Dataset Information

Short-term effects of particulate matter on stroke attack: meta-regression and meta-analyses.

ABSTRACT:

Background and purpose

Currently there are more and more studies on the association between short-term effects of exposure to particulate matter (PM) and the morbidity of stroke attack, but few have focused on stroke subtypes. The objective of this study is to assess the relationship between PM and stroke subtypes attack, which is uncertain now.

Methods

Meta-analyses, meta-regression and subgroup analyses were conducted to investigate the association between short-term effects of exposure to PM and the morbidity of different stroke subtypes from a number of epidemiologic studies (from 1997 to 2012).

Results

Nineteen articles were identified. Odds ratio (OR) of stroke attack associated with particular matter ("thoracic particles" [PM10]<10 µm in aerodynamic diameter, "fine particles" [PM2.5]<2.5 µm in aerodynamic diameter) increment of 10 µg/m3 was as effect size. PM10 exposure was related to an increase in risk of stroke attack (OR per 10 µg/m3 = 1.004, 95%CI: 1.001 ? 1.008) and PM2.5 exposure was not significantly associated with stroke attack (OR per 10 µg/m3 = 0.999, 95%CI: 0.994 ? 1.003). But when focused on stroke subtypes, PM2.5 (OR per 10 µg/m3 = 1.025; 95%CI, 1.001?1.049) and PM10 (OR per 10 µg/m3 = 1.013; 95%CI, 1.001 ? 1.025) exposure were statistically significantly associated with an increased risk of ischemic stroke attack, while PM2.5 (all the studies showed no significant association) and PM10 (OR per 10 µg/m3 = 1.007; 95%CI, 0.992 ? 1.022) exposure were not associated with an increased risk of hemorrhagic stroke attack. Meta-regression found study design and area were two effective covariates.

Conclusion

PM2.5 and PM10 had different effects on different stroke subtypes. In the future, it's worthwhile to study the effects of PM to ischemic stroke and hemorrhagic stroke, respectively.

SUBMITTER: Yu XB

PROVIDER: S-EPMC4011734 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Short-term effects of particulate matter on stroke attack: meta-regression and meta-analyses.

Yu Xiao-Bo XB Su Jun-Wei JW Li Xiu-Yang XY Chen Gao G

PloS one 20140506 5

<h4>Background and purpose</h4>Currently there are more and more studies on the association between short-term effects of exposure to particulate matter (PM) and the morbidity of stroke attack, but few have focused on stroke subtypes. The objective of this study is to assess the relationship between PM and stroke subtypes attack, which is uncertain now.<h4>Methods</h4>Meta-analyses, meta-regression and subgroup analyses were conducted to investigate the association between short-term effects of ...[more]

PMID: 24802512

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Project description:BACKGROUND:The link between PM2.5 exposure and adverse health outcomes is well documented from studies across the world. However, the reported effect estimates vary across studies, locations and constituents. We aimed to conduct a meta-analysis on associations between short-term exposure to PM2.5 constituents and mortality using city-specific estimates, and explore factors that may explain some of the observed heterogeneity. METHODS:We systematically reviewed epidemiological studies on particle constituents and mortality using PubMed and Web of Science databases up to July 2015.We included studies that examined the association between short-term exposure to PM2.5 constituents and all-cause, cardiovascular, and respiratory mortality, in the general adult population. Each study was summarized based on pre-specified study key parameters (e.g., location, time period, population, diagnostic classification standard), and we evaluated the risk of bias using the Office of Health Assessment and Translation (OHAT) Method for each included study. We extracted city-specific mortality risk estimates for each constituent and cause of mortality. For multi-city studies, we requested the city-specific risk estimates from the authors unless reported in the article. We performed random effects meta-analyses using city-specific estimates, and examined whether the effects vary across regions and city characteristics (PM2.5 concentration levels, air temperature, elevation, vegetation, size of elderly population, population density, and baseline mortality). RESULTS:We found a 0.89% (95% CI: 0.68, 1.10%) increase in all-cause, a 0.80% (95% CI: 0.41, 1.20%) increase in cardiovascular, and a 1.10% (95% CI: 0.59, 1.62%) increase in respiratory mortality per 10?g/m3 increase in PM2.5. Accounting for the downward bias induced by studies of single days, the all-cause mortality estimate increased to 1.01% (95% CI: 0.81, 1.20%). We found significant associations between mortality and several PM2.5 constituents. The most consistent and stronger associations were observed for elemental carbon (EC) and potassium (K). For most of the constituents, we observed high variability of effect estimates across cities. CONCLUSIONS:Our meta-analysis suggests that (a) combustion elements such as EC and K have a stronger association with mortality, (b) single lag studies underestimate effects, and (c) estimates of PM2.5 and constituents differ across regions. Accounting for PM mass in constituent's health models may lead to more stable and comparable effect estimates across different studies. SYSTEMATIC REVIEW REGISTRATION:PROSPERO: CRD42017055765.

