The burden of ambient air pollution on years of life lost in Wuxi, China, 2012–2015: A time-series study using a distributed lag non-linear model

Zhu, Jingying; Zhang, Xuhui; Zhang, Xi; Dong, Ming; Wu, Jianwen; Dong, Yunqiu; Chen, Rong; Ding, Xinliang; Huang, Chunhua; Zhang, Qi; Zhou, Weijie

doi:10.1016/j.envpol.2017.02.053

Cited by 71 publications

(26 citation statements)

References 54 publications

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“…However, no significant association between air pollutants and hospitalization for respiratory disease was observed in multi-pollutant models. The possible reason was that the stronger correlations between air pollutants would influence the effects of them on respiratory disease hospitalization [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…A time-series analysis approach in Generalized Additive Model (GAM) was applied to estimate the association between ambient air pollution and hospitalization for respiratory disease [ 15 ]. Since daily hospitalization counts typically followed an over-dispersed Poisson distribution, the quasi-Poisson distribution was adopted in the GAM model in our study [ 16 , 17 , 18 ]. The GAM process in the present study included the following steps: (1) The cubic spline smoothing function of time was incorporated into the GAM to control the long-term trends and seasonal changes of daily respiratory disease hospitalization [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

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Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis

Luo

Zhang

Jiang

et al. 2018

IJERPH

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In this study, we estimated the short-term effects of ambient air pollution on respiratory disease hospitalization in Taiyuan, China. Daily data of respiratory disease hospitalization, daily concentration of ambient air pollutants and meteorological factors from 1 October 2014 to 30 September 2017 in Taiyuan were included in our study. We conducted a time-series study design and applied a generalized additive model to evaluate the association between every 10-μg/m3 increment of air pollutants and percent increase of respiratory disease hospitalization. A total of 127,565 respiratory disease hospitalization cases were included in this study during the present period. In single-pollutant models, the effect values in multi-day lags were greater than those in single-day lags. PM2.5 at lag02 days, SO2 at lag03 days, PM10 and NO2 at lag05 days were observed to be strongly and significantly associated with respiratory disease hospitalization. No significant association was found between O3 and respiratory disease hospitalization. SO2 and NO2 were still significantly associated with hospitalization after adjusting for PM2.5 or PM10 into two-pollutant models. Females and younger population for respiratory disease were more vulnerable to air pollution than males and older groups. Therefore, some effective measures should be taken to strengthen the management of the ambient air pollutants, especially SO2 and NO2, and to enhance the protection of the high-risk population from air pollutants, thereby reducing the burden of respiratory disease caused by ambient air pollution.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis

Luo

Zhang

Jiang

et al. 2018

IJERPH

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“…Much evidence suggested that environmental air pollution, such as particulate matter (PM 10 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and ozone (O 3 ), have adverse consequences for respiratory diseases (Luong et al 2017 ; Phung et al 2016 ; Shen et al 2017 ; Zhu et al 2017 ). Additionally, climatic variables (temperature, humidity, and rain) have also been reported by a number of studies to be associated with pneumonia hospitalization (Kim et al 2016 ; Liu et al 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Time series modeling of pneumonia admissions and its association with air pollution and climate variables in Chiang Mai Province, Thailand

Ruchiraset

Tantrakarnapa

2018

Environ Sci Pollut Res

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This study aimed to predict the number of pneumonia cases in Chiang Mai Province. An autoregressive integrated moving average (ARIMA) was used in data fitting and to predict future pneumonia cases monthly. Total pneumonia cases of 67,583 were recorded in Chiang Mai during 2003–2014 that the monthly pattern of case was similar every year. Monthly pneumonia cases were increased during February and September, which are the periods of winter and rainy season in Thailand and decreased during April to July (the period of summer season to early rainy season). Using available data on 12 years of pneumonia cases, air pollution, and climate in Chiang Mai, the optimum ARIMA model was investigated based on several conditions. Seasonal change was included in the models due to statistically strong season conditions. Twelve ARIMA model (ARMODEL1–ARMODEL12) scenarios were investigated. Results showed that the most appropriate model was ARIMA (1,0,2)(2,0,0)[12] with PM10 (ARMODEL5) exhibiting the lowest AIC of − 38.29. The predicted number of monthly pneumonia cases by using ARMODEL5 during January to March 2013 was 727, 707, and 658 cases, while the real number was 804, 868, and 783 cases, respectively. This finding indicated that PM10 held the most important role to predict monthly pneumonia cases in Chiang Mai, and the model was able to predict future pneumonia cases in Chiang Mai accurately.

