A Multipollutant Approach to Estimating Causal Effects of Air Pollution Mixtures on Overall Mortality in a Large, Prospective Cohort

Association between trajectories of the neighborhood social exposome and mental health in late adolescence: A FinnTwin12 cohort study

BACKGROUND

Adolescent mental health problems impose a significant burden. Exploring evolving social environments could enhance comprehension of their impact on mental health. We aimed to depict the trajectories of the neighborhood social exposome from middle to late adolescence and assess the intricate relationship between them and late adolescent mental health.

METHODS

Participants (n = 3965) from the FinnTwin12 cohort with completed questionnaires at age 17 were used. Nine mental health measures were assessed. The social exposome comprised 28 neighborhood social indicators. Trajectories of these indicators from ages 12 to 17 were summarized via latent growth curve modeling into growth factors, including baseline intercept. Mixture effects of all growth factors were assessed through quantile-based g-computation. Repeated generalized linear regressions identified significant growth factors. Sex stratification was performed.

RESULTS

The linear-quadratic model was the most optimal trajectory model. No mixture effect was detected. Regression models showed some growth factors saliently linked to the p-factor, internalizing problems, anxiety, hyperactivity, and aggression. The majority of them were baseline intercepts. Quadratic growth factors about mother tongues correlated with anxiety among sex-combined participants and males. The linear growth factor in the proportion of households of couples without children was associated with internalizing problems in females.

LIMITATIONS

We were limited to including only neighborhood-level social exposures, and the multilevel contextual exposome situation interfered with our assessment.

CONCLUSIONS

Trajectories of the social neighborhood exposome modestly influenced late adolescent mental health. Tackling root causes of social inequalities through targeted programs for living conditions could improve adolescent mental health.

Relationship of long-term exposure to air pollutant mixture with metabolic-associated fatty liver disease and subtypes: A retrospective cohort study of the employed population of Southwest China

BACKGROUND

While evidence suggests that PM2.5 is associated with overall prevalence of Metabolic (dysfunction)-Associated Fatty Liver Disease (MAFLD), effects of comprehensive air pollutant mixture on MAFLD and its subtypes remain unclear.

OBJECTIVE

To investigate individual and joint effects of long-term exposure to comprehensive air pollutant mixture on MAFLD and its subtypes.

METHODS

Data of 27,699 participants of the Chinese Cohort of Working Adults were analyzed. MAFLD and subtypes, including overweight/obesity, lean, and diabetes MAFLD, were diagnosed according to clinical guidelines. Concentrations of NO3-, SO42-, NH4+, organic matter (OM), black carbon (BC), PM2.5, SO2, NO2, O3 and CO were estimated as a weighted average over participants' residential and work addresses for the three years preceding outcome assessment. Logistic regression and weighted quantile sum regression were used to estimate individual and joint effects of air pollutant mixture on presence of MAFLD.

RESULTS

Overall prevalence of MAFLD was 26.6 % with overweight/obesity, lean, and diabetes MAFLD accounting for 92.0 %, 6.4 %, and 1.6 %, respectively. Exposure to SO42-, NO3-, NH4+, BC, PM2.5, NO2, O3and CO was significantly associated with overall MAFLD, overweight/obesity MAFLD, or lean MAFLD in single pollutant models. Joint effects of air pollutant mixture were observed for overall MAFLD (OR = 1.10 [95 % CI: 1.03, 1.17]), overweight/obesity (1.09 [1.02, 1.15]), and lean MAFLD (1.63 [1.28, 2.07]). Contributions of individual air pollutants to joint effects were dominated by CO in overall and overweight/obesity MAFLD (Weights were 42.31 % and 45.87 %, respectively), while SO42- (36.34 %), SO2 (21.00 %) and BC (12.38 %) were more important in lean MAFLD. Being male, aged above 45 years and smoking increased joint effects of air pollutant mixture on overall MAFLD.

