International Journal of
Environmental Research and Public Health Article
Gaseous Air Pollution and the Risk for Stroke Admissions: A Case-Crossover Study in Beijing, China Fangfang Huang 1,2,† , Yanxia Luo 1,2,† , Peng Tan 3 , Qin Xu 1,2 , Lixin Tao 1,2 , Jin Guo 1,2 , Feng Zhang 1,2 , Xueqin Xie 3, * and Xiuhua Guo 1,2, * 1
2 3
* †
School of Public Health, Capital Medical University, Beijing 100069, China; [email protected] (F.H.); [email protected] (Y.L.); [email protected] (Q.X.); [email protected] (L.T.); [email protected] (J.G.); [email protected] (F.Z.) Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China Beijing Public Health Information Center, Beijing 100050, China; [email protected] Correspondence: [email protected] (X.X.); [email protected] (X.G.); Tel./Fax: +86-10-8391-1508 (X.G.) Fangfang Huang and Yanxia Luo contributed equally to this study.
Academic Editor: Sayed M. Hassan Received: 25 November 2016; Accepted: 27 January 2017; Published: 14 February 2017
Abstract: Background: Though increasing evidence supports association between gaseous air pollution and stroke, it remains unclear whether the effects differ in season, sex and age. The aim of this study was to examine the associations of gaseous air pollution with stroke admissions in Beijing, 2013–2014 in different subgroups. Methods: Case-crossover design and conditional logistic regression were used to perform the analyses. We examined the exposure-response relationship between air pollution and stroke. Stratified analyses were performed in different seasons, sex, and age groups. Results: There were 147,624 stroke admissions during the study period. In the whole study period, percent changes of stroke admissions were 0.82% (95% CI: 0.52% to 1.13%) and 0.73% (95% CI: 0.44% to 1.03%) per 10 µg/m3 increase in the same day conentration of nitrogen dioxide (NO2 ) and sulfur dioxide (SO2 ). The positive associations were higher in warm seasons and with patients >65 years (p < 0.05). Contrary effects of carbon monoxide (CO) and ozone on stroke admissions were observed in different seasons. Conclusions: NO2 and SO2 were positively associated with stroke admissions, with stronger effects in warm seasons and with patients >65 years. The associations of CO and ozone with stroke admissions differed across seasons. Keywords: stroke; air pollution; hospital admission; exposure-response relationship
1. Introduction Due to increasing motorized traffic and industrial and agricultural activities, concentrations of air pollutants increased substantially in recent decades [1–3]. Stroke is a leading cause of death and disability globally, which accounts for five million deaths each year representing nearly 10% of all deaths, and 44 million disability-adjusted life-years are lost annually due to stroke [4]. In China, stroke prevalence estimates in 2013 were statistically greater than those reported in China three decades ago. The age-standardized prevalence, incidence and mortality rates were 1114.8 per 100,000 people, 246.8 and 114.8 per 100,000 person-years, respectively [5]. Therefore, effects of air pollution on stroke should be explored given the great stroke burden in terms of mortality and disability worldwide for primary prevention efforts. Although exposure to air pollution has been associated with an increase in mortality and morbidity of stroke, these studies were mainly focused on particulate matters (PM) and air pollutants from
Int. J. Environ. Res. Public Health 2017, 14, 189; doi:10.3390/ijerph14020189
www.mdpi.com/journal/ijerph
