Previous meta-analyses on associations between air pollution (AP) and type 2 diabetes mellitus (T2DM) were mainly focused on studies conducted in high-income countries. Evidence should be updated by ...including more recent studies, especially those conducted in low- and middle-income countries. We therefore conducted a systematic review and meta-analysis of epidemiological studies to conclude an updated pooled effect estimates between long-term AP exposure and the prevalence and incidence of T2DM. We searched PubMed, Embase, and Web of Science to identify studies regarding associations of AP with T2DM prevalence and incidence prior to January 2019. A random-effects model was employed to analyze the overall effects. A total of 30 articles were finally included in this meta-analysis. The pooled results showed that higher levels of AP exposure were significantly associated with higher prevalence of T2DM (per 10 μg/m3 increase in concentrations of particles with aerodynamic diameter < 2.5 μm (PM2.5): odds ratio (OR) = 1.09, 95% confidence interval (95%CI): 1.05, 1.13; particles with aerodynamic diameter < 10 μm (PM10): OR = 1.12, 95%CI: 1.06, 1.19; nitrogen dioxide (NO2): OR = 1.05, 95%CI:1.03, 1.08). Besides, higher level of PM2.5 exposure was associated with higher T2DM incidence (per 10 μg/m3 increase in concentration of PM2.5: hazard ratio (HR) = 1.10, 95%CI:1.04, 1.16), while the associations between PM10, NO2 and T2DM incidence were not statistically significant. The associations between AP exposure and T2DM prevalence showed no significant difference between high-income countries and low- and middle-incomes countries. However, different associations were identified between PM2.5 exposure and T2DM prevalence in different geographic areas. No significant differences were found in associations of AP and T2DM prevalence/incidence between females and males, except for the effect of NO2 on T2DM incidence. Overall, AP exposure was positively associated with T2DM. There still remains a need for evidence from low- and middle-income countries on the relationships between AP and T2DM.
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•Long-term exposure to air pollution was associated with an increased risk of T2DM.•Different associations between females and males were not identified.•Epidemiologic studies on exposure-response relationship should be encouraged.
Long-term exposure to air pollution was positively associated with T2DM.
Air pollution has been associated with elevated blood pressure in adults. However, epidemiological evidence from children and adolescents is limited. We investigated the associations between ...long-term exposure to particulate matter (PM) air pollution and blood pressure in a large population of children and adolescents.
A cross-sectional analysis was performed in a nationally representative sample consisting of 43,745 children and adolescents aged 7 to 18 years in seven provinces in China. Exposure to ambient fine particles (PM2.5) and thoracic particles (PM10) was estimated using spatiotemporal models based on satellite remote sensing, meteorological data and land use information. Mixed-effects (two-level) linear and logistic regression models were used to investigate the associations between PM exposure and systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension.
After adjustment for a wide range of covariates, every 10 μg/m3 increment in PM2.5 and PM10 concentration was associated with 1.46 95% confidence interval (CI): 0.05, 2.88 and 1.36 (95% CI: 0.34, 2.39) mmHg increases in SBP, respectively. PM10 was also associated with higher prevalence of hypertension odds ratio per 10 μg/m3 increment: 1.45 (95% CI: 1.07, 1.95).
Long-term exposure to ambient PM air pollution was associated with increased blood pressure and higher prevalence of hypertension in children and adolescents. Our findings support air pollution reduction strategies as a prevention measure of childhood hypertension, a well-recognized risk factor of future cardiovascular health.
•There is limited evidence on air pollution and childhood blood pressure.•Exposure to PM 2.5 and PM10 was associated with increased systolic blood pressure in children and adolescents.•Exposure to PM10 was also associated with higher prevalence of hypertension.•No significant effect modification by sex or obesity was observed.
The evidence for adverse effects of ambient particulate matter (PM) pollution on mental health is limited. Studies in Western countries suggested higher risk of autism spectrum disorder (ASD) ...associated with PM air pollution, but no such study has been done in developing countries.
