Kai Huang1, Kun Ding1, Xiao-Jing Yang1, Cheng-Yang Hu1, Wen Jiang1, Xiao-Guo Hua1, Jie Liu2, Ji-Yu Cao2, Tao Zhang3, Xiao-Hong Kan4, Xiu-Jun Zhang5. 1. Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China. 2. Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China. 3. Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, China. 4. Anhui Medical University Clinical College of Chest, 397 Jixi Road, Hefei, 230022, China; Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, China. Electronic address: kanxiaohong@ahmu.edu.cn. 5. Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China. Electronic address: zhangxiujun@ahmu.edu.cn.
Abstract
BACKGROUND: The current evidence has presented mixed results between air pollutants exposure and the progression of tuberculosis (TB). The purpose of this study was to explore the association between short-term exposure to air pollutants and the risk of TB outpatient visits in Hefei, China. METHODS: Time-series analysis was used to assess the effect of short-term exposure to ambient air pollutants on the risk of TB outpatient visits. A Poisson generalized linear regression model combined with a distributed lag non-linear model (DLNM) was applied to explore the association. The effects of different gender (male, female), age (≤65 years old, >65 years old) and season (cold season, warm season) on the risk of TB were investigated by stratified analysis. Sensitivity analyses were conducted to test the robustness of our findings. RESULTS: A total of 22,749 active TB cases were identified from November 1, 2013 to December 31, 2018 in Hefei. The overall exposure-response curve showed that the concentration of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) and nitrogen dioxide (NO2) exposure were positively correlated with the risk of TB outpatient visits, while ozone (O3) and sulfur dioxide (SO2) exposure were negatively correlated with the risk of TB outpatient visits. The maximum lag-specific and cumulative relative risk (RR) of TB outpatient visits were 1.057 [95%CI: 1.002-1.115, lag 3 day] and 1.559 (95%CI: 1.057-2.300, lag 13 days) for each 10 μg/m³ increase in PM2.5; 1.026 (95% CI: 1.008-1.044, lag 0 day) and 1.559 (95%CI: 1.057-2.300, lag 07 days) for each 10 μg/m³ increase in NO2; 0.866 (95% CI: 0.801-0.935, lag 5 day) and 0.852 (95%CI: 1.01-1.11, lag 0-14 days) for each 10 μg/m³ increase in SO2 in the single-pollutant model. There was only a negative association between O3 exposure and the cumulative risk of TB outpatient visits (RR = 0.960, 95%CI: 0.936-0.984, lag 07 days). Stratified analyses showed that the effects of SO2 and O3 exposure were different between warm and cold seasons. The effect of NO2 exposure remained statistically significant in male, younger, and cold season subgroups. Besides, elderly people are more susceptible to PM2.5 exposure. CONCLUSION: This study suggests that exposure to PM2.5, NO2, SO2, and O3 are associated with the risk of TB outpatient visits. Seasonal variation may have a greater impact on the risk of TB outpatient visits compared with gender and age.
BACKGROUND: The current evidence has presented mixed results between air pollutants exposure and the progression of tuberculosis (TB). The purpose of this study was to explore the association between short-term exposure to air pollutants and the risk of TBoutpatient visits in Hefei, China. METHODS: Time-series analysis was used to assess the effect of short-term exposure to ambient air pollutants on the risk of TBoutpatient visits. A Poisson generalized linear regression model combined with a distributed lag non-linear model (DLNM) was applied to explore the association. The effects of different gender (male, female), age (≤65 years old, >65 years old) and season (cold season, warm season) on the risk of TB were investigated by stratified analysis. Sensitivity analyses were conducted to test the robustness of our findings. RESULTS: A total of 22,749 active TB cases were identified from November 1, 2013 to December 31, 2018 in Hefei. The overall exposure-response curve showed that the concentration of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) and nitrogen dioxide (NO2) exposure were positively correlated with the risk of TBoutpatient visits, while ozone (O3) and sulfur dioxide (SO2) exposure were negatively correlated with the risk of TBoutpatient visits. The maximum lag-specific and cumulative relative risk (RR) of TBoutpatient visits were 1.057 [95%CI: 1.002-1.115, lag 3 day] and 1.559 (95%CI: 1.057-2.300, lag 13 days) for each 10 μg/m³ increase in PM2.5; 1.026 (95% CI: 1.008-1.044, lag 0 day) and 1.559 (95%CI: 1.057-2.300, lag 07 days) for each 10 μg/m³ increase in NO2; 0.866 (95% CI: 0.801-0.935, lag 5 day) and 0.852 (95%CI: 1.01-1.11, lag 0-14 days) for each 10 μg/m³ increase in SO2 in the single-pollutant model. There was only a negative association between O3 exposure and the cumulative risk of TBoutpatient visits (RR = 0.960, 95%CI: 0.936-0.984, lag 07 days). Stratified analyses showed that the effects of SO2 and O3 exposure were different between warm and cold seasons. The effect of NO2 exposure remained statistically significant in male, younger, and cold season subgroups. Besides, elderly people are more susceptible to PM2.5 exposure. CONCLUSION: This study suggests that exposure to PM2.5, NO2, SO2, and O3 are associated with the risk of TBoutpatient visits. Seasonal variation may have a greater impact on the risk of TBoutpatient visits compared with gender and age.
Authors: Yuqing Feng; Jing Wei; Maogui Hu; Chengdong Xu; Tao Li; Jinfeng Wang; Wei Chen Journal: Int J Environ Res Public Health Date: 2022-05-09 Impact factor: 4.614
Authors: Yun-Peng Chen; Le-Fan Liu; Yang Che; Jing Huang; Guo-Xing Li; Guo-Xin Sang; Zhi-Qiang Xuan; Tian-Feng He Journal: Int J Environ Res Public Health Date: 2022-04-28 Impact factor: 4.614