Short-term exposure to ambient air pollution and type 2 diabetes mortality: A population-based time series study

Associations of short-term exposure to air pollution with outpatient visits and treatment costs for chronic obstructive pulmonary disease in Xinxiang, China (2016-2021)

The acute health effects of air pollution on the risk of chronic obstructive pulmonary disease (COPD) have not been adequately studied and results remain inconsistent. Furthermore, fewer studies have explored the impact of air pollution on the cost of treating patients with COPD. Generalized additive models (GAM) based on Poisson distribution and gamma were applied to evaluate the association between short-term exposure to air pollution and daily COPD outpatient visits and daily COPD treatment costs. A total of 14,611 outpatient in Xinxiang from 2016 to 2021 were included for analysis. We found that short-term exposure to PM2.5, PM10, NO2 and CO were positively associated with COPD outpatient visits, and gaseous pollutants appeared to have greater effects on outpatient visits than particulate matter. For the largest effect, per 10 μg/m3 increment in (per 1 mg/m3 increment in CO concentration) CO (lag 01), NO2 (lag 01), PM2.5 (lag 02) and PM10 (lag 06) were significantly associated with 7.859 % (95 % CI:3.421,12.488), 4.894 % (95 % CI:3.422,6.386), 0.627 % (95 % CI:0.010, 1.248) and 0.531 % (95 % CI:0.050,1.014) increase in daily COPD outpatient visits, respectively. Short-term exposure to air pollutants (PM10, CO and NO2) was positively associated with COPD treatment costs. No significant sex or age differences were found in the stratified analysis of outpatient visits. The effect of gaseous pollutants (NO2) on COPD outpatient visits was greater in the cold season (October to March) (P < 0.05), whereas the effect of particulate matter (PM2.5 and PM10) was greater in the warm season (April to September) (P < 0.05). Greater health benefits could be obtained when pollutant concentrations meet WHO standards. In conclusion, short-term exposure to PM2.5, PM10, NO2 and CO was significantly associated with increased COPD outpatient visits, and gaseous pollutants appeared to have greater effects on outpatient visits than particulate matter. Further larger-scale studies are needed to validate our findings.

Exposure to air pollution and the risk of type II diabetes mellitus: a time-series study

Background

Environmental factors have been identified as primary risk factors for type 2 diabetes mellitus (T2DM). However, studies on the association between environmental factors and T2DM have mainly focused on morbidity and mortality, which do not fully reflect the disease burden stemming from air pollution. Therefore, we aimed to evaluate the correlation between air pollution and T2DM, including hospital length of stay (LOS) and costs.

Methods

We collected data on patients with T2DM from three healthcare institutions in Xinxiang from 2016-2021. Data on particulate and gaseous pollutants in Xinxiang and daily meteorological data were collected from national databases. The distribution lag nonlinear model was used to evaluate the correlation between air pollution and the number of inpatients with T2DM, LOS, and hospital costs. Subgroup analyses were conducted to identify potential modifying factors.

Results

Overall, 13,797 patients with T2DM were included in our analysis. Within the cumulative lag of 7 days, with every increase of 1 mg/m3 of carbon monoxide (CO) and 10 μg/m3 of 2.5 microns particulate matter, nitrogen dioxide and ozone exhibited significant associations with an increase in diabetes hospitalization risk. CO exhibited adverse effects on LOS on most lag days. Moreover, hospital costs were significantly associated with the attributable fraction of LOS and hospital costs attributed to diabetes.

Conclusions

Exposure to air pollutants increased T2DM risk, imposing significant economic and social burdens in Xinxiang, China. Implementing policies to reduce air pollutant exposure may decrease T2DM admissions, costs, and LOS.

The burden of type 2 diabetes attributable to air pollution across China and its provinces, 1990-2021: an analysis for the Global Burden of Disease Study 2021

Background

Temporal trends and geographical disparities in type 2 diabetes burden attributable to air pollution, including ambient and household, are not fully understood within China. This study aims to estimate the burden of type 2 diabetes attributable to air pollution at national and provincial levels from 1990 to 2021.

Methods

We assessed air pollution exposure across 33 Chinese provinces, autonomous regions, municipalities, and special administrative regions, focusing on two common forms of air pollution: ambient particulate matter pollution (defined as the annual gridded concentration of PM2.5) and household air pollution (defined as the percentage of households using solid cooking fuels and their corresponding exposure to PM2.5). We employed the methods from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 to estimate the attribution of air pollution on type 2 diabetes deaths and disability-adjusted life years (DALYs) by age, sex, year, and province.

Findings

In 2021, about a fifth of the national type 2 diabetes burden was attributable to air pollution, with an age-standardised estimate of 1.76 deaths and 110.79 DALYs per 100,000 population, higher in males. Ambient PM2.5 contributed to 16.89% of deaths and 16.36% of DALYs, while household air pollution contributed to 3.24% of deaths and 3.07% of DALYs. From 1990 to 2021, type 2 diabetes mortality rates due to ambient PM2.5 pollution increased by 264.23%, whereas those from household air pollution decreased by 80.8%. In 2021, Beijing had the highest population attributable fraction (PAFs) of DALYs due to ambient PM2.5 pollution at 19.63%, while Tibet had the highest PAFs for household air pollution at 13.72%. The age-standardised DALYs rates for type 2 diabetes due to ambient PM2.5 varied widely across provinces, from 143.8 per 100,000 people in Tianjin to 21.6 per 100,000 people in Tibet.

Interpretation

Air pollution, especially ambient PM2.5, is a significant risk factor for type 2 diabetes in China. Urgent action is needed to enhance air pollution control and develop locally adapted preventive strategies to reduce the burden of air pollution-related type 2 diabetes.

Funding

Sanming Project of Medicine in Shenzhen (NO. SZSM202111001).

The association between ozone exposure and blood pressure in a general Chinese middle-aged and older population: a large-scale repeated-measurement study

BACKGROUND

The relationship between ozone (O3) exposure and blood pressure (BP) remains inconclusive. Given the scarcity of Chinese epidemiological data, more research on this association is of paramount importance, particularly among middle-aged and older Chinese populations.

METHODS

This study involved 10,875 participants (median age: 60.0 years) in Xiamen, China, from 2013 to 2019, with 34,939 repeated BP measurements. Air pollutant exposure data, including O3, particulate matter, nitrogen dioxide, sulfur dioxide, and carbon monoxide were derived from China High Air Pollutants and High-resolution Air Quality Reanalysis datasets using a k-nearest neighbor algorithm. The relationship between mixed air pollutant exposure and BP was evaluated using Bayesian kernel machine regression model. The effects of daily-specific O3 exposure on BP were assessed by distributed lag models integrated into a linear mixed-effects framework. The mediating role of total cholesterol (TC), serum total bilirubin (STB), triglyceride (TG), and low-density lipoprotein (LDL) were examined using multilevel mediation analysis with a fully adjusted model.

