The association between ambient air pollution concentrations and psychological distress

BACKGROUND

A growing number of epidemiological studies have linked air pollution exposure to psychological conditions. Laboratory studies indicate that air pollutants can activate the neuroendocrine stress axis and modulate stress hormone levels, which could contribute to the development or exacerbation of psychological distress. The present study examined the spatial associations between air pollutants (fine particulate matter [PM_2.5], nitrogen dioxide [NO₂] and ground-level ozone [O₃]) and psychological distress among subjects in the most populous provinces in Canada.

DATA AND METHODS

Subjects were sampled from the Canadian Community Health Survey in three regions (Quebec in 2005 [n=25,800], British Columbia and Alberta in 2005 [n=23,000], and Ontario in 2011 [n=36,000]), and were assigned estimates of annual exposure to three ambient air pollutants (PM_2.5, NO₂ and O₃) for the same years. Individual psychological distress was assessed using the Kessler Psychological Distress Scale (K10), based on anxiety and depressive symptoms in the past month. Regression models (both ordinary least squares and simultaneous autoregressive models) were applied to estimate associations between K10 distress scores and each air pollutant, after adjusting for individual (demographic, socioeconomic and behavioural) and neighbourhood covariates..

RESULTS

Psychological distress was positively associated with PM_2.5 and NO₂ in all three regions, and with O₃ in Quebec. However, after further adjusting for individual and neighbourhood covariates, the associations between distress and air pollution remained statistically significant only in Quebec.

DISCUSSION

Some evidence for positive associations between psychological distress and ambient air pollution after adjusting for spatial autocorrelation was found.

Effects of COVID-19 lockdowns on fine particulate matter concentrations

Lockdowns during the COVID-19 pandemic provide an unprecedented opportunity to examine the effects of human activity on air quality. The effects on fine particulate matter (PM2.5) are of particular interest, as PM2.5 is the leading environmental risk factor for mortality globally. We map global PM2.5 concentrations for January to April 2020 with a focus on China, Europe, and North America using a combination of satellite data, simulation, and ground-based observations. We examine PM2.5 concentrations during lockdown periods in 2020 compared to the same periods in 2018 to 2019. We find changes in population-weighted mean PM2.5 concentrations during the lockdowns of -11 to -15 μg/m3 across China, +1 to -2 μg/m3 across Europe, and 0 to -2 μg/m3 across North America. We explain these changes through a combination of meteorology and emission reductions, mostly due to transportation. This work demonstrates regional differences in the sensitivity of PM2.5 to emission sources.

Examining PM2.5 concentrations and exposure using multiple models

Epidemiologic studies have found associations between fine particulate matter (PM_2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM_2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM_2.5 concentrations in 2011, ii) potential changes in PM_2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM_2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM_2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m^-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM_2.5 concentrations were estimated to decrease by about 1 μg m^-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m^-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM_2.5 concentrations less than 10 μg m^-3 in 2011 and PM_2.5 improvements of about 2 μg m^-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

Ambient Fine Particulate Matter Air Pollution and Risk of Weight Gain and Obesity in United States Veterans: An Observational Cohort Study

BACKGROUND

Experimental evidence and studies of children and adolescents suggest that ambient fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] air pollution may be obesogenic, but the relationship between PM2.5 and the risk of body weight gain and obesity in adults is uncertain.

OBJECTIVES

Our goal was to characterize the association between PM2.5 and the risks of weight gain and obesity.

METHODS

We followed 3,902,440 U.S. Veterans from 2010 to 2018 (median 8.1 y, interquartile range: 7.3-8.4) and assigned time-updated PM2.5 exposures by linking geocoded residential street addresses with satellite-based estimates of surface-level PM2.5 mass (at ∼1-km2 resolution). Associations with PM2.5 were estimated using Cox proportional hazards models for incident obesity [body mass index (BMI)≥30 kg/m2] and a 10-lb increase in weight relative to baseline and linear mixed models for associations with intra-individual changes in BMI and weight.