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“…The structure of our model for air pollution was as follows:

where Yt represented the expected number of diabetes cases; βZ t , l represented the cross-basis objects used to estimate the effects of air pollutants; Z was determined by each pollutant (PM 2.5 , PM 10 , NO 2 , SO 2 , O 3 , and CO); β was the coefficient for Z t , l , the logarithmic increase in the number of diabetes cases caused by the increase of air pollutant by one unit; t represented the observation day, and l represented the lag days; ns was a natural cubic spline function, and df was its degree of freedom; time was a date variable used to control time trends and seasonal fluctuations; DOW represented the day of the week, controlling the natural fluctuations of the number of people with diabetes in a week; and temperature/⋯ referred to meteorological conditions, which were used to adjust the impact of meteorological factors on diabetes incidence. To fit the model, we set the df of natural cubic spline functions for time to 11 per year, which was based on the Akaike information criterion (AIC) and the principle of the partial autocorrelation function (PACF) [ 31 ]. And we set the df of meteorological factors to 3, based on experience and previous studies.…”

Section: Methodsmentioning

confidence: 99%

Risk Analysis of Air Pollution and Meteorological Factors Affecting the Incidence of Diabetes in the Elderly Population in Northern China

Lin

Zhou

Liu

et al. 2020

Journal of Diabetes Research

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Background. Research investigating the effect of air pollution on diabetes incidence is mostly conducted in Europe and the United States and often produces conflicting results. The link between meteorological factors and diabetes incidence remains to be explored. We aimed to explore associations between air pollution and diabetes incidence and to estimate the nonlinear and lag effects of meteorological factors on diabetes incidence. Methods. Our study included 19,000 people aged ≥60 years from the Binhai New District without diabetes at baseline. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) were used to explore the effect of air pollutants and meteorological factors on the incidence of diabetes. In the model combining the GAM and DLNM, the impact of each factor (delayed by 30 days) was first observed separately to select statistically significant factors, which were then incorporated into the final multivariate model. The association between air pollution and the incidence of diabetes was assessed in subgroups based on age, sex, and body mass index (BMI). Results. We found that cumulative RRs for diabetes incidence were 1.026 (1.011-1.040), 1.019 (1.012-1.026), and 1.051 (1.019-1.083) per 10 μg/m3 increase in PM2.5, PM10, and NO2, respectively, as well as 1.156 (1.058-1.264) per 1 mg/m3 increase in CO in a single-pollutant model. Increased temperature, excessive humidity or dryness, and shortened sunshine duration were positively correlated with the incidence of diabetes in single-factor models. After adjusting for temperature, humidity, and sunshine, the risk of diabetes increased by 9.2% (95% confidence interval (CI):2.1%-16.8%) per 10 μg/m3 increase in PM2.5. We also found that women, the elderly (≥75 years), and obese subjects were more susceptible to the effect of PM2.5. Conclusion. Our data suggest that PM2.5 is positively correlated with the incidence of diabetes in the elderly, and the relationship between various meteorological factors and diabetes in the elderly is nonlinear.

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The burden of ambient air pollution on years of life lost in Wuxi, China, 2012–2015: A time-series study using a distributed lag non-linear model

Cited by 71 publications

References 54 publications

Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis

Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis

Time series modeling of pneumonia admissions and its association with air pollution and climate variables in Chiang Mai Province, Thailand

Risk Analysis of Air Pollution and Meteorological Factors Affecting the Incidence of Diabetes in the Elderly Population in Northern China

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