CONCLUSIONS

Air pollutant mixture was associated with MAFLD, particularly the lean MAFLD subtype. CO played a pivotal role in both overall and overweight/obesity MAFLD, whereas SO42- were associated with lean MAFLD.

Ambient Ultrafine Particulate Matter and Clinical Outcomes in Fibrotic Interstitial Lung Disease

Rationale: Particulate matter with an aerodynamic diameter ⩽2.5 μm is associated with adverse outcomes in fibrotic interstitial lung disease (fILD), but the impact of ultrafine particulates (UFPs; aerodynamic diameter ⩽100 nm) remains unknown. Objective: To evaluate UFP associations with clinical outcomes in fILD. Methods: We conducted a multicenter, prospective cohort study enrolling patients with fILD from the University of Pittsburgh Dorothy P. and Richard P. Simmons Center and the Pulmonary Fibrosis Foundation Patient Registry (PFF-PR). Using a national-scale UFP model, we linked exposures using three approaches in the Simmons cohort (residential address geocoordinates, ZIP code centroid geocoordinates, and ZIP code average) and two in the PFF-PR for which only five-digit ZIP code was available (ZIP code centroid and ZIP code average). We tested UFP associations with transplantation-free survival using multivariable Cox proportional-hazards models, baseline percentage predicted FVC and DlCO using multivariable linear regressions, and decline in FVC and DlCO using linear mixed models adjusting for age, sex, smoking, race, socioeconomic status, site, particulate matter with an aerodynamic diameter ⩽2.5, and nitrogen dioxide. Measurements and Main Results: Annual mean outdoor UFP concentrations for 2017 were estimated for 1,416 Simmons and 1,919 PFF-PR patients. Increased UFP concentration was associated with transplantation-free survival in fully adjusted Simmons residential address models (hazard ratio, 1.08 per 1,000 particles/cm3 [95% confidence interval, 1.01-1.15]; P = 0.02) but not PFF-PR models, which used less precise linkage approaches. Higher UFP exposure was associated with lower baseline FVC and more rapid FVC decline in the Simmons registry. Conclusions: Increased UFP exposure was associated with transplantation-free survival and lung function in the cohort with precise residential location linkage. This work highlights the need for more robust regulatory networks to study the health effects of UFPs nationwide.

Impact of Gestational Exposure to Individual and Combined Per- and Polyfluoroalkyl Substances on a Placental Structure and Efficiency: Findings from the Ma'anshan Birth Cohort

Prenatal exposure to perfluoroalkyl and polyfluoroalkyl substances (PFASs) is inevitable among pregnant women. Nevertheless, there is a scarcity of research investigating the connections between prenatal PFAS exposure and the placental structure and efficiency. Based on 712 maternal-fetal dyads in the Ma'anshan Birth Cohort, we analyzed associations between individual and mixed PFAS exposure and placental measures. We repeatedly measured 12 PFAS in the maternal serum during pregnancy. Placental weight, scaling exponent, chorionic disc area, and disc eccentricity were used as the outcome variables. Upon adjusting for confounders and implementing corrections for multiple comparisons, we identified positive associations between branched perfluorohexane sulfonate (br-PFHxS) and 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA) with placental weight. Additionally, a positive association was observed between br-PFHxS and the scaling exponent, where a higher scaling exponent signified reduced placental efficiency. Based on neonatal sex stratification, female infants were found to be more susceptible to the adverse effects of PFAS exposure. Mixed exposure modeling revealed that mixed PFAS exposure was positively associated with placental weight and scaling exponent, particularly during the second and third trimesters. Furthermore, br-PFHxS and 6:2 Cl-PFESA played major roles in the placental measures. This study provides the first epidemiological evidence of the relationship between prenatal PFAS exposure and placental measures.

Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia

BACKGROUND

Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts.

OBJECTIVES

Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach.

METHODS

BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO 2 ), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups.

RESULTS

Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO 2 (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO 2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants].

DISCUSSION

Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.

