Association between ambient air pollution and hospitalization for ischemic and hemorrhagic stroke in China: A multicity case-crossover study

Environmental Pollution 230 (2017) 234e241

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Association between ambient air pollution and hospitalization for ischemic and hemorrhagic stroke in China: A multicity case-crossover study* Hui Liu a, b, 1, Yaohua Tian a, 1, Yan Xu c, Zhe Huang a, Chao Huang a, Yonghua Hu a, **, Jun Zhang c, * a b c

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No.38 Xueyuan Road, 100191 Beijing, China Medical Informatics Center, Peking University, No.38 Xueyuan Road, 100191 Beijing, China Department of Neurology, Peking University People's Hospital, No.11 South Xizhimen Street, 100044 Beijing, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 April 2017 Received in revised form 15 June 2017 Accepted 17 June 2017

There is growing interest in the association between ambient air pollution and stroke, but few studies have investigated the association in developing countries. The primary objective of this study was to examine the association between levels of ambient air pollutants and hospital admission for stroke in China. A time-stratified case-crossover analysis was conducted between 2014 and 2015 in 14 large Chinese cities among 200,958 ischemic stroke and 41,746 hemorrhagic stroke hospitalizations. We used conditional logistic regression to estimate the percentage changes in stroke admissions in relation to interquartile range increases in air pollutants. Air pollution was positively associated with ischemic stroke. A difference of an interquartile range of the 6-day average for particulate matter less than 10 mm in aerodynamic diameter, sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone corresponded to 0.7% (95% CI: 0%, 1.4%), 1.6% (95% CI: 1.0%, 2.3%), 2.6% (95% CI: 1.8%, 3.5%), 0.5% (95% CI:
0.2%, 1.1%), and 1.3% (95% CI: 0.3%, 2.3%) increases in ischemic stroke admissions, respectively. For hemorrhagic stroke, we observed the only significant association in relation to nitrogen dioxide on the current day (percentage change: 1.6%; 95% CI: 0.3%, 2.9%). Our findings contribute to the limited scientific literature concerning the effect of ambient air pollution on stroke in developing countries. Our findings may have significant public health implications for primary prevention of stroke in China. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Stroke Air pollution Hospitalization China

1. Introduction Stroke is the second-leading cause of death and the third major cause of adult disability worldwide (2015; Murray et al., 2015). Approximately 10.3 million new cases of stroke were diagnosed in 2013, and 6.5 million people are estimated to have died of stroke that year; two-thirds of all strokes occurred in low- and middleincome countries (Feigin et al., 2015). In China, stroke has emerged as the most common cause of death and adult disability in

*

This paper has been recommended for acceptance by Dr. Chen Da. * Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (Y. Hu), [email protected] (J. Zhang). 1 Hui Liu and Yaohua Tian contributed equally to this study and should be considered as coefirst authors. http://dx.doi.org/10.1016/j.envpol.2017.06.057 0269-7491/© 2017 Elsevier Ltd. All rights reserved.

recent years (Liu et al., 2011). A nationally-representative population-based survey involving 480,687 Chinese adults aged 20 years estimated that the age-standardized incidence and mortality rates of stroke were 246.8 and 114.8 per 100,000 person-years in 2013, respectively (Wang et al., 2017). Despite the implementation of several primary and secondary prevention strategies in various countries that have been shown as effective in randomized trials (Kernan et al., 2014; Wang et al., 2007), the incidence of stroke continues to rise, particularly in economically transitioning countries (Feigin et al., 2014). Stroke exerts considerable patient suffering and immense economic burdens (Liu et al., 2011; Tong et al., 2016). Therefore, identification of modifiable risk factors for stroke has significant public health implications. Research has provided compelling evidence linking outdoor air pollution to hospital admissions or death from stroke (Andersen et al., 2010; Hong et al., 2002; Mateen and Brook, 2011; Wellenius et al., 2012). Unlike the established risk factors for

