental factors and mortality; however, little is known on the combined effects of these factors and health. We aimed to evaluate the association between external exposome and all-cause mortality in large administrative and traditional adult cohorts in Europe.MethodsData from six administrative cohorts (Catalonia, Greece, Rome, Sweden, Switzerland and the Netherlands, totaling 27,913,545 subjects) and three traditional adult cohorts (CEANS-Sweden, EPIC-NL-the Netherlands, KORA–Germany, totaling 57,653 participants) were included. Multiple exposures were assigned at the residential addresses, and were divided into three a priori defined domains: (1) air pollution [fine particulate matter (PM2.5), nitrogen dioxide (NO₂), black carbon (BC) and warm-season Ozone (warm-O3)]; (2) land/built environment (Normalized Difference Vegetation Index—NDVI, impervious surfaces, and distance to water); (3) air temperature (cold- and warm-season mean and standard deviation). Each domain was synthesized through Principal Component Analysis (PCA), with the aim of explaining at least 80% of its variability. Cox proportional-hazards regression models were applied and the total risk of the external exposome was estimated through the Cumulative Risk Index (CRI). The estimates were adjusted for individual- and area-level covariates.ResultsMore than 205 million person-years at risk and more than 3.2 million deaths were analyzed. In single-component models, IQR increases of the first principal component of the air pollution domain were associated with higher mortality [HRs ranging from 1.011 (95% CI: 1.005–1.018) for the Rome cohort to 1.076 (1.071–1.081) for the Swedish cohort]. In contrast, lower levels of the first principal component of the land/built environment domain, pointing to reduced vegetation and higher percentage of impervious surfaces, were associated with higher risks. Finally, the CRI of external exposome increased mortality for almost all cohorts. The associations found in the traditional adult cohorts were generally consistent with the results from the administrative ones, albeit without reaching statistical significance.DiscussionVarious components of the external exposome, analyzed individually or in combination, were associated with increased mortality across European cohorts. This sets the stage for future research on the connections between various exposure patterns and human health, aiding in the planning of healthier cities.

COVID-19 lockdowns reduced nitrogen dioxide (NO2) and fine particulate matter (PM2.5) emissions in many countries. We aim to quantify the changes in these pollutants and to assess the attributable changes in mortality in Jiangsu, China; California, U.S.; Central-southern Italy; and Germany during COVID-19 lockdowns in early 2020. Accounting for meteorological impacts and air pollution time trends, we use a machine learning-based meteorological normalization technique and the difference-in-differences approach to quantify the changes in NO2 and PM2.5 concentrations due to lockdowns. Using region-specific estimates of the association between air pollution and mortality derived from a causal modeling approach using data from 2015 to 2019, we assess the changes in mortality attributable to the air pollution changes caused by the lockdowns in early 2020. During the lockdowns, NO2 reductions avoided 1.41 (95% empirical confidence interval [eCI]: 0.94, 1.88), 0.44 (95% eCI: 0.17, 0.71), and 4.66 (95% eCI: 2.03, 7.44) deaths per 100,000 people in Jiangsu, China; California, U.S.; and Central-southern Italy, respectively. Mortality benefits attributable to PM2.5 reductions were also significant, albeit of a smaller magnitude. For Germany, the mortality benefits attributable to NO2 changes were not significant (0.11; 95% eCI: −0.03, 0.25), and an increase in PM2.5 concentrations was associated with an increase in mortality of 0.35 (95% eCI: 0.22, 0.48) deaths per 100,000 people during the lockdown. COVID-19 lockdowns overall improved air quality and brought attributable health benefits, especially associated with NO2 improvements, with notable heterogeneity across regions. This study underscores the importance of accounting for local characteristics when policymakers adapt successful emission control strategies from other regions.

Background: Heatwaves are expected to increase with climate change, posing a significant threat to population health. In India, with the world’s largest population, heatwaves occur annually but have not been comprehensively studied. Accordingly, we evaluated the association between heatwaves and all-cause mortality and quantifying the attributable mortality fraction in India. Methods: We obtained all-cause mortality counts for ten cities in India (2008–2019) and estimated daily mean temperatures from satellite data. Our main extreme heatwave was defined as two-consecutive days with an intensity above the 97th annual percentile. We estimated city-specific heatwave associations through generalised additive Poisson regression models, and meta-analysed the associations. We reported effects as the percentage change in daily mortality, with 95% confidence intervals (CI), comparing heatwave vs non-heatwave days. We further evaluated heatwaves using different percentiles (95th, 97th, 99th) for one, two, three and five-consecutive days. We also evaluated the influence of heatwave duration, intensity and timing in the summer season on heatwave mortality, and estimated the number of heatwave-related deaths. Findings: Among ∼ 3.6 million deaths, we observed that temperatures above 97th percentile for 2-consecutive days was associated with a 14.7 % (95 %CI, 10.3; 19.3) increase in daily mortality. Alternative heatwave definitions with higher percentiles and longer duration resulted in stronger relative risks. Furthermore, we observed stronger associations between heatwaves and mortality with higher heatwave intensity. We estimated that around 1116 deaths annually (95 %CI, 861; 1361) were attributed to heatwaves. Shorter and less intense definitions of heatwaves resulted in a higher estimated burden of heatwave-related deaths. Conclusions: We found strong evidence of heatwave impacts on daily mortality. Longer and more intense heatwaves were linked to an increased mortality risk, however, resulted in a lower burden of heatwave-related deaths. Both definitions and the burden associated with each heatwave definition should be incorporated into planning and decision-making processes for policymakers.