RESULTS

Mixed air pollutant exposure was positively correlated with BP, with O3 being a predominant contributor exhibiting an inverse effect. O3 exposure had immediate effects on pulse pressure (PP), while systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) showed delayed responses, with 3-, 14-, and 8-day lags, respectively. During the study period of up to 30 days, each 10 μg/m3 increase in maximum daily 8-h average O3 concentration was associated with reductions in SBP (β =  - 1.176 mm Hg), DBP (- 0.237 mm Hg), PP (β =  - 0.973 mm Hg), and MAP (β =  - 0.544 mm Hg). Stronger correlations were observed in the older participants (aged ≥ 65 years), overweight/obese individuals, smokers and alcohol consumers, and those with hypertension or type 2 diabetes mellitus. STB and LDL mediated these effects, while TC and TG played mitigating roles.

CONCLUSIONS

Short-term O3 exposure is negatively associated with BP in middle-aged and older Chinese individuals. The findings provide preliminary evidence for the impact of O3 exposure on BP regulation and underscore the urgent need to reassess public health policies in response to O3 pollution.

Secular trends in type 2 diabetes mellitus attributable to PM2.5 exposure in China from 1990 to 2019: an age-period-cohort analysis

Secular trends of mortality and disability-adjusted life years (DALY) in type 2 diabetes mellitus (T2DM) attributable to PM2.5 exposure in China remain unclear. This study applied the joinpoint regression analysis and age-period-cohort model to assess the secular trends. There was a slight alternation in age-standardized rate of mortality and DALY in the total population, while the changes were increased in males and decreased in females from 1990 to 2019. Meanwhile, the changes attributable to ambient particular matter pollution exposure (APE) increased significantly and reduced household air pollution from solid fuels exposure (HPE). Longitudinal age curves showed that T2DM mortality and DALY increased with age. Period rate ratios (RR) attributable to APE increased but fell to HPE. Similar trends were observed in the cohort RR. PM2.5 exposure is more harmful to males and older people. The type of air pollution responsible for T2DM has changed from HPE to APE.

Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period

OBJECTIVE

At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.

METHODS

Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day.

RESULTS

In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4-12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B.

CONCLUSIONS

The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.

Risk of heavy metal(loid) compositions in fine particulate matter on acute cardiovascular mortality: a poisson analysis in Anyang, China

Fine particulate matter (PM2.5) is a risk factor of cardiovascular disease. Associations between PM2.5 compositions and cardiovascular disease are a point of special interest but inconsistent. This study aimed to explore the cardiovascular effects of heavy metal(loid) compositions in PM2.5. Data for mortality, air pollutants and meteorological factors in Anyang, China from 2017 to 2021 were collected. Heavy metal(loid) in PM2.5 were monitored and examined monthly. A Case-crossover design was applied to the estimated data set. The interquartile range increase in cadmium (Cd), antimony (Sb) and arsenic (As) at lag 1 was associated with increment of 8.1% (95% CI: 3.3, 13.2), 4.8% (95% CI: 0.2, 9.5) and 3.5% (95% CI: 1.1, 6.0) cardiovascular mortality. Selenium in lag 2 was inversely associated with cerebrovascular mortality (RR = 0.920 95% CI: 0.862, 0.983). Current-day exposure of aluminum was positively associated with mortality from ischemic heart disease (RR = 1.083 95% CI: 1.001, 1.172). Stratified analysis indicated sex, age and season modified the cardiovascular effects of As (P < 0.05). Our study reveals that heavy metal(loid) play key roles in adverse effects of PM2.5. Cd, Sb and As were significant risk factors of cardiovascular mortality. These findings have potential implications for accurate air pollutants control and management to improve public health benefits.

Effects of sulfur dioxide inhalation on human health: a review

Sulfur dioxide (SO2) is one of the most important gaseous air pollutants and the chemical index of sulfur oxides (SOx). SO2 is one of the six criteria pollutants in the air quality index (AQI). SO2 can be emitted by natural and anthropogenic sources. Although efforts have been made to reduce sulfur dioxide emissions worldwide, this pollutant and its adverse effects remain a major concern, especially in developing countries. The aim of this study was the investigated the effects of sulfur dioxide inhalation on human health. This narrative review was done based on the literature published from 2000 to 2022 through PubMed, Springer, Web of Science, Science Direct, and Google Scholar databases. In this study, was done screened first based on the abstract and Final assessment done based on the full text of the article. Finally, 38 articles were selected for inclusion in the study. The results of this study showed that sulfur dioxide has adverse health effects on the human respiratory, cardiovascular, and nervous systems and causes type 2 diabetes and non-accidental deaths. Although some evidence suggests that sulfur dioxide in given concentrations has no adverse health effect, its synergistic effects in combination with other air pollutants may be significant. Among the most important practical results of this study can be mentioned to increase the health awareness of the general public, help the politicians of the health sector in making decisions in the health field, creating awareness among polluting producing units and industries and efforts to reduce the emission of Sulfur dioxide.

Air pollution and cancer daily mortality in Hangzhou, China: an ecological research

BACKGROUND

Long-term exposure to air pollution has been linked to cancer incidence. However, the evidence is limited regarding the effect of short-term exposure to air pollution on cancer mortality.

OBJECTIVES

This study aimed to investigate associations between short-term exposure to air pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter <10 mm (PM10) and PM2.5) and cancer daily mortality.

METHODS

This study used air quality, meteorological and daily cancer death data from 2014 to 2019 in Hangzhou, China. Generalised additive models (GAM) with quasi-Poisson regression were used to analyse the associations between air pollutants and cancer mortality with adjustment for confounding factors including time trends, day of week, temperature and humidity. Then, we conducted stratified analyses by sex, age, season and education. In addition, stratified analyses of age, season and education were performed within each sex to determine whether sex difference was modified by such factors.

RESULTS

After adjusting for potential confounders, the GAM results indicated a statistically significant relationship between increased cancer mortality and elevated air pollution concentrations, but only in the female population. For every 10 μg/m3 rise in pollutant concentration, the increased risk of cancer death in females was 6.82% (95% CI 3.63% to 10.10%) for SO2 on lag 03, and 2.02% (95% CI 1.12% to 2.93%) for NO2 on lag 01 and 0.89% (95% CI 0.46% to 1.33%) for PM10 on lag 03 and 1.29% (95% CI 0.64% to 1.95%) for PM2.5 on lag 03. However, no statistically significant association was found among males. Moreover, the differences in effect sizes between males and females were more pronounced during the cold season, among the elderly and among subjects with low levels of education.

