Ning Wang1, Kerrie Mengersen2, Shilu Tong3, Michael Kimlin4, Maigeng Zhou5, Lijun Wang5, Peng Yin5, Zhiwei Xu1, Jian Cheng1, Yuzhou Zhang1, Wenbiao Hu6. 1. School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. 2. School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia. 3. School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia; Shanghai Children's Medical Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China. 4. Health Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia. 5. National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China. 6. School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. Electronic address: w2.hu@qut.edu.au.
Abstract
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.
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 cancerpatients were more sensitive to the short-term effect of air pollution. CONCLUSIONS:Lung cancerpatients 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.