BackgroundWe evaluated the independent and joint effects of air pollution, land/built environment characteristics, and ambient temperature on all-cause mortality as part of the EXPANSE project.MethodsWe collected data from six administrative cohorts covering Catalonia, Greece, the Netherlands, Rome, Sweden, and Switzerland and three traditional cohorts in Sweden, the Netherlands, and Germany. Participants were linked to spatial exposure estimates derived from hybrid land use regression models and satellite data for: air pollution [fine particulate matter (PM2.5), nitrogen dioxide (NO₂), black carbon (BC), warm season ozone (O3)], land/built environment [normalized difference vegetation index (NDVI), distance to water, impervious surfaces], and ambient temperature (the mean and standard deviation of warm and cool season temperature). We applied Cox proportional hazard models accounting for several cohort-specific individual and area-level variables. We evaluated the associations through single and multiexposure models, and interactions between exposures. The joint effects were estimated using the cumulative risk index (CRI). Cohort-specific hazard ratios (HR) were combined using random-effects meta-analyses.ResultsWe observed over 3.1 million deaths out of approximately 204 million person-years. In administrative cohorts, increased exposure to PM2.5, NO2, and BC was significantly associated with all-cause mortality (pooled HRs: 1.054, 1.033, and 1.032, respectively). We observed an adverse effect of increased impervious surface and mean season-specific temperature, and a protective effect of increased O3, NDVI, distance to water, and temperature variation on all-cause mortality. The effects of PM2.5 were higher in areas with lower (10th percentile) compared to higher (90th percentile) NDVI levels [pooled HRs: 1.054 (95% confidence interval (CI) 1.030–1.079) vs. 1.038 (95% CI 0.964–1.118)]. A similar pattern was observed for NO2. The CRI of air pollutants (PM2.5 or NO2) plus NDVI and mean warm season temperature resulted in a stronger effect compared to single-exposure HRs: [PM2.5 pooled HR: 1.061 (95% CI 1.021–1.102); NO2 pooled HR: 1.041 (95% CI 1.025–1.057)]. Non-significant effects of similar patterns were observed in traditional cohorts.DiscussionThe findings of our study not only support the independent effects of long-term exposure to air pollution and greenness, but also highlight the increased effect when interplaying with other environmental exposures.

Background: Evidence available on the associations between urban greenness and mental health is mainly based on cross-sectional studies and has relied on 2D indicators of greenness. This longitudinal study aimed at investigating the association between 2D and 3D indicators of green and grey spaces and incident mental health-related outcomes in a large population-based cohort. Methods: Our study used data from 593,894 Italian adults (≥30 years) from the Rome Longitudinal Study. Mental health outcomes were defined using either drug prescriptions (antidepressants, antipsychotics, lithium and other mood stabilisers, and anxiolytics, hypnotics and sedatives), or hospitalisation records (for schizophrenia spectrum disorder, depression, anxiety, stress-related and somatoform, or substance use disorders). We obtained 2D and 3D indicators of green and grey exposures including Normalized Difference Vegetation Index (NDVI), green volume, grey volume, number of trees, and Normalized Difference Green-Grey Volume Index around participants’ homes. Cox proportional hazards regression models were developed to estimate the association of green and grey space exposure and psychiatric conditions and medicine use, adjusted for relevant covariates. Results: We found beneficial associations of NDVI and the number of trees with antipsychotic and lithium and other mood stabiliser drugs. We also observed detrimental associations between grey volume and lithium and other mood stabilisers and anxiolytic, hypnotic and sedative drugs. Finally, we found a protective association of the NDGG with lithium and other mood stabilisers (HR: 0.977; 95% CI: 0.965–0.990) and anxiolytic, hypnotic and sedative drugs (HR: 0.851; 95% CI: 0.762–0.950). The associations for hospitalisation for psychiatric conditions were less consistent and generally not statistically significant. Conclusions: Findings suggested that higher greenness areas around residential addresses are associated with reduced use of drugs for psychiatric conditions, while the opposite is true for higher grey space exposure. The study highlights the importance of accurately characterising green and grey spaces, using novel exposure indicators.

Abstract Background Long-term exposure to air pollution and noise is detrimental to health; but studies that evaluated both remain limited. This study explores associations with natural and cause-specific mortality for a range of air pollutants and transportation noise. Methods Over 4 million adults in Switzerland were followed from 2000 to 2014. Exposure to PM2.5, PM2.5 components (Cu, Fe, S and Zn), NO2, black carbon (BC) and ozone (O3) from European models, and transportation noise from source-specific Swiss models, were assigned at baseline home addresses. Cox proportional hazards models, adjusted for individual and area-level covariates, were used to evaluate associations with each exposure and death from natural, cardiovascular (CVD) or non-malignant respiratory disease. Analyses included single and two exposure models, and subset analysis to study lower exposure ranges. Results During follow-up, 661,534 individuals died of natural causes (36.6% CVD, 6.6% respiratory). All exposures including the PM2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m3 PM2.5, 1.050 (1.041, 1.059) per 10 µg/m3 NO2, 1.057 (1.048, 1.067) per 0.5 × 10–5/m BC and 1.045 (1.040, 1.049) per 10 dB Lden total transportation noise. NO2, BC, Cu, Fe and noise were consistently associated with CVD and respiratory mortality, whereas PM2.5 was only associated with CVD mortality. Natural mortality associations persisted

