Few cohort studies explored the long-term effects of ambient fine particulate matter (PM2.5) on incidence of cardiovascular diseases (CVDs), especially in countries with higher levels of air pollution. We aimed to evaluate the association between long-term exposure to PM2.5 and incidence of CVD in China. We performed a prospective cohort study in ten regions that recruited 512,689 adults during 2004-2008, with follow-up until 2017. Annual PM2.5 concentrations were estimated using a satellite-based model with national coverage and 1 x 1 km spatial resolution. Time-varying Cox proportional hazard regression models were used to estimate hazard ratios (HRs) for all-cause and cause-specific CVDs associated with PM2.5, adjusting for conventional covariates. During 5.08 million person-years of follow-up, 148,030 incident cases of CVD were identified. Long-term exposure to PM2.5 showed positive and linear association with incidence of CVD, without a threshold below any concentration. The adjusted HRs per 10 μg/m3 increase in PM2.5 was 1.04 (95%CI: 1.02, 1.07) for total CVD. The risk estimates differed between certain population subgroups, with greater HRs in men, in household with higher income, and in people using unclean heating fuels. This prospective study of large Chinese population provided essential epidemiological evidence for CVD incident risk associated with PM2.5.
Although emerging researches have linked ambient fine particulate matter (PM2.5) to obesity, evidence from high-polluted regions is still lacking. We thus assessed the long-term impacts of PM2.5 on body mass index (BMI) and the risk of the prevalence of overweight/obesity (BMI≥25 kg/m2), by incorporating the well-established Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project comprising 77,609 participants with satellite-based PM2.5 estimates at 1-km spatial resolution. The average of long-term PM2.5 level was 70.4 μg/m3, with the range of 32.1–94.2 μg/m3. Each 10 μg/m3 increment of PM2.5 was associated with 0.421 kg/m2 (95% confidence interval [CI]: 0.402, 0.439) and 13.5% (95% CI: 12.8%, 14.3%) increased BMI and overweight/obesity risk, respectively. Moreover, compared with the lowest quartile of PM2.5 (≤57.5 μg/m3), the relative risk of the prevalence of overweight/obesity from the highest quartile (>85.9 μg/m3) was 1.611 (95% CI: 1.566, 1.657). The exposure-response curve suggested a non-linear relationship between PM2.5 exposure and overweight/obesity. Besides, the association was modified by age, diabetes mellitus, hypertension and dyslipidemia status. Our study provides the evidence for the adverse impacts of long-term PM2.5 on BMI and overweight/obesity in China, and the findings are important for policy development on air quality, especially in severely polluted areas.
Background: A growing body of evidence has confirmed the association between fine particulate matter (PM2.5) and ocular diseases, but little is known on the effect of long-term PM2.5 exposure on glaucoma. Methods: A national cross-sectional study of the Rural Epidemiology for Glaucoma was conducted in 10 provinces of China, and 33,701 adults aged 40 years or more were included. A satellite-based model at 1-km resolution level was used to estimate PM2.5 concentrations which were assigned to each participant according to geocoded home addresses. Logistic regression model was performed to investigate associations of long-term PM2.5 exposure with glaucoma and its subtypes. Results: Estimated PM2.5 concentrations ranged from 28.0 to 96.4 μg/m3. For each 10 μg/m3 increment in PM2.5, the adjusted odds ratios (ORs) were 1.07 (95% CI: 1.00–1.15) and 1.14 (95% CI: 1.02–1.26) for glaucoma and primary angle-closure glaucoma (PACG), respectively. A positive but non-significant association (OR = 1.05, 95% CI: 0.92–1.18) was detected between long-term exposure to PM2.5 and odds of primary open-angle glaucoma. The middle aged residents and non-smokers were more sensitive to the adverse effects of PM2.5. Conclusions: Long-term PM2.5 exposure was associated with glaucoma and PACG in Chinese adults, which provided new insights on adverse ophthalmic effect of PM2.5.
Background: Few studies have estimated effects of maternal PM2.5 exposure on birth outcomes in China due to the lack of historical air pollution data. Objectives: We estimated the associations between maternal PM2.5 exposure and birth outcomes using gap-filled satellite estimates in Shanghai, China. Methods: We obtained birth registration records of 132,783 singleton live births during 2011–2014 in Shanghai. PM2.5 exposures were assessed from satellite-derived estimates or central-site measurements. Linear and logistic regressions were used to estimate associations with term birth weight and term low birth weight (LBW), respectively. Logistic and discrete-time survival models were used to estimate associations with preterm birth. Effect modification by maternal age and parental education levels was investigated. Results: A 10 μg/m3 increase in gap-filled satellite-based whole-pregnancy PM2.5 exposure was associated with a −12.85 g (95% CI: −18.44, −7.27) change in term birth weight, increased risk of preterm birth (OR 1.27, 95% CI: 1.20, 1.36), and increased risk of term LBW (OR 1.22, 95% CI: 1.06, 1.41). Sensitivity analyses during 2013–2014, when ground PM2.5 measurements were available, showed that the health associations using gap-filled satellite PM2.5 concentrations were higher than those obtained using satellite PM2.5 concentrations without accounting for missingness. The health associations using gap-filled satellite PM2.5 had similar magnitudes to those using central-site measurements, but with narrower confidence intervals. Conclusions: The magnitude of associations between maternal PM2.5 exposure and adverse birth outcomes in Shanghai was higher than previous findings. One reason could be reduced exposure error of the gap-filled high-resolution satellite PM2.5 estimates.
