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
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PROVIDER: S-EPMC9476567 | biostudies-literature | 2021 Jan
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Project description:ObjectiveTo investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines.DesignPooled analysis of eight cohorts.SettingMulticentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries.Participants325 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 (PM2.5), nitrogen dioxide, ozone, and black carbon.Main outcome measuresDeaths due to natural causes and cause specific mortality.ResultsOf 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM2.5, nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m3 in PM2.5 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 PM2.5, 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 PM2.5 was associated with 29.6% (14% to 47.4%) increase in natural deaths.ConclusionsOur 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.
| S-EPMC8409282 | biostudies-literature
Project description:We assessed mortality risks associated with source-specific fine particles (PM2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 μg/m3 increase) across five identified sources. On a 1 μg/m3 basis, the residual oil-related PM2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM2.5 mass, suggesting that past estimates using the generic PM2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.
| S-EPMC9261290 | biostudies-literature
Project description:IntroductionAir pollution represents a serious threat to health on a global scale, being responsible for a large portion of the global burden of disease from environmental factors. Current evidence about the association between air pollution exposure and Diabetes Mellitus (DM) is still controversial. We aimed to evaluate the association between area-level ambient air pollution and self-reported DM in a large population sample in Italy.Materials and methodsWe extracted information about self-reported and physician diagnosed DM, risk factors and socio-economic status from 12 surveys conducted nationwide between 1999 and 2013. We obtained annual averaged air pollution levels for the years 2003, 2005, 2007 and 2010 from the AMS-MINNI national integrated model, which simulates the dispersion and transformation of pollutants. The original maps, with a resolution of 4 x 4 km2, were normalized and aggregated at the municipality class of each Italian region, in order to match the survey data. We fit logistic regression models with a hierarchical structure to estimate the relationship between PM10, PM2.5, NO2 and O3 four-years mean levels and the risk of being affected by DM.ResultsWe included 376,157 individuals aged more than 45 years. There were 39,969 cases of DM, with an average regional prevalence of 9.8% and a positive geographical North-to-South gradient, opposite to that of pollutants' concentrations. For each 10 μg/m3 increase, the resulting ORs were 1.04 (95% CI 1.01-1.07) for PM10, 1.04 (95% CI 1.02-1.07) for PM2.5, 1.03 (95% CI 1.01-1.05) for NO2 and 1.06 (95% CI 1.01-1.11) for O3, after accounting for relevant individual risk factors. The associations were robust to adjustment for other pollutants in two-pollutant models tested (ozone plus each other pollutant).ConclusionsWe observed a significant positive association between each examined pollutant and prevalent DM. Risk estimates were consistent with current evidence, and robust to sensitivity analysis. Our study adds evidence about the effects of air pollution on diabetes and suggests a possible role of ozone as an independent factor associated with the development of DM. Such relationship is of great interest for public health and deserves further investigation.
| S-EPMC5771616 | biostudies-literature
Project description:BackgroundMany studies have reported associations of air pollutants and death, but fewer examined multiple pollutants, or used causal methods. We present a method for directly estimating changes in the distribution of age at death using propensity scores.MethodsWe included all participants in Medicare from 2000 to 2016 (637,207,589 person-years of follow-up). We fit separate logistic regressions modeling the probability of death at each year of age from 65 to 98 or older as a function of exposure to particulate matter less tha 2.5 µM in diameter (PM2.5), NO2, and O3, using separate propensity scores for each age. We estimated the propensity score using gradient boosting. We estimated the distribution of life expectancy at three counterfactual exposures for each pollutant.ResultsThe estimated increase in mean life expectancy had the population been exposed to 7 versus 12 µg/m3 PM2.5 was 0.29 years (95% CI = 0.28, 0.30). The change in life expectancy had the population been exposed to 10 versus 20 ppb of NO2 was -0.01 years (95% CI = -0.015, -0.006). The increase in mean life expectancy had the population been exposed to 35 versus 45 ppb of O3 was 0.15 years (95% CI = 0.14, 0.16). Each of these effects was independent and additive.ConclusionsWe estimated that reducing PM2.5 and O3 concentrations to levels below current standards would increase life expectancy by substantial amounts compared with the recent increase of life expectancy at age 65 of 0.7 years in a decade. Our results are not consistent with the hypothesis that exposure to NO2 decreases life expectancy.