Background: Recent epidemiological evidence suggests associations between air pollution exposure and major depressive disorders, but the literature is inconsistent for other mental illnesses. We investigated the associations of several air pollutants and road traffic noise with the incidence of different categories of mental disorders in a large population-based cohort. Methods: We enrolled 1,739,277 individuals 30 + years from the 2011 census in Rome, Italy, and followed them up until 2019. In detail, we analyzed 1,733,331 participants (mean age 56.43 +/- 15.85 years; 54.96 % female) with complete information on covariates of interest. We excluded subjects with prevalent mental disorders at baseline to evaluate the incidence (first hospitalization or co-pay exemption) of schizophrenia spectrum disorders, bipolar, anxiety, personality, or substance use disorders. In addition, we studied subjects with first prescriptions of antipsychotics, antidepressants, and mood stabilizers. Annual average concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO₂), Black Carbon (BC), ultrafine particles (UFP), and road traffic noise were assigned to baseline residential addresses. We applied Cox regression models adjusted for individual and area-level covariates. Results: Each interquartile range (1.13 µg/m3) increase in PM2.5 was associated with a hazard ratio (HR) of 1.070 (95 % confidence interval [CI]: 1.017, 1.127) for schizophrenia spectrum disorder, 1.135 (CI: 1.086, 1.186) for depression, 1.097 (CI: 1.030, 1.168) for anxiety disorders. Positive associations were also detected for BC and UFP, and with the three categories of drug prescriptions. Bipolar, personality, and substance use disorders did not show clear associations. The effects were highest in the age group 30–64 years, except for depression. Conclusions: Long-term exposure to ambient air pollution, especially fine and ultrafine particles, was associated with increased risks of schizophrenia spectrum disorder, depression, and anxiety disorders. The association of the pollutants with the prescriptions of specific drugs increases the credibility of the results.

Background Air pollution is one of the main risk factors for cardiovascular disease globally, but its association with out‐of‐hospital cardiac arrest at low air pollution levels is unclear. This nationwide study in Sweden aims to investigate if air pollution is associated with a higher risk of out‐of‐hospital cardiac arrest in an area with relatively low air pollution levels. Methods and Results This study was a nationwide time‐stratified case‐crossover study investigating the association between short‐term air pollution exposures and out‐of‐hospital cardiac arrest using data from the SRCR (Swedish Registry for Cardiopulmonary Resuscitation) between 2009 and 2019. Daily air pollution levels were estimated in 1×1‐km grids for all of Sweden using a satellite‐based machine learning model. The association between daily air pollutant levels and out‐of‐hospital cardiac arrest was quantified using conditional logistic regression adjusted for daily air temperature. Particulate matter

Background: The epidemiological evidence on the interaction between heat and ambient air pollution on mortality is still inconsistent. Objectives: To investigate the interaction between heat and ambient air pollution on daily mortality in a large dataset of 620 cities from 36 countries. Methods: We used daily data on all-cause mortality, air temperature, particulate matter ≤ 10 μm (PM10), PM ≤ 2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) from 620 cities in 36 countries in the period 1995–2020. We restricted the analysis to the six consecutive warmest months in each city. City-specific data were analysed with over-dispersed Poisson regression models, followed by a multilevel random-effects meta-analysis. The joint association between air temperature and air pollutants was modelled with product terms between non-linear functions for air temperature and linear functions for air pollutants. Results: We analyzed 22,630,598 deaths. An increase in mean temperature from the 75th to the 99th percentile of city-specific distributions was associated with an average 8.9 % (95 % confidence interval: 7.1 %, 10.7 %) mortality increment, ranging between 5.3 % (3.8 %, 6.9 %) and 12.8 % (8.7 %, 17.0 %), when daily PM10 was equal to 10 or 90 μg/m3, respectively. Corresponding estimates when daily O3 concentrations were 40 or 160 μg/m3 were 2.9 % (1.1 %, 4.7 %) and 12.5 % (6.9 %, 18.5 %), respectively. Similarly, a 10 μg/m3 increment in PM10 was associated with a 0.54 % (0.10 %, 0.98 %) and 1.21 % (0.69 %, 1.72 %) increase in mortality when daily air temperature was set to the 1st and 99th city-specific percentiles, respectively. Corresponding mortality estimate for O3 across these temperature percentiles were 0.00 % (-0.44 %, 0.44 %) and 0.53 % (0.38 %, 0.68 %). Similar effect modification results, although slightly weaker, were found for PM2.5 and NO2. Conclusions: Suggestive evidence of effect modification between air temperature and air pollutants on mortality during the warm period was found in a global dataset of 620 cities.

Background: Short-term associations between heat and cardiovascular disease (CVD) mortality have been examined mostly in large cities. However, different vulnerability and exposure levels may contribute to spatial heterogeneity. This study assessed heat effects on CVD mortality and potential vulnerability factors using data from three European countries, including urban and rural settings. Methods: We collected daily counts of CVD deaths aggregated at the small-area level in Norway (small-area level: municipality), England and Wales (lower super output areas), and Germany (district) during the warm season (May-September) from 1996 to 2018. Daily mean air temperatures estimated by spatial–temporal models were assigned to each small area. Within each country, we applied area-specific Quasi-Poisson regression using distributed lag nonlinear models to examine the heat effects at lag 0–1 days. The area-specific estimates were pooled by random-effects meta-analysis to derive country-specific and overall heat effects. We examined individual- and area-level heat vulnerability factors by subgroup analyses and meta-regression, respectively. Results: We included 2.84 million CVD deaths in analyses. For an increase in temperature from the 75th to the 99th percentile, the pooled relative risk (RR) for CVD mortality was 1.14 (95% CI: 1.03, 1.26), with the country-specific RRs ranging from 1.04 (1.00, 1.09) in Norway to 1.24 (1.23, 1.26) in Germany. Heat effects were stronger among women [RRs (95% CIs) for women and men: 1.18 (1.08, 1.28) vs. 1.12 (1.00, 1.24)]. Greater heat vulnerability was observed in areas with high population density, high degree of urbanization, low green coverage, and high levels of fine particulate matter. Conclusion: This study provides evidence for the heat effects on CVD mortality in European countries using high-resolution data from both urban and rural areas. Besides, we identified individual- and area-level heat vulnerability factors. Our findings may facilitate the development of heat-health action plans to increase resilience to climate change.

