Short-term exposure to air pollution and occurrence of emergency stroke in Chongqing, China

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.

The impact of ambient air pollutants on childhood respiratory system disease and the resulting disease burden: a time-series study

PURPOSE

The effects of air pollution on human health have long been a hot topic of research. For respiratory diseases, a large number of studies have proved that air pollution is one of the main causes. The purpose of this study was to investigate the risk of hospitalization of children with respiratory system diseases (CRSD) caused by six pollutants (PM2.5, PM10, NO2, SO2, CO, and O3) in Hefei City, and further calculate the disease burden.

METHOD

In the first stage, the generalized additive models were combined with the distributed lag non-linear models to evaluate the impact of air pollution on the inpatients for CRSD in Hefei. In the second stage, this study used the cost-of-illness approach to calculate the attributable number of hospitalizations and the extra disease burden.

RESULT

Overall, all the six kinds of pollutants had the strongest effects on CRSD inpatients within lag10 days. SO2 and CO caused the highest and lowest harm, respectively, and the RR values were SO2 (lag0-5): 1.1 20 (1.053, 1.191), and CO (lag0-6): 1.002 (1.001, 1.003). During the study period (January 1, 2014 to December 30, 2020), the 7-year cumulative burden of disease was 36.19 million CNY under the WHO air pollution standards.

CONCLUSION

In general, we found that six air pollutants were risk factors for CRSD in Hefei City, and create a huge burden of disease.

Air Pollution and Its Adverse Effects on the Central Nervous System

Air pollution is recognized as a significant public health problem and is associated with illnesses of the central nervous system (CNS) as well as neuroinflammation and neuropathology. Air pollution may cause chronic brain inflammation, white matter abnormalities, and microglia activation, which increases the risk of autism spectrum disorders, neurodegenerative disorders, stroke, and multiple sclerosis (MS). Methods: A literature review was done on "PubMed, EMBASE and Web of Science" on the relationship of air pollution with MS and stroke, using the keywords "air pollution" OR "pollution"; "ambient air pollution," "particulate matter, ozone, black carbon" AND "stroke" OR "cerebrovascular diseases," "multiple sclerosis," "neuroinflammation," or "neurodegeneration." Results: We first identified 128 articles and their related websites, of which 44 articles were further selected for analysis mainly based on study relevance, study quality and reliability, and date of publication. Further studies on air pollution and its adverse effects on the CNS are needed. The findings of such studies will support the development of appropriate preventive measures in the future.

Association between ambient carbon monoxide levels and hospitalization costs of patients with myocardial infarction: Potential effect modification by ABO blood group

Previous researches have reported the association between air pollution and various diseases. However, few researches have investigated whether air pollutants are associated with the economic loss resulting from patients' hospitalization, especially the economic loss of hospitalization due to acute cardiovascular events. The purpose of our research was to explore the association between the levels of carbon monoxide (CO), taken as an index of pollution, and the hospitalization costs of myocardial infarction (MI), and the potential effect modification by the ABO blood group. A total of 3237 MI inpatients were included in this study. A multiple linear regression model was used to evaluate the association between ambient CO levels and hospitalization costs of MI patients. Moreover, we performed stratified analyses by age, gender, body mass index (BMI), season, hypertension, and ABO blood types. There was a positive association between the levels of CO in the air and the costs of hospitalization caused by MI. Furthermore, such association was stronger in males, BMI ≥25, <65 years, with hypertension, and non-O blood group. Interestingly, we found the association was particularly significant in patients with blood group B. Overall, our study first found that ambient CO levels could have an impact on the hospitalization costs for MI patients, and those with blood group B can be more sensitive.

Acute Gaseous Air Pollution Exposure and Hospitalizations for Acute Ischemic Stroke: A Time-Series Analysis in Tianjin, China

BACKGROUND

Stroke has always been an important problem troubling human health. Short-term exposure to air pollutants is associated with increased hospital admissions. The rise of pollutants such as O₃ has caused a huge social and economic burden. This study aims to explore the relationship between short-term exposure to ambient gaseous pollutants and daily hospitalizations for ischemic stroke, utilizing a four-year time-series study in Tianjin.

