TY - JOUR
T1 - Short-term association between ambient air pollution and lung cancer mortality
AU - Wang, Ning
AU - Mengersen, Kerrie
AU - Tong, Shilu
AU - Kimlin, Michael
AU - Zhou, Maigeng
AU - Wang, Lijun
AU - Yin, Peng
AU - Xu, Zhiwei
AU - Cheng, Jian
AU - Zhang, Yuzhou
AU - Hu, Wenbiao
N1 - Funding Information:
For continuous support and assistance, we thank Jiangmei Liu and Yunning Liu ( National Center for Chronic and Noncommunicable Disease Control and Prevention , Chinese Center for Disease Control and Prevention ). We thank all research staff from local Center for Disease Control and Prevention for collection of data. N Wang was supported by the Queensland University of Technology Postgraduate Research Award and Queensland University of Technology Higher Degree Research International Tuition Fee Sponsorship. K Mengersen is supported by the ARC Center of Excellence in Mathematics and Statistical Frontiers . M Kimlin is supported through a Cancer Council Queensland Professorial Chair. W Hu is supported by Australian Research Council future fellowship ( FT140101216 ).
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/12
Y1 - 2019/12
N2 - Rationale: Long-term exposure to air pollution has been associated with increased lung cancer incidence and mortality. However, the short-term association between air pollution and lung cancer mortality (LCM) remains largely unknown. Methods: We collected daily data on particulate matter with diameter <2.5 μm (PM2.5), particulate matter with diameter < 10 μm (PM10), sulfur dioxide (SO2), and ozone (O3), and LCM in three of the biggest cities in China, i.e. Beijing, Chongqing, and Guangzhou, from 2013 to 2015. We first estimated city-specific relationships between air pollutants and LCM using time-series generalized linear models, adjusting for potential confounders. A classification and regression tree (CART) model was used to stratify LCM risk based on combinations of air pollutants and meteorological factors in each city. Then we pooled the city-specific associations using random-effects meta-analysis. Meta regression was used to explore if city-specific characteristics modified the air pollution-LCM association. Finally, we stratified the analyses by season, age, and sex. Results:Over the entire period, the current-day concentrations of PM2.5 and PM10 in Chongqing and PM2.5, PM10, and SO2 in Guangzhou were positively associated with LCM (Excess risk ranged from 0.72% (95% CI 0.27%–1.17%) to 6.06% (95% CI 0.76%–11.64%) with each 10 μg/m3 increment in different pollutants), but the association between current-day air pollution and LCM in Beijing was not significant (P > 0.05). When considering the environmental and weather factors simultaneously, current-day PM2.5, relative humidity, and PM10 were the most important factors associated with LCM in Beijing, Chongqing, and Guangzhou, respectively. LCM risk related with daily PM2.5, PM10, and SO2 significantly increased with the increasing annual mean temperature and humidity of the city, while LCM risk related with daily O3 significantly increased with the increases of latitude, annual mean O3 concentration, and socioeconomic level. After stratification, the current-day PM2.5, PM10, and O3 during the warm season in Beijing and PM2.5, PM10, and SO2 during the cool season in Chongqing and Guangzhou were positively associated with LCM (Excess risk ranged from 0.93% (95% CI 0.42%–1.45%) to 7.16% (95% CI 0.64%–14.09%) with each 10 μg/m3 increment in different pollutants). Male and the elderly lung cancer patients were more sensitive to the short-term effect of air pollution. Conclusions: Lung cancer patients should enhance protection measures against air pollution. More attentions should be paid for the high PM2.5, PM10, and O3 during the warm season in Beijing, and high PM2.5, PM10, and SO2 during the cool season in Chongqing and Guangzhou.
AB - Rationale: Long-term exposure to air pollution has been associated with increased lung cancer incidence and mortality. However, the short-term association between air pollution and lung cancer mortality (LCM) remains largely unknown. Methods: We collected daily data on particulate matter with diameter <2.5 μm (PM2.5), particulate matter with diameter < 10 μm (PM10), sulfur dioxide (SO2), and ozone (O3), and LCM in three of the biggest cities in China, i.e. Beijing, Chongqing, and Guangzhou, from 2013 to 2015. We first estimated city-specific relationships between air pollutants and LCM using time-series generalized linear models, adjusting for potential confounders. A classification and regression tree (CART) model was used to stratify LCM risk based on combinations of air pollutants and meteorological factors in each city. Then we pooled the city-specific associations using random-effects meta-analysis. Meta regression was used to explore if city-specific characteristics modified the air pollution-LCM association. Finally, we stratified the analyses by season, age, and sex. Results:Over the entire period, the current-day concentrations of PM2.5 and PM10 in Chongqing and PM2.5, PM10, and SO2 in Guangzhou were positively associated with LCM (Excess risk ranged from 0.72% (95% CI 0.27%–1.17%) to 6.06% (95% CI 0.76%–11.64%) with each 10 μg/m3 increment in different pollutants), but the association between current-day air pollution and LCM in Beijing was not significant (P > 0.05). When considering the environmental and weather factors simultaneously, current-day PM2.5, relative humidity, and PM10 were the most important factors associated with LCM in Beijing, Chongqing, and Guangzhou, respectively. LCM risk related with daily PM2.5, PM10, and SO2 significantly increased with the increasing annual mean temperature and humidity of the city, while LCM risk related with daily O3 significantly increased with the increases of latitude, annual mean O3 concentration, and socioeconomic level. After stratification, the current-day PM2.5, PM10, and O3 during the warm season in Beijing and PM2.5, PM10, and SO2 during the cool season in Chongqing and Guangzhou were positively associated with LCM (Excess risk ranged from 0.93% (95% CI 0.42%–1.45%) to 7.16% (95% CI 0.64%–14.09%) with each 10 μg/m3 increment in different pollutants). Male and the elderly lung cancer patients were more sensitive to the short-term effect of air pollution. Conclusions: Lung cancer patients should enhance protection measures against air pollution. More attentions should be paid for the high PM2.5, PM10, and O3 during the warm season in Beijing, and high PM2.5, PM10, and SO2 during the cool season in Chongqing and Guangzhou.
UR - http://www.scopus.com/inward/record.url?scp=85072543676&partnerID=8YFLogxK
U2 - 10.1016/j.envres.2019.108748
DO - 10.1016/j.envres.2019.108748
M3 - Article
C2 - 31561053
AN - SCOPUS:85072543676
SN - 0013-9351
VL - 179
JO - Environmental Research
JF - Environmental Research
M1 - 108748
ER -