A case-control study was performed in Shanghai with a multi-stage random sampling design. Children's exposures to PM1, PM2.5 and PM10 (particulate matter with aerodynamic diameter < 1 μm, < 2.5 μm and < 10 μm, respectively) during the first three years after birth were estimated with satellite remote sensing data. Conditional logistic regression was used to examine the PM-ASD association.
In total, 124 ASD cases and 1240 healthy controls were included in this study. The median levels of PM1, PM2.5 and PM10 exposures during the first three years of life were 48.8 μg/m3, 66.2 μg/m3 and 95.4 μg/m3, respectively, and the interquartile range (IQR) for these three pollutants were 4.8 μg/m3, 3.4 μg/m3 and 4.9 μg/m3, respectively. The adjusted odds ratios (and 95% confidence intervals) of ASD associated with an IQR increase for PM1, PM2.5 and PM10 were 1.86 (1.09, 3.17), 1.78 (1.14, 2.76) and 1.68 (1.09, 2.59), respectively. Higher ORs of ASD associated with PM pollution were observed in the second and the third year after birth.
Exposures to PM1, PM2.5 and PM10 during the first three years of life were associated with the increased risk of ASD and there appeared to be stronger effects of ambient PM pollution on ASD in the second and the third years after birth.
•Post-natal exposure to PM1 significantly increased the risk of autism (OR = 1.86).•Post-natal exposure to PM2.5 significantly increased the risk of autism (OR = 1.78).•Stronger associations were observed in the second and the third year after birth.
•Long-term exposure to PM2.5 was associated with poor sleep quality (OR = 1.15).•Long-term exposure to NO2 was associated with poor sleep quality (OR = 1.14).•Exposure to air pollution showed adverse ...effects on sleep quality in rural China.•Health effects of air pollution in rural areas should be given more attention.
Poor sleep quality is associated with poor quality of life and may even lead to mental illnesses. Several studies have indicated the association between exposure to air pollution and sleep quality. However, the evidence is very limited in China, especially in rural areas.
Participants in this study were obtained from the Henan Rural Cohort established during 2015–2017. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) in the baseline survey. Poor sleep quality was defined by the global score of PSQI > 5. Participants’ exposures to PM2.5, PM10 (particulate matter with aerodynamic diameters ≤2.5 μm and 10 μm, respectively) and NO2 (nitrogen dioxide) during the three years before the baseline survey were estimated using a satellite-based prediction. The associations between long-term exposure to air pollutants and sleep quality were examined using both the linear regression and logistic regression models.
The IQRs (interquartile range) of mean levels of participants’ exposures to PM2.5, PM10 and NO2 were 3.3 µg/m3, 8.8 µg/m3, and 4.8 µg/m3, respectively. After adjusted for potential confounders, the global score of PSQI (and 95%CI, 95% confidence intervals) increased by 0.16 (0.04, 0.27), 0.09 (−0.01, 0.19) and 0.14 (0.03, 0.24), associated with per IQR increase in PM2.5, PM10 and NO2, respectively. The odds ratios (and 95%CI) of poor sleep quality associated with per IQR increase in PM2.5, PM10 and NO2 were 1.15 (1.03, 1.29), 1.11 (1.02, 1.21) and 1.14 (1.03, 1.25), respectively.
Long-term exposures to PM2.5, PM10 and NO2 were associated with poor sleep quality in rural China. Improvement of air quality may help to improve sleep quality among rural population of China.
Limited evidence is available on the health effects of particulate matter with an aerodynamic diameter of <1 μm (PM1), mainly due to the lack of its ground measurement worldwide.
To identify and ...examine the mortality risks and mortality burdens associated with PM1, PM2.5, and PM10 in Zhejiang province, China.
We collected daily data regarding all-cause (stratified by age and gender), cardiovascular, stroke, respiratory, and chronic obstructive pulmonary disease (COPD) mortality, and PM1, PM2.5, and PM10, from 11 cities in Zhejiang province, China during 2013 and 2017. We used a quasi-Poisson regression model to estimate city-specific associations between mortality and PM concentrations. Then we used a random-effect meta-analysis to pool the provincial estimates. To show the mortality burdens of PM1, PM2.5, and PM10, we calculated the mortality fractions and deaths attributable to these PMs.