METHODS

Collecting the data of gaseous pollutants (NO₂, SO₂, CO, O₃), meteorological data (including daily average temperature and relative humidity) and the number of hospitalizations due to ischemic stroke in Tianjin Medical University General Hospital from 2013 to 2016. Poisson regression generalized additive model with single-day and multi-day moving average lag structure was used to estimate adverse effects of gaseous pollutants on daily hospitalizations. Subgroup analysis was performed to detect modification effect by gender and age.

RESULTS

In total, there were 9081 ischemic stroke hospitalizations. After controlling for the meteorological factors in the same period, no significant findings were found with the increase of NO₂, SO₂, CO and O₃ concentrations at most of the time in the single-pollutant model. Similarly, in the stratified analysis, no associations between gaseous pollutants and ischemic stroke were observed in this study.

CONCLUSIONS

Short-term exposure to NO₂, SO₂, CO and O₃ was not distinctly associated with daily hospitalizations for ischemic stroke in Tianjin. Multicenter studies in the future are warranted to explore the associations between gaseous pollution exposure and ischemic stroke.

The Association Between Air Pollutants and Daily Outpatient Visits for Allergic Rhinitis: A Time-Series Analysis Based on Distribution Lag Nonlinear Model in Chongqing, China

Background

Allergic rhinitis (AR) is a severe and the most common chronic allergic disease, affecting 10-40% of the world population. The effect of air pollutants on AR has been confirmed in clinical experiments.

Purpose

This study aimed to quantify the association between air pollutants and daily outpatient visits for AR in Chongqing, China.

Methods

Based on the data of AR outpatients in the primary urban area of Chongqing from 2016 to 2017, along with the atmospheric pollutants and meteorological data in the same period, the distributed lag nonlinear model (DLNM) and generalized additive model (GAM) were used to analyze the time-series. We examined the effects of the single and double pollutant models with a maximum lag day of 30 days. Effect estimates were described as relative risk (RR) and 95% confidence intervals (CIs) in daily outpatient visits for AR per 10 μg/m³ increases in PM_2.5, PM₁₀, SO₂, NO₂, O₃, and per 1 mg/m³ increase in CO.

Results

A single pollutant's O₃ level had an immediate positive effect on AR within two days, the relative risks (RR, 95% CI) were 1.066 (1.008-1.127), 1.057 (1.005-1.112) and 1.048 (1.002-1.097). PM_2.5 had a lag effect within 11-18 days, the max relative risks (RR, 95% CI) were 1.083 (1.010-1.160). Moreover, O₃, PM_2.5, PM₁₀, SO₂ and NO₂ had significant effects on AR in the two-pollutant model. The RR cumulative effect of PM_2.5 became more pronounced as the concentration increased. The cumulative effect of NO₂ was lesser than PM_2.5.

Conclusion

Air pollutants were associated with the daily outpatient visits for AR, which may have considerable implications for developing tailored health policies and services to prevent AR in Chongqing and even all over the world.

Short-term effects and economic burden assessment of ambient air pollution on hospitalizations for schizophrenia

The evidence on the health and economic impacts of air pollution with schizophrenia is scarce, especially in developing countries. In this study, we aimed to systemically examine the short-term effects of PM_2.5 (particulate matter ≤ 2.5 μm in diameter), PM₁₀ (≤ 10 μm in diameter), NO₂ (nitrogen dioxide), SO₂ (sulfur dioxide), CO (carbon monoxide), and O₃ (ozone) on hospital admissions for schizophrenia in a Chinese coastal city (Qingdao) and to further assess the corresponding attributable risk and economic burden. A generalized additive model (GAM) was applied to model the impact of air pollution on schizophrenia, and the corresponding economic burden including the direct costs (medical expenses) and indirect costs (productivity loss). Stratified analyses were also performed by age, gender, and season (warm or cold). Our results showed that for a 10 μg/m³ increase in the concentrations of PM_2.5, PM₁₀, SO₂, and CO at lag5, the corresponding relative risks (RRs) were 1.0160 (95% CI: 1.0038-1.0282), 1.0097 (1.0018-1.0177), 1.0738 (1.0222-1.01280), and 1.0013 (1.0001-1.0026), respectively. However, no significant effect of NO₂ or O₃ on schizophrenia admissions was found. The stratified analysis indicated that females and younger individuals (< 45 years old) appeared to be more vulnerable, but no significant difference was found between seasons. Furthermore, 12.41% of schizophrenia hospitalizations were attributable to exposure to air pollution exceeding the World Health Organization (WHO) air quality standard, with a total economic burden of 89.67 million RMB during the study period. At the individual level, excessive air pollution exposure resulted in an economic burden of 8232.08 RMB per hospitalization. Our study found that short-term exposure to air pollutants increased the risk of hospital admissions for schizophrenia and resulted in a substantial economic burden. Considerable health benefits can be achieved by further reducing air pollution.