CONCLUSIONS

Increased cancer mortality was only observed in females with rising concentrations of air pollutants. Further research is required to confirm this sex difference. Advocate for the reduction of air pollutant emissions to protect vulnerable groups.

Associations between PM2.5 Components and Mortality of Ischemic Stroke, Chronic Obstructive Pulmonary Disease and Diabetes in Beijing, China

Ischemic stroke (IS), chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM) account for a large burden of premature deaths. However, few studies have investigated the associations between fine particular matter (PM2.5) components and mortality of IS, COPD and DM. We aimed to examine these associations in Beijing, China. Data on daily mortality, air pollutants and meteorological factors from 2008 to 2011 in Beijing were collected. Daily concentrations of five PM2.5 components, namely, sulfate ion (SO42-), ammonium ion (NH4+), nitrate ion (NO3-), organic matter (OM) and black carbon (BC), were obtained from the Tracking Air Pollution (TAP) database in China. The association between PM2.5 components and daily deaths was explored using a quasi-Poisson regression with the distributed lag nonlinear model (DLNM). The average daily concentrations of SO42-, NH4+, NO3-, OM and BC were 11.24, 8.37, 12.00, 17.34 and 3.32 μg/m3, respectively. After adjusting for temperature, relative humidity, pressure, particulate matter less than 10 μm in aerodynamic diameter (PM10), nitrogen dioxide (NO2) and sulfur dioxide (SO2), an IQR increase in OM at lag day 2 and lag day 6 was associated with an increased DM mortality risk (RR 1.038; 95% CI: 1.005-1.071) and COPD mortality risk (RR 1.013; 95% CI: 1.001-1.026). An IQR increase in BC at lag day 0 and lag day 6 was associated with increased COPD mortality risk (RR 1.228; 95% CI: 1.017-1.48, RR 1.059; 95% CI: 1.001-1.121). Cumulative exposure to SO42- and NH4+ was associated with an increased mortality risk for IS, with the highest effect found for lag of 0-7 days (RR 1.085; 95% CI: 1.010-1.167, RR 1.083; 95% CI: 1.003-1.169). These effects varied by sex and age group. This study demonstrated associations of short-term exposure to PM2.5 components with increased risk of IS, COPD and DM mortality in the general population. Our study also highlighted susceptible subgroups.

Short-Term Exposure to Air Pollution and the Incidence and Mortality of Stroke: A Meta-Analysis

BACKGROUND

The relationship between short-term exposure to various air pollutants [particulate matter <10 μm (PM 10 ), particulate matter <2.5 μm (PM 2.5 ), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), carbon monoxide, and ozone (O 3 )] and the incidence and mortality of stroke remain unclear.

REVIEW SUMMARY

We conducted a comprehensive search across databases, including PubMed, Web of Science, and others. A random-effects model was employed to estimate the odds ratios (OR) and their 95% CIs. Short-term exposure to PM 10 , PM 2.5 , NO 2 , SO 2 , and O 3 was associated with increased stroke incidence [per 10 μg/m 3 increase in PM 2.5 : OR = 1.005 (95% CI: 1.004-1.007), per 10 μg/m 3 increase in PM 10 : OR = 1.006 (95% CI: 1.004-1.009), per 10 μg/m 3 increase in SO 2 : OR = 1.034 (95% CI: 1.020-1.048), per 10 μg/m 3 increase in NO 2 : OR = 1.029 (95% CI: 1.015-1.043), and O 3 for per 10 μg/m 3 increase: OR: 1.006 (95% CI: 1.004-1.007)]. In addition, short-term exposure to PM 2.5 , PM 10 , SO 2, and NO 2 was correlated with increased mortality from stroke [per 10 μg/m 3 increase in PM 2.5 : OR = 1.010 (95% CI: 1.006-1.013), per 10 μg/m 3 increase in PM 10 : OR = 1.004 (95% CI: 1.003-1.006), per 10 μg/m 3 increase in SO 2 : OR = 1.013 (95% CI: 1.007-1.019) and per 10 μg/m 3 increase in NO 2 : OR = 1.012 (95% CI: 1.008-1.015)].

CONCLUSION

Reducing outdoor air pollutant levels may yield a favorable outcome in reducing the incidence and mortality associated with strokes.

Association between short-term ambient air pollutants and type 2 diabetes outpatient visits: a time series study in Lanzhou, China

Diabetes is a global public health problem, and the impact of air pollutants on type 2 diabetes mellitus (T2DM) has attracted people's attention. This study aimed to assess the association of short-term exposure to six criteria air pollutants with T2DM outpatient visits in Lanzhou, China. We collected data on daily outpatient visits for T2DM, daily meteorological data and hourly concentrations of air pollutants in Lanzhou from 2013 to 2019. An over-dispersed passion generalized addictive model combined with a distributed lag non-linear model was applied to estimate the associations and stratified analyses were performed by gender, age, and season. The models were fitted with different lag structures, including single lag days from the current to the previous seven days (lag0 to lag7) and moving average concentrations over seven lag days (lag01 to lag07). A positive association between multiple air pollutants, especially PM2.5, NO2, O38h and CO and hospital outpatient visits for T2DM was observed. The largest association between T2DM outpatient visits and PM2.5 was observed at lag06 (RR 1.013, 95% CI: 1.001, 1.027), NO2 at lag03 (RR 1.034, 95% CI: 1.018, 1.050), O38h at lag05 (RR 1.012, 95% CI: 1.001, 1.023) for an increase of 10 μg m-3 and CO at lag03 (RR 1.084, 95% CI: 1.029, 1.142) for an increase of 1 mg m-3 in the concentrations. In addition, people aged <65 and males are more susceptible, and air pollutants have a greater impact on the cold season. This study showed that although the air pollution in Lanzhou was improved, there was still a statistical correlation between air pollution exposure and T2DM outpatient visits. Therefore, the local government still needs to strengthen the control of air pollution and enhance the protection awareness of the diabetic population through education and publicity.

Impact of short-term exposure to air pollution on natural mortality and vulnerable populations: a multi-city case-crossover analysis in Belgium

BACKGROUND

The adverse effect of air pollution on mortality is well documented worldwide but the identification of more vulnerable populations at higher risk of death is still limited. The aim of this study was to evaluate the association between natural mortality (overall and cause-specific) and short-term exposure to five air pollutants (PM2.5, PM10, NO2, O3 and black carbon) and identify potential vulnerable populations in Belgium.

METHODS

We used a time-stratified case-crossover design with conditional logistic regressions to assess the relationship between mortality and air pollution in the nine largest Belgian agglomerations. Then, we performed a random-effect meta-analysis of the pooled results and described the global air pollution-mortality association. We carried out stratified analyses by individual characteristics (sex, age, employment, hospitalization days and chronic preexisting health conditions), living environment (levels of population density, built-up areas) and season of death to identify effect modifiers of the association.