H. Liu et al. / Environmental Pollution 230 (2017) 234e241

stroke, such as smoking, alcohol consumption, and physical inactivity (O'Donnell et al., 2010), air pollution represents a potentially modifiable risk factor that is independent of individual behavioral change. Improving air quality may offer a unique advantage in enhancing prevention efforts aimed at reducing the incidence of stroke. However, most previous studies of this topic were conducted in developed countries, and only limited research data have been generated in developing countries. In view of the considerable differences in pollutant characteristics (e.g., pollution levels and components), meteorological patterns, population susceptibility, and socio-demographic status (e.g., age structure and socioeconomic characteristics) between developed and developing countries and the enormous stroke burden in the latter, an urgent need remains to evaluate the effect of outdoor air pollution on the incidence of stroke in developing countries. China has been experiencing the worst air pollution problem in the world (Kan et al., 2012). Ambient air pollution has been a major cause of mortality and morbidity in China, accounting for an estimated 1.2 million premature deaths and 25 million disabilityadjusted life years annually (Lim et al., 2012). The detrimental health effects of air pollution are of increasing concern to the public, particularly in relation to haze days. However, only a handful of studies have examined the association between air pollution and stroke in China, yielding inconsistent findings, and these studies were restricted to a single city, such as Shanghai (Kan et al., 2003), Wuhan (Xiang et al., 2013), and Beijing (Huang et al., 2016). To the best of our knowledge, only one multicity study, the China Air Pollution and Health Effects Study, has examined the acute effects of air pollution on stroke mortality in eight Chinese cities (Chen et al., 2013). However, this study did not differentiate between ischemic and hemorrhagic stroke. In addition, the study used death data rather hospitalization data. Stroke hospitalization data would yield higher numbers of patients in a given population, increasing the statistical power. Furthermore, hospitalization data can better evaluate the temporal sequence between exposure to air pollution and clinical presentation of stroke (Villeneuve et al., 2006). The objective of this study was to examine the association between short-term exposure to air pollution and hospital admission for stroke in 14 large Chinese cities using a time-stratified casecrossover design. 2. Methods 2.1. Study population Data on daily admissions for ischemic and hemorrhagic stroke were collected from electronic hospitalization summary reports (HSRs) of the top-ranked hospitals for care safety and quality as evaluated by the National Hospital Performance Evaluation Project of the National Healthcare Data Center of China. The hospital ranking system considers several aspects, including hospital infrastructure, medical service and management, technical level and efficiency, and quality and safety of clinical care. The information recorded on the HSR includes basic demographics (e.g., sex and age), date of admission and discharge, hospitalization and discharge diagnoses and their corresponding International Classification of Diseases, 10th Revision (ICD-10) codes, treatments, discharge status (survival status, drug allergy, and hospitalization infection), and financial costs. The present study is considered exempt from institutional review board approval since the data used was collected for administrative purpose without any personal identifiers. Daily hospital admissions with a primary discharge diagnosis of ischemic stroke or hemorrhagic stroke between January 1, 2014,

235

and December 31, 2015, were identified from the HSR database using ICD-10 codes I63, and I61 and I62, respectively. To decrease the influence of coding inaccuracy, we used the corresponding diagnoses to check the identified hospitalizations. Individuals aged <18 years were excluded from this study. 14 large cities across China were analyzed in this study, including two municipalities (Beijing and Tianjin), 11 provincial capital cities (Harbin, Shenyang, Urumchi, Changchun, Yinchuan, Shijiazhuang, Jinan, Xining, Lanzhou, Xi'an, and Zhengzhou), and Dalian City (Fig. 1). 2.2. Air pollution and meteorological data Data on air pollution, including levels of particulate matter less than 10 mm in aerodynamic diameter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) between January 1, 2014, and December 31, 2015, were obtained from the China National Air Pollution Monitoring System, which is run by the Ministry of Environmental Protection. There are 4e15 fixed-site air monitoring stations in each city. To fulfill the quality assurance and quality control programs mandated by the Chinese government, each monitoring station must provide hourly air pollution data to the China National Air Pollution Monitoring System. For each city, the daily (24-h) mean concentrations for pollutants were averaged from the available monitoring data across various stations (Wong et al., 2008). All the hospitals included in this study are located in the center of corresponding city. The maximum distance between the hospitals and air monitoring stations were less than 40 km. It has been suggested that the monitoring data could be used as a proxy for personal exposure among individuals residing <40 km from the monitoring station (Dockery et al., 2005; Wellenius et al., 2012; Xie et al., 2015). To allow for the potential confounding effects of meteorological conditions, daily weather data on temperature ( C) and relative humidity (%) for each city were obtained from the Chinese Meteorological Bureau. 2.3. Study design A time-stratified case-crossover study design was applied to assess the short-term effects of ambient air pollution on hospital admissions for stroke. In this design, each case serves as its own control (Carracedo-Martinez et al., 2010). For each case of stroke, ambient air pollution exposure on the case day (the day of hospitalization) was compared with exposure on a series of referent days occurring on the same days of the week within the same month and