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fossil fuel combustion [6–9]. For carbon monoxide (CO) and ozone, studies reported inconsistent results [10–13]. For example, Tian et al. (2015) found that low environmental CO was associated with reduced risk of daily stroke admissions [10] and Männistö et al. (2015) observed decreased odds of stroke events with exposure to ozone [12]. These findings suggest that there may be a non-linear exposure-response relationship which was not well researched. Besides, it remains unclear how air pollution influences stroke in different seasons with varied concentrations. It is important to understand the characteristics of individuals who are at increased risk of adverse stroke events due to air pollution. Some subpopulations may be particularly sensitive to PM exposure, such as elderly subjects, diabetic patients, and individuals with known coronary artery disease [14,15]. However, prior findings about the modifying effects of characteristics of individuals for gaseous air pollution remain inconsistent. For example, Kan et al. (2008) found that females and the elderly were more vulnerable to nitrogen dioxide (NO2 ), sulfur dioxide (SO2 ), and ozone [16]. Zheng et al. (2013) also found elderly people had higher estimates for cerebrovascular admissions for NO2 and SO2 than the younger [17]. However, other studies suggest opposite results or no modifying effects of age and sex [8,18]. The aim of this study was to examine the associations of gaseous air pollutants with stroke admissions and their exposure-response relationships, using a case-crossover design, and to determine whether the associations differed in season, sex, and age, in order to capture the susceptible subpopulations. 2. Materials and Methods 2.1. Air Pollution and Health Data NO2 , SO2 , CO, ozone, and particulate matter that is 2.5 µm or less in diameter (PM2.5 ) measurements in Beijing, China during 1 January 2013 to 31 December 2014 were retrieved from the Centre of City Environmental Protection Monitoring Website Platform of Beijing. The District-level 24-h mean concentrations of air pollutants were used as metrics of exposures. Meteorological data in 16 districts of Beijing were obtained from the Chinese Meteorological Bureau. We obtained disease data from the medical record database for cardiovascular and cerebrovascular diseases in Beijing. The medical record database contains all the hospitals that have the capability to diagnose and treat cardiovascular and cerebrovascular disease in Beijing. Hospital admissions for stroke (International Classification of Diseases, 10th revision, ICD10: I61-63) and demographic characteristics, including sex and age, between 1 January 2013 to 31 December 2014 were retrieved from the database. The protocol of this study was approved by the School of Public Health, Capital Medical University (SPHCMU) Institutional Review Board (IRB00009511). Informed consent was not required for this study because all health data were analyzed at the aggregated level, no individual record information for patients was involved and no patients were contacted. 2.2. Statistical Analysis A bidirectional case-crossover design was used to examine the associations of gaseous air pollutants and stroke admissions. The case-crossover design is a variant of the matched case control study and consists of only cases, which serve as their own controls in the analysis. This design can control the effects of day of week, season, and slowly varying confounders. In this study, case period was defined as the day of hospital admission. Control periods were chosen in a two-week window before and after the case period for the same days of the week as the hospital admission [19]. Conditional logistic regression was used to perform the analyses. Temperature, relative humidity, and holidays were controlled as previous studies have shown that these variables are the potential confounding factors [20,21]. We used natural cubic splines with three degrees of freedom (df ) to include the effects of temperature and relative humidity on stroke on