Real-World Evidence of Multiple Air Pollutants and Mortality: A Prospective Cohort Study in an Oldest-Old Population

We aimed to report real-world longitudinal ambient air pollutants levels compared to WHO Air Quality Guidelines (AQG) and analyze multiple air pollutants' joint effect on longevity, and the modification and confounding from the climate and urbanization with a focus on the oldest-old. This study included 13,207 old participants with 73.3% aged 80 and beyond, followed up from 2008 to 2018 in 23 Chinese provinces. We used the Cox-proportional hazards model and quantile-based g-computation model to measure separate and joint effects of the multiple pollutants. We adjusted for climate and area economic factors based on a directed acyclic graph. In 2018, no participants met the WHO AQG for PM2.5 and O3, and about one-third met the AQG for NO2. The hazard ratio (HR) for mortality was 1.07 (95% confidence interval-CI: 1.05, 1.09) per decile increase in all three pollutants, with PM2.5 being the dominant contributor according to the quantile-based g-computation model. In the three-pollutant model, the HRs (95% CI) for PM2.5 and NO2 were 1.27 (1.25, 1.3) and 1.08 (1.05, 1.12) per 10 μg/m3 increase, respectively. The oldest-old experienced a much lower mortality risk from air pollution compared to the young-old. The mortality risk of PM2.5 was higher in areas with higher annual average temperatures. The adjustment of road density considerably intensified the association between NO2 and mortality. The ambient PM2.5 and O3 levels in China exceeded the WHO AQG target substantially. Multiple pollutants coexposure, confounding, and modification of the district economic and climate factors should not be ignored in the association between air pollution and mortality.

Disentangling associations between multiple environmental exposures and all-cause mortality: an analysis of European administrative and traditional cohorts

Background

We 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.

Methods

We 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.

Results

We 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.

Discussion

The 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.

Exposure to a Mixture of Endocrine-Disrupting Chemicals and Metabolic Outcomes in Belgian Adolescents

Childhood exposure to endocrine-disrupting chemicals (EDCs), either alone or in mixtures, may affect metabolic outcomes, yet existing evidence remains inconclusive. In our study of 372 adolescents from the Flemish Environment and Health Study (FLEHS IV, 2017-2018), we measured 40 known and suspected EDCs and assessed metabolic outcomes, including body mass index z-score (zBMI), abdominal obesity (AO), total cholesterol (TC), and triglycerides (TG). We applied Bayesian kernel machine regression (BKMR) and Bayesian penalized horseshoe regression for variable selection and then built multivariate generalized propensity score (mvGPS) models to provide an overview of the effects of selected EDCs on metabolic outcomes. As a result, BKMR and horseshoe together identified five EDCs associated with zBMI, three with AO, three with TC, and five with TG. Through mvGPS analysis, monoiso-butyl phthalate (MIBP), polychlorinated biphenyl (PCB-170), and hexachlorobenzene (HCB) each showed an inverse association with zBMI, as did PCB-170 with AO. Copper (Cu) was associated with higher TC and TG, except in boys where it was linked to lower TG. Additionally, monoethyl phthalate (MEP) and monobenzyl phthalate (MBzP) were associated with higher TG. To conclude, our findings support the association between certain chemicals (Cu, MEP, and MBzP) and elevated lipid levels, aligning with prior studies. Further investigation is needed for sex-specific effects.

Mixtures of long-term exposure to ambient air pollution, built environment and temperature and stroke incidence across Europe

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 < 2.5 μm (PM2.5), black carbon (BC), ozone), built environment (green/blue spaces, impervious surfaces) and meteorology (seasonal mean and standard deviation of temperatures). Associations between environmental exposures and stroke were estimated in single and multiple-exposure Cox proportional hazard models, and Principal Component (PC) Analyses derived prototypes for specific exposures domains. We carried out random effects meta-analyses by cohort type.