| S-EPMC8162225 | biostudies-literature
Project description:BackgroundInconsistent associations between long-term exposure to particles with an aerodynamic diameter ≤2.5 μm [fine particulate matter (PM2.5)] components and mortality have been reported, partly related to challenges in exposure assessment.ObjectivesWe investigated the associations between long-term exposure to PM2.5 elemental components and mortality in a large pooled European cohort; to compare health effects of PM2.5 components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms.MethodsWe 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 PM2.5 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 PM2.5 mass and nitrogen dioxide (NO2) separately.ResultsWe 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 PM2.5 and NO2. 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/m3, adjusting for PM2.5 mass. Associations with cause-specific mortality were less consistent in two-pollutant models.ConclusionLong-term exposure to V in PM2.5 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.
| S-EPMC8041432 | biostudies-literature
Project description:BackgroundThe evidence linking ambient air pollution to bladder cancer is limited and mixed.MethodsWe assessed the associations of bladder cancer incidence with residential exposure to fine particles (PM2.5), nitrogen dioxide (NO2), black carbon (BC), warm season ozone (O3) and eight PM2.5 elemental components (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in a pooled cohort (N = 302,493). Exposures were primarily assessed based on 2010 measurements and back-extrapolated to the baseline years. We applied Cox proportional hazard models adjusting for individual- and area-level potential confounders.ResultsDuring an average of 18.2 years follow-up, 967 bladder cancer cases occurred. We observed a positive though statistically non-significant association between PM2.5 and bladder cancer incidence. Hazard Ratios (HR) were 1.09 (95% confidence interval (CI): 0.93-1.27) per 5 µg/m3 for 2010 exposure and 1.06 (95% CI: 0.99-1.14) for baseline exposure. Effect estimates for NO2, BC and O3 were close to unity. A positive association was observed with PM2.5 zinc (HR 1.08; 95% CI: 1.00-1.16 per 10 ng/m3).ConclusionsWe found suggestive evidence of an association between long-term PM2.5 mass exposure and bladder cancer, strengthening the evidence from the few previous studies. The association with zinc in PM2.5 suggests the importance of industrial emissions.
| S-EPMC9090745 | biostudies-literature
Project description:Socioeconomic inequalities in the exposome have been found to be complex and highly context-specific, but studies have not been conducted in large population-wide cohorts from multiple countries. This study aims to examine the external exposome, encompassing individual and environmental factors influencing health over the life course, and to perform dimension reduction to derive interpretable characterization of the external exposome for multicountry epidemiological studies. Analyzing data from over 25 million individuals across seven European countries including 12 administrative and traditional cohorts, we utilized domain-specific principal component analysis (PCA) to define the external exposome, focusing on air pollution, the built environment, and air temperature. We conducted linear regression to estimate the association between individual- and area-level socioeconomic position and each domain of the external exposome. Consistent exposure patterns were observed within countries, indicating the representativeness of traditional cohorts for air pollution and the built environment. However, cohorts with limited geographical coverage and Southern European countries displayed lower temperature variability, especially in the cold season, compared to Northern European countries and cohorts including a wide range of urban and rural areas. The individual- and area-level socioeconomic determinants (i.e., education, income, and unemployment rate) of the urban exposome exhibited significant variability across the European region, with area-level indicators showing stronger associations than individual variables. While the PCA approach facilitated common interpretations of the external exposome for air pollution and the built environment, it was less effective for air temperature. The diverse socioeconomic determinants suggest regional variations in environmental health inequities, emphasizing the need for targeted interventions across European countries.