Introduction: The complex interplay of multiple environmental factors and cardiovascular has scarcely been studied. Within the EXPANSE project, we evaluated the association between long-term exposure to multiple environmental indices and stroke incidence across Europe. Methods: Participants from three traditional adult cohorts (Germany, Netherlands and Sweden) and four administrative cohorts (Catalonia [region Spain], Rome [city-wide], Greece and Sweden [nationwide]) were followed until incident stroke, death, migration, loss of follow-up or study end. We estimated exposures at residential addresses from different exposure domains: air pollution (nitrogen dioxide (NO2), particulate matter

Objectives: We explored temporal variations in disease burden of ambient particulate matter 2.5 μm or less in diameter (PM2.5) and ozone in Italy using estimates from the Global Burden of Disease Study 2019.Methods: We compared temporal changes and percent variations (95% Uncertainty Intervals [95% UI]) in rates of disability adjusted life years (DALYs), years of life lost, years lived with disability and mortality from 1990 to 2019, and variations in pollutant-attributable burden with those in the overall burden of each PM2.5- and ozone-related disease.Results: In 2019, 467,000 DALYs (95% UI: 371,000, 570,000) were attributable to PM2.5 and 39,600 (95% UI: 18,300, 61,500) to ozone. The crude DALY rate attributable to PM2.5 decreased by 47.9% (95% UI: 10.3, 65.4) from 1990 to 2019. For ozone, it declined by 37.0% (95% UI: 28.9, 44.5) during 1990–2010, but it increased by 44.8% (95% UI: 35.5, 56.3) during 2010–2019. Age-standardized rates declined more than crude ones.Conclusion: In Italy, the burden of ambient PM2.5 (but not of ozone) significantly decreased, even in concurrence with population ageing. Results suggest a positive impact of air quality regulations, fostering further regulatory efforts.

Background: Few comparisons between causal inference and traditional approaches have been performed. We applied “causal” and “traditional” methods to investigate the association between long-term air pollution exposure (PM2.5 and NO2) and mortality. Methods: We analyzed pooled data from eight well-characterized cohorts and one administrative cohort. We defined the generalized propensity score (GPS) as the conditional likelihood of exposure given confounders, and derived corresponding inverse-probability weights (IPW). We applied Cox-proportional hazard models weighted by IPW, adjusted for GPS, and directly adjusting for all confounders. Results: In IPW models, PM2.5 5 µg/m3 increases were associated with hazard ratios (HR) = 1.141 (95% confidence interval (CI): 1.107, 1.176) and 1.050 (1.014, 1.088) in the pooled and administrative cohorts. Corresponding estimates for traditional Cox models were 1.132 (1.107, 1.158) and 1.057 (1.025, 1.089). Almost identical results were found for all approaches and both pollutants, when unbalanced covariates were adjusted for in causal models. Conclusions: Traditional and causal approaches provided consistent associations between long-term exposure to air pollution and mortality.

Background: Evidence on the potential interactive effects of heat and ambient air pollution on cause-specific mortality is inconclusive and limited to selected locations. Objectives: We investigated the effects of heat on cardiovascular and respiratory mortality and its modification by air pollution during summer months (six consecutive hottest months) in 482 locations across 24 countries. Methods: Location-specific daily death counts and exposure data (e.g., particulate matter with diameters ≤ 2.5 µm [PM2.5]) were obtained from 2000 to 2018. We used location-specific confounder-adjusted Quasi-Poisson regression with a tensor product between air temperature and the air pollutant. We extracted heat effects at low, medium, and high levels of pollutants, defined as the 5th, 50th, and 95th percentile of the location-specific pollutant concentrations. Country-specific and overall estimates were derived using a random-effects multilevel meta-analytical model. Results: Heat was associated with increased cardiorespiratory mortality. Moreover, the heat effects were modified by elevated levels of all air pollutants in most locations, with stronger effects for respiratory than cardiovascular mortality. For example, the percent increase in respiratory mortality per increase in the 2-day average summer temperature from the 75th to the 99th percentile was 7.7% (95% Confidence Interval [CI] 7.6–7.7), 11.3% (95%CI 11.2–11.3), and 14.3% (95% CI 14.1–14.5) at low, medium, and high levels of PM2.5, respectively. Similarly, cardiovascular mortality increased by 1.6 (95%CI 1.5–1.6), 5.1 (95%CI 5.1–5.2), and 8.7 (95%CI 8.7–8.8) at low, medium, and high levels of O3, respectively. Discussion: We observed considerable modification of the heat effects on cardiovascular and respiratory mortality by elevated levels of air pollutants. Therefore, mitigation measures following the new WHO Air Quality Guidelines are crucial to enhance better health and promote sustainable development.

Abstract Desert dust and sandstorms are recurring environmental phenomena that are reported to produce serious health risks worldwide. This scoping review was conducted to identify the most likely health effects of desert dust and sandstorms and the methods used to characterize desert dust exposure from the existing epidemiological literature. We systematically searched PubMed/MEDLINE, Web of Science, and Scopus to identify studies that reported the effects of desert dust and sandstorms on human health. Search terms referred to desert dust or sandstorm exposure, names of major deserts, and health outcomes. Health effects were cross‐tabulated with study design variables (e.g., epidemiological design and methods to quantify dust exposure), desert dust source, health outcomes and conditions. We identified 204 studies that met the inclusion criteria for the scoping review. More than half of the studies (52.9%) used a time‐series study design. However, we found a substantial variation in the methods used to identify and quantify desert dust exposure. The binary metric of dust exposure was more frequently used than the continuous metric for all desert dust source locations. Most studies (84.8%) reported significant associations between desert dust and adverse health effects, mainly for respiratory and cardiovascular mortality and morbidity causes. Although there is a large body of evidence on the health effects of desert dust and sandstorms, the existing epidemiological studies have significant limitations related to exposure measurement and statistical analysis that potentially contribute to inconsistencies in determining the effect of desert dust on human health.