RESULTS

A 10-μg/m3 higher average annual PM2.5 concentration was associated with risk of incident obesity [n=2,325,769; hazard ratio (HR)=1.08 (95% CI: 1.06, 1.11)] and the risk of a 10-lb (4.54 kg) increase in weight [HR=1.07 (95% CI: 1.06, 1.08)] and with higher intra-individual changes in BMI [0.140 kg/m2 per year (95% CI: 0.139, 0.142)] and weight [0.968 lb/y (95% CI: 0.955, 0.981)]. Nonlinear exposure-response models indicated associations at PM2.5 concentrations below the national standard of 12 μg/m3. As expected, a negative exposure control (ambient air sodium) was not associated with obesity or weight gain. Associations were consistent in direction and magnitude across sensitivity analyses that included alternative outcomes and exposures assigned at different spatial resolutions.

DISCUSSION

PM2.5 air pollution was associated with the risk of obesity and weight gain in a large predominantly male cohort of U.S. Veterans. Discussions about health effects of PM2.5 should include its association with obesity, and deliberations about the epidemiology of obesity should consider its association with PM2.5. Investigation in other cohorts will deepen our understanding of the relationship between PM2.5 and weight gain and obesity. https://doi.org/10.1289/EHP7944.

A Population-Based Cohort Study of Respiratory Disease and Long-Term Exposure to Iron and Copper in Fine Particulate Air Pollution and Their Combined Impact on Reactive Oxygen Species Generation in Human Lungs

Metal components in fine particulate matter (PM_2.5) from nontailpipe emissions may play an important role in underlying the adverse respiratory effects of PM_2.5. We investigated the associations between long-term exposure to iron (Fe) and copper (Cu) in PM_2.5 and their combined impact on reactive oxygen species (ROS) generation in human lungs, and the incidence of asthma, chronic obstructive pulmonary disease (COPD), COPD mortality, pneumonia mortality, and respiratory mortality. We conducted a population-based cohort study of ∼0.8 million adults in Toronto, Canada. Land-use regression models were used to estimate the concentrations of Fe, Cu, and ROS. Outcomes were ascertained using validated health administrative databases. We found positive associations between long-term exposure to Fe, Cu, and ROS and the risks of all five respiratory outcomes. The associations were more robust for COPD, pneumonia mortality, and respiratory mortality than for asthma incidence and COPD mortality. Stronger associations were observed for ROS than for either Fe or Cu. In two-pollutant models, adjustment for nitrogen dioxide somewhat attenuated the associations while adjustment for PM_2.5 had little influence. Long-term exposure to Fe and Cu in PM_2.5 and estimated ROS concentration in lung fluid was associated with increased incidence of respiratory diseases, suggesting the adverse respiratory effects of nontailpipe emissions.

Ambient PM2.5 exposure and rapid spread of COVID-19 in the United States

It has been posited that populations being exposed to long-term air pollution are more susceptible to COVID-19. Evidence is emerging that long-term exposure to ambient PM2.5 (particulate matter with aerodynamic diameter 2.5 μm or less) associates with higher COVID-19 mortality rates, but whether it also associates with the speed at which the disease is capable of spreading in a population is unknown. Here, we establish the association between long-term exposure to ambient PM2.5 in the United States (US) and COVID-19 basic reproduction ratio R0- a dimensionless epidemic measure of the rapidity of disease spread through a population. We inferred state-level R0 values using a state-of-the-art susceptible, exposed, infected, and recovered (SEIR) model initialized with COVID-19 epidemiological data corresponding to the period March 2-April 30. This period was characterized by a rapid surge in COVID-19 cases across the US states, implementation of strict social distancing measures, and a significant drop in outdoor air pollution. We find that an increase of 1 μg/m3 in PM2.5 levels below current national ambient air quality standards associates with an increase of 0.25 in R0 (95% CI: 0.048-0.447). A 10% increase in secondary inorganic composition, sulfate-nitrate-ammonium, in PM2.5 associates with ≈10% increase in R0 by 0.22 (95% CI: 0.083-0.352), and presence of black carbon (soot) in the ambient environment moderates this relationship. We considered several potential confounding factors in our analysis, including gaseous air pollutants and socio-economical and meteorological conditions. Our results underscore two policy implications - first, regulatory standards need to be better guided by exploring the concentration-response relationships near the lower end of the PM2.5 air quality distribution; and second, pollution regulations need to be continually enforced for combustion emissions that largely determine secondary inorganic aerosol formation.

Fine particulate matter constituents and sub-clinical outcomes of cardiovascular diseases: A multi-center study in China

BACKGROUND

Limited evidence is available on the associations of long-term exposure to various fine particulate matter (PM_2.5) constituents with sub-clinical outcomes of cardiovascular disease (CVD) in China.