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the day of hospital admission. The public holidays were controlled through binary variable (coded as public holiday = 1 and no public holiday = 0) in the model. All results were from models including temperature, relative humidity, and holidays. We graphically examined the exposure-response relationship of air pollutants with stroke admissions. We applied natural cubic splines with 3 df for NO2 , SO2 and ozone, and 4 df for CO to model the relationship. Two-pollutant models adjusting the other gaseous air pollutants were fitted to check the robustness of our results. Percent change and 95% confidence interval (CI) in stroke admissions associated with a 10.0 µg/m3 increase in daily concentration of NO2 , SO2 and ozone, and a 1.0 mg/m3 increase in daily concentration of CO were estimated on the same day (lag0), the previous day (lag1), the two preceding days (lag2), and three days moving average (lag0–2). Stratified analyses were performed to examine whether the associations differed in season (warm season: May to October and cold season: November to April), sex (men and women) and age (>65 years and ≤65 years). Two-pollutant models adjusting PM2.5 were fitted to check the modifying effect of PM2.5 on associations of gaseous air pollutants and stroke admissions. We tested the statistical significance of subgroup differences through Z test [10]. Sensitivity analyses were performed to check the robustness of the results. We used a different method to choose controls, i.e., time-stratified case-crossover design. Degrees of freedom were changed for meteorological variables. We also used temperature and relative humidity lagged by up to two weeks to control the potential lagged effects of meteorological variables. The conditional logistic regression was performed using the PHREG procedure in SAS statistical software (Version 9.2; SAS Institute, Inc., Cary, NC, USA). We used the Akaike’s Information Criterion to choose the df for the splines and determine the goodness of the model fit. All statistical tests were two-sided, and p-values less than 0.05 were considered statistically significant. 3. Results There were 147,624 stroke admissions in 2013–2014 that formed the basis for this study. On average there were 205.1 hospital admissions per day for stroke, including 125.7 for men and 119.2 for patients >65 years (Table 1). Table 1. Summary statistics of stroke admissions in Beijing, China, 2013–2014. Count/Day
Mean
SD
Median
IQR
Minimum
Maximum
All days All Men Women Age > 65 Age ≤ 65
205.1 125.7 79.4 119.2 85.8
62.8 40.1 24.5 37.9 26.7
212.0 128.0 82.0 126.0 85.0
113.0 70.0 42.0 69.0 43.0
81.0 42.0 28.0 41.0 27.0
375.0 225.0 150.0 215.0 179.0
Warm days All Men Women Age > 65 Age ≤ 65
206.6 127.2 79.4 120.5 86.1
64.4 41.5 24.7 39.5 26.7
210.0 129.0 81.5 126.5 86.0
118.5 75.0 42.5 72.0 44.5
81.0 51.0 28.0 45.0 36.0
375.0 225.0 150.0 208.0 179.0
Cold days All Men Women Age > 65 Age ≤ 65
203.6 124.1 79.5 118.0 85.6
61.2 38.6 24.4 36.3 26.8
213.5 128.0 82.5 126.0 85.0
108.0 65.0 41.0 64.0 43.0
81.0 42.0 30.0 41.0 27.0
340.0 218.0 134.0 215.0 152.0
SD, standard deviation; IQR, interquartile range.