RESULTS

The study included 304,754 natural deaths registered between 2010 and 2015. We found percentage increases for overall natural mortality associated with 10 μg/m3 increases of air pollution levels of 0.6% (95% CI: 0.2%, 1.0%) for PM2.5, 0.4% (0.1%, 0.8%) for PM10, 0.5% (-0.2%, 1.1%) for O3, 1.0% (0.3%, 1.7%) for NO2 and 7.1% (-0.1%, 14.8%) for black carbon. There was also evidence for increases of cardiovascular and respiratory mortality. We did not find effect modification by individual characteristics (sex, age, employment, hospitalization days). However, this study suggested differences in risk of death for people with preexisting conditions (thrombosis, cardiovascular diseases, asthma, diabetes and thyroid affections), season of death (May-September vs October-April) and levels of built-up area in the neighborhood (for NO2).

CONCLUSIONS

This work provided evidence for the adverse health effects of air pollution and contributed to the identification of specific population groups. These findings can help to better define public-health interventions and prevention strategies.

Impact of environmental factors on diabetes mortality: A comparison between inland and coastal areas

BACKGROUND

Diabetes mortality varies between coastal and inland areas in Shandong Province, China. However, evidence about the reasons for this disparity is limited. We assume that distinct environmental conditions may contribute to the disparities in diabetes mortality patterns between coastal and inland areas.

METHOD

Qingdao and Jinan were selected as typical coastal and inland cities in Shandong Province, respectively, with similar socioeconomic but different environmental characteristics. Data on diabetes deaths and environmental factors (i.e., temperature, relative humidity and air pollution particles with a diameter of 2.5 μm or less (PM2.5)) were collected from 2013 to 2020. Spatial kriging methods were used to estimate the aggregated diabetes mortality at the city level. A distributed lag non-linear model (DLNM) was used to quantify the possible cumulative and non-cumulative associations between environmental factors and diabetes mortality by age, sex and location.

RESULTS

In the coastal city (Qingdao), the maximum cumulative relative risks (RRs) of temperature and PM2.5 associated with diabetes deaths were 2.54 (95 % confidence interval (CI): 1.25-5.15), and 1.17 (95 % CI: 1.01-1.37) respectively, at lag 1 week. In the inland city (Jinan), only temperature exhibited significant cumulative associations with diabetes deaths (RR = 1.54, 95 % CI: 1.07-2.23 at 29 °C). Lower relative humidity (22 %-45 %) had a lag-specific association with diabetes deaths in inland areas at lag 3 weeks (RR = 1.33, 95 % CI: 1.03-1.70 at 22 %).

CONCLUSION

Despite the lower PM2.5 concentrations in the coastal location, diabetes mortality exhibited stronger links to environmental variables in the coastal city than in the inland city. These findings suggest that the control of air pollution could decrease the mortality burden of diabetes, even in the region with relatively good air quality. Additionally, the spatial estimation method is recommended to identify associations between environmental factors and diseases in studies with limited data.

Influence of Air Pollution Exposures on Cardiometabolic Risk Factors: a Review

PURPOSE OF REVIEW

The increasing prevalence of cardiometabolic risk factors (CRFs) contributes to the rise in cardiovascular disease. Previous research has established a connection between air pollution and both the development and severity of CRFs. Given the ongoing impact of air pollution on human health, this review aims to summarize the latest research findings and provide an overview of the relationship between different types of air pollutants and CRFs.

RECENT FINDINGS

CRFs include health conditions like diabetes, obesity, hypertension etc. Air pollution poses significant health risks and encompasses a wide range of pollutant types, air pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O2). More and more population epidemiological studies have shown a positive correlation between air pollution and CRFs. Although various pollutants have diverse effects on specific cellular molecular pathways, their main influence is on oxidative stress, inflammation response, and impairment of endothelial function. More and more studies have proved that air pollution can promote the occurrence and development of cardiovascular and metabolic risk factors, and the research on the relationship between air pollution and CRFs has grown intensively. An increasing number of studies are using new biological monitoring indicators to assess the occurrence and development of CRFs resulting from exposure to air pollution. Abnormalities in some important biomarkers in the population (such as homocysteine, uric acid, and C-reactive protein) caused by air pollution deserve more attention. Further research is warranted to more fully understand the link between air pollution and novel CRF biomarkers and to investigate potential prevention and interventions that leverage the mechanistic link between air pollution and CRFs.

The Short-Term Effects and Burden of Ambient Air Pollution on Hospitalization for Type 2 Diabetes: Time-Stratified Case-Crossover Evidence From Sichuan, China

Type 2 diabetes mellitus (T2DM), a complicated metabolic disease, might be developed or exacerbated by air pollution, resulting in economic and health burden to patients. So far, limited studies have estimated associations between short-term exposure to air pollution and disease burden of T2DM in China. Hence, we aimed to estimate the associations and burden of ambient air pollutants (NO2, PM10, PM2.5, SO2, and CO) on hospital admissions (HAs) for T2DM using a time-stratified case-crossover design. Data on HAs for T2DM during 2017-2019 were collected from hospital electronic health records in nine cities in Sichuan Province using conditional poisson regression. Totally, 92,381 T2DM hospitalizations were recorded. There were significant short-term effects of NO2, PM10, PM2.5, SO2 and CO on HAs for T2DM. A 10 μg/m3 increment of NO2, PM10, PM2.5, SO2 and CO as linked with a 3.39% (95% CI: 2.26%, 4.54%), 0.33% (95% CI: 0.04%, 0.62%), 0.76% (95% CI: 0.35%, 1.16%), 12.68% (95% CI: 8.14%, 17.42%) and 79.00% (95% CI: 39.81%, 129.18%) increase in HAs for T2DM at lag 6. Stratified analyses modified by age, sex, and season showed old (≥65 years) and female patients linked with higher impacts. Using WHO's air quality guidelines of NO2, PM10, PM2.5, and CO as the reference, the attributable number of T2DM HAs exceeding these pollutants exposures were 786, 323, 793, and 2,127 during 2017-2019. Besides, the total medical costs of 25.83, 10.54, 30.74, and 67.78 million China Yuan were attributed to NO2, PM10, PM2.5, and CO. In conclusion, short-term exposures to air pollutants were associated with higher risks of HAs for T2DM.

Effects of Ambient O3 on Respiratory Mortality, Especially the Combined Effects of PM2.5 and O3

BACKGROUND

In China, the increasing concentration of ozone (O3) has emerged as a significant air pollution issue, leading to adverse effects on public health, particularly the respiratory system. Despite the progress made in managing air pollution in China, it is crucial to address the problem of environmental O3 pollution at present.