RESULTS

In over 15 million participants, increased levels of NO2 and BC were associated with increased higher stroke incidence in both cohort types. Increased Normalized Difference Vegetation Index (NDVI) was associated with a lower stroke incidence in both cohort types, whereas an increase in impervious surface was associated with an increase in stroke incidence. The first PC of the air pollution domain (PM2.5, NO2 and BC) was associated with an increase in stroke incidence. For the built environment, higher levels of NDVI and lower levels of impervious surfaces were associated with a protective effect [%change in HR per 1 unit = -2.0 (95 %CI, -5.9;2.0) and -1.1(95 %CI, -2.0; -0.3) for traditional adult and administrative cohorts, respectively]. No clear patterns were observed for distance to blue spaces or temperature parameters.

CONCLUSIONS

We observed increased HRs for stroke with exposure to PM2.5, NO2 and BC, lower levels of greenness and higher impervious surface in single and combined exposure models.

Relationship of long-term air pollution exposure with asthma and rhinitis in Italy: an innovative multipollutant approach

BACKGROUND

air pollution is a complex mixture; novel multipollutant approaches could help understanding the health effects of multiple concomitant exposures to air pollutants.

AIM

to assess the relationship of long-term air pollution exposure with the prevalence of respiratory/allergic symptoms and diseases in an Italian multicenter study using single and multipollutant approaches.

METHODS

14420 adults living in 6 Italian cities (Ancona, Pavia, Pisa, Sassari, Turin, Verona) were investigated in 2005-2011 within 11 different study cohorts. Questionnaire information about risk factors and health outcomes was collected. Machine learning derived mean annual concentrations of PM₁₀, PM_2.5, NO₂ and mean summer concentrations of O₃ (μg/m³) at residential level (1-km resolution) were used for the period 2013-2015. The associations between the four pollutants and respiratory/allergic symptoms/diseases were assessed using two approaches: a) logistic regression models (single-pollutant models), b) principal component logistic regression models (multipollutant models). All the models were adjusted for age, sex, education level, smoking habits, season of interview, climatic index and included a random intercept for cohorts.

RESULTS

the three-year average (± standard deviation) pollutants concentrations at residential level were: 20.3 ± 6.8 μg/m³ for PM_2.5, 29.2 ± 7.0 μg/m³ for PM₁₀, 28.0 ± 11.2 μg/m³ for NO₂, and 70.9 ± 4.3 μg/m³ for summer O_3. Through the multipollutant models the following associations emerged: PM₁₀ and PM_2.5 were related to 14-25% increased odds of rhinitis, 23-34% of asthma and 30-33% of night awakening; NO₂ was related to 6-9% increased odds of rhinitis, 7-8% of asthma and 12% of night awakening; O₃ was associated with 37% increased odds of asthma attacks. Overall, the Odds Ratios estimated through the multipollutant models were attenuated when compared to those of the single-pollutant models.

CONCLUSIONS

this study enabled to obtain new information about the health effects of air pollution on respiratory/allergic outcomes in adults, applying innovative methods for exposure assessment and multipollutant analyses.

The causal links between long-term exposure to major PM2.5 components and the burden of tuberculosis in China

BACKGROUND

We aimed to estimate the causal impacts of long-term exposure to major PM_2.5 components - including black carbon, organic matter, sulfate, nitrate, and ammonium - on the incidence and mortality of tuberculosis in China.

METHODS

We collected annual and provincial-level tuberculosis incidence and mortality, concentrations of PM_2.5 components, and socioeconomic indicators from between 2004 and 2018 in mainland China. We used the difference-in-differences (DID) causal inference approach with a generalized weighted quantile sum (gWQS) regression model to estimate the long-term effects and relative contributions of PM_2.5 components' exposure on tuberculosis incidence and mortality.

RESULTS

We found that long-term multi-components exposure was significantly associated with tuberculosis incidence (WQS index IR%:8.34 %, 95 % CI:4.54 %-12.27 %) and mortality (WQS index IR%:19.49 %, 95 % CI: 9.72 %-30.13 %). Primary pollutants, black carbon and organic matter, contributed most of the overall mixture effect (over 85 %). Nitrate showed a critical role in tuberculosis burden in not-aging provinces and in regions at the Q3 stratum (i.e., the 3rd quartile) of GDP per capita and urbanization rate. Meanwhile the contribution of sulfate to tuberculosis burden in regions at the Q1 stratum of GDP per capita and urbanization rate was the largest among the effect of secondary pollutants (i.e., sulfate, nitrate, and ammonium).

