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. 2022 Feb 1;6(2):e2021GH000529.
doi: 10.1029/2021GH000529. eCollection 2022 Feb.

Association Between Air Pollutants and Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Time Stratified Case-Crossover Design With a Distributed Lag Nonlinear Model

Yanchen Liu  1 , Xiaoli Han  1   2 , Xudong Cui  1 , Xiangkai Zhao  1 , Xin Zhao  1 , Hongmiao Zheng  1 , Benzhong Zhang  3 , Xiaowei Ren  1   4
Affiliations

Association Between Air Pollutants and Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Time Stratified Case-Crossover Design With a Distributed Lag Nonlinear Model

Yanchen Liu et al. Geohealth. .

Abstract

Acute exacerbation of chronic obstruction pulmonary disease (AECOPD) as a respiratory disease, is considered to be related to air pollution by more and more studies. However, the evidence on how air pollution affect the incidence of AECOPD and whether there are population differences is still insufficient. Therefore, we select PM10, PM2.5, SO2, NO2, CO, and O3 as representatives combined with daily AECOPD admission data from 1 January 2015 to 26 June 2016 in the rural areas of Qingyang, northwestern China to explore the associations of air pollution with AECOPD. Based on a time-stratified case-crossover design, we constructed a distributed lag nonlinear model to qualify the single and cumulative lagged effects of air pollution on AECOPD. Stratified related risks by sex and age were also reported. The cumulative exposure-response curves were approximately linear for PM2.5, "V"-shaped for PM10, "U"-shaped for NO2 and inverted-"V" for SO2, CO and O3. Exposure to high-PM2.5 (42 μg/m3), high-PM10 (91 μg/m3), high-SO2 (58 μg/m3), low-NO2 (12 μg/m3), and high-CO (1.55 mg/m3) increased the risk of AECOPD. Females aged 15-64 were more susceptible under extreme concentrations of PM2.5, SO2, CO, and low-PM10 than other subgroups. In addition, adults aged 15-64 were more sensitive to extreme concentrations of NO2 compared with the elderly ≥65 years old, while the latter were more sensitive to high-PM10. High-SO2, high-NO2, and extreme concentrations of PM2.5 had the greatest effects on the day of exposure, while low-SO2 and low-CO had lagged effects on AECOPD. Precautionary measures should be taken with a focus on vulnerable subgroups, to control hospitalization for AECOPD associated with air pollutants.

Keywords: AECOPD; air pollutants; distributed lag nonlinear model; time‐stratified case‐crossover study.

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Conflict of interest statement

The authors declare no conflicts of interest relevant to this study.