METHODS

The connection between O3 exposure and respiratory mortality in Shenyang, China, from 2014 to 2018 was analyzed by a time-series generalized additive regression model (GAM) with quasi-Poisson regression. Additionally, the potential combined effects of fine particulate matter (PM2.5) and O3 were investigated using the synergy index (SI).

RESULTS

Our findings indicate that each 10 μg/m3 increase in O3 at lag 2 days was associated with a maximum relative risk (RR) of 1.0150 (95% CI: 1.0098-1.0202) for respiratory mortality in the total population. For individuals aged ≥55 years, unmarried individuals, those engaged in indoor occupations, and those with low educational attainment, each 10 μg/m3 increase in O3 at lag 07 days was linked to RR values of 1.0301 (95% CI: 1.0187-1.0417), 1.0437 (95% CI: 1.0266-1.0610), 1.0317 (95% CI: 1.0186-1.0450), and 1.0346 (95% CI: 1.0222-1.0471), respectively. Importantly, we discovered a synergistic effect of PM2.5 and O3, resulting in an SI of 2.372 on the occurrence of respiratory mortality.

CONCLUSIONS

This study confirmed a positive association between O3 exposure and respiratory mortality. Furthermore, it highlighted the interaction between O3 and PM2.5 in exacerbating respiratory deaths.

Criteria air pollutants and diabetes mortality classified by different subtypes and complications: A nationwide, case-crossover study

The associations between air pollution and diabetes mortality of different subtypes and complications were largely unclear. We performed an individual-level, time-stratified case-crossover study among over 0.9 million diabetes deaths from all administrative regions of Chinese mainland during 2013-2019. Daily concentrations of fine particles (PM2.5), coarse particles (PM2.5-10), nitrogen dioxide (NO2) and ozone (O3) were obtained for each decedent using high-resolution prediction models. Conditional logistic regression models were utilized to analyze the data. Each interquartile range increment in PM2.5, PM2.5-10, NO2 and O3 concentrations on lag 0-2 d increased the risks of overall diabetes mortality by 2.81 %, 1.92 %, 3.96 % and 2.15 %, respectively. Type 2 diabetes had stronger associations with air pollution than type 1 diabetes. Air pollutants were associated with diabetic ketoacidosis and diabetic nephropathy, but not other complications. The exposure-response curves were approximately linear with a plateau at higher concentrations of PM2.5, PM2.5-10, and NO2, while the associations for O3 appear to be statistically significant beyond 60 μg/m3. This nationwide study reinforces the evidence of higher risks of acute diabetic events following short-term air pollution exposure. We identified differential effects of air pollutants on various subtypes and complications of diabetes, which require further mechanistic investigations.

Effect of short-term ambient air pollution exposure on early miscarriage and pregnancy hormones with critical window identification

BACKGROUND

Pregnancy hormones are particularly important in early miscarriage, and some evidence suggests that exposure to air pollution is associated with pregnancy hormones and miscarriage. However, the effects of air pollution on pregnancy hormone-mediated miscarriages have not yet been investigated.

METHODS

We collected air pollution exposure measurements and pregnancy hormone tests from the participants. Logistic regression models were used to investigate the association between air pollution and early miscarriages. A distributed lag nonlinear model (DLNM) was used to investigate non-linear and delayed associations and identify the crucial window. We performed mediation analysis to estimate the potential association that may exist between pregnancy hormone levels and early miscarriage.

RESULTS

Short-term exposure to CO and SO2 was associated with early miscarriage. Lag 22-28 days of exposure to both CO and SO2 and lag 15-21 days of exposure to CO were significantly positively associated with early miscarriage, with an obvious exposure dose response. Serum progesterone concentration explained 36.79 % of the association between lag 15-28 days of CO exposure and early miscarriage.

CONCLUSION

This study provides evidence for the association between short-term exposure to air pollution and early miscarriage, and provides clues for further exploration of biological mechanisms.

An update on the association between ambient short-term air pollution exposure and daily outpatient visits for conjunctivitis: a time-series study in Hangzhou, China

Air pollution is a major public health problem that can lead to conjunctivitis. This study aimed to explore the associations between air pollutants and outpatient visits for conjunctivitis in Hangzhou, China. This study collected data on 50,772 patients with conjunctivitis and the concentrations of six air pollutants from February 1, 2014, to August 31, 2018. A time series analysis using a generalized additive model (GAM) was conducted. We found that the risk of conjunctivitis was related to the air pollutants PM2.5, PM10, SO2, NO2, and O3, which had concentration hysteresis effects. The risk of conjunctivitis increased by 1.009 (95% confidence interval (CI): 1.003, 1.014), 1.011 (95% CI: 1.008, 1.015), 1.238 (95% CI: 1.186, 1.292), 1.028 (95% CI: 1.019, 1.038), and 1.013 (95% CI: 1.008, 1.017) for every 10 µg/m3 increase in PM2.5, PM10, SO2, NO2, and O3 concentrations, respectively. The lag effects of SO2 and NO2 were stronger than those of particulate matter. Females exposed to PM10, PM2.5, SO2, and O3 had a higher risk of conjunctivitis than males, while males exposed to NO2 had a nearly identical risk of conjunctivitis as females. People aged 19-59 were more likely to suffer from conjunctivitis. The risk of conjunctivitis caused by PM10, SO2, and O3 was highest in the transitional season, while the risk caused by NO2 was highest in the winter season. In conclusion, females and middle-aged adults were at higher risk of conjunctivitis. People were more susceptible to conjunctivitis during the transitional season. These findings highlight the importance of atmospheric pollution governance and reference for public health measures.

Acute effects of air pollution on type II diabetes mellitus hospitalization in Lanzhou, China

Studies on the effects of short-term air pollution exposure on hospitalization for type 2 diabetes mellitus (T2DM) are relatively scarce in developing regions. The time-series study was used to explore the acute effects of air pollutants on hospitalization for T2DM in Lanzhou, China. A distribution lag nonlinear model based on the generalized additive model was used to analyze the hospitalization impact of air pollution on T2DM. Stratified analysis by gender, age and season was obtained. The results were indicated as the relative risk (RR) with 95% confidence interval (CI) for single-day lags (from lag0 to lag7) and cumulative lag days (from lag0-1 to lag0-7). The strongest correlations (RR, 95% CI) of hospitalization for T2DM and PM10 (RR = 1.003, 95% CI 1.000, 1.001) at lag7 and NO2 (RR = 1.022, 95% CI 1.000, 1.045) at lag0-4 were observed for an increase of 10 µg/m3 in the concentrations and CO (RR = 1.091, 95% CI 1.017, 1.170) at lag0-4 for an increase of 1 mg/m3 in the concentration. The hazardous impacts of PM10, NO2 and CO were greater for females, people aged ≥ 65 years and in the cold season. However, there was no significant association between PM2.5, SO2 and O38h and the number of hospitalizations for T2DM.