Fig. 1. Locations of the 14 Chinese cities included in this study.

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year as the case day. This design allowed for controlling the influence of seasonality, time trends, sex, genetics, and other factors. 2.4. Statistical analysis Spearman's correlation tests were used to estimate the associations between exposure variables. Conditional logistic regression was used to examine the associations between air pollutants and stroke. For adjustment of the delayed and non-linear effects of temperature and humidity, the distributed lag non-linear models with three degrees of freedom in the natural cubic splines and a maximum lag of 3 days were used (Goldberg et al., 2011). Public holiday was also incorporated in the model. To examine the temporal association of air pollution with stroke, we fitted the models with single-day lags (from lag0 to lag5) and multiple-day lags (lag0e2 and lag0e5). Smoothing function with three degrees of freedom in the natural cubic splines was applied to graphically analyze the exposure-response associations between air pollutants concentrations and ischemic stroke hospitalizations. To examine the stability of air pollutants' effects, multi-pollutant analyses were performed for air pollutants that were significant in the single pollutant model, and the lag with the strongest univariate effect was tested. To address the collinearity between air pollutants, only those air pollutants with r < 0.7 were entered into the model (Ko et al., 2007a, 2007b). To explore the potential modification, we examined outcomes by age (65 years and <65 years) and by sex. The differences between risk estimates from stratified analyses were assessed using a Z-test (Altman and Bland, 2003). The results are reported as the percentage change and 95% confidence intervals (CIs) in the daily stroke admissions associated with a difference of an interquartile range (IQR) in daily pollutant levels. Percentage change equals odds ratio minus 1 and then multiplies by 100. All analyses were conducted using the R programming language (V.3.2.2, R Development Core Team). All statistical tests were twosided, and P < 0.05 was considered statistically significant. 3. Results There were 200,958 hospital admissions for ischemic stroke and 41,746 admissions for hemorrhagic stroke that formed the basis for this study (Table 1). Demographic characteristics of stroke admissions are present in Table 1. Table 2 shows the summary statistics of air pollutants and meteorological variables in the 14 Chinese cities during the study period. The air pollution levels in this study were much higher than those reported in developed countries. The daily concentrations of PM10, NO2, SO2, and CO were highly and positively correlated with each other (correlation coefficient r ¼ 0.56e0.65, p < 0.001). The daily concentrations of O3 were negatively correlated with other air pollutants (correlation coefficient r ¼ (
0.38)e(
0.12), p < 0.001) (Table 3). Table 4 summarizes the results of the single-pollutant model (lag 0e5) for stroke hospitalizations after controlling for

Table 1 Demographic characteristics of stroke admissions in 14 Chinese cities in 2014e2015. Variable

Ischemic stroke

Hemorrhagic stroke

Total Gender Male (%) Female (%) Age (year) (mean ± SD) <65 (%) 65 (%)

200,958

41,746

127,636 (63.5) 73,322 (36.5) 64.4 ± 12.6 102,275 (50.9) 98,693 (49.1)