Acute effect of air pollutants' peak-hour concentrations on ischemic stroke hospital admissions among hypertension patients in Beijing, China, from 2014 to 2018

Air pollutants' effect on ischemic stroke (IS) has been widely reported. But the effect of high-level concentrations during people's outdoor periods among hypertension patients was unknown. Peak-hour concentrations were defined considering air pollutants' high concentrations as well as people's outdoor periods. We conducted a time-series study and used the generalized additive model to analyze peak-hour concentrations' acute effect. A total of 315,499 IS patients comorbid with hypertension were admitted to secondary and above hospitals in Beijing from 2014 to 2018. A 10 µg/m³ (CO: 1 mg/m³) increase of the peak-hour concentrations was positively associated with IS hospital admissions among hypertension patients. The maximum effect sizes were as follows: for PM_2.5, 0.17% (95% confidence interval [CI]: 0.10-0.24%) at Lag0 and 0.22% (95% CI: 0.12-0.33%) at Lag0-5; for PM₁₀, 0.09% (95% CI: 0.05-0.13%) at Lag5 and 0.17% (95% CI: 0.09-0.26%) at Lag0-5; for SO₂, 0.87% (95% CI: 0.46-1.29%) at Lag5; for NO₂, 0.83% (95% CI: 0.62-1.04%) at Lag0 and 0.86% (95% CI: 0.59-1.13%) at Lag0-1; for CO 1.23% (95% CI: 0.66-1.80%) at Lag0 and 1.33% (95% CI: 0.33-2.35%) at Lag0-5; for O₃ 0.23% (95% CI: 0.12-0.35%) at Lag0 and 0.20% (95% CI: 0.05-0.34%) at Lag0-1. The effect sizes of PM_2.5, NO₂, and O₃ remained significant after adjusting daily mean. Larger effect sizes were observed for PM_2.5 and PM₁₀ in cool season and for O₃ in warm season. As significant exposure indicators of air pollution, peak-hour concentrations exposure increased the risk of IS hospital admissions among hypertension patients and it is worthy of consideration in relative environmental standard. It is suggested for hypertension patients to avoid outdoor activity during peak hours. More relevant searches are required to further illustrate air pollutant's effect on chronic disease population.

Air pollution and hospital admissions for critical illness in emergency department: a tertiary-center research in Changsha, China, 2016-2020

We aimed to comprehensively investigate the associations of air pollutants with hospital admissions for critical illness in ED. Patients with critical illness including level 1 and level 2 of the Emergency Severity Index admitted in ED of Changsha Central Hospital from January 2016 to December 2020 were enrolled. Meteorological and air pollutants data source were collected from the National Meteorological Science Data Center. A Poisson generalized linear regression combined with a polynomial distributed lag model (PDLM) was utilized to explore the effect of air pollution on hospital admissions for critical illness in ED. Benchmarks as references (25th) were conducted for comparisons with high levels of pollutant concentrations (75th). At first, lagged effects of all different air pollutants were analyzed. Then, based on the most significant factor, analyses in subgroups were performed by gender (male and female), age (< 45, 45-65, and > 65), disorders (cardiovascular, neurological, respiratory), and seasons (spring, summer, autumn, and winter). A total of 47,290 patients with critical illness admitted in ED were included. The effects of air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃ and CO) on critical illness ED visits were statistically significant. Strong collinearity between PM_2.5 and PM₁₀ (r = 0.862) was found. Both single-day lag and cumulative-day lag day models showed that PM_2.5 had the strongest effects (lag 0, RR = 1.025, 95% CI 1.008-1.043, and lag 0-14, RR = 1.067, 95% CI 1.017-1.120, respectively). In both PM_2.5 and PM₁₀, the risks of critical illness in male, > 65 ages, respiratory diseases, and winter increased the most significant. Air pollutants, especially PM_2.5 and PM₁₀ exposure, could increase the risk of critical illness admission.