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Summary statistics of air pollutants and meteorological variables for all days in Beijing, China, 2013–2014 are presented in Table 2. The means (standard deviation, SD) of air pollutants and meteorological variables were 52.5 (28.1) µg/m3 for NO2 , 24.5 (24.8) µg/m3 for SO2 , 1.7 (1.2) mg/m3 for CO, 117.5 (73.2) µg/m3 for ozone, 89.8 (73.2) µg/m3 for PM2.5 , 11.9 (11.2) ◦ C for temperature, and 56.1 (17.5) % for relative humidity, respectively. On warm days, the means (SD) of NO2 , SO2 , CO, and ozone were 44.9 (21.5) µg/m3 , 11.4 (11.0) µg/m3 , 1.2 (0.6) mg/m3 , and 163.7 (69.1) µg/m3 , respectively. On cold days, the corresponding values were 60.1 (31.2) µg/m3 , 43.7 (38.7) µg/m3 , 2.2 (1.4) mg/m3 , and 70.6 (39.9) µg/m3 , respectively. Table 2. Summary statistics of air pollutants and meteorological variables in Beijing, China, 2013–2014. Variables
Mean
SD
Med
IQR
Min
Max
All days NO2 (µg/m3 ) SO2 (µg/m3 ) CO (mg/m3 ) Ozone (µg/m3 ) PM2.5 (µg/m3 ) Temperature (◦ C) Relative humidity (%)
52.5 24.5 1.7 117.5 89.8 11.9 56.1
28.1 24.8 1.2 73.2 73.2 11.2 17.5
48.3 14.8 1.3 99.0 71.4 13.5 56.5
36.3 27.1 1.0 102.5 82.0 20.7 27.2
1.0 1.0 0.0 0.0 4.0 −13.3 12.8
196.8 197.1 11.5 703.0 685.5 31.1 96.0
Warm season NO2 (µg/m3 ) SO2 (µg/m3 ) CO (mg/m3 ) Ozone (µg/m3 ) PM2.5 (µg/m3 ) Temperature (◦ C) Relative humidity (%)
44.9 11.4 1.2 163.7 79.8 20.9 64.1
21.5 11.0 0.6 69.1 57.7 5.5 15.2
42.4 8.0 1.0 160.0 66.6 22.0 65.3
58.0 14.0 1.5 101.1 70.3 7.7 21.1
1.0 1.0 0.0 2.0 4.0 5.0 18.4
159.9 197.1 9.0 703.0 373.0 31.1 94.3
Cold season NO2 (µg/m3 ) SO2 (µg/m3 ) CO (mg/m3 ) Ozone (µg/m3 ) PM2.5 (µg/m3 ) Temperature (◦ C) Relative humidity (%)
60.1 43.7 2.2 70.6 100.6 2.5 47.8
31.2 38.7 1.4 39.9 84.8 7.1 15.7
55.7 34.0 2.0 67.0 80.0 1.3 46.8
79.0 57.5 3.0 44.2 92.9 10.5 22.0
1.0 2.0 0.1 0.0 4.3 −13.3 12.8
196.8 537.0 11.5 331.0 685.5 20.9 96.0
SD, standard deviation; Med, median; IQR, interquartile range; Min, minimum value; Max, maximum value; NO2 , nitrogen dioxide; SO2 , sulfur dioxide; CO, carbon monoxide; PM2.5 , particulate matter that is 2.5 µm or less in diameter.
Spearman correlation coefficients for correlations among the exposure variables are presented in Table 3. NO2 , SO2 , and CO were positively correlated with each other (correlation coefficient, r = 0.58 to 0.68, p < 0.001) and negatively correlated with ozone (r = −0.43 to –0.29, p < 0.001). Correlations between temperature and NO2 , SO2 , and CO were negative (r = −0.29 to –0.42, p < 0.001). Correlation between temperature and ozone was positive (r = 0.79, p < 0.001). Exposure-response relationships of stroke admissions with air pollutants in different pollutant models are shown in Figure 1. The exposure-response curves suggest an approximately linear rise in stroke risk with daily changes in NO2 , SO2 , and ozone concentrations in both single- and two-pollutant models. For CO, there was a linear increase in stroke risk at higher CO concentrations, with a threshold of around 5 mg/m3 for a 24-h average exposure.
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Table 3. Spearman correlation coefficients among the exposure variables. Table 3. Spearman correlation coefficients among the exposure variables.
Variable NO2 SO2 *2 NO 2 1.00NO2 0.58SO Variable SO2 NO - 1.00 1.00 0.58 * 2 CO SO2 - - 1.00 OzoneCO - - Ozone PM2.5 - PM2.5 T T RH RH - - -
CO 0.61 CO * 0.68 0.61 ** 1.00* 0.68 1.00 ---
Ozone PM2.5 T RH −0.29 * PM0.61 Ozone T−0.24 * RH0.13 * 2.5 * −0.46 * 0.48 * −0.60 * −0.29 * 0.61 * −0.24 * * 0.13−0.21 * −0.43 * 0.68 * −0.42 * 0.18 * −0.46 * 0.48 * −0.60 * −0.21 * −0.43 * 0.68 * *−0.42 * * 0.18 *0.15 * −0.09 0.79 1.00 * 0.79 * 1.00−0.091.00 −0.05 * 0.15 *0.40 * 1.00 −0.05 * 0.40 * 0.36 * 1.00 1.00 0.36 * - 1.00 1.00 - - -
NO, nitrogen 2, nitrogen dioxide; SO2, sulfur dioxide; CO, carbon monoxide; PM2.5, particulate matter that is 2.5 NO dioxide; SO2 , sulfur dioxide; CO, carbon monoxide; PM2.5 , particulate matter that is 2.5 µm or less in 2 µ m or less in diameter; T, relative temperature; RH,* prelative diameter; T, temperature; RH, humidity. < 0.001.humidity. * p < 0.001.