28,223 (67.6) 13,523 (32.4) 59.0 ± 13.8 27,626 (66.2) 24,120 (33.8)

temperature, relative humidity and public holiday. All analyzed air pollutants, with the exception of CO, were positively associated with ischemic stroke, while CO showed significant association at lag 3 and lag 4 days. We also observed significant association of hemorrhagic stroke with NO2 on the current day. The air pollutants showed strong temporal associations with a 4e5 day lag (Fig. 2). Fig. 3 shows the exposure-response associations between air pollutants concentrations (lag 0e5 day) and hospital admissions for ischemic stroke. Table 5 shows the associations between air pollutants (a difference of an IQR in different best lag days) and stroke in multi-pollutant models. The associations of ischemic stroke with NO2, SO2, and O3 remained stable and significant after adjusting for other air pollutants. However, the associations of PM10 and CO with ischemic stroke were weakened even towards null. The association between NO2 and hemorrhagic stroke even became stronger after adjusting for other air pollutants. There was no evidence of effect modification by sex or age in any lag structure (all p > 0.05) (Fig. S1). 4. Discussion Our multicity analysis showed that all five major air pollutants were positively associated with risk of hospital admission for ischemic stroke in the study area. We also observed significant association of NO2 with hospitalization for hemorrhagic stroke. To the best of our knowledge, this is the first multicity study in mainland China to examine the short-term effects of various air pollutants on hospital admissions for stroke. In the present study, a difference of an IQR of 6-day moving average concentrations of PM10, NO2, SO2, CO, and O3 corresponded to increases in hospital admissions for ischemic stroke, and an increase in average concentration of NO2 was significantly associated with admissions for hemorrhagic stroke. The magnitude of our risk estimates was generally comparable with prior reports. For example, Wellenius et al. (2005). examined the association of air pollution with hospital admissions for ischemic and hemorrhagic stroke in nine US cities, limiting the cohort to patients aged 65. They found that a difference of an IQR of PM10, SO2, NO2, and CO concentration was associated with 1.03%, 1.35%, 2.94%, and 2.83% increases in hospital admissions for ischemic stroke, respectively. However, for hemorrhagic stroke, no significant associations with any pollutants were observed (Wellenius et al., 2005). Another large-scale multicity analysis, the China Air Pollution and Health Effects Study, estimated that a 10 mg/m3 increase of 2-day moving average concentrations of PM10, SO2, and NO2 corresponded to a 0.54%, 0.88%, and 1.47% increase in stroke mortality, respectively (Chen et al., 2013). In a meta-analysis of 103 studies on air pollution and stroke morbidity and mortality, most of which were conducted in Europe and North America, Shah et al. (2015). estimated that the excess risks of stroke associated with a 10 ppb increase of SO2, NO2, and O3 concentrations were 1.9%, 1.4%, and 0.1%, respectively. PM10 and CO were also significantly associated with increased risk of stroke (Shah et al., 2015). The broad consistency in the literature indicates that the association of air pollution with stroke is unlikely to be spurious due to confounding, publication bias or flaws in study design. It should be noted that, although the magnitude of risk estimates in this study was similar to the magnitude indicated in previous reports, the stroke burden resulting from exposure to air pollution is greater in China than in developed countries because of higher air pollution levels and consequent higher incidence. While our study showed consistent positive associations between air pollution and ischemic stroke, the associations with hemorrhagic stroke were more variable and imprecise; this finding is consistent with those of earlier studies (Huang et al., 2016; Shah et al., 2015; Villeneuve et al., 2006; Wellenius et al., 2005). The

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Table 2 Summary statistics for air pollutants concentrations and meteorological variables in 14 Chinese cities in 2014e2015. Mean ± SD

Variable

PM10 (mg/m3) NO2 (mg/m3) SO2 (mg/m3) CO (mg/m3) O3 (mg/m3) Temperature ( C) Relative humidity (%)

Minimum

127.1 ± 82.2 46.6 ± 20.6 39.6 ± 41.2 1.28 ± 0.78 91.5 ± 51.1 11.2 ± 12.0 62.0 ± 45.4