Air pollution is a major environmental and public health challenge in China and the Chinese government has implemented a series of strict air quality policies. However, particulate nitrate (NO3−) concentration remains high or even increases at monitoring sites despite the total PM2.5 concentration has decreased. Unfortunately, it has been difficult to estimate NO3− concentration across China due to the lack of a PM2.5 speciation monitoring network. Here, we use a machine learning model incorporating ground measurements and satellite data to characterize the spatiotemporal patterns of NO3−, thereby understanding the disease burden associated with long-term NO3− exposure in China. Our results show that existing air pollution control policies are effective, but increased NO3− of traffic emissions offset reduced NO3− of industrial emissions. In 2018, the national mean mortality burden attributable to NO3− was as high as 0.68 million, indicating that targeted regulations are needed to control NO3− pollution in China.
In January 2013, severe haze events over northeastern China sparked substantial health concerns. This study explores the associations of fine particulate matter less than 2.5 _m (PM 2.5 ) and black carbon (BC) with hospital emergency room visits (ERVs) during a haze season in Beijing. During that period, daily counts of ERVs for respiratory, cardiovascular and ocular diseases were obtained from a Level-3A hospital in Beijing from 1 December 2012 to 28 February 2013, and associations of which with PM 2.5 and BC were estimated by time-stratified case-crossover analysis in singleand two-pollutant models. We found a 27.5% (95% confidence interval (CI): 13.0, 43.9%) increase in respiratory ERV (lag02), a 19.4% (95% CI: 2.5, 39.0%) increase in cardiovascular ERV (lag0), and a 12.6% (95% CI: 0.0, 26.7%) increase in ocular ERV (lag0) along with an interquartile range (IQR) increase in the PM 2.5 . An IQR increase of BC was associated with 27.6% (95% CI: 9.6, 48.6%) (lag02), 18.8% (95% CI: 1.4, 39.2%) (lag0) and 11.8% (95% CI: ‑1.4, 26.8%) (lag0) increases for changes in these same health outcomes respectively. Estimated associations were consistent after adjusting SO2 or NO2 in two-pollutant models. This study provides evidence that improving air quality and reducing haze days would greatly benefit the population health.
Despite the recent development of using satellite remote sensing to predict surface NO2 levels in China, methods for estimating reliable historical NO2 exposure, especially before the establishment of NO2 monitoring network in 2013, are still rare. A gap-filling model was first adopted to impute the missing NO2 column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO2 concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO2 associated mortality burdens in China. The coverage of satellite NO2 column densities increased from 46.9% to 100% after gap-filling. The ensemble model predictions had good agreement with observations, and the sample-based, temporal and spatial cross-validation (CV) R2 were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO2 concentrations, with both by-year CV R2 and external separate year validation R2 achieving 0.80. The estimated national NO2 levels showed a increasing trend during 2005–2011, then decreased gradually until 2020, especially in 2012–2015. The estimated annual mortality burden attributable to long-term NO2 exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite-based ensemble model could provide reliable long-term NO2 predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO2 and call for more targeted policies to reduce the emission of nitrogen oxides in China.
Background: Associations between ambient particulate matter < 2.5 μm (PM2.5) and asthma morbidity have been suggested in previous epidemiologic studies but results are inconsistent for areas with lower PM2.5 levels. We estimated the associations between early-life short-term PM2.5 exposure and the risk of asthma or wheeze clinical encounters among Massachusetts children in the innovative Pregnancy to Early Life Longitudinal (PELL) cohort data linkage system. Methods: We used a semi-bidirectional case-crossover study design with short-term exposure lags for asthma exacerbation using data from the PELL system. Cases included children up to 9 years of age who had a hospitalization, observational stay, or emergency department visit for asthma or wheeze between January 2001 and September 2009 (n = 33,387). Daily PM2.5 concentrations were estimated at a 4-km resolution using satellite remote sensing, land use, and meteorological data. We applied conditional logistic regression models to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CI). We also stratified by potential effect modifiers. Results: The median PM2.5 concentration among participants was 7.8 μg/m3 with an interquartile range of 5.9 μg/m3. Overall, associations between PM2.5 exposure and asthma clinical encounters among children at lags 0, 1 and 2 were close to the null value of OR = 1.0. Evidence of effect modification was observed by birthweight for lags 0, 1 and 2 (p < 0.05), and season of clinical encounter for lags 0 and 1 (p < 0.05). Children with low birthweight (LBW) (< 2500 g) had increased odds of having an asthma clinical encounter due to higher PM2.5 exposure for lag 1 (OR: 1.08 per interquartile range (IQR) increase in PM2.5; 95% CI: 1.01, 1.15). Conclusion: Asthma or wheeze exacerbations among LBW children were associated with short-term increases in PM2.5 concentrations at low levels in Massachusetts.
