The association between inhalable particulate matter and YLL caused by COPD in a typical city in northern China

Atmospheric Environment 172 (2018) 26–31

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The association between inhalable particulate matter and YLL caused by COPD in a typical city in northern China

MARK

Qiang Zenga,1, Ziting Wub,1, Guohong Jianga, Pei Lia, Yang Nia, Guoxing Lib,∗, Xiaochuan Panb a b

Tianjin Centers for Disease Control and Prevention, No. 6, Huayue Road, Hedong District, Tianjin 300171, China Department of Occupational and Environmental Health, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China

A R T I C L E I N F O

A B S T R A C T

Keywords: PM10 Time series study GAM COPD YLL

Background: Chronic obstructive pulmonary disease (COPD) has become the fourth-leading cause of death. The association between air pollution and years of life lost (YLL) caused by COPD is currently a hot topic; however, few studies have been published regarding COPD YLL around the world, especially in China, a highly polluted area. Aim: We investigated the exposure-response association between ambient particulate matter with an aerodynamic diameter of less than 10 μm (PM10) and COPD YLL. Methods: We applied a generalized additive model (GAM) to estimate the short-term effects of PM10 on COPD YLL and mortality from 2002 to 2010 in Tianjin. Results: The annual mean concentration of PM10 was 111.6 μg/m3. An increase in PM10 was significantly associated with daily YLL and mortality in a single pollutant model. A 10 μg/m3 increase in the two-day moving average of PM10 was associated with the maximum YLL increment of 0.30 (95% confidence interval: 0.06, 0.54) person-years and an excess risk (ER) of mortality of 0.60% (0.20%, 1.01%). For YLL increases, the association is stronger in elderly (0.27 (0.06, 0.48), the cumulative effect) populations than in younger populations. Conclusions: An increment of COPD YLL is associated with an increased PM10 concentration. Elderly groups are more susceptible to air pollution. Strict air pollutant emission control is needed to protect public health.

1. Introduction Air pollution has received close attention in China because it ranked as the fourth highest risk factor for health and caused approximately 1.2 million premature deaths and 25 million disability-adjusted life years (DALY) in 2010 in China (Lim et al., 2012; Yang et al., 2013). Tianjin, a typical industrial city, is the most important city in the Beijing-Tianjin-Hebei district in China, with annual mean ambient particulate matter with an aerodynamic diameter of less than 10 μm (PM10) concentration of 111.6 μg/m3 in 2002-10; this concentration level is far above the recommended level by the World Health Organization's guidelines of 20 μg/m3 (annual mean) (WHO, 2016). Chronic Obstructive Pulmonary Disease (COPD) is the fourthleading cause of death all over the world, and it may become the thirdleading cause by 2020 (Hasford and Fruhmann, 1998). Epidemiological studies have investigated the association between PM10 and COPD (Li et al., 2016a; Wang et al., 2016; Pothirat et al., 2016; Song et al., 2014; Schikowski et al., 2014). For example, One study in Guangzhou

investigated the association between PM10 and COPD mortality, and the results showed that per 10 μg/m3 increase in PM10 was associated with 1.58% (95% confidence interval (CI):0.12, 3.06). Another study in Thailand also found positive relationship between air pollution and COPD emergency visits. However, less attention has been paid to the age at time of death. Years of life lost (YLL) is one index which usually measure disease burden. Compared with mortality, YLL could assign more weights on younger deaths because it consider the life expectancy at death age (Li et al., 2016b). So that this index could provide more information about the relationship between PM10 and mortality. Untill now, only one study in southern China, Ningbo explored the relationship between particulate matter and COPD YLL, and the study showed that per 10 μg/ m3 increase in PM2.5 was associated with 0.91% (95% CI:0.16, 1.66) years increase in YLL. Further study should be carried out in this field. In this study, we conducted a time-series study to explore the disease burden from short-term PM10 exposure in Tianjin, China, 2002–2010, using the index of YLL.

∗ Corresponding author. Department of Occupational and Environmental Health, School of Public Health, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China. E-mail address: [email protected] (G. Li). 1 Contribute equally to this paper.

http://dx.doi.org/10.1016/j.atmosenv.2017.10.046 Received 27 February 2017; Received in revised form 22 September 2017; Accepted 22 October 2017 Available online 26 October 2017 1352-2310/ © 2017 Elsevier Ltd. All rights reserved.

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Fig. 1. Tianjin map which shows the urban districts (the target districts in our study) and rural districts of Tianjin, China. The dots denote the monitoring stations.

2. Methods

of the values for the days before and after the missing day.