Daily concentrations of PM1, PM2.5, and PM10 ranged between 0–199 μg/m3, 0–218 μg/m3, and 0–254 μg/m3, respectively; Mortality effects were significant in lag 0–2 days. The relative risks for all-cause mortality were 1.0064 (95% CI: 1.0034, 1.0094), 1.0061 (95% CI: 1.0034, 1.0089), and 1.0060 (95% CI: 1.0038, 1.0083) associated with a 10 μg/m3 increase in PM1, PM2.5, and PM10, respectively. Age- and gender-stratified analysis shows that elderly people (aged 65+) and females are more sensitive to PMs. The mortality fractions of all-cause mortality were estimated to be 2.39% (95% CI: 1.28, 3.48) attributable to PM1, 2.53% (95% CI: 1.42, 3.63) attributable to PM2.5, and 3.08% (95% CI: 1.95, 4.19) attributable to PM10. The ratios of attributable cause-specific deaths for PM1/PM2.5, PM1/PM10, and PM2.5/PM10 were higher than the ratios of their respective concentrations.
PM1, PM2.5 and PM10 are risk factors of all-cause, cardiovascular, stroke, respiratory, and COPD mortality. PM1 accounts for the vast majority of short-term PM2.5- and PM10-induced mortality. Our analyses support the notion that smaller size fractions of PM have a more toxic mortality impacts, which suggests to develop strategies to prevent and control PM1 in China, such as to foster strict regulations for automobile and industrial emissions.
•PM1, PM2.5 and PM10 are risk factors of all-cause, cardiovascular, stroke, respiratory, and COPD mortality.•PM1 accounts for the vast majority of short-term PM2.5- and PM10-induced mortality.•Smaller size fractions of PM have a more toxic mortality impacts.•PM1 has stronger effects on all-cause, cardiovascular, and stroke mortality in the warm season than in the cold season.
Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban-rural differences in the temperature impacts on health outcomes.
We investigated whether ...temperature-mortality relationships vary between urban and rural counties in China.
We collected daily data on 1 km gridded temperature and mortality in 89 counties of Zhejiang Province, China, for 2009 and 2015. We first performed a two-stage analysis to estimate the temperature effects on mortality in urban and rural counties. Second, we performed meta-regression to investigate the modifying effect of the urbanization level. Stratified analyses were performed by all-cause, nonaccidental (stratified by age and sex), cardiopulmonary, cardiovascular, and respiratory mortality. We also calculated the fraction of mortality and number of deaths attributable to nonoptimum temperatures associated with both cold and heat components. The potential sources of the urban-rural differences were explored using meta-regression with county-level characteristics.
Increased mortality risks were associated with low and high temperatures in both rural and urban areas, but rural counties had higher relative risks (RRs), attributable fractions of mortality, and attributable death counts than urban counties. The urban-rural disparity was apparent for cold (first percentile relative to minimum mortality temperature), with an RR of 1.47 95% confidence interval (CI): 1.32, 1.62 associated with all-cause mortality for urban counties, and 1.98 (95% CI: 1.87, 2.10) for rural counties. Among the potential sources of the urban-rural disparity are age structure, education, GDP, health care services, air conditioners, and occupation types.
Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly. The findings suggest past studies using exposure-response functions derived from urban areas may underestimate the mortality burden for the population as a whole. The public health agencies aimed at controlling temperature-related mortality should develop area-specific strategies, such as to reduce the urban-rural gaps in access to health care and awareness of risk prevention. Future projections on climate health impacts should consider the urban-rural disparity in mortality risks. https://doi.org/10.1289/EHP3556.
•Positive associations of air pollutants with MetS were found in rural Chinese adults.•Physical activity was negatively associated with MetS in rural Chinese adults.•Air pollutants attenuated the ...negative association of physical activity with MetS.