Figure 1. Exposure-response relationships of stroke admissions with air pollutants on the concurrent Figure 1. Exposure-response relationships of stroke admissions with air pollutants on the concurrent day in different pollutant models. day in different pollutant models. 3 increase Percent (95% CI) CI) in hospital admissions for stroke associated with a 10.0 Percentchanges changes (95% in hospital admissions for stroke associated withµg/m a 10.0 μg/m3 3 3 inincrease NO2 , SO and2, ozone, and a 1.0 and mg/m in CO are Tablein4.Table 4. in2 ,NO SO2, and ozone, a 1.0increase mg/m increase in shown CO are in shown
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Table 4. Percent change (95% CI) in hospital admissions for stroke associated with a 10.0 µg/m3 increase in NO2 , SO2 , and ozone, and 1.0 mg/m3 increase in CO stratified by season.
Variables
Season
All Days
Test for Difference between Seasons
Warm Season
Cold Season
Z Value
p Value
NO2 Lag0 Lag1 Lag2 3 days average
0.82 (0.52 to 1.13) # 0.39 (0.11 to 0.67) * −0.14 (−0.40 to 0.12) 0.22 (−0.18 to 0.61)
2.35 (1.76 to 2.94) # 1.80 (1.22 to 2.37) # 0.34 (−0.22 to 0.90) 2.24 (1.44 to 3.05) #
0.40 (0.02 to 0.78) * −0.13 (−0.46 to 0.20) −0.08 (−0.38 to 0.23) −0.37 (−0.84 to 0.10)
5.426 5.735 1.281 5.516
Environmental Research and Public Health Article
Gaseous Air Pollution and the Risk for Stroke Admissions: A Case-Crossover Study in Beijing, China Fangfang Huang 1,2,† , Yanxia Luo 1,2,† , Peng Tan 3 , Qin Xu 1,2 , Lixin Tao 1,2 , Jin Guo 1,2 , Feng Zhang 1,2 , Xueqin Xie 3, * and Xiuhua Guo 1,2, * 1
2 3
* †
School of Public Health, Capital Medical University, Beijing 100069, China; [email protected] (F.H.); [email protected] (Y.L.); [email protected] (Q.X.); [email protected] (L.T.); [email protected] (J.G.); [email protected] (F.Z.) Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China Beijing Public Health Information Center, Beijing 100050, China; [email protected] Correspondence: [email protected] (X.X.); [email protected] (X.G.); Tel./Fax: +86-10-8391-1508 (X.G.) Fangfang Huang and Yanxia Luo contributed equally to this study.
Academic Editor: Sayed M. Hassan Received: 25 November 2016; Accepted: 27 January 2017; Published: 14 February 2017
Abstract: Background: Though increasing evidence supports association between gaseous air pollution and stroke, it remains unclear whether the effects differ in season, sex and age. The aim of this study was to examine the associations of gaseous air pollution with stroke admissions in Beijing, 2013–2014 in different subgroups. Methods: Case-crossover design and conditional logistic regression were used to perform the analyses. We examined the exposure-response relationship between air pollution and stroke. Stratified analyses were performed in different seasons, sex, and age groups. Results: There were 147,624 stroke admissions during the study period. In the whole study period, percent changes of stroke admissions were 0.82% (95% CI: 0.52% to 1.13%) and 0.73% (95% CI: 0.44% to 1.03%) per 10 µg/m3 increase in the same day conentration of nitrogen dioxide (NO2 ) and sulfur dioxide (SO2 ). The positive associations were higher in warm seasons and with patients >65 years (p < 0.05). Contrary effects of carbon monoxide (CO) and ozone on stroke admissions were observed in different seasons. Conclusions: NO2 and SO2 were positively associated with stroke admissions, with stronger effects in warm seasons and with patients >65 years. The associations of CO and ozone with stroke admissions differed across seasons. Keywords: stroke; air pollution; hospital admission; exposure-response relationship
1. Introduction Due to increasing motorized traffic and industrial and agricultural activities, concentrations of air pollutants increased substantially in recent decades [1–