2.1. Data collection

2.2. YLL calculation

The study period was from January 1st, 2002 to December 31st, 2010, and the study area was the urban districts (Heping district, Hexi district, Hedong district, Hongqiao district, Nankai district, Hebei district) of Tianjin (Fig. 1). We collected the air pollutant monitoring data, meteorological data and mortality data. The daily air pollutant monitoring data (including PM10, nitrogen dioxide (NO2) and sulphur dioxide (SO2)) were obtained from Tianjin Environmental Monitoring Center. There were six monitoring stations in urban areas (Fig. 1).as for the calculation of daily (24-h) average concentrations of air pollutants, at least 75% of the 1-h values must be available on that particular day. The location of monitoring stations is mandated not to be influenced by local pollution sources, such as traffic road, coal-,waste-, oil-burning boilers and furnaces, etc. The daily air pollutants’ concentrations were averaged from the monitoring results among various stations. Daily meteorological data (including temperature and relative humidity) were obtained from the Tianjin Meteorological Bureau. The daily mortality data on COPD deaths were obtained from the Death Register and Report Information System, Tianjin Centers for Disease Control and Prevention. Information, such as birth date, gender, age, death date, cause of death and its code, was included in the system. Based on the International Classification of Diseases 10th version (ICD-10), COPD was coded J41 to J44. The data collected in this study were almost complete, the percentage of missing data regarding air pollutants was less than 0.5%, and that of meteorological data was less than 1%. The missing air pollution data and meteorological data were simply interpolated as the median value

YLL was an indicator for the years of lost life because of premature death. We used the equation from Global Burden of Disease (GBD) (2010) for the sake of comparison with results from other countries and districts. YLL data were estimated using the Standard Expected Years of Life Lost approach based on the World Health Organization (WHO) standard life table for YLL (WHO, 2013). The YLL for each death was calculated by matching the age to the life table. Daily YLL were the sum of the YLL for all deaths on that day. And then, based on data availability, the sums were stratified by gender and age group (< 65 years and ≥65 years) (Guo et al., 2013). 2.3. Statistical analyses A generalized additive model (GAM) was used to estimate the association between PM10 and COPD YLL. Based on previous studies (Guo et al., 2013; Zeng et al., 2017), the link function was the identity function, and the basic function for the GAM was: E(Yt) = α + βxt−l + s(time,df) + s(relative humidity,df) + s (temp,df) + DOW + ε (1) where Yt was the observed daily YLL for day t; E(Yi) was the expected value of the observed daily YLL for day t; l was the lag day of PM10 exposure; β was the coefficient of the explanatory variable, a changing value of the logarithm of the daily COPD YLL when the air pollutant concentration changed one unit. S (time , df ), S (temp , df ) and 27

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Table 1 Descriptive statistic of PM10, NO2, SO2, temperature and relative humidity in Tianjin, 2002–2010. Year

PM10

NO2

2002 2003 2004 2005 2006 2007 2008 2009 2010 Over all

150.3 ± 98.1 134.4 ± 61.3 112.5 ± 57.0 115.8 ± 58.4 116.8 ± 64.5 89.5 ± 49.4 88.4 ± 53.2 100.7 ± 62.0 96.0 ± 53.4 111.6 ± 66.3

51.6 57.9 57.9 53.6 54.7 44.7 44.6 46.7 45.4 50.8

SO2 ± ± ± ± ± ± ± ± ± ±

23.1 20.1 21.3 20.6 18.0 19.5 18.6 18.2 16.2 20.2

80.2 79.5 79.7 85.2 69.2 61.8 64.6 56.2 51.0 69.7

± ± ± ± ± ± ± ± ± ±

73.8 63.2 60.9 68.4 50.8 47.9 51.6 48.6 45.7 58.6

Temperature

RH

13.5 12.9 13.4 13.1 13.5 14.1 13.8 13.0 12.1 13.3

60.6 62.5 62.2 61.7 68.6 67.9 65.2 63.3 57.9 63.3

± ± ± ± ± ± ± ± ± ±

10.7 10.6 10.2 11.5 11.0 10.8 11.1 11.5 11.8 11.0

± ± ± ± ± ± ± ± ± ±

20.5 18.9 20.1 21.1 19.9 20.3 21.3 19.1 17.6 20.1

Note:PM10: ambient particulate matter with an aerodynamic diameter of less than 10 μm; NO2:nitrogen dioxide; SO2:sulfur dioxide; RH: relative humidity.

We used a multi-pollutant model to test the stability of the results. Other pollutants were added as nonparametric values to reduce the bias cause by collinearity. All data processing work, statistical analyses and model construction were conducted with the “mgcv” package in statistical software R (version3.1.2).