Project description:BackgroundAir pollution can cause various respiratory and neurological diseases and continuous exposure can lead to death. Previous studies have reported that particulate matter (PM) exposure increases the risk of depression, suicidal thoughts, and suicidal death; however, the results are inconsistent and limited. This study aimed to examine the relationship between short-term PM exposure and suicide deaths, as well as investigate the short-term effects of PM on suicide death within vulnerable groups based on factors such as sex, age group, suicide-related information (note, method, and cause), psychiatric disorders, and physical diseases.MethodsData on a total of 28,670 suicide deaths from 2013 to 2017, provided by the Korea Foundation for Suicide Prevention, were analyzed. The study design employed a time-series analysis with a two-stage approach. In the first step, a generalized additive model combined with a distributed lag nonlinear model was used to estimate the short-term effect of PM exposure on suicide risk specific to each city. In the second step, the estimated results from each city were pooled through a meta-analysis to derive the overall effect. We determined the effects of single lag, cumulative lag, and moving average PM concentrations from days 0-7 before suicide.ResultsWe confirmed an association between exposure to PM10 (≤ 10 μm in diameter) and deaths due to suicide. In particular, among individuals with psychiatric disorders and those who employed non-violent suicide methods, increased exposure to PM10 was associated with a higher risk of death by suicide, with percentage changes of 5.92 (95% confidence interval [CI]: 3.95-7.92) and 11.47 (95% CI: 7.95-15.11), respectively. Furthermore, in the group with psychiatric disorders, there was an observed tendency of increasing suicide risk as PM10 levels increased up to 120 µg/m3, whereas in the group with non-violent suicide deaths, there was a pronounced trend of rapid increase in suicide risk with an increase in PM10 up to 100 µg/m3.ConclusionsThese results show an association between short-term exposure to PM and suicide. Our study adds evidence for the benefits of reducing PM in preventing diseases and improving mental health.

Project description:Inflammation and the coagulation cascade are considered to be the potential mechanisms of ambient particulate matter (PM) exposure-induced adverse cardiovascular events. Tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), interleukin-8 (IL-8), and fibrinogen are arguably the four most commonly assayed markers to reflect the relationships of PM with inflammation and blood coagulation. This review summarized and quantitatively analyzed the existing studies reporting short- and long-term associations of PM2.5(PM with an aerodynamic diameter ≤2.5 μm)/PM10 (PM with an aerodynamic diameter≤10 μm) with important inflammation and blood coagulation markers (TNF-α, IL-6, IL-8, fibrinogen). We reviewed relevant studies published up to July 2020, using three English databases (PubMed, Web of Science, Embase) and two Chinese databases (Wang-Fang, China National Knowledge Infrastructure). The OHAT tool, with some modification, was applied to evaluate risk of bias. Meta-analyses were conducted with random-effects models for calculating the pooled estimate of markers. To assess the potential effect modifiers and the source of heterogeneity, we conducted subgroup analyses and meta-regression analyses where appropriate. The assessment and correction of publication bias were based on Begg's and Egger's test and "trim-and-fill" analysis. We identified 44 eligible studies. For short-term PM exposure, the percent change of a 10 μg/m3 PM2.5 increase on TNF-α and fibrinogen was 3.51% (95% confidence interval (CI): 1.21%, 5.81%) and 0.54% (95% confidence interval (CI): 0.21%, 0.86%) respectively. We also found a significant short-term association between PM10 and fibrinogen (percent change = 0.17%, 95% CI: 0.04%, 0.29%). Overall analysis showed that long-term associations of fibrinogen with PM2.5 and PM10 were not significant. Subgroup analysis showed that long-term associations of fibrinogen with PM2.5 and PM10 were significant only found in studies conducted in Asia. Our findings support significant short-term associations of PM with TNF-α and fibrinogen. Future epidemiological studies should address the role long-term PM exposure plays in inflammation and blood coagulation markers level change.

Dataset Information

Short-term effects of particulate matter on stroke attack: meta-regression and meta-analyses.

Background and purpose

Methods

Results

Conclusion

Publications

Short-term effects of particulate matter on stroke attack: meta-regression and meta-analyses.

Similar Datasets

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