Long-term exposure to ambient air pollution and physical activity are linked to metabolic syndrome (MetS). However, the joint effect of physical activity and ambient air pollution on MetS remains largely unknown in rural Chinese adult population.
In this study, 39 089 individuals were included from the Henan Rural Cohort study that recruited 39 259 individuals at the baseline. Participants' exposure to air pollutants (including particulate matter with an aerodynamic diameter ≤ 1.0 µm (PM1), ≤2.5 µm (PM2.5), or ≤ 10 µm (PM10) and nitrogen dioxide (NO2)) were evaluated by using a spatiotemporal model based on satellites data. Individuals were defined as MetS according to the recommendation of the Joint Interim Societies. Physical activity-metabolic equivalent (MET) was calculated based on the formula of MET coefficient of activity × duration (hour per time) × frequency (times per week). Generalized linear models were used to analyze the individual air pollutant or physical activity and their interaction on MetS. Interaction effects of individual air pollutant and physical activity on MetS were assessed by using Interaction plots which exhibited the estimated effect of physical activity on MetS as a function of individual air pollutant.
The prevalence of MetS was 30.8%. The adjusted odd ratio of MetS with a per 5 µg/m3 increase in PM1, PM2.5, PM10, NO2 or a 10 MET (hour/day) of physical activity increment was 1.251(1.199, 1.306), 1.424(1.360, 1.491), 1.228(1.203, 1.254), 1.408(1.363, 1.455) or 0.814(0.796, 0.833). The protective effect of physical activity on MetS was decreased with accompanying air pollutant concentrations increased.
The results indicated that long-term exposure to ambient air pollutants related to increased risk of MetS and physical activity attenuated the effects of ambient air pollutants on increased risk for MetS.
The epidemiological evidence on relationships between long-term exposure to particulate matter and hypertension and blood pressure has been inconclusive. Limited evidence was available for ...particulate matter with an aerodynamic diameter ≤ 1 μm (PM1) in rural areas of developing countries.
This study aimed to investigate the associations between long-term exposure to PM1 and hypertension and blood pressure among rural Chinese population.
This study included 39,259 participants who had completed the baseline survey from Henan Rural Cohort. Participants' exposure to PM1 was assessed by a satellite-based spatiotemporal model. The binary logistic regression model was used to examine the association between long-term PM1 exposure and hypertension, and multivariable linear regression model was used to investigate the associations between long-term PM1 exposure and systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP). Moreover, we examined potential effect modifications by demographic, lifestyle and diet factors.
The mean concentration of PM1 for all participants during the 3-year before baseline survey was 59.98 μg/m3. Each 1 μg/m3 increase in PM1 concentration was significantly associated with an increase of 4.3% Odds ratio(OR) = 1.043, 95% confidence interval(CI): 1.033, 1.053 in odds for hypertension, an increase of 0.401 mm Hg (95% CI, 0.335, 0.467), 0.328 mm Hg (95% CI, 0.288, 0.369), 0.353 mm Hg (95% CI, 0.307, 0.399) and 0.073 mm Hg (95% CI, 0.030, 0.116) in SBP, DBP, MAP and PP, respectively. Further stratified analyses showed that the effect of PM1 on hypertension and blood pressure could be modified by sex, lifestyle and diet.
This study suggests that long-term exposure to ambient PM1 increases the risk of hypertension and is associated with elevations in blood pressure in rural Chinese adults, especially in male and those with unhealthy habits.
•Rural cohort participants were exposed to high level of PM1 during the study period.•PM1 was related to increased risk of hypertension and elevated blood pressure.•Males and those with unhealthy habits were more susceptible to the adverse effect.
•Higher residential greenness was associated with better sleep quality in rural China.•The association was stronger in males and participants with higher household income.•The association was ...stronger in participants with higher educational attainment.•Exposure to air pollution modified the association of greenness with sleep quality.
Epidemiological studies on the association of residential greenness with sleep quality are limited in China.
This study aims to investigate the association of long-term exposure to residential greenness with sleep quality in rural China.
