Estimating causal links of long-term exposure to particulate matters with all-cause mortality in South China

•Higher all-cause mortality was attributed to greater PM1, PM2.5 and PM10 exposure.•The risk from PM1 tends to be 1.1% and 1.3% greater than that from PM2.5 and PM10.•The elderly, unmarried and less educated ones were more vulnerable to the impact of PMs.•Participants generally at low PM exposures w...

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Vydané v:Environment international Ročník 171; s. 107726
Hlavní autori: Wang, Ying, Wei, Jing, Zhang, Yuqin, Guo, Tong, Chen, Shirui, Wu, Wenjing, Chen, Shimin, Li, Ziqiang, Qu, Yanji, Xiao, Jianpeng, Deng, Xinlei, Liu, Yu, Du, Zhicheng, Zhang, Wangjian, Hao, Yuantao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier Ltd 01.01.2023
Elsevier
Predmet:
Aged
Air Pollutants - analysis
Air Pollution - adverse effects
Air Pollution - analysis
All-cause mortality
Causal inference model
China
China - epidemiology
chronic exposure
confidence interval
elderly
environment
Environmental Exposure - adverse effects
Environmental Exposure - analysis
Humans
lifestyle
Long-term effect
low educational status
mortality
Particulate matter
Particulate Matter - analysis
particulates
risk
Risk Factors
toxicity
China
Causal inference model
All-cause mortality
Long-term effect
Particulate matter
ISSN:0160-4120, 1873-6750, 1873-6750
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Shrnutí:•Higher all-cause mortality was attributed to greater PM1, PM2.5 and PM10 exposure.•The risk from PM1 tends to be 1.1% and 1.3% greater than that from PM2.5 and PM10.•The elderly, unmarried and less educated ones were more vulnerable to the impact of PMs.•Participants generally at low PM exposures were more susceptible than others. The association between long-term particulate matter (PM) exposure and all-cause mortality has been well-documented. However, evidence is still limited from high-exposed cohorts, especially for PM1 which is smaller while more toxic than other commonly investigated particles. We aimed to examine the potential casual links of long-term PMs exposure with all-cause mortality in high-exposed areas. A total of 580,757 participants in southern China were enrolled during 2009–2015 and followed up to 2020. The annual average concentration of PM1, PM2.5, and PM10 at 1 km2 spatial resolution was assessed for each residential address through validated spatiotemporal models. We used marginal structural Cox models to estimate the PM-mortality associations which were further stratified by sociodemographic, lifestyle factors and general exposure levels. 37,578 deaths were totally identified during averagely 8.0 years of follow-up. Increased exposure to all 3 PM size fractions were significantly associated with increased risk of all-cause mortality, with hazard ratios (HRs) of 1.042 (95 % confidence interval (CI): 1.037–1.046), 1.031 (95 % CI: 1.028–1.033), and 1.029 (95 % CI: 1.027–1.031) per 1 μg/m3 increase in PM1, PM2.5, and PM10 concentrations, respectively. We observed greater effect estimates among the elderly (age ≥ 65 years), unmarried participants, and those with low education attainment. Additionally, the effect of PM1, PM2.5, and PM10 tend to be higher in the low-exposure group than in the general population. We provided comprehensive evidence for the potential causal links betweenlong-term PM exposureand all-cause mortality, and suggested stronger links for PM1compared to large particles and among certain vulnerable subgroups.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0160-4120
1873-6750
1873-6750
DOI:10.1016/j.envint.2022.107726