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

Abstract 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 PM2.5) 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 PM2.5 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) 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.

Background: The link between exposure to ambient air pollution and mortality from cardiorespiratory diseases is well established, while evidence on neurodegenerative disorders including Parkinson’s Disease (PD) remains limited. Objective: We examined the association between long-term exposure to ambient air pollution and PD mortality in seven European cohorts. Methods: Within the project ‘Effects of Low-Level Air Pollution: A Study in Europe’ (ELAPSE), we pooled data from seven cohorts among six European countries. Annual mean residential concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC), and ozone (O3), as well as 8 PM2.5 components (copper, iron, potassium, nickel, sulphur, silicon, vanadium, zinc), for 2010 were estimated using Europe-wide hybrid land use regression models. PD mortality was defined as underlying cause of death being either PD, secondary Parkinsonism, or dementia in PD. We applied Cox proportional hazard models to investigate the associations between air pollution and PD mortality, adjusting for potential confounders. Results: Of 271,720 cohort participants, 381 died from PD during 19.7 years of follow-up. In single-pollutant analyses, we observed positive associations between PD mortality and PM2.5 (hazard ratio per 5 µg/m3: 1.25; 95% confidence interval: 1.01–1.55), NO2 (1.13; 0.95–1.34 per 10 µg/m3), and BC (1.12; 0.94–1.34 per 0.5 × 10-5m-1), and a negative association with O3 (0.74; 0.58–0.94 per 10 µg/m3). Associations of PM2.5, NO2, and BC with PD mortality were linear without apparent lower thresholds. In two-pollutant models, associations with PM2.5 remained robust when adjusted for NO2 (1.24; 0.95–1.62) or BC (1.28; 0.96–1.71), whereas associations with NO2 or BC attenuated to null. O3 associations remained negative, but no longer statistically significant in models with PM2.5. We detected suggestive positive associations with the potassium component of PM2.5. Conclusion: Long-term exposure to PM2.5, at levels well below current EU air pollution limit values, may contribute to PD mortality.

Ambient air pollution is an established risk factor for premature mortality from chronic cardiovascular, respiratory and metabolic diseases, while evidence on neurodegenerative diseases and psychiatric disorders remains limited. We examined the association between long-term exposure to air pollution and mortality from dementia, psychiatric disorders, and suicide in seven European cohorts. Within the multicenter project ‘Effects of Low-Level Air Pollution: A Study in Europe’ (ELAPSE), we pooled data from seven European cohorts from six countries. Based on the residential addresses, annual mean levels of fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC), ozone (O3), and 8 PM2.5 components were estimated using Europe-wide hybrid land-use regression models. We applied stratified Cox proportional hazard models to investigate the associations between air pollution and mortality from dementia, psychiatric disorders, and suicide. Of 271,720 participants, 900 died from dementia, 241 from psychiatric disorders, and 164 from suicide, during a mean follow-up of 19.7 years. In fully adjusted models, we observed positive associations of NO2 (hazard ratio [HR] = 1.38; 95 % confidence interval [CI]: 1.13, 1.70 per 10 µg/m3), PM2.5 (HR = 1.29; 95 % CI: 0.98, 1.71 per 5 µg/m3), and BC (HR = 1.37; 95 % CI: 1.11, 1.69 per 0.5 × 10−5/m) with psychiatric disorders mortality, as well as with suicide (NO2: HR = 1.13 [95 % CI: 0.92, 1.38]; PM2.5: HR = 1.19 [95 % CI: 0.76, 1.87]; BC: HR = 1.08 [95 % CI: 0.87, 1.35]), and no association with dementia mortality. We did not detect any positive associations of O3 and 8 PM2.5 components with any of the three mortality outcomes. Long-term exposure to NO2, PM2.5, and BC may lead to premature mortality from psychiatric disorders and suicide.

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.

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 (PM2.5), 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 PM2.5 (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 PM2.5 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 PM2.5, NO2, and/or BC in Denmark were associated with mortality beyond cardiorespiratory diseases, including diabetes, dementia, psychiatric disorders, asthma, and ALRI.

Abstract Background Although sex differences in cardiovascular diseases are recognised, including differences in incidence, clinical presentation, response to treatments, and outcomes, most of the practice guidelines are not sex-specific. Heart failure (HF) is a major public health challenge, with high health care expenditures, high prevalence, and poor clinical outcomes. The objective was to analyse the sex-specific association of socio-demographics, life-style factors and health characteristics with the prevalence of HF and diastolic left ventricular dysfunction (DLVD) in a cross-sectional population-based study. Methods A random sample of 2001 65–84 year-olds underwent physical examination, laboratory measurements, including N-terminal pro-B-type natriuretic peptide (NT-proBNP), electrocardiography, and echocardiography. We selected the subjects with no missing values in covariates and echocardiographic parameters and performed a complete case analysis. Sex-specific multivariable logistic regression models were used to identify the factors associated with the prevalence of the diseases, multinomial logistic regression was used to investigate the factors associated to asymptomatic and symptomatic LVD, and spline curves to display the relationship between the conditions and both age and NT-proBNP. Results In 857 men included, there were 66 cases of HF and 408 cases of DLVD (77% not reporting symptoms). In 819 women, there were 51 cases of HF and 382 of DLVD (79% not reporting symptoms). In men, the factors associated with prevalence of HF were age, ischemic heart disease (IHD), and suffering from three or more comorbid conditions. In women, the factors associated with HF were age, lifestyles (smoking and alcohol), BMI, hypertension, and atrial fibrillation. Age and diabetes were associated to asymptomatic DLVD in both genders. NT-proBNP levels were more strongly associated with HF in men than in women. Conclusions There were sex differences in the factors associated with HF. The results suggest that prevention policies should consider the sex-specific impact on cardiac function of modifiable cardiovascular risk factors.