Maternal 25(OH)D attenuates the relationship between ambient air pollution during pregnancy and fetal hyperinsulinism

Inflammatory responses have been demonstrated to link air pollution with insulin resistance and type 2 diabetes in adults. However, few studies have focused on the relationship between prenatal air pollution and fetal β-cell function and the mediating effect of systematic inflammation remains elusive. Whether the anti-inflammatory effect of vitamin D could attenuate the β-cell dysfunction in early life warrants further investigations. We aimed to determine whether maternal blood 25(OH)D attenuates the associations of ambient air pollution during pregnancy with fetal hyperinsulinism mediated by maternal inflammatory response. A total of 8250 mother-newborn pairs were included between 2015 and 2021 in the Maternal & Infants Health in Hefei study. Weekly mean air pollution exposure to fine particles (PM_2.5 and PM₁₀), SO₂, and CO was estimated across pregnancy. Maternal serum samples in the third trimester were used to measure the high-sensitivity c-reactive protein (hs-CRP) and 25(OH)D. Cord blood samples at delivery were collected for the measurement of C-peptide. Fetal hyperinsulinism was based on cord C-peptide >90th centile. An increased fetal hyperinsulinism risk was associated with per 10 μg/m³ increase in PM_2.5 [odds ratios (OR): 1.45 (95% confidence interval (CI):1.32, 1.59)], per 10 μg/m³ increase in PM₁₀ [OR = 1.49 (95% CI:1.37, 1.63)], per 5 μg/m³ increase in SO₂ [OR = 1.91 (95% CI: 1.70, 2.15)], and per 0.1 mg/m³ increase in CO [OR = 1.48 (95% CI:1.37, 1.61)] across pregnancy. Mediation analysis showed a 16.3% contribution of maternal hsCRP to the relationship between air pollution throughout pregnancy and fetal hyperinsulinism. Air pollution-associated higher levels of hsCRP and risk of fetal hyperinsulinism could be attenuated by higher maternal 25(OH)D levels. Prenatal ambient air pollution exposures were associated with an increased fetal hyperinsulinism risk mediated by maternal serum hsCRP. Higher antenatal 25(OH)D levels could attenuate air pollution-induced inflammatory responses and hyperinsulinism risk.

Effects of ambient air pollution on cause-specific hospitalizations in Wuhan during 2017-2019

BACKGROUND

Few studies have focused on the associations between air pollutants and multiple organ system diseases in the entire hospitalized population. The present study aims to explore the short-term effects of six routinely monitored air pollutants on the broad causes of hospital admissions and estimate the resulting hospital admission burdens.

METHODS

Daily hospital admission records from 2017 to 2019 were obtained from the Wuhan Information center of Health and Family Planning. Generalized additive models (GAMs) were employed to evaluate the effects of air pollutants on the percent increase in the cause-specific daily number of hospital admissions. Increased hospital admission numbers, days, and expenses were also estimated.

RESULTS

A total of 2636,026 hospital admissions were identified. We found that both PM_2.5 and PM₁₀ increased the risk of hospital admissions for most disease categories. Short-term exposure to PM_2.5 was positively associated with hospitalizations of several rarely studied disease categories, such as diseases of the eye and adnexa (2.83%, 95%CI: 0.96-4.73%, P < 0.01) and diseases of the musculoskeletal system and connective tissue (2.17%, 95% CI: 0.88-3.47%, P < 0.001). NO₂ was observed to have a robust effect on diseases of the respiratory system (1.36%, 95%CI: 0.74-1.98%, P < 0.001). CO was significantly associated with hospital admissions for six disease categories. Furthermore, each 10-μg/m³ increase in PM_2.5 was associated with an annual increase of 13,444 hospital admissions (95% CI: 6239-20,649), 124,344 admission days (95% CI: 57,705-190,983), and 166-million-yuan admission expenses (95% CI: 77-255).

CONCLUSION

Our study suggested that particulate matter (PM) had a short-term effect on hospital admissions of most major disease categories and resulted in a considerable hospital admission burden. In addition, the health effects of NO₂ and CO emissions require more attention in megacities.

Short-term effects of air pollution on hospital admissions for cardiovascular diseases and diabetes mellitus in Sofia, Bulgaria (2009-2018)

Bulgaria has a very high incidence of cardiometabolic diseases and air pollution-related mortality rate. This study investigated the relationship between daily air pollution levels and hospital admissions for ischaemic heart diseases (IHD), cerebral infarction (CI), and type 2 diabetes mellitus (T2DM) in Sofia, Bulgaria. We obtained daily data on hospitals admissions and daily average air pollution levels from 2009 to 2018. Pollutants of interest were particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), ozone (O₃), and carbon monoxide (CO). Negative binomial regressions were fitted to study the effects of air pollution on hospital admission over the course of seven days prior to that event, accounting for autocorrelations and time trend in the data, day of the week, temperature, and relative humidity. Our findings confirm that higher air pollution levels generally increase the risk of hospital admissions for IHD and CI. For T2DM the association is less clear. Admissions often lagged several days behind and were more common in specific demographic subgroups or when pollution crossed a particular threshold. However, we did not expect to find the risk of hospital admissions increased in warmer rather than colder months of the year. Our findings are to be taken with reservation but do provide an idea about how air pollution could trigger acute episodes of related cardiovascular diseases, and our model may serve to investigate similar associations across the country.

Effects of air pollution and residential greenness on sleep disorder: A 8-year nationwide cohort study

BACKGROUND

Sleep disorder influencing the quality of life, however, its contributing factors have not been fully identified yet. Recently the potential effects of environmental exposures like air pollution and greenness on sleep disorder have attracted attention, but the evidence in China is limited, particularly in the middle-aged and elderly.

METHODS

We conducted a nationwide prospective study that included 21,878 Chinese citizens aged 45 years or above. For each participant, the 3-year averaged exposure concentrations of air pollutants (including PM₁₀, PM_2.5, PM₁, NO₂) and greenness (assessed by NDVI) were estimated based on residential address. We used mixed-effects logistic models to examine the associations of sustained air pollutants and greenness exposures with the occurrence of sleep disorder, and used linear mixed-effects models to assess the associations with sleep duration. Specifically, interaction effects models were employed to identify potential modificators of the above associations.