OBJECTIVES

We aimed to explore the associations of PM_2.5 and its constituents with blood pressure (BP), fasting glucose, and cardiac electrophysiological (ECG) properties based on a national survey of 5852 Chinese adults, who participated in the Sub-Clinical Outcome of Polluted Air study, from July 2017 to March 2019.

METHODS

Annual residential exposure to PM_2.5 and its constituents of each subject was predicted by a satellite-based mode. We assessed the associations between five main constituents [organic matter (OM), black carbon (BC), sulfate (SO₄^2-), nitrate (NO₃^-), ammonium (NH₄⁺)] of PM_2.5 and systolic BP (SBP), diastolic BP (DBP), fasting glucose, and ECG measurements (PR, QRS, QT, and QTc interval) using multivariable linear regression models.

RESULTS

Long-term PM_2.5 exposure was significantly associated with increased levels of fasting glucose, DBP, and ECG measurements. An IQR increase in OM (8.2 μg/m³) showed considerably stronger associations with an elevated fasting glucose of 0.39 mmol/L (95%CI confidence interval: 0.28, 0.49) compared with other PM_2.5 constituents. Meanwhile, an IQR increase in NO₃^-, NH₄⁺ and OM had stronger associations with DBP and ECG parameters compared with BC and SO₄^2-.

CONCLUSIONS

This nationwide multi-center study in China indicated that some constituents (i.e., OM, NO₃^-, and NH₄⁺) might be mainly responsible for the association of PM_2.5 with sub-clinical outcomes of CVD including BP, fasting glucose, and ECG measurements.

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

BACKGROUND

Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.

OBJECTIVES

We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.

METHODS

We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.

RESULTS

Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean=30 (95% CI: 9, 50)], BC [mean=2 (95% CI: 1, 2)], and PM2.5, [mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell-level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean=38 (95% CI: 11, 64); PM2.5 CBG mean=59 (95% CI: 40, 77); and PM2.5 county mean=55 (95% CI: 37, 71)].

DISCUSSION

Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.

Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State

BACKGROUND

Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited.

OBJECTIVE

We examined the potential association between long-term exposure to particulate matter ≤ 2.5 μ m in aerodynamic diameter [fine particulate matter (PM 2.5 )] and disease aggravation in Alzheimer's (AD) and Parkinson's (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation.

METHODS

We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000-2014) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual PM 2.5 concentrations estimated by a prediction model at a 1 -km 2 resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y PM 2.5 exposure. We allowed for nonlinear exposure-outcome relationships using penalized splines and accounted for potential confounders.

RESULTS

We found a positive nonlinear PM 2.5 - PD association that plateaued above 11   μ g / m 3 (RR = 1.09 , 95% CI: 1.04, 1.14 for a PM 2.5 increase from 8.1 to 10.4   μ g / m 3 ). We also found a linear PM 2.5 - ALS positive association (RR = 1.05 , 95% CI: 1.01, 1.09 per 1 - μ g / m 3 PM 2.5 increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients < 70  years of age but found insufficient evidence of effect modification by sex or urbanization level for any of the outcomes.

CONCLUSION

Our findings suggest that annual increase in county-level PM 2.5 concentrations may contribute to clinical aggravation of PD and ALS. Importantly, the average annual PM 2.5 concentration in our study was 8.1   μ g / m 3 , below the current American national standards, suggesting the standards may not adequately protect the aging population. https://doi.org/10.1289/EHP7425.

Ambient PM2.5 and its chemical constituents on lifetime-ever pneumonia in Chinese children: A multi-center study