Background: Increased physical activity is associated with a reduced risk of cardiovascular disease, but outdoor physical activity can be accompanied by increased inhalation of fine particulate matter (PM2·5). The extent to which long-term exposure to PM2·5 can offset the cardiovascular benefits of physical activity is unknown. We aimed to evaluate whether the associations between active commuting or farming activity and incident risks of cerebrovascular disease and ischaemic heart disease were consistent between populations with different ambient PM2·5 exposures. Methods: We did a prospective cohort study using data from people aged 30–79 years without cardiovascular disease at baseline from the China Kadoorie Biobank (CKB). Active commuting and farming activity were assessed at baseline using questionnaires. A high-resolution (1 × 1 km) satellite-based model was used to estimate annual average PM2·5 exposure during the study period. Participants were stratified according to PM2·5 exposure (54 μg/m3 or greater vs less than 54 μg/m3). Hazard ratios (HRs) and 95% CIs for incident cerebrovascular disease and ischaemic heart disease by active commuting and farming activity were estimated using Cox proportional hazard models. Effect modifications by PM2·5 exposure were tested by likelihood ratio tests. Analyses were restricted to the period from Jan 1, 2005, to Dec 31, 2017. Findings: Between June 25, 2004, and July 15, 2008, 512 725 people were enrolled in the CKB cohort. 322 399 eligible participants completed the baseline survey and were included in the analysis of active commuting (118 274 non-farmers and 204 125 farmers). Among 204 125 farmers, 2985 reported no farming time and 201 140 were included in the farming activity analysis. During a median follow-up of 11 years, 39 514 cerebrovascular disease cases and 22 313 ischaemic heart disease cases were newly identified. Among non-farmers with exposure to annual average PM2·5 concentrations of less than 54 μg/m3, increased active commuting was associated with lower risks of cerebrovascular disease (highest active commuting vs lowest active commuting HR 0·70, 95% CI 0·65–0·76) and ischaemic heart disease (0·60, 0·54–0·66). However, among non-farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater, there was no association between active commuting and cerebrovascular disease or ischaemic heart disease. Among farmers with exposure to annual average PM2·5 concentrations of less than 54 μg/m3, increased active commuting (highest active commuting vs lowest active commuting HR 0·77, 95% CI 0·63–0·93) and increased farming activity (highest activity vs lowest activity HR 0·85, 95% CI 0·79–0·92) were both associated with a lower cerebrovascular disease risk. However, among farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater, increases in active commuting (highest active commuting vs lowest active commuting HR 1·12, 95% CI 1·05–1·19) and farming activity (highest activity vs lowest activity HR 1·18, 95% CI 1·09–1·28) were associated with an elevated cerebrovascular disease risk. The above associations differed significantly between PM2·5 strata (all interaction p values <0·0001). Interpretation: For participants with long-term exposure to higher ambient PM2·5 concentrations, the cardiovascular benefits of active commuting and farming activity were significantly attenuated. Higher levels of active commuting and farming activity even increased the cerebrovascular disease risk among farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater. Funding: National Natural Science Foundation of China, National Key Research and Development Program of China, Kadoorie Charitable Foundation, UK Wellcome Trust.
China faces increasing health risks from climate change. The structure and function of the eye and vision were affected by extreme heat and cold. The study aimed to evaluate the impacts of heatwaves and cold spells on glaucoma. A national cross-sectional study of the Rural Epidemiology for Glaucoma (REG-China) was conducted in ten provinces of China, and 36,081 adults aged 40 years or more were included. Glaucoma signs were assessed via a standard examination. A total of 15 heatwave definitions, based on intensity (95th to 99th percentiles of temperature distribution) and duration (≥2 days, 3 days, and 4 days), were used to quantify heatwave effects, and 6 cold spell definitions were defined based on threshold temperature percentile (5th and 10th) and duration (3 days, 5 days, and 9 days). Multivariable-adjusted logistic regression models paired with interaction analysis were performed to investigate the impacts of heatwaves and cold spells on glaucoma, and the dose-response relationships were assessed using a restricted cubic spline (RCS) model. Subgroup analysis was conducted stratified by gender, age, smoking status, occupation, and family history of glaucoma. The overall prevalence of glaucoma was 2.1% (95% CI 1.94–2.25%). Higher heatwaves were significantly correlated with higher OR of glaucoma, with the OR (95% CI) ranging from 1.014 (1.009, 1.018) to 1.090 (1.065, 1.115) by different definitions. Glaucoma was affected by heatwaves more strongly than by cold spells. The effects of both heatwaves and cold spells were higher in males than females and in smokers than nonsmokers. These results of the present study evoked the attention of prospective research to elucidate the relationship between extreme temperatures and eye diseases.