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.

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

Background and aim: Accumulation of evidence has raised concern regarding the harmful effect of air pollution on cognitive function, but results are diverging. We aimed to investigate the longitudinal association of long-term exposure to air pollutants and cognitive impairment and its further progression to dementia in older adults residing in an urban area. Methods: Data were obtained from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). Cognitive impairment, no dementia (CIND) was assessed by a comprehensive neuropsychological battery (scoring ≥1.5 standard deviations below age-specific means in ≥1 cognitive domain). We assessed long-term residential exposure to particulate matters (PM2.5 and PM10) and nitrogen oxides (NOx) with dispersion modeling. The association with CIND was estimated using Cox proportional hazards models with 3-year moving average air pollution exposure. We further estimated the effect of long-term air pollution exposure on the progression of CIND to dementia using Cox proportional hazards models. Results: Among 1987 cognitively intact participants, 301 individuals developed CIND during the 12-year follow-up. A 1-μg/m3 increment in PM2.5 exposure was associated with a 75% increased risk of incident CIND (HR = 1.75, 95 %CI: 1.54, 1.99). Weaker associations were found for PM10 (HR for 1-μg/m3 = 1.08, 95 %CI: 1.03–1.14) and NOx (HR for 10 μg/m3 = 1.18, 95 %CI: 1.04–1.33). Among those with CIND at baseline (n = 607), 118 participants developed dementia during follow-up. Results also show that exposure to air pollution was a risk factor for the conversion from CIND to dementia (PM2.5: HR for 1-μg/m3 = 1.90, 95 %CI: 1.48–2.43; PM10: HR for 1-μg/m3 = 1.14, 95 %CI: 1.03–1.26; and NOx: HR for 10 μg/m3 = 1.34, 95 %CI: 1.07–1.69). Conclusion: We found evidence of an association between long-term exposure to ambient air pollutants and incidence of CIND. Of special interest is that air pollution also was a risk factor for the progression from CIND to dementia.

Background: Death from cardiovascular diseases (CVD) has been associated with transportation noise. This nationwide cohort, with state-of-the-art exposure assessment, evaluates these associations by noise source. Methods: Road traffic, railway and aircraft noise for 2001 and 2011 were linked to 4.1 million adults in the Swiss National Cohort, accounting for address history. Mean noise exposure in 5-year periods was calculated. Time-varying Cox regression models, with age as timescale, were applied to all and cause-specific cardiovascular causes of death. Models included all three noise sources plus PM2.5, adjusted for individual and spatial covariates. Nighttime noise events for all sources combined (expressed as intermittency ratio or number of events) were considered in sensitivity analyses. Absolute excess risk was calculated by multiplying deaths/100,000 person-years by the excess risk (hazard ratio-1) within each age/sex group. Results: During a 15-year follow-up, there were 277,506 CVD and 34,200 myocardial infarction (MI) deaths. Associations (hazard ratio; 95%-CIs) for road traffic, railway and aircraft noise and CVD mortality were 1.029 (1.024–1.034), 1.013 (1.010–1.017), and 1.003 (0.996–1.010) per 10 dB Lden, respectively. Associations for MI mortality were a respective 1.043 (1.029–1.058), 1.020 (1.010–1.030) and 1.040 (1.020–1.060) per 10 dB Lden. Blood pressure-related, ischemic heart disease, and all stroke mortality were significantly associated with road traffic and railway noise, while ischemic stroke mortality was associated with aircraft noise. Associations were mostly linear, often starting below 40 dB Lden for road traffic and railway noise. Higher levels of noise intermittency were also independently associated with each outcome. While the absolute number of deaths attributed to noise increased with age, the hazard ratios declined with age. Relative and absolute risk was higher in males compared to females. Conclusion: Independent of air pollution, transportation noise exposure is associated with all and cause-specific CVD mortality, with effects starting below current guideline limits.

Summary: 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 (PM2·5), 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 PM2·5, NO2, and black carbon, with a hazard ratio (HR) of 1·053 (95% CI 1·021–1·085) per 5 μg/m3 increment in PM2·5, 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 PM2·5, 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 PM2·5 (10 μg/m3) and NO2 (40 μg/m3) were 1·078 (1·046–1·111) per 5 μg/m3 PM2·5 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 PM2·5 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.