Figures

Figure 1
Figure 1
Time‐series diagram of daily acute exacerbation of chronic obstruction pulmonary disease admissions by subgroup and air pollutants from 1 January 2015 to 26 June 2016.
Figure 1
Figure 1
Time‐series diagram of daily acute exacerbation of chronic obstruction pulmonary disease admissions by subgroup and air pollutants from 1 January 2015 to 26 June 2016.
Figure 2
Figure 2
Three‐dimensional plots for the relative risks of air pollutants on acute exacerbation of chronic obstruction pulmonary disease in Qingyang's rural areas from 1 January 2015 to 26 June 2016.
Figure 3
Figure 3
Cumulative exposure‐response curves of different air pollutants for total acute exacerbation of chronic obstruction pulmonary disease admissions over lag0–27 days in Qingyang's rural areas from 1 January 2015 to 26 June 2016. Each curve depicts the mean relative risk, and the gray region represents the 95% confidence interval. Solid vertical lines are the median concentrations of air pollutants, and dashed vertical lines are the 25th and 75th percentiles; dotted vertical lines are the 2.5th and 97.5th percentiles.
Figure 4
Figure 4
Statistically significant single‐day lagged effect curves of extreme air pollutants for acute exacerbation of chronic obstruction pulmonary disease (AECOPD) in each subgroup at different lags in Qingyang's rural areas from 1 January 2015 to 26 June 2016. The effects were expressed by the relative risk of AECOPD. The high‐concentration effects were estimated by comparing the 75th percentile of daily air pollutants to the median value, whereas the low‐concentration effects were estimated by comparing the 25th percentile of daily air pollutants to the median value. Elderly and adult patients were sub‐grouped according to age (adult, 15–64 years old; the elderly, ≥65 years old).
Figure 4
Figure 4
Statistically significant single‐day lagged effect curves of extreme air pollutants for acute exacerbation of chronic obstruction pulmonary disease (AECOPD) in each subgroup at different lags in Qingyang's rural areas from 1 January 2015 to 26 June 2016. The effects were expressed by the relative risk of AECOPD. The high‐concentration effects were estimated by comparing the 75th percentile of daily air pollutants to the median value, whereas the low‐concentration effects were estimated by comparing the 25th percentile of daily air pollutants to the median value. Elderly and adult patients were sub‐grouped according to age (adult, 15–64 years old; the elderly, ≥65 years old).
Figure 5
Figure 5
Forest plots for statistically significant cumulative lagged effects of extreme air pollutants on acute exacerbation of chronic obstruction pulmonary disease (AECOPD) at different lags in each subgroup in Qingyang's rural areas from 1 January 2015 to 26 June 2016. The effects were expressed by the relative risk of AECOPD. The high‐concentration effects were estimated by comparing the 75th percentile of daily air pollutants to the median value, whereas the low‐concentration effects were estimated by comparing the 25th percentile of daily air pollutants to the median value. Elderly and adult individuals were sub‐grouped according to age (adult, 15–64 years old and the elderly, ≥65 years old).
Figure 5
Figure 5
Forest plots for statistically significant cumulative lagged effects of extreme air pollutants on acute exacerbation of chronic obstruction pulmonary disease (AECOPD) at different lags in each subgroup in Qingyang's rural areas from 1 January 2015 to 26 June 2016. The effects were expressed by the relative risk of AECOPD. The high‐concentration effects were estimated by comparing the 75th percentile of daily air pollutants to the median value, whereas the low‐concentration effects were estimated by comparing the 25th percentile of daily air pollutants to the median value. Elderly and adult individuals were sub‐grouped according to age (adult, 15–64 years old and the elderly, ≥65 years old).
Figure 5
Figure 5
Forest plots for statistically significant cumulative lagged effects of extreme air pollutants on acute exacerbation of chronic obstruction pulmonary disease (AECOPD) at different lags in each subgroup in Qingyang's rural areas from 1 January 2015 to 26 June 2016. The effects were expressed by the relative risk of AECOPD. The high‐concentration effects were estimated by comparing the 75th percentile of daily air pollutants to the median value, whereas the low‐concentration effects were estimated by comparing the 25th percentile of daily air pollutants to the median value. Elderly and adult individuals were sub‐grouped according to age (adult, 15–64 years old and the elderly, ≥65 years old).
Figure 6
Figure 6
Forest plots for cumulative lagged effects on acute exacerbation of chronic obstruction pulmonary disease (AECOPD) in each subgroup using the two‐pollutant model in Qingyang's rural areas from 1 January 2015 to 26 June 2016. The effects were expressed by the relative risk of extreme concentrations for AECOPD development, with the median concentration as a reference. Elderly and adult individuals were sub‐grouped according to age (adult, 15–64 years old and the elderly, ≥65 years old).
Figure 6
Figure 6
Forest plots for cumulative lagged effects on acute exacerbation of chronic obstruction pulmonary disease (AECOPD) in each subgroup using the two‐pollutant model in Qingyang's rural areas from 1 January 2015 to 26 June 2016. The effects were expressed by the relative risk of extreme concentrations for AECOPD development, with the median concentration as a reference. Elderly and adult individuals were sub‐grouped according to age (adult, 15–64 years old and the elderly, ≥65 years old).
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