The long-term effects of ambient PM_2.5 and chemical constituents on childhood pneumonia were still unknown. A cross-sectional study was conducted in 30,315 children in the China Children, Homes, Health (CCHH) project, involving 205 preschools in six cities in China, to investigate the long-term effects of PM_2.5 constituents on lifetime-ever diagnosed pneumonia. Information on the lifetime-ever pneumonia and demographics were collected by validated questionnaires. The lifetime annual average ambient PM_2.5, ozone and five main PM_2.5 constituents, including SO₄^2-, NO₃^-, NH₄⁺, organic matter (OM) and black carbon (BC), were estimated according to preschool addresses by a combination of satellite remote sensing, chemical transport modeling and ground-based monitors. The prevalence of lifetime-ever diagnosed pneumonia was 34.5% across six cities and differed significantly among cities (p = 0.004). The two-level logistic regression models showed that the adjusted odds ratio for PM_2.5 (per 10 µg/m³) and its constituents (per 1 µg/m³)-SO₄^2-, NO₃^-, NH₄⁺, and OM were 1.12 (95% CI:1.07-1.18), 1.02 (1.00-1.04), 1.06 (1.04-1.09), 1.05 (1.03-1.07) and 1.09 (1.06-1.12), respectively. Children in urban area, aged < 5 years and breastfeeding time < 6 months enhanced the risks of pneumonia. Our study provided robust results that long-term levels of ambient PM_2.5 and its constituents increased the risk of childhood pneumonia, especially NH₄⁺, NO₃^- and OM.

Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest

We developed Europe-wide models of long-term exposure to eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter <2.5 μm (PM_2.5) using standardized measurements for one-year periods between October 2008 and April 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithms. Potential predictor variables were obtained from satellites, chemical transport models, land-use, traffic, and industrial point source databases to represent different sources. Overall model performance across Europe was moderate to good for all elements with hold-out-validation R-squared ranging from 0.41 to 0.90. RF consistently outperformed SLR. Models explained within-area variation much less than the overall variation, with similar performance for RF and SLR. Maps proved a useful additional model evaluation tool. Models differed substantially between elements regarding major predictor variables, broadly reflecting known sources. Agreement between the two algorithm predictions was generally high at the overall European level and varied substantially at the national level. Applying the two models in epidemiological studies could lead to different associations with health. If both between- and within-area exposure variability are exploited, RF may be preferred. If only within-area variability is used, both methods should be interpreted equally.

Ambient ultrafine particle concentrations and incidence of childhood cancers

BACKGROUND

Ambient air pollution has been associated with childhood cancer. However, little is known about the possible impact of ambient ultrafine particles (<0.1 μm) (UFPs) on childhood cancer incidence.

OBJECTIVE

This study aimed to evaluate the association between prenatal and childhood exposure to UFPs and development of childhood cancer.

METHODS

We conducted a population-based cohort study of within-city spatiotemporal variations in ambient UFPs across the City of Toronto, Canada using 653,702 singleton live births occurring between April 1, 1998 and March 31, 2017. Incident cases of 13 subtypes of paediatric cancers among children up to age 14 were ascertained using a cancer registry. Associations between ambient air pollutant concentrations and childhood cancer incidence were estimated using random-effects Cox proportional hazards models. We investigated both single- and multi-pollutant models accounting for co-exposures to PM_2.5 and NO₂.

RESULTS

A total of 1,066 childhood cancers were identified. We found that first trimester exposure to UFPs (Hazard Ratio (HR) per 10,000/cm³ increase = 1.13, 95% CI: 1.03-1.22) was associated with overall cancer incidence diagnosed before 6 years of age after adjusting for PM_2.5, NO₂, and for personal and neighborhood-level covariates. Association between UFPs and overall cancer incidence exhibited a linear shape. No statistically significant associations were found for specific cancer subtypes.

CONCLUSION

Ambient UFPs may represent a previously unrecognized risk factor in the aetiology of cancers in children. Our findings reinforce the importance of conducting further research on the effects of UFPs given their high prevalence of exposure in urban areas.

Self-rated stress, distress, mental health, and health as modifiers of the association between long-term exposure to ambient pollutants and mortality

BACKGROUND

Individual and neighbourhood-scale socioeconomic characteristics modify associations between exposure to air pollution and mortality. The role of stress, which may integrate effects of social and environmental exposures on health, is unknown. We examined whether an individual's perspective on their own well-being, as assessed using self-rated measures of stress and health, modifies the pollutant-mortality relationship.

METHODS

The Canadian Community Health Survey (CCHS)-mortality cohort includes respondents from surveys administered between 2001 and 2012 linked to vital statistics and postal codes from 1981 until 2016. Annual fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) exposure estimates were attached to a sample of cohort members aged 30-89 years (n = 398,300 respondents/3,848,400 person-years). We examined whether self-rated stress, distress, mental health, and general health modified associations between long-term exposure to each pollutant (three-year moving average with one-year lag) and non-accidental mortality using Cox survival models, adjusted for individual- (i.e. socioeconomic and behavioural) and neighbourhood-scale covariates.