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

Abstract Background Due to the complex interplay among different urban-related exposures, a comprehensive approach is advisable to estimate the health effects. We simultaneously assessed the effect of “green”, “grey” and air pollution exposure on respiratory/allergic conditions and general symptoms in schoolchildren. Methods This study involved 219 schoolchildren (8–10 years) of the Municipality of Palermo, Italy. Data were collected through questionnaires self-administered by parents and children. Exposures to greenness and greyness at the home addresses were measured using the normalized difference vegetation index (NDVI), residential surrounding greyness (RSG) and the CORINE land-cover classes (CLC). RSG was defined as the percentage of buffer covered by either industrial, commercial and transport units, or dump and construction sites, or urban fabric related features. Two specific categories of CLC, namely “discontinuous urban fabric - DUF” - and “continuous urban fabric - CUF” - areas were found. Exposure to traffic-related nitrogen dioxide (NO2) was assessed using a Land-Use Regression model. A symptom score ranging from 0 to 22 was built by summing affirmative answers to twenty-two questions on symptoms. To avoid multicollinearity, multiple Logistic and Poisson ridge regression models were applied to assess the relationships between environmental factors and self-reported symptoms. Results A very low exposure to NDVI ≤0.15 (1st quartile) had a higher odds of nasal symptoms (OR = 1.47, 95% CI [1.07–2.03]). Children living in CUF areas had higher odds of ocular symptoms (OR = 1.49, 95% CI [1.10–2.03]) and general symptoms (OR = 1.18, 95% CI [1.00–1.48]) than children living in DUF areas. Children living in proximity (≤200 m) to High Traffic Roads (HTRs) had increased odds of ocular (OR = 1.68, 95% CI [1.31–2.17]) and nasal symptoms (OR = 1.49, 95% CI [1.12–1.98]). A very high exposure to NO2 ≥ 60 μg/m3 (4th quartile) was associated with a higher odds of general symptoms (OR = 1.28, 95% CI [1.10–1.48]). No associations were found with RGS. A Poisson ridge regression model on the symptom score showed that children living in proximity to HTRs (≤200 m) had a higher symptoms score (RR = 1.09, 95% CI [1.02–1.17]) than children living > 200 m from HTRs. Children living in CUF areas had a higher symptoms score (RR = 1.11, 95% CI [1.03–1.19]) than children living in DUF areas. Conclusions Multiple exposures related to greenness, greyness (measured by CORINE) and air pollution within the urban environment are associated with respiratory/allergic and general symptoms in schoolchildren. No associations were found when considering the individual exposure to greyness measured using the RSG indicator.

Summary: Background: Epidemiological evidence on short-term association between ambient carbon monoxide (CO) and mortality is inconclusive and limited to single cities, regions, or countries. Generalisation of results from previous studies is hindered by potential publication bias and different modelling approaches. We therefore assessed the association between short-term exposure to ambient CO and daily mortality in a multicity, multicountry setting. Methods: We collected daily data on air pollution, meteorology, and total mortality from 337 cities in 18 countries or regions, covering various periods from 1979 to 2016. All included cities had at least 2 years of both CO and mortality data. We estimated city-specific associations using confounder-adjusted generalised additive models with a quasi-Poisson distribution, and then pooled the estimates, accounting for their statistical uncertainty, using a random-effects multilevel meta-analytical model. We also assessed the overall shape of the exposure–response curve and evaluated the possibility of a threshold below which health is not affected. Findings: Overall, a 1 mg/m3 increase in the average CO concentration of the previous day was associated with a 0·91% (95% CI 0·32–1·50) increase in daily total mortality. The pooled exposure–response curve showed a continuously elevated mortality risk with increasing CO concentrations, suggesting no threshold. The exposure–response curve was steeper at daily CO levels lower than 1 mg/m3, indicating greater risk of mortality per increment in CO exposure, and persisted at daily concentrations as low as 0·6 mg/m3 or less. The association remained similar after adjustment for ozone but was attenuated after adjustment for particulate matter or sulphur dioxide, or even reduced to null after adjustment for nitrogen dioxide. Interpretation: This international study is by far the largest epidemiological investigation on short-term CO-related mortality. We found significant associations between ambient CO and daily mortality, even at levels well below current air quality guidelines. Further studies are warranted to disentangle its independent effect from other traffic-related pollutants. Funding: EU Horizon 2020, UK Medical Research Council, and Natural Environment Research Council.

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.

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 (PM2.5), 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, PM2.5, 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 PM2.5 (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 PM2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines.

Background: Air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD), but evidence is sparse and inconsistent. Objectives: We examined the association between long-term exposure to low-level air pollution and COPD incidence. Methods: Within the ‘Effects of Low-Level Air Pollution: A Study in Europe’ (ELAPSE) study, we pooled data from three cohorts, from Denmark and Sweden, with information on COPD hospital discharge diagnoses. Hybrid land use regression models were used to estimate annual mean concentrations of particulate matter with a diameter

Introduction: To characterize air pollution exposure at a fine spatial scale, different exposure assessment methods have been applied. Comparison of associations with health from different exposure methods are scarce. The aim of this study was to evaluate associations of air pollution based on hybrid, land-use regression (LUR) and dispersion models with natural cause and cause-specific mortality. Methods: We followed a Dutch national cohort of approximately 10.5 million adults aged 29+ years from 2008 until 2012. We used Cox proportional hazard models with age as underlying time scale and adjusted for several potential individual and area-level socio-economic status confounders to evaluate associations of annual average residential NO2, PM2.5 and BC exposure estimates based on two stochastic models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion models. Results: Spatial variability of PM2.5 and BC exposure was smaller for LUR compared to hybrid and dispersion models. NO2 exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO2 and BC ranged from 0.72 to 0.83; correlations for PM2.5 were slightly lower (0.61–0.72). In general, all three models showed stronger associations of air pollutants with respiratory disease and lung cancer mortality than with natural cause and cardiovascular disease mortality. The strength of the associations differed between the three exposure models. Associations of air pollutants estimated by LUR were generally weaker compared to associations of air pollutants estimated by hybrid and dispersion models. For natural cause mortality, we found a hazard ratio (HR) of 1.030 (95% confidence interval (CI): 1.019, 1.041) per 10 µg/m3 for hybrid modeled NO2, a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO2. Conclusion: Air pollution was positively associated with natural cause and cause-specific mortality, but the strength of the associations differed between the three exposure models. Our study documents that the selected exposure model may contribute to heterogeneity in effect estimates of associations between air pollution and health.