CONCLUSION

The mitigation of black carbon and organic matter pollution may significantly reduce the tuberculosis burden in China. Controlling nitrate emissions and increasing clean energy (i.e., energy sources with limited pollution emissions, such as natural gas and clean coal) may also be effective in certain regions.

Ambient air pollution, temperature and hospital admissions due to respiratory diseases in a cold, industrial city

Background

The influences of air pollution exposure and temperature on respiratory diseases have become major global health concerns. This study investigated the relationship between ambient air pollutant concentrations and temperature in cold industrial cities that have the risk of hospitalization for respiratory diseases.

Methods

A time-series study was conducted in Changchun, China, from 2015 to 2019 to analyse the number of daily admissions for respiratory diseases, air pollutant concentrations, and meteorological factors. Time-series decomposition was applied to analyse the trend and characteristics of the number of admissions. Generalized additive models and distributed lag nonlinear models were constructed to explore the effects of air pollutant concentrations and temperature on the number of admissions.

Results

The number of daily admissions showed an increasing trend, and the seasonal fluctuation was obvious, with more daily admissions in winter and spring than in summer and autumn. There were positive and gradually decreasing lag effects of PM10, PM2.5, NO₂, and CO concentrations on the number of admissions, whereas O₃ showed a J-shaped trend. The results showed that within the 7-day lag period, 0.5°C was the temperature associated with the lowest relative risk of admission due to respiratory disease, and extremely low and high temperatures (<-18°C, >27°C, respectively) increased the risk of hospitalization for respiratory diseases by 8.3% and 12.1%, respectively.

Conclusions

From 2015 to 2019, respiratory diseases in Changchun showed an increasing trend with obvious seasonality. The increased concentrations of SO₂, NO₂, CO, PM2.5, O₃ and PM10 lead to an increased risk of hospitalization for respiratory diseases, with a significant lag effect. Both extreme heat and cold could lead to increases in the risk of admission due to respiratory disease.

Identification of Health Effects of Complex Air Pollution in China

After the Chinese government introduced a series of policies to strengthen the control of air pollution, the concentration of particulate matter has decreased, but the concentration of ozone has increased, and the problem of complex air pollution still exists, posing a serious threat to public health. Therefore, disentangling the health effect of multi-pollutants has been a long-discussed challenge in China. To evaluate the adverse effects of complex air pollution, a generalized additive model was used to assess the health risks of different pollution types in eight metropolises in different climates in China from 2013 to 2016. Instead of directly introducing multiple pollutant concentrations, we integrated the concentration levels of PM_2.5, NO₂, and O₃ into a set of predictors by grouping methods and divided air pollution into three high single-pollutant types and four high multi-pollutant types to calculate mortality risk in different types. The comprehensive results showed that the impact of high multi-pollutant types on mortality risk was greater than that of high single-pollutant types. Throughout the study period, the high multi-pollutant type with high PM_2.5, NO₂, and O₃ and the high multi-pollutant type with high PM_2.5 and NO₂ were more associated with death, and the highest RRs were 1.129 (1.080, 1.181) and 1.089 (1.066, 1.113), respectively. In addition, the pollution types that most threaten people are different in different cities. These differences may be related to different pollution conditions, pollutant composition, and indoor-outdoor activity patterns in different cities. Seasonally, the risk of complex air pollution is greater in most cities in the warm season than in the cold season. This may be caused by the modifying effects of high temperature on pollutants in addition to different indoor-outdoor activity patterns in different seasons. The results also show that calculating the effect of individual air pollutants separately and adding them together may lead to an overestimation of the combined effect. It further highlights the urgency and need for air pollution health research to move towards a multi-pollutant approach that considers air pollution as a whole in the context of atmospheric abatement and global warming.