RESULTS

In fully-adjusted models, the relationship between exposure to pollutants and mortality was stronger among those with poor self-rated mental health, including a significant difference for NO₂ (hazard ratio (HR) = 1.15, 95% CI 1.06-1.25 per IQR) compared to those with very good/excellent mental health (HR = 1.05, 95% CI 1.01-1.08; Cochran's Q = 4.01; p < 0.05). Poor self-rated health was similarly associated with higher pollutant-associated HRs, but only in unadjusted models. Stress and distress did not modify pollutant-mortality associations.

CONCLUSIONS

Poor self-rated mental and general health were associated with increased mortality attributed to exposure to ambient pollutants.

Prenatal exposure to fine particles, premature rupture of membranes and gestational age: A prospective cohort study

BACKGROUND

The associations between maternal exposure to fine particles with aerodynamic diameter ≤ 2.5 μm (PM_2.5) and gestational age as well as premature rupture of membranes (PROM) remain unclear. Few studies have focused on preconception exposure and components of fine particles in China.

METHODS

A total of 1715 pregnant women were enrolled at hospitals affiliated with Nanjing Medical University from 2014 to 2015. Personal exposure to PM_2.5 was estimated from preconception to the first trimester. Gestational age and PROM were investigated to explore their associations with PM_2.5 and its components.

RESULTS

From 12 weeks before conception to the end of the first trimester, the gestational age was reduced by 0.89 days (95% CI: -1.37, -0.40) per 10 μg/m³ increment in PM_2.5 exposure. After the exposure period was separated into two groups, PM_2.5 exposure reduced the gestational age by 0.35 days (95% CI: -0.59, -0.11) in the 12 weeks before pregnancy. With maternal exposure to PM_2.5 early in the first trimester, gestational age was reduced by 0.62 days (95% CI: -1.09, -0.14). After mediation analysis, we found that PROM mediated the association between PM_2.5 and gestational age from preconception to the first trimester. Components analysis indicated that exposure to black carbon, organic matter, and nitrate increased the risk of PROM and decreased gestational age.

CONCLUSION

Exposure to PM_2.5 as well as some components of PM_2.5 before and during early pregnancy was associated with PROM and gestational age. PROM might be a potential mediator in associations between PM_2.5 as well as various components and gestational age.

Prenatal Exposure to Specific PM2.5 Chemical Constituents and Preterm Birth in China: A Nationwide Cohort Study

Exposure to fine particulate matter (PM_2.5) during pregnancy has been associated with preterm birth (PTB). However, the existing evidence is inconsistent, and the roles of specific PM_2.5 chemical constituents remain unclear. Based on the China Labor and Delivery Survey, we included birth data from 89 hospitals in 25 provinces in mainland China, and conducted a national multicenter cohort study to examine the associations of PM_2.5 and its chemical constituents with PTB risk in China. We applied satellite-based models to predict prenatal PM_2.5 mass and six main component exposure. Multilevel logistic regression analysis was used to examine the associations, controlling for sociodemographic characteristics, seasonality, and spatial variation. We observe an increased PTB risk with an increase in PM_2.5 mass and the most significant association is found during the third trimester when the adjusted odds ratio (OR) per interquartile range increases in PM_2.5 total mass is 1.12 (95% confidence Interval, CI: 1.05-1.20). Infants conceived by assisted reproductive technology (ART) show greater PTB risk associated with PM_2.5 exposure (OR = 1.33, 95% CI: 1.05-1.69) than those conceived naturally (OR = 1.11, 95% CI: 1.03-1.19). We also find black carbon, sulfate, ammonium and nitrate, often linked to fossil combustion, have comparable or larger estimates of the effect (OR = 1.07-1.14) than PM_2.5. Our findings provide evidence that components mainly from fossil fuel combustion may have a perceptible influence on increased PTB risk associated with PM_2.5 exposure in China. Additionally, compared to natural conception, conception through ART may be more susceptible to PM_2.5 exposure.

Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease.

METHODS

GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden.