S (relative humimidy, df ) were penalized splines and nonparametric smoothing functions that could control the long-term tendency in a time series study, as well as some confounders related to the tendency, such as seasonal and meteorological factors; DOW was a dummy variable to control short-term fluctuations. Furthermore, we also investigated the association between daily mortality and PM10 in our study. The same independent variables were used as in the YLL model, except that the time series function with the Poisson link, under a GAM framework, was used; thus, the link function was a log function, and the basic function for the GAM was:

3. Results 3.1. Air pollutants and weather conditions

Log[E(Yt)] = α + βxt−l + s(time,df) + s(relative humidity,df) + s (temp,df) + DOW + ε (2)

During the study period, the annual mean concentrations of PM10, NO2 and SO2 in Tianjin were 111.6 μg/m3, 50.8 μg/m3 and 69.7 μg/m3, respectively. Though the air pollutant levels showed a decreasing trend, the annual mean concentrations of PM10 from 2002 to 2010 exceeded the China national air quality standard II (annual mean: 70 μg/m3). The daily means of the temperature and relative humidity in the corresponding period were 13.3 °C and 63.3%, respectively (Table 1).

where Yt was the observed daily mortality for day t; E(Yi) was the expected value of the observed daily mortality for day t; β was the coefficient of the explanatory variable, a changing value of the logarithm of the daily COPD mortality when the air pollutant concentration changed one unit. The interpretation of the other parameters has been mentioned above. In equations (1) and (2), there were 7 degrees of freedom (df) per year for time (63 df for 9 years) in the two mentioned GAMs, and 3 degrees of freedom for temperature and relative humidity were used to control for meteorological factors. DOW, as a factor in the model, was used to control for the effect of the day of the week. We used the unit of 10 μg/m3 to measure the effect of air pollutants. The effect of air pollutants on COPD daily YLL was measured by the value β, where β was that defined for model (1). The increase in YLL was years of life lost for the whole population. The effect of air pollutants on COPD daily death was measured by excess risk (ER). The equation to calculate ER is:

3.2. COPD YLL Of the 160,739 person years in the COPD YLL, 49.5% of the data were from males and 85.8% were from the elderly, and the daily mean YLL was 48.90 person years for the nine years. The annual trends of daily YLL and mortality are shown in Fig. 2. Interestingly, they also show a decreasing trend, similar to that of PM10 (Supplementary Table S1). 3.3. Associations between PM10 and COPD YLL

ER = (RR − 1) × 100% = (eβ − 1) × 100%

As shown in Table 2, PM10 was significantly associated with COPD YLL in single pollutant models, except in female and non-elderly populations. In the overall level, the maximum effect appeared at lag01, with a 0.30 (95% CI: 0.06, 0.54) years increase for every 10 μg/m3 increment of PM10. The biggest association is in the elderly (0.27 (95% CI: 0.06, 0.48), lag01) populations compared with in younger populations, about 13 times. We also estimated the ERs of COPD mortality caused by every 10 μg/m3 increase in PM10. There is no obvious difference between different gender groups or different age groups (Table 2).

where RR is the relative risk and β is as defined for model (2). A large number of studies in the field of air pollution epidemiology have shown that the daily mortality was associated closely with the concentration of air pollutants from that day and one day before (Schwartz, 2000); thus, the average value of the two concentrations was usually adopted in most studies as the exposure concentration (Touloumi et al., 2004). We fitted the concentration of air pollutants on the observed day (lag1), the day after that day (lag0), and the moving average value of the two days (lag01) to estimate their associations with the daily YLL and COPD mortality. Autocorrelations in all of the models were assessed by plots of autocorrelation functions for the residuals in order to test the adequacy of the models; no clear evidence of autocorrelations was found (Supplementary Fig. S1). A sensitivity analysis of the model was conducted. After we changed the degrees of freedom for the relative humidity, temperature and time-trend, the association between PM10 and COPD YLL showed robustness compared with the former results (Supplementary Table S2).

3.4. Multi-pollutant model of exposure-response relationship between PM10 and COPD disease burden Table 3 shows the results of a multi-pollutant model of the relationships between PM10 exposure and COPD YLL and mortality in Tianjin from 2002 to 2010. In our analysis, the lag01 concentration of PM10 was used. In general, the effect estimates in the multi-pollutant model are quite close to those in the single pollutant model. After we controlled 28

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Fig. 2. Annual mean of daily COPD YLL (top) and death counts (bottom) in Tianjin, 2002–2010.