Association between Particulate Matter Pollution Concentration and Hospital Admissions for Hypertension in Ganzhou, China

Fine particulate matter (PM_2.5) and respirable particulate matter (PM₁₀) are two major air pollutants with toxic effects on the cardiovascular system. Hypertension, as a chronic noncommunicable cardiovascular disease, is also a risk factor for several diseases. We applied generalized linear models with a quasi-Poisson link to assess the effect of air pollution exposure on the number of daily admissions for patients with hypertension. In addition, we established a two-pollutant model to evaluate PM_2.5 and PM₁₀ hazard effect stability by adjusting the other gaseous pollutants. Results showed that during the study period, 24 h mean concentrations of ambient PM_2.5 and PM₁₀ at 38.17 and 59.84 μg/m³, respectively, and a total of 2,611 hypertension hospital admissions were recorded. Air pollution concentrations significantly affected the number of hospitalizations for hypertension approximately 2 months after exposure. For each 10 μg/m³ increase in PM_2.5 and PM₁₀ in single-pollutant models, the number of hospitalizations for hypertension increased by 7.92% (95% CI: 5.48% to 10.42%) and 4.46% (95% CI: 2.86% to 5.65%), respectively, at the lag day with the strongest effect. NO₂, O₃, CO, and SO₂ had different significant effects on the number of hospitalizations over the same time period, and PM_2.5 and PM₁₀ still showed robust significant effects after adjustment of gas pollutants through a two-pollutant model. These findings may contribute to a better understanding of the health effects of ambient particulate matter.

Ambient Air Pollution and Hospitalizations for Ischemic Stroke: A Time Series Analysis Using a Distributed Lag Nonlinear Model in Chongqing, China

Short-term exposure to air pollution has been associated with ischemic stroke (IS) hospitalizations, but the evidence of its effects on IS in low- and middle-income countries is limited and inconsistent. We aimed to quantitatively estimate the association between air pollution and hospitalizations for IS in Chongqing, China. This time series study included 2,299 inpatients with IS from three hospitals in Chongqing from January 2015 to December 2016. Generalized linear regression models combined with a distributed lag nonlinear model (DLNM) were used to investigate the impact of air pollution on IS hospitalizations. Stratification analysis was further implemented by sex, age, and season. The maximum lag-specific and cumulative percentage changes of IS were 1.2% (95% CI: 0.4–2.1%, lag 3 day) and 3.6% (95% CI: 0.5–6.7%, lag 05 day) for each 10 μg/m³ increase in PM_2.5; 1.0% (95% CI: 0.3–1.7%, lag 3 day) and 2.9% (95% CI: 0.6–5.2%, lag 05 day) for each 10 μg/m³ increase in PM₁₀; 4.8% (95% CI: 0.1–9.7%, lag 4 day) for each 10 μg/m³ increase in SO₂; 2.5% (95% CI: 0.3–4.7%, lag 3 day) and 8.2% (95% CI: 0.9–16.0%, lag 05 day) for each 10 μg/m3 increase in NO₂; 0.7% (95% CI: 0.0–1.5%, lag 6 day) for each 10 μg/m³ increase in O₃. No effect modifications were detected for sex, age, and season. Our findings suggest that short-term exposure to PM_2.5, PM₁₀, SO₂, NO₂, and O₃ contributes to more IS hospitalizations, which warrant the government to take effective actions in addressing air pollution issues.