FINDINGS

The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51-12·1) deaths (19·2% [16·9-21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12-9·31) deaths (15·4% [14·6-16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253-350) DALYs (11·6% [10·3-13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0-9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10-24 years, alcohol use for those aged 25-49 years, and high systolic blood pressure for those aged 50-74 years and 75 years and older.

INTERPRETATION

Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public.

FUNDING

Bill & Melinda Gates Foundation.

Fine particular matter and its constituents in air pollution and gestational diabetes mellitus

BACKGROUND

Ambient air pollution has been linked to the development of gestational diabetes mellitus (GDM). However, previous studies provided inconsistent findings and no study has examined the effects of complex chemical constituents of the particular matter on GDM, especially in developing countries. Therefore, we aim to investigate the associations of exposure to PM_2.5 (particular matter ≤ 2.5 μm) and its constituents with GDM, and to identify susceptible exposure window in a large survey in China.

METHODS

The China Labor and Delivery Survey was a cross-sectional investigation conducted in 24 provinces in China between 2015 and 2016. A random sample of all deliveries in each participating hospital was selected and detailed obstetric and newborn information was extracted from medical records. Average concentrations of PM_2.5 and six constituents (organic matter, black carbon, sulfate, nitrate, ammonium and soil dust) were estimated (1 km × 1 km) using a combined geoscience-statistical model. GDM was diagnosed based on an oral glucose tolerance test (OGTT) between 24 to 28 weeks of gestation and according to IADPSG criteria. Generalized linear mixed models were used to adjust for potential confounders.

RESULTS

A total of 54,517 subjects from 55 hospitals were included. The incidence of GDM was 10.8%. An interquartile range (IQR) increase in PM_2.5 exposure in the 2nd trimester of pregnancy was associated with an increased GDM risk in the single pollutant model, [adjusted odds ratio (aOR) = 1.11 and 95% confidence interval (CI): 1.01-1.22]. Exposure to organic matter (aOR = 1.14; 95%CI: 1.05-1.23), black carbon (aOR = 1.15; 95%CI: 1.07-1.25) and nitrate (aOR = 1.13; 95%CI: 1.02-1.24) during 2nd trimester were associated with increased risks of GDM. Associations between constituents and GDM were robust after controlling for total PM_2.5 mass and accounting for multi-collinearity.

CONCLUSIONS

Exposure to PM_2.5 in 2^nd trimester of pregnancy was associated with an increased risk of GDM. Organic matter, black carbon and nitrate may be the main culprits for the association.

Fine particulate matter exposure and renal function: A population-based study among pregnant women in China

BACKGROUND

Fine particulate matter (PM_2.5) is the most serious environmental threat worldwide. The nephrotoxicity of PM_2.5 has been demonstrated in older adults, but no study has addressed the impacts of PM_2.5 exposure on renal function in pregnant women, who are recognized to be vulnerable and susceptible to PM_2.5 exposure.

OBJECTIVE

To evaluate whether exposures to PM_2.5 total mass and its chemical constituents were associated with reduced renal function among pregnant women in China.

METHODS

We measured serum concentrations of urea nitrogen (UN), uric acid (UA) and creatinine for 10,052 pregnant women in Shanghai, China. Exposures to PM_2.5 total mass and its 5 key chemical constituents during the whole pregnancy and each trimester of pregnancy was represented by satellite-based models.

RESULTS

Exposures to PM_2.5 total mass and its chemical constituents of organic matter (OM), black carbon (BC), nitrate (NO₃^-) and ammonium (NH₄⁺) were positively associated with serum levels of UN and UA, and negatively associated with estimated glomerular filtration rate (eGFR). An interquartile rang (IQR) increase in PM_2.5 total mass, OM, BC, NO₃^- and NH₄⁺ exposure in third trimester was associated with 1.33 (β = -1.33, 95% CI, -1.79, -0.87), 1.67 (β = -1.67, 95% CI, -2.26, -1.07), 1.29 (β = -1.29, 95% CI,-1.89, -0.70), 1.16 (β = -1.16, 95% CI,-1.66, -0.65) and 0.76 (β = -0.76, 95% CI, -1.08, -0.44) mL/min/1.73 m² decrease in eGFR, respectively.

CONCLUSION

We concluded that exposures to PM_2.5 during pregnancy were associated with decreased renal function among pregnant women.