Lag01 in Tianjin from 2002 to 2010. Few epidemiological evidence exploring the short-term adverse effect of particulate matter on COPD YLL in China has been published. One study concerning the association between PM10 and COPD YLL found that a 10 μg/m3 increase in PM10 was associated with a 0.81 (95% CI: 0.30, 1.33) person years' increase in YLL and a 1.07% (95% CI: 0.51%, 1.63%) increase in ER (Li et al., 2017). However, this study was carried out in Ningbo, in southern China, where annual concentration levels were around 30 μg/m3 lower than that in Tianjin. Thus, this is the first time; our study explored the effects of PM10 on COPD YLL in heavy polluted area, China. In our study, the increment of YLL is less than that of ER, which was consistent with ningbo's study, while different from the effects of air

for SO2, the effect in the multi-pollutant model was slightly lower than that of the single pollutant model. After controlling for NO2 or simultaneously controlling for the two pollutants, the effect value was slightly higher than that of the single pollution model. 4. Discussion 4.1. Effect of PM10 on COPD YLL and mortality In this study, we found that the YLL increment and ER of COPD mortality were 0.30 (95% CI: 0.06, 0.54) person years and 0.60% (95% CI:0.20%, 1.01%), respectively, for every 10 μg/m3 increase in PM10 for

Table 2 Estimated changes with 95% confidence intervals in COPD YLL increment and excess risk of mortality associated with 10 μg/m3 increasing in PM10 in overall level and subgroups in Tianjin, 2002–2010.

YLLa

Lag

Overall

Lag0

0.26 (0.06, 0.46)* 0.14 (−0.06, 0.34) 0.30 (0.06, 0.54)* 0.46 (0.14, 0.79)* 0.34 (0.01, 0.66)* 0.60 (0.20, 1.01)*

Lag1 Lag01 Mortalityb

Lag0 Lag1 Lag01

Male 0.22 (0.09, 0.36)* 0.04 (−0.10, 0.17) 0.19 (0.03, 0.36)* 0.65 (0.21, 1.10)* 0.20 (−0.26, 0.66) 0.65 (0.09, 1.21)*

Female

Elderly (≥65y)

Non-elderly (< 65y)

0.03 (−0.1, 0.17) 0.10 (−0.03, 0.24) 0.10 (−0.06, 0.27) 0.27 (−0.19, 0.73) 0.47 (0.02, 0.92)* 0.56 (0.00, 1.12)*

0.23 (0.05, 0.40)* 0.14 (−0.03, 0.31) 0.27 (0.06, 0.48)* 0.46 (0.12, 0.80)* 0.37 (0.03, 0.70)* 0.62 (0.21, 1.04)*

0.03 (−0.06, 0.00 (−0.10, 0.02 (−0.09, 0.51 (−0.72, −0.08 (−1.37, 0.33 (−1.22,

0.13) 0.10) 0.14) 1.76) 1.22) 1.91)

Note:*P < 0.05; lag0: the concentration of PM10 the observed day; lag1: the concentration of PM10 the day before the observed day; lag01: the average value of lag0 and lag1; a: YLL increment(person-years); b: excess risk of mortality(%).

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Table 3 Multi-pollution models of the effects of PM10 on daily mortality and daily YLL of COPD in Tianjin.

YLLa

Model

Overall

Male

Female

Elderly (≥65y)

Non-elderly (< 65y)

Single

0.30 (0.06, 0.54)* 0.22 (−0.05, 0.49) 0.33 (0.02, 0.64)* 0.30 (−0.01, 0.61) 0.60 (0.20, 1.01)* 0.60 (0.13, 1.06)* 0.65 (0.13, 1.18)* 0.63 (0.11, 1.16)*

0.19 (0.03, 0.36)* 0.12 (−0.07, 0.30) 0.21 (0.00, 0.42) 0.19 (−0.03, 0.40) 0.65 (0.09, 1.21)* 0.48 (−0.17, 1.14) 0.72 (0.00, 1.45) 0.66 (−0.07, 1.40)

0.10 (−0.06, 0.27) 0.10 (−0.08, 0.29) 0.12 (−0.09, 0.33) 0.11 (−0.10, 0.32) 0.56 (0.00, 1.12)* 0.70 (0.06, 1.35)* 0.59 (−0.14, 1.32) 0.59 (−0.14, 1.33)

0.27 (0.06, 0.24 (0.01, 0.30 (0.03, 0.28 (0.01, 0.62 (0.21, 0.66 (0.18, 0.67 (0.13, 0.66 (0.12,