Percentile

8.9 5.6 1.9 0.14 2
25.7 8

25th

50th

75th

73.1 31.8 12.5 0.76 54 1.9 42

108.4 42.8 25.4 1.07 80 12.8 57

158.8 57.2 50.8 1.57 121 21.5 71

Maximum

IQR

977.3 170.9 316.9 8.41 290 35.5 95

85.7 25.4 38.3 0.81 67 19.6 53

IQR: interquartile range.

previous reports (Chen et al., 2013; Villeneuve et al., 2006). NO2 generally serves as a surrogate measure for vehicular pollution because of its close association with vehicle exhaust emissions (Seaton and Dennekamp, 2003). It is possible that other trafficrelated components, such as ultrafine particles, are responsible for the observed effects. However, the robust and consistent risk

Table 3 Spearman correlation coefficients among the exposure variables in 14 Chinese cities in 2014e2015. Variables

PM10

NO2

SO2

CO

O3

Temp

RH

PM10 NO2 SO2 CO O3 Temp RH

1.00 e e e e e e

0.61a 1.00 e e e e e

0.56a 0.56a 1.00 e e e e

0.63a 0.65a 0.56a 1.00 e e e


0.12a
0.27a
0.38a
0.35a 1.00 e e


0.18a
0.33a
0.59a
0.37a 0.76a 1.00 e


0.12a 0.10a 0.01a 0.12a
0.16a 0.18a 1.00

Temp: temperature ( C); RH: relative humidity (%). a P < 0.001.

underlying mechanism of cardiovascular changes in relation to air pollution and the differences in the etiology of stroke subtype may partly explain the heterogeneity in the association between stroke subtype and air pollution. Several plausible biological mechanistic pathways for the adverse health effects associated with air pollution have been advanced. Epidemiological and toxicological evidence indicates that air pollution exposure may provoke platelet activation, leading to enhanced blood coagulation and thrombosis formation (Franchini and Mannucci, 2011; Lucking et al., 2008). Several studies have posited associations between exposure to air pollution and artery calcification (Kaufman et al., 2016) or vascular endothelial dysfunction (Tornqvist et al., 2007). This hypothesis is also supported by findings in animal models (Sun et al., 2005). Exposure to air pollution has been associated with increased levels of plasma cytokines including tumor necrosis factor alpha, interleukin 1 beta, and interleukin 6 (van Eeden et al., 2001), suggesting that systemic inflammatory responses induced by air pollution may also play a role in the development of stroke. These pathophysiologic changes associated with air pollution may be related to the development and progression of ischemic stroke. The lower incidence of hemorrhagic stroke may lead to larger imprecision in the estimates (Shah et al., 2015). The risk estimates from multi-pollutant models found that NO2 contributed most to the increased risk of stroke, in line with

Fig. 2. Percentage change (95% CI) in hospital admissions for ischemic stroke and hemorrhagic stroke associated with a difference of an interquartile range (IQR) of PM10 (85.7 mg/m3), NO2 (25.4 mg/m3), SO2 (38.3 mg/m3), CO (0.81 mg/m3), and O3 (67 mg/m3) for different lag structures in 14 Chinese cities, 2014e2015. PM10, particulate matter less than 10 mm in aerodynamic diameter; NO2, nitrogen dioxide; SO2, sulfur dioxide; CO, carbon monoxide; O3, ozone.

Table 4 Percentage change with 95% CI in ischemic and hemorrhagic stroke admissions associated with an interquartile range increases in air pollutants in 14 Chinese cities in 2014e2015. Variables

PM10 NO2 SO2 CO O3 a

Ischemic stroke

Hemorrhagic stroke

Percentage changea

95% CI

P

Percentage change

95% CI

P

0.7 1.6 2.6 0.5 1.3

0e1.4 1.0e2.3 1.8e3.5
0.2e1.1 0.3e2.3

0.0479 3.54e-07 8.45e-10 0.176 0.0103

0.3 0.6 1.4
0.8 2


1.1e1.8
0.7e2
0.4e3.2
2.2e0.7
0.2e4.3

0.641 0.342 0.125 0.287 0.0771

The association was adjusted for temperature, relative humidity and public holiday.