Aim There is growing interest in the health effects following exposure to ambient particles with a diameter

Background: Increased atmospheric particulate matter (PM) concentrations are commonly observed during desert dust days in Iran, but there is still no evidence of their effects on human health. We aimed to evaluate the association between daily mortality and exposure to PM10 and PM2.5 during dust and non-dust days in Tehran and Ahvaz, two major Middle Eastern cities with different sources, intensity, and frequency of desert dust days. Methods: We identified desert dust days based on exceeding a daily PM10 concentration threshold of 150 µg/m3 between 2014 and 2017, checking for low PM2.5/PM10 ratio typical of dust days. We used a time-stratified case-crossover design to estimate the short-term effects of PM10 and PM2.5 concentrations on daily mortality during dust and non-dust days. Data was analyzed using conditional Poisson regression models. Results: Higher concentrations of PM and frequency of desert dust days were observed in Ahvaz rather than Tehran. In Ahvaz, the effect of PM10 at lag 0 was much higher during dust days, an increment of 10 μg/m3 was associated with 3.28% (95%CI = [2.42, 4.15]) increase of daily mortality, than non-dust days, 1.03% (95%CI = [−0.02, 2.08]), while in Tehran, was slightly higher during non-dust days, 0.72% (95%CI = [0.23, 1.23]), than in dust days, 0.49% (95%CI = [−0.22, 1.20]). No statistically significant associations were observed between PM2.5 and daily mortality in Ahvaz, while in Teheran the effect of PM2.5 increased significantly during non-dust days at lag 2, 1.89% (95%CI = [0.83, 1.2.95] and lag 3, 1.88% (95%CI = [0.83, 1.2.95]). Conclusion: The study provides evidence that exposure to PM during Middle East dust days is an important risk factor to human health in arid regions and areas affected by desert dust events. Keywords: Air pollution, Desert dust, Particulate matter, PM10, PM2.5, Mortality, Tehran, Ahvaz

Background: Despite the relevance for occupational safety policies, the health effects of temperature on occupational injuries have been scarcely investigated. A nationwide epidemiological study was carried out to estimate the risk of injuries for workers exposed to extreme temperature and identify economic sectors and jobs most at risk. Materials and methods: The daily time series of work-related injuries in the industrial and services sector from the Italian national workers' compensation authority (INAIL) were collected for each of the 8090 Italian municipalities in the period 2006–2010. Daily air temperatures with a 1 × 1 km resolution derived from satellite land surface temperature data using mixed regression models were included. Distributed lag non-linear models (DLNM) were used to estimate the association between daily mean air temperature and injuries at municipal level. A meta-analysis was then carried out to retrieve national estimates. The relative risk (RR) and attributable cases of work-related injuries for an increase in mean temperature above the 75th percentile (heat) and for a decrease below the 25th percentile (cold) were estimated. Effect modification by gender, age, firm size, economic sector and job type were also assessed. Results: The study considered 2,277,432 occupational injuries occurred in Italy in the period 2006–2010. There were significant effects for both heat and cold temperatures. The overall relative risks (RR) of occupational injury for heat and cold were 1.17 (95% CI: 1.14–1.21) and 1.23 (95% CI: 1.17–1.30), respectively. The number of occupational injuries attributable to temperatures above and below the thresholds was estimated to be 5211 per year. A higher risk of injury on hot days was found among males and young (age 15–34) workers occupied in small-medium size firms, while the opposite was observed on cold days. Construction workers showed the highest risk of injuries on hot days while fishing, transport, electricity, gas and water distribution workers did it on cold days. Conclusions: Prevention of the occupational exposure to extreme temperatures is a concern for occupational health and safety policies, and will become a critical issue in future years considering climate change. Epidemiological studies may help identify vulnerable jobs, activities and workers in order to define prevention plans and training to reduce occupational exposure to extreme temperature and the risk of work-related injuries. Keywords: Climate change, Extreme outdoor air temperature, Occupational injuries, Heat impacts, Cold impacts, Case-crossover study

In the original article [...]

Background: A large steel plant close to the urban area of Taranto (Italy) has been operating since the sixties. Several studies conducted in the past reported an excess of mortality and morbidity from various diseases at the town level, possibly due to air pollution from the plant. However, the relationship between air pollutants emitted from the industry and adverse health outcomes has been controversial. We applied a variant of the “difference-in-differences” (DID) approach to examine the relationship between temporal changes in exposure to industrial PM10 from the plant and changes in cause-specific mortality rates at area unit level. Methods: We examined a dynamic cohort of all subjects (321,356 individuals) resident in the Taranto area in 1998–2010 and followed them up for mortality till 2014. In this work, we included only deaths occurring on 2008–2014. We observed a total of 15,303 natural deaths in the cohort and age-specific annual death rates were computed for each area unit (11 areas in total). PM10 and NO2 concentrations measured at air quality monitoring stations and the results of a dispersion model were used to estimate annual average population weighted exposures to PM10 of industrial origin for each year, area unit and age class. Changes in exposures and in mortality were analyzed using Poisson regression. Results: We estimated an increased risk in natural mortality (1.86%, 95% confidence interval [CI]: −0.06, 3.83%) per 1 μg/m3 annual change of industrial PM10, mainly driven by respiratory causes (8.74%, 95% CI: 1.50, 16.51%). The associations were statistically significant only in the elderly (65+ years). Conclusions: The DID approach is intuitively simple and reduces confounding by design. Under the multiple assumptions of this approach, the study indicates an effect of industrial PM10 on natural mortality, especially in the elderly population. Keywords: Air pollution, Mortality, PM10, Steel industry, Confounding, Difference-in-differences

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58–0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48–0.57; EV R2 0.39–0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites. Keywords: Land use regression, Fine particles, Nitrogen dioxide, Machine learning

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

An amendment to this paper has been published and can be accessed via the original article.