| S-EPMC11411702 | biostudies-literature
Project description:Environmental exposures during pregnancy and early life may have adverse health effects. Single birth cohort studies often lack statistical power to tease out such effects reliably. To improve the use of existing data and to facilitate collaboration among these studies, an inventory of the environmental exposure and health data in these studies was made as part of the ENRIECO (Environmental Health Risks in European Birth Cohorts) project. The focus with regard to exposure was on outdoor air pollution, water contamination, allergens and biological organisms, metals, pesticides, smoking and second hand tobacco smoke (SHS), persistent organic pollutants (POPs), noise, radiation, and occupational exposures. The review lists methods and data on environmental exposures in 37 European birth cohort studies. Most data is currently available for smoking and SHS (N=37 cohorts), occupational exposures (N=33), outdoor air pollution, and allergens and microbial agents (N=27). Exposure modeling is increasingly used for long-term air pollution exposure assessment; biomonitoring is used for assessment of exposure to metals, POPs and other chemicals; and environmental monitoring for house dust mite exposure assessment. Collaborative analyses with data from several birth cohorts have already been performed successfully for outdoor air pollution, water contamination, allergens, biological contaminants, molds, POPs and SHS. Key success factors for collaborative analyses are common definitions of main exposure and health variables. Our review emphasizes that such common definitions need ideally be arrived at in the study design phase. However, careful comparison of methods used in existing studies also offers excellent opportunities for collaborative analyses. Investigators can use this review to evaluate the potential for future collaborative analyses with respect to data availability and methods used in the different cohorts and to identify potential partners for a specific research question.
| S-EPMC3564791 | biostudies-literature
Project description:Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3-0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.
| S-EPMC7795951 | biostudies-literature
Project description:Background and purposeStroke is a common cause of death and a leading cause of disability and morbidity. Stroke risk assessment remains a challenge, but circulating biomarkers may improve risk prediction. Controversial evidence is available on the predictive ability of troponin concentrations and the risk of stroke in the community. Furthermore, reports on the predictive value of troponin concentrations for different stroke subtypes are scarce.MethodsHigh-sensitivity cardiac troponin I (hsTnI) concentrations were assessed in 82 881 individuals (median age, 50.7 years; 49.7% men) free of stroke or myocardial infarction at baseline from 9 prospective European community cohorts. We used Cox proportional hazards regression to determine relative risks, followed by measures of discrimination and reclassification using 10-fold cross-validation to control for overoptimism. Follow-up was based upon linkage with national hospitalization registries and causes of death registries.ResultsOver a median follow-up of 12.7 years, 3033 individuals were diagnosed with incident nonfatal or fatal stroke (n=1654 ischemic strokes, n=612 hemorrhagic strokes, and n=767 indeterminate strokes). In multivariable regression models, hsTnI concentrations were associated with overall stroke (hazard ratio per 1-SD increase, 1.15 [95% CI, 1.10-1.21]), ischemic stroke (hazard ratio, 1.14 [95% CI, 1.09-1.21]), and hemorrhagic stroke (hazard ratio, 1.10 [95% CI, 1.01-1.20]). Adding hsTnI concentrations to classical cardiovascular risk factors (C indices, 0.809, 0.840, and 0.736 for overall, ischemic, and hemorrhagic stroke, respectively) increased the C index significantly but modestly. In individuals with an intermediate 10-year risk (5%-20%), the net reclassification improvement for overall stroke was 0.038 (P=0.021).ConclusionsElevated hsTnI concentrations are associated with an increased risk of incident stroke in the community, irrespective of stroke subtype. Adding hsTnI concentrations to classical risk factors only modestly improved estimation of 10-year risk of stroke in the overall cohort but might be of some value in individuals at an intermediate risk.
| S-EPMC7447179 | biostudies-literature