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Fig. 3. The exposure-response curves for 6-day (lag0e5) moving average concentrations of particulate matter, nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone (degree of freedom ¼ 3) associated with hospital admissions for ischemic stroke in 14 Chinese cities, 2014e2015. Note: The X-axis is the 6-day (lag0e5) moving average concentrations of air pollutants. The Y-axis is the log relative risk (RR). The solid line represents the predicted log relative risk (RR), and the dotted lines represent the 95% CI.

estimates for NO2 when controlling for other air pollutants point to an independent effect. This should be interpreted with caution because of the high correlation between pollutants. Future studies are required to identify the specific toxic agent which is directly responsible for increased risk of stroke. Possibly because of the higher levels of air pollution in China, a stronger temporal association was observed compared with prior

reports of a 1e2 day lag (Andersen et al., 2010; Villeneuve et al., 2006; Wellenius et al., 2005, 2012). However, it should be noted that these risk estimates were based on hospital admission data rather than on the timing of symptom onset, possibly leading to misclassification of time and underestimation of effects (Zeger et al., 2000). However, because stroke events usually require urgent care and hospitalizations, the confounding bias caused by

H. Liu et al. / Environmental Pollution 230 (2017) 234e241

239

Table 5 Percentage change with 95% CI in ischemic and hemorrhagic stroke admissions associated with an interquartile range increases in air pollutants concentrations in different best lag days in multi-pollutant models. Variable

PM10

NO2

SO2

CO

O3

Ischemic stroke Hemorrhagic stroke


0.2 (
0.9e0.5) 0.6 (
0.9e2.1)

1.9 (1.0e2.9)a 3.7 (1.7e5.7)a

0.9 (0.3e1.5)a 0.4 (
1.0e1.8)


0.5 (
1.3e0.3)
0.9 (
2.5e0.8)

1.6 (0.8e2.4)a 1.7 (
0.2e3.6)

a

P < 0.05 (The association was adjusted for temperature, relative humidity and public holiday).

exposure misclassification is expected to be minor. A populationbased study of 1101 acute ischemic stroke patients in one city in the U.S. found that hospital admission occurred a median of one calendar day after the onset of symptoms, and this delay in presentation was likely to result in underestimation of the strength of association between air pollution and stroke (Lokken et al., 2009). Therefore, the lag effects of ambient air pollution exposure on stroke should be interpreted with caution. Unlike the majority of previous studies, which had been conducted in Western developed nations where air pollution levels are generally low, this study was carried out in multiple heavily polluted cities. We were therefore able to examine the exposureresponse relationship in a much wider range of air pollution levels, and have an opportunity to sketch a more complete picture of the association. Our study also had some potential limitations. First, the data used in this study were derived from 14 large cities. Due to the topography of China, also in relation to the variability in air pollution levels across varying city sizes, the generalizability of our findings to smaller cities should be interpreted with caution. The association between air pollution and stroke in smaller cities should be examined in future studies. Second, the use of citywide average air pollution levels calculated from various monitoring stations as a proxy for personal exposure is expected to result in exposure measurement error, which may underestimate the effects of air pollution (Goldman et al., 2011). Third, PM2.5 has also been shown to contribute to stroke risk. However, data on PM2.5 is not available in this study. Future studies are needed to evaluate the effect of PM2.5 on stroke risk in China. In addition, we were not able to differentiate the ischemic stroke subtypes, because that information was not available in our database. Future studies are needed to examine whether the acute effects of air pollution differed across strata defined by ischemic stroke etiology. Moreover, we could not determine if individuals that are admitted for stroke are from outside areas of the city, or what if those that are the ones exposed to the pollution at those levels present at another hospital outside of the city. However, according to China's medical system, patients ought to go to their designated local hospitals to seek medical care (Babiarz et al., 2010; Dong, 2009; Xu et al., 1995). In addition, stroke events usually require urgent care and hospitalizations. A previous study indicated that hospital admission occurred a median of one calendar day after the onset of symptoms (Lokken et al., 2009). Therefore, the confounding bias caused by this potential exposure misclassification is expected to be minor. Another limitation was our inability to account for factors associated with personal air pollution exposure, e.g., activities of daily living for individual patients. The potential misclassifications for the diseases diagnosis should also be considered when interpreting the findings. However, all the hospitals included in this study are top-ranked public hospitals, and enjoy the prestigious esteems for quality in all aspects of healthcare, including treatment, diagnosis, coding, hospital management, and electronic medical record systems. The Beijing Municipal Health Bureau had conducted quality control study, and demonstrated that over 95% of diagnostic codes on the HSR were accurate according to manual examinations of electronic medical records (Zhao et al., 2013). Only inclusion of top-ranked hospitals in this study can help with eliminating the potential misclassifications