RESULTS

A total of 39,580 survey responses were received, with the overall occurrence rate of sleep disorder was 25.7%. A 10 μg/m³ increment in PM₁₀ and PM_2.5 were associated with increased occurrence of sleep disorder at 2% (aOR = 1.02, 95%CI:1.01, 1.04) and 7% (aOR = 1.07, 95%CI: 1.04, 1.11), and were associated with reduced sleep duration by 0.07 (95% CI: 0.08, 0.05) and 0.04 (95% CI: 0.05, 0.03) hours, respectively. Residential greenness appears to the potential protective factor for sleep disorder, that a 0.1 higher of the NDVI was associated a 9% (aOR = 0.91, 95%CI: 0.86, 0.96) decreased occurrence of sleep disorder and 0.09 h (95% CI: 0.05, 0.13) longer of sleep duration. Age and residence were identified as modificators of the above significant associations.

CONCLUSION

Sustained exposure to air pollutants can increase the occurrence of sleep disorder and can reduce sleep duration, while exposure to higher levels of greenness can protect sleep health from the side effects of air pollutants.

Short-term exposure to ambient particulate matter and mortality among HIV/AIDS patients: Case-crossover evidence from all counties of Hubei province, China

BACKGROUND

Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) has been a worrisome public health problem in the world. However, evidence for associations between short-term exposure to particulate matter (PM) and mortality among HIV/AIDS patients is scarce.

METHODS

We collected daily death records in people with HIV/AIDS from all counties (N = 103) of Hubei province, China from 2018 to 2019. The county-level daily concentrations of PM₁, PM_2.5 and PM₁₀ in the same period were extracted from ChinaHighAirPollutants dataset. A time-stratified case-crossover design with conditional logistic regression analysis was performed to assess the associations between PM and mortality.

RESULTS

Each 1 μg/m³ increased in PM₁ corresponded with 0.89 % elevated in all-cause deaths (ACD) at lag 0-4 days. The largest effects of PM₁, PM_2.5 and PM₁₀ on AIDS-related deaths (ARD) were detected at lag 0-4 days, and PM₁ [percent changes in odds ratio: 2.51 % (95 % CIs: 0.82, 4.22)] appeared greater health hazards than PM_2.5 [1.24 % (95 % CIs: 0.33, 2.15)] as well as PM₁₀ [0.65 % (95 % CIs: 0.01, 1.30)]. In subgroup analyses, the significant associations of PM₁/PM_2.5 and ACD were only found in male and the cold season. We also observed the effects of PM₁ and PM₁₀ on ARD were significantly stronger (P for interaction <0.05) in males than females. In addition, we caught sight of HIV/AIDS patients aged over 60 years old were more susceptible to ARD caused by PM than younger population.

CONCLUSIONS

Our study suggested PM₁ was positively linked with the risk of ACD and ARD. Male patients with HIV/AIDS were more significantly susceptible to PM₁, PM_2.5 and PM₁₀. PM₁/PM_2.5 appeared stronger associations with ARD in HIV/AIDS patients aged over 60 years old and in the cold season.

A novel Energy Resources Allocation Management model for air pollution reduction

Although air pollution has been reduced in various industrial and crowded cities during the COVID-19 pandemic, curbing the high concentration of the crisis of air pollution in the megacity of Tehran is still a challenging issue. Thus, identifying the major factors that play significant roles in increasing contaminant concentration is vital. This study aimed to propose a mathematical model to reduce air pollution in a way that does not require citizen participation, limitation on energy usage, alternative energies, any policies on fuel-burn style, extra cost, or time to ensure that consumers have access to energy adequately. In this study, we proposed a novel framework, denoted as the Energy Resources Allocation Management (ERAM) model, to reduce air pollution. The ERAM is designed to optimize the allocation of various energies to the recipients. To do so, the ERAM model is simulated based on the magnitude of fuel demand consumption, the rate of air pollution emission generated by each energy per unit per consumer, and the air pollution contribution produced by each user. To evaluate the reflectiveness and illustrate the feasibility of the model, a real-world case study, i.e., Tehran, was employed. The air pollution emission factors in Tehran territory were identified by considering both mobile sources, e.g., motorcycles, cars, and heavy-duty vehicles, and stationary sources, e.g., energy conversion stations, industries, and household and commercial sectors, which are the main contributors to particulate matter and nitrogen dioxide. An elaborate view of the results indicates that the ERAM model on fuel distribution could remarkably reduce Tehran's air pollution concentration by up to 14%.

Association between short-term exposure to air pollutants and cause-specific daily mortality in Italy. A nationwide analysis

BACKGROUND/AIM

Daily air pollution has been linked with mortality from urban studies. Associations in rural areas are still unclear and there is growing interest in testing the role that air pollution has on other causes of death. This study aims to evaluate the association between daily air pollution and cause-specific mortality in all 8092 Italian municipalities.

METHODS

Natural, cardiovascular, cardiac, ischemic, cerebrovascular, respiratory, metabolic, diabetes, nervous and psychiatric causes of death occurred in Italy were extracted during 2013-2015. Daily ambient PM₁₀, PM_2.5 and NO₂ concentrations were estimated through machine learning algorithms. The associations between air pollutants and cause-specific mortality were estimated with a time-series approach using a two-stage analytic protocol where area-specific over-dispersed Poisson regression models where fit in the first stage, followed by a meta-analysis in the second. We tested for effect modification by sex, age class and the degree of urbanisation of the municipality.

RESULTS

We estimated a positive association between PM₁₀ and PM_2.5 and the mortality from natural, cardiovascular, cardiac, respiratory and nervous system causes, but not with metabolic or psychiatric causes of death. In particular, mortality from nervous diseases increased by 4.55% (95% CI: 2.51-6.63) and 9.64% (95% CI: 5.76-13.65) for increments of 10 μg/m³ in PM₁₀ and PM_2.5 (lag 0-5 days), respectively. NO₂ was positively associated with respiratory (6.68% (95% CI: 1.04-12.62)) and metabolic (7.30% (95% CI: 1.03-13.95)) mortality for increments of 10 μg/m³ (lag 0-5). Higher associations with natural mortality were found among the elderly, while there were no differential effects between sex or between rural and urban areas.

CONCLUSIONS

Short-term exposure to particulate matter was associated with mortality from nervous diseases. Mortality from metabolic diseases was associated with NO₂ exposure. Other associations are confirmed and updated, including the contribution of lowly urbanised areas. Health effects were also found in suburban and rural areas.