0.02 (−0.09, −0.03 (−0.16, 0.03 (−0.12, 0.02 (−0.14, 0.33 (−1.22, −0.36 (−2.23, 0.37 (−1.68, 0.13 (−1.95,

+SO2 +NO2 +SO2+NO2 Mortalityb

Single +SO2 +NO2 +SO2+NO2

0.48) * 0.48) * 0.57) * 0.55) * 1.04)* 1.14)* 1.22)* 1.21)*

0.14) 0.11) 0.18) 0.17) 1.91) 1.54) 2.46) 2.26)

Note:*P < 0.05; a: YLL increment (person-years); b: excess risk of mortality(%); PM10: ambient particulate matter with an aerodynamic diameter of less than 10 μm; NO2:nitrogen dioxide; SO2:sulfur dioxide.

relationship.

pollution on non-accidental diseases show the opposite results (Guo et al., 2013; Li et al., 2017; He et al., 2016; Lu et al., 2015). This may be a result of the characteristics of COPD, in which a large body of patients is the elderly (Supplementary Table S1 and Fig. 2); older patients contribute to less YLL but induce the excess risk caused by PM10 exposure at a quite high level. This confirmed that effective air quality improvement measures should be carried out, and the elderly population should protect themselves from air pollution in order to prevent COPD development and deterioration.

4.3. Innovation and limitations The innovation of this study is that for the first time, a 9-year longterm data series in Tianjin was used to establish the exposure response relationships between PM10 and COPD YLL and mortality of the exposed population. Moreover, we used YLL as an outcome because it is more accurate in reflecting the effect of PM10 on COPD. The findings are important in developing public policy, determining the population at higher risk and carrying out more corresponding measures. Our study also has some limitations. Firstly, the exposure data of air pollutants in this study are derived from fixed monitoring sites and cannot accurately reflect individual exposure. Secondly, this is an ecological study, and ecological fallacies may exist (Sedgwick, 2015). Thirdly, the study areas were located in urban areas, we should be cautious when extending the results to rural COPD patients or in other areas in China.

4.2. Effects of PM10 on COPD YLL and mortality among people of different genders and ages The results showed that there is no significant difference as for the effects of PM10 on COPD mortality in genders. For gender modification, the results were inconsistent. One study showed that female were more susceptible, and per inter-quartile range increase in PM10 was associated with a 5.68% (95% CI 0.54, 11.09) increase for COPD (Wang et al., 2016), while one study in Ningbo also found no significant difference between genders (Li et al., 2017). However, when we found the effects of PM10 on COPD YLL in males were slightly higher than that in females. Smoking might be one of the impacting factors. It is well known that smoking is one of the main risk factors of COPD, especially with a co-existence of air pollution (Faustini et al., 2012; Tamayo-Uria et al., 2016; Liu et al., 2017), and the male population has a relatively higher smoking rate than females in China (Chen et al., 2015). Additionally, Chinese men are more likely to be exposed to traffic pollutants and are more likely to suffer from social pressure, stimulating the adverse effects (Tamayo-Uria et al., 2016). More studies were needed to get convincing evidence in heavy polluted areas. The effect estimates of both outcomes were more obvious in elderly people (> =65 years) than that in younger ones (< 65 years). A large number of epidemiological studies have also come to the similar conclusions. For example, studies in Tianjin, Beijing and Ningbo showed that older people exposed to PM10 demonstrated higher risks (Wang et al., 2016; Zeng et al., 2017; He et al., 2016). The difference was biologically plausible because compared to the non-elderly, the physiological functions of the respiratory system and multiple organs in the elderly population are decreased, resulting in a slower immune response, increased allergic reactions, etc (Sandstrom et al., 2003; Heidi Cover, 1994; Eckel et al., 2012). At present, few studies have been published regarding the effects of air pollution on YLL in different genders and ages, especially in urban areas, China, which has relatively high PM10 concentrations; therefore, further relative epidemiological studies are needed to confirm the

5. Conclusion The association of PM10 with YLL of COPD can be demonstrated well by a GAM. Increased YLL and ER of death are associated with increased PM10 concentrations. YLL provides an alternative method for examining the effect of air pollution on population mortality. More awareness should be given to elderly on high pollution days.

Acknowledgements We thank the Tianjin municipal environmental monitoring center for providing air pollution data, the Tianjin Center for Diseases Prevention and Control for providing mortality data, and the Tianjin Meteorological Bureau for providing meteorological data. The study was supported by the Natural Science Foundation of China (81372950); Key Scientific and Technological Project on Health of Tianjin (16KG170). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx. doi.org/10.1016/j.atmosenv.2017.10.046. 30

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