Residential Greenness and Cardiovascular Disease Incidence, Readmission, and Mortality

BACKGROUND

Living in greener areas of cities was linked to increased physical activity levels, improved mental well-being, and lowered harmful environmental exposures, all of which may affect human health. However, whether living in greener areas may be associated with lower risk of cardiovascular disease incidence, progression, and premature mortality is unclear.

OBJECTIVES

We conducted a cohort study to examine the associations between residential green spaces and the incidence of acute myocardial infarction (AMI) and heart failure (HF), post-AMI and HF hospital readmissions, and mortality.

METHODS

We simultaneously followed four large population-based cohorts in Ontario, Canada, including the entire adult population, adults free of AMI and HF, and survivors of AMI or HF from 2000 to 2014. We estimated residential exposure to green spaces using satellite-derived observations and ascertained health outcomes using validated disease registries. We estimated the associations using spatial random-effects Cox proportional hazards models. We conducted various sensitivity analyses, including further adjusting for property values and performing exploratory mediation analysis.

RESULTS

Each interquartile range increase in residential greenness was associated with a 7% [95% confidence interval (CI): 4%, 9%] decrease in incident AMI and a 6% (95% CI: 4%, 7%) decrease in incident HF. Residential greenness was linked to a ∼10% decrease in cardiovascular mortality in both adults free of AMI and HF and the entire adult population. These associations remained consistent in sensitivity analyses and were accentuated among younger adults. Additionally, we estimated that the decreases in AMI and HF incidence associated with residential greenness explained ∼53% of the protective association between residential greenness and cardiovascular mortality. Conversely, residential greenness was not associated with any delay in readmission or mortality among AMI and HF patients.

CONCLUSIONS

Living in urban areas with more green spaces was associated with improved cardiovascular health in people free of AMI and HF but not among individuals who have already developed these conditions. https://doi.org/10.1289/EHP6161.

Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)

Exposure to outdoor fine particulate matter (PM_2.5) is a leading risk factor for mortality. We develop global estimates of annual PM_2.5 concentrations and trends for 1998-2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM_2.5 concentrations using geophysical relationships between surface PM_2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM_2.5 estimates are highly consistent with globally distributed ground monitors (R² = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM_2.5 estimates to predict and account for the residual bias with PM_2.5 monitors, yielding even higher cross validated agreement (R² = 0.90-0.92; slope = 0.90-0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (-0.28 ± 0.03 μg/m³/yr), Europe (-0.15 ± 0.03 μg/m³/yr), India (1.13 ± 0.15 μg/m³/yr), and globally (0.04 ± 0.02 μg/m³/yr). The positive trend (2.44 ± 0.44 μg/m³/yr) for India over 2005-2013 and the negative trend (-3.37 ± 0.38 μg/m³/yr) for China over 2011-2018 are remarkable, with implications for the health of billions of people.

Urban green space and the risks of dementia and stroke

INTRODUCTION

It is unknown whether urban green space is associated with reduced risk of major neurological conditions, especially dementia and stroke.

METHODS

Retrospective, population-based cohorts were created for each study outcome, including 1.7 and 4.3 million adults in Ontario, Canada for dementia and stroke, respectively. Residential green space was quantified using the satellite-derived Normalized Difference Vegetation Index. Incidence was ascertained using health administrative data with validated algorithms. Mixed-effects Cox models were used to estimate hazard ratios per interquartile range increase in green space exposure.

RESULTS

Between 2001 and 2013, 219,013 individuals were diagnosed with dementia and 89,958 had a stroke. The hazard ratio per interquartile range increase in green space was 0.97 (95% CI: 0.96-0.98) for dementia and 0.96 (0.95-0.98) for stroke. Estimates remained generally consistent in sensitivity analyses.

DISCUSSION

Increased exposure to urban green space was associated with reduced incidence of dementia and stroke. To our knowledge, this is the first population-based cohort study to assess these relationships.

The role of cardiovascular disease in the relationship between air pollution and incident dementia: a population-based cohort study

BACKGROUND

Evidence suggests a link between air pollution and dementia. Cardiovascular disease (CVD) may be a potential determinant of dementia. This motivated us to quantify the contribution of CVD to the association between air pollution and dementia.