Association between short-term ambient nitrogen dioxide and type 2 diabetes outpatient visits: A large hospital-based study

Type 2 diabetes (T2DM) as a non-communicable disease imposes heavy disease burdens on society. Limited studies have been conducted to assess the effects of short-term air pollution exposure on T2DM, especially in Asian regions. Our research aimed to determine the association between short-term exposure to ambient nitrogen dioxide (NO₂) and outpatient visits for T2DM in Chongqing, the largest city in western China, based on the data collected from November 28, 2013 to December 31, 2019. A generalized additive model (GAM) was applied, and stratified analyses were performed to investigate the potential modifying effects by age, gender, and season. Meanwhile, the disease burden was revealed from attributable risk. Positive associations between short-term NO₂ and daily T2DM outpatient visits were observed. The strongest association was observed at lag 04, with per 10 μg/m³ increase of NO₂ corresponded to increased T2DM outpatient visits at 1.57% [95% confidence interval (CI): 0.48%, 2.65%]. Stronger associations were presented in middle-aged group (35-64 years old), male group, and cool seasons (October to March). Moreover, there were 1.553% (8664.535 cases) of T2DM outpatient visits attributable to NO₂. Middle-aged adults, males, and patients who visited in cool seasons suffered heavier burdens. Conclusively, short-term exposure to NO₂ was associated with increased outpatient visits for T2DM. Attention should be paid to the impact of NO₂ on the burden of T2DM, especially for those vulnerable groups.

Association of long-term exposure to PM2.5 in workplace with fasting plasma glucose among asymptomatic adults: A multicenter study in North China

BACKGROUND

The impacts of long-term high exposure to PM2.5 in workplace on glucose metabolism in asymptomatic working adults (AWAs) have rarely been explored.

OBJECTIVES

To assess the relationship between long-term exposure to workplace PM2.5 and glucose metabolism in asymptomatic general working adults in heavily polluted regions.

METHODS

We used the baseline data of the asymptomatic working participants from the Beijing-Tianjin-Hebei Medical Examination Cohort, which recruited adults undergoing medical examinations. A machine learning-based spatial-temporal model was used to estimate daily average PM2.5 concentrations in the participants' workplaces. We assessed the association of long-term PM2.5 concentrations (three years prior to the interview) and fasting plasma glucose (FPG) using generalized linear mixed-effects models (GLMM) with inclusion of potential confounders. Stratified analyses by sex, age, BMI and smoking status, and two pollutant models were further performed.

RESULTS

A total of 37,619 individuals were interviewed and 28,865 were included in the analyses. The mean FPG was 5.20 (0.96) mmol/L, and the estimated three-year average concentration of PM2.5 exposure was 69.51 (6.92) μg/m3. We detected a significant association of long-term exposure to workplace PM2.5 and FPG, a 10 µg/m3 increase in the long-term workplace PM2.5 exposure was associated with 0.075 (95%CI: 0.050-0.100) mmol/L elevated FPG and 25% (OR = 1.25, 95%CI: 1.05-1.50) elevated odds of abnormal fasting glucose metabolism with control of the potential confounding. The detected association between workplace PM2.5 and FPG metabolism remained significant in males, individuals aged > 44 years, overweight and/or obese people, both smokers and non-smokers, and when NO2, SO2, O3, and CO were included in the model.

CONCLUSIONS

Long-term exposure to workplace PM2.5 was associated with elevated FPG and/or odds of abnormal glucose metabolism among AWAs. Male, middle-aged, overweight and/or obese AWAs were more susceptible to workplace PM2.5 regardless of smoking status.

Secular trends in global burden of diabetes attributable to particulate matter pollution from 1990 to 2019

Increasing evidence suggests an association between fine particulate matter (PM2.5) exposure and type 2 diabetes mellitus. However, there is still a lack of comparative evaluation regarding diabetes burden due to ambient and indoor PM2.5 pollution at a global scale. This study attempts to provide a systematic and comprehensive profile for PM2.5-attributable burden of diabetes and its spatiotemporal trends, globally and regionally. Comparative estimates of diabetes attributable to ambient PM2.5 and household air pollution (HAP) from solid fuels for 204 countries and territories were derived from the Global Burden of Disease Study 2019. Globally, 292.5 (95% uncertainty interval: 207.1, 373.4) thousand deaths and 13.0 (9.1, 17.2) million disability-adjusted life years (DALYs) from diabetes were attributed to PM2.5 pollution in 2019, wherein more than two-thirds (67.3% deaths and 69.7% DALYs) were contributed by ambient PM2.5. Compared to 1990, age-standardized DALY rate (ASDR) in 2019 attributable to ambient PM2.5 increased by 85.9% (APC: 2.21% [2.15, 2.27]), while HAP-associated ASDR decreased by 37.9% (APC: - 1.66% [- 1.82, - 1.50]). We observed a negative correlation between SDI and APC in ASMR (rs =  - 0.5, p < 0.001) and ASDR (rs = -0.4, p < 0.001) among 204 countries and territories. HAP-related diabetes experienced a sharp decline during 1990-2019, while global burden of diabetes attributable to ambient PM2.5 was rising rapidly. The elderly and people in low-SDI countries suffered from the greatest burden of diabetes due to PM2.5 pollution. More targeted interventions should be taken by governments to reduce PM2.5 exposure and related diabetes burden.

Acute effect of particulate matter pollution on hospital admissions for cause-specific respiratory diseases among patients with and without type 2 diabetes in Beijing, China, from 2014 to 2020

BACKGROUND

Scientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated.

METHODS

A two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM_2.5 and PM₁₀ were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline.

RESULTS

A total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM_2.5 corresponded to 1.91% (95% CI: 1.33-2.49%), 2.16% (95% CI: 1.08-3.25%), and 1.92% (95% CI: 1.46-2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0-8 days, lag 8 days, and lag 8 days. The effect size of PM_2.5 was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P < 0.01). The effect sizes of PM₁₀ were 3.86% (95% CI: 2.48-5.27%), 3.73% (95% CI: 1.72-5.79%), and 3.92% (95% CI: 2.65-5.21%), respectively, at lag 0-13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM₁₀ on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions.

CONCLUSIONS

Short-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM_2.5 was higher in patients with T2D than those without T2D.

Effects of SO2 Pollution on Household Insurance Purchasing in China: A Cross-Sectional Study

There have been considerable concerns regarding the effects of air pollution on health and economy over the past decades across the world. As insurance coverage has been closely related to household welfare, we aim to investigate the influence of air pollution, in particular, the sulfur dioxide (SO2) pollution on household purchases of commercial health insurance using data from the 2017 China Household Financial Survey (CHFS). The results show that the rise in SO2 emission has a significant positive association with tendency of residents to participate in commercial health insurance. The possibility of household commercial health insurance purchasing increases by 4% per 1,000 tons of SO2 emission. In addition, the proportion of commercial health insurance expenditure in household annual income increases by 29% per 1,000 tons of SO2 emission. The effects are also found to differ among resident groups. Residents in eastern parts of China are more likely to buy commercial health insurance facing SO2 pollution compared to those in western parts of China; people with higher income are more likely to be affected compared to those with lower income; families with the household head being female are more likely to be affected compared to those with the household head being male. This research provides baseline information on the formulation and implementation of future operation strategy in commercial health insurance companies of China.