In our study, 27,654 rural residents were selected from 4 counties of Henan Province by a multi-stage stratified cluster sampling method. Participants’ sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), two satellite-derived vegetation indexes, were used to assess the level of residential greenness. Long-term greenness exposure was defined as the averages of NDVI and EVI during the three years prior to the baseline survey. The relationship between sleep quality and greenness was assessed using the mixed-effect linear regression models.
Among 27,654 rural residents, the mean age was 55.89 years (standard deviation, SD = 12.22) and 60.18% of them were female. In the crude model, the PSQI score decreased with per interquartile range (IQR) increase in EVI and NDVI ΔPSQI score (95% confidence interval, 95%CI): −0.073 (−0.115, −0.030) and −0.047 (−0.089, −0.002). After controlling potential confounders, ΔPSQI scores and 95%CIs were −0.055 (−0.095, −0.012) and −0.090 (−0.151, −0.025) associated with per IQR increment in EVI and NDVI. The results of stratified analyses showed the effect of residential greenness on sleep was stronger among males and individuals with higher household income and educational attainment than females and those with lower household income and educational attainment. Moreover, the modification effect of air pollution was observed in the greenness-sleep association.
Our study indicated that higher residential greenness was significantly associated with better sleep quality in Chinese rural population, which highlights the significant effect of green space on human health.
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•First study on the risk of preterm birth/term low birth weight associated with wildfire-specific PM2.5 in New South Wales, Australia.•14.30% preterm births and 8.04% term low birth ...weight cases attributable to maternal exposure to wildfire-specific PM2.5.•Susceptibility higher in male infants and mothers > 40 years, suffering medical conditions, temperature extremes, conceived in spring or from inner region for preterm birth.•Vulnerability greater in male infants and mothers < 20 years, smoking, experiencing heat, conceived in spring or from very remote areas for term low birth weight.
Exposure to wildfire smoke has been linked with a range of health outcomes. However, to date, evidence is limited for the association between wildfire-specific PM2.5, a primary emission of wildfire smoke, and adverse birth outcomes.
We aimed to estimate the risk and burden of preterm birth/term low birth weight, associated with maternal exposure to wildfire-specific PM2.5.
A total of 330,884 birth records with maternal information were collected from the New South Wales Australia from 2015 to 2019, covering 523 residential communities. Daily wildfire-specific PM2.5 at a 0.25° × 0.25° (≈ 25 km × 25 km) resolution was estimated by a machine learning method combining 3-D chemical transport model (GEOS-Chem) and reanalysis meteorological data. Cox proportional hazards models were implemented to evaluate the association between wildfire-specific PM2.5 and preterm birth/term low birth weight. Number and fraction of preterm birth/term low birth weight attributable to wildfire-specific PM2.5 during pregnancy were calculated.
Per one interquartile-range rise in wildfire-specific PM2.5 was found to be associated with 6.9% (HR: 1.069, 95% CI: 1.058–1.081) increased risk of preterm birth and 3.6% (HR: 1.036, 95% CI: 1.014–1.058) higher risk of term low birth weight. The most susceptible gestational window was the 2nd trimester for preterm birth whereas the 1st for term low birth weight. We estimated that 14.30% preterm births and 8.04% term low birth weight cases were attributable to maternal exposure to wildfire-specific PM2.5 during the whole pregnancy. Male infants and mothers aged ≥ 40, experiencing temperature extremes or living in the inner region, and concepted during spring had higher risks of preterm birth/term low birth weight associated with wildfire-specific PM2.5. Comparatively, mothers with advanced age have a higher risk of preterm birth while younger mothers were more likely to deliver term newborns with low birth weight, when being exposed to wildfire-specific PM2.5. Pregnancy-induced hypertension enhanced the risk of preterm birth associated with wildfire-specific PM2.5.
This study strengthened robust evidence on the enhanced risk of preterm birth/term low birth weight associated with maternal exposure to wildfire-specific PM2.5. In light of higher frequency and intensity of wildfire occurrences globally, more special attention should be paid to pregnant women by policy makers.
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