METHODS

A cohort of Canadian-born residents of Ontario, who participated in the 1996-2003 Canadian Community Health Surveys, was followed through 2013 or until dementia diagnosis. Exposure to nitrogen dioxide (NO2) and fine particulate matter (PM2.5) was estimated with a 3-year average and 5-year lag before dementia diagnosis. Incident CVD was evaluated as a mediator. We used multi-level Cox proportional and Aalen additive hazard regression models, adjusting for individual- and neighbourhood-level risk factors to estimate associations with NO2 and PM2.5. We estimated the total, direct and indirect effects of air pollution on dementia through cardiovascular disease.

RESULTS

This study included 34 391 older adults. At baseline, the mean age of this cohort was 59 years. The risk of dementia was moderately higher among those more exposed to NO2 (hazard ratio (HR) 1.10, 95% confidence interval (CI) 0.99-1.19; and 100 additional cases per 100 000 [standard error (SE) <100x10-5]) and PM2.5 [(HR 1.29, 95% CI 0.99-1.64; 200 additional cases per 100 000] [SE 100x10-5]) after adjusting for covariates; however, these estimates are imprecise. A greater proportion of the relationship between PM2.5 and dementia was mediated through CVD than NO2 for both scales.

CONCLUSIONS

These results suggest some of the association between air pollution and dementia is mediated through CVD, indicating that improving cardiovascular health may prevent dementia in areas with higher exposure to air pollution.

Ambient air pollution and incidence of early-onset paediatric type 1 diabetes: A retrospective population-based cohort study

BACKGROUND

Studies have reported increasing incidence rates of paediatric diabetes, especially among those aged 0-5 years. Epidemiological evidence linking ambient air pollution to paediatric diabetes remains mixed.

OBJECTIVE

This study investigated the association between maternal and early-life exposures to common air pollutants (NO₂, PM_2.5, O₃, and oxidant capacity [Ox; the redox-weighted average of O₃ and NO₂]) and the incidence of paediatric diabetes in children up to 6 years of age.

METHODS

All registered singleton births in Ontario, Ca nada occurring between April 1st^, 2006 and March 31st^, 2012 were included through linkage from health administrative data. Monthly exposures to NO₂, PM_2.5, O₃, and O_x were estimated across trimesters, the entire pregnancy period and during childhood. Random effects Cox proportional hazards models were used to assess the relationships with paediatric diabetes incidence while controlling for important covariates. We also modelled the shape of concentration-response (CR) relationships.

RESULTS

There were 1094 children out of a cohort of 754,698 diagnosed with diabetes before the age of six. O₃ exposures during the first trimester of pregnancy were associated with paediatric diabetes incidence (hazard ratio (HR) per interquartile (IQR) increase = 2.00, 95% CI: 1.04-3.86). The CR relationship between O₃ during the first trimester and paediatric diabetes incidence appeared to have a risk threshold, in which there was little-to-no risk below 25 ppb of O₃, while above this level risk increased sigmoidally. No other associations were observed.

CONCLUSION

O₃ exposures during a critical period of development were associated with an increased risk of paediatric diabetes incidence.

Understanding the Joint Impacts of Fine Particulate Matter Concentration and Composition on the Incidence and Mortality of Cardiovascular Disease: A Component-Adjusted Approach

Past health impact assessments of ambient fine particulate matter (particles with an aerodynamic diameter ≤2.5 μm; PM_2.5) have generally considered mass concentration only, despite PM_2.5 being a heterogeneous mixture. Given constant changes in the concentration and the composition of atmospheric aerosol, uncertainty exists as to whether the current focus on PM_2.5 mass or individual components may fully characterize the health burden of PM_2.5. We proposed a component-adjusted method that jointly estimates the health impacts of PM_2.5 and its major components while allowing for a potential nonlinear PM_2.5-outcome relationship. Using this method, we quantified the effects of PM_2.5 on the risks of developing acute myocardial infarction (AMI) and dying from cardiovascular causes in comparison to three traditional approaches in the entire adult population across Ontario, Canada. We observed that PM_2.5 was positively associated with AMI incidence and cardiovascular mortality with all four methods. Compared to the traditional approaches, however, the new component-adjusted approach demonstrated a significant improvement in explaining the health impacts of PM_2.5, especially in the presence of a nonlinear PM_2.5-outcome relationship. Using the new approach, we found that the effects of PM_2.5 on AMI incidence and cardiovascular mortality may be 10% to 27% higher than what would be estimated from the conventional approaches examining PM_2.5 alone.