for the diseases diagnosis. In addition, the corresponding Chinese diagnoses were applied to check the identified admissions. Natural language processing has been suggested to be an efficient method for identifying cases in large clinical databases (Data, 2016; Nadkarni et al., 2011). Both ICD codes and the corresponding patient diagnoses were used to identify eligible hospital admissions for stroke, which would significantly reduce the potential bias caused by misclassification of stroke. However, on the other hand, only inclusion of top-ranked hospitals may cause selection bias. Finally, although the associations between air pollutants and stroke remained significant after Bonferroni correction, we acknowledged that the results could be due to chance because of multiple testing and the sparsely significant results. Future studies are warranted to confirm our findings. In conclusion, our study found that short-term exposure to air pollution was significantly associated with increased hospital admissions for stroke in China. Our findings contribute to the limited scientific literature concerning the effect of air pollution on stroke in developing countries, where air pollution is more severe. Our findings may have significant public health implications for prevention of stroke in China. Conflict of interest The authors declare that they have no conflict of interest. Acknowledgments This research work was funded by the National Natural Science Foundation of China (Grant No. 71402003). Appendix A. Supplementary data Supplementary data related to this chapter can be found at http://dx.doi.org/10.1016/j.envpol.2017.06.057. References Altman, D.G., Bland, J.M., 2003. Interaction revisited: the difference between two estimates. Bmj 326, 219. Andersen, Z.J., Olsen, T.S., Andersen, K.K., Loft, S., Ketzel, M., Raaschou-Nielsen, O., 2010. Association between short-term exposure to ultrafine particles and hospital admissions for stroke in Copenhagen, Denmark. Eur. Heart J. 31, 2034e2040. Babiarz, K.S., Miller, G., Yi, H., Zhang, L., Rozelle, S., 2010. New evidence on the impact of China's new rural cooperative medical scheme and its implications for rural primary healthcare: multivariate difference-in-difference analysis. Bmj 21. Carracedo-Martinez, E., Taracido, M., Tobias, A., Saez, M., Figueiras, A., 2010. Casecrossover analysis of air pollution health effects: a systematic review of methodology and application. Environ. Health Perspect. 118, 1173e1182. Chen, R., Zhang, Y., Yang, C., Zhao, Z., Xu, X., Kan, H., 2013. Acute effect of ambient air pollution on stroke mortality in the China air pollution and health effects study. Stroke 44, 954e960. Data, M.C., 2016. Secondary Analysis of Electronic Health Records. Dockery, D.W., Luttmann-Gibson, H., Rich, D.Q., Link, M.S., Mittleman, M.A., Gold, D.R., Koutrakis, P., Schwartz, J.D., Verrier, R.L., 2005. Association of air pollution with increased incidence of ventricular tachyarrhythmias recorded by implanted cardioverter defibrillators. Environ. Health Perspect. 113, 670e674. Dong, K., 2009. Medical insurance system evolution in China. China Econ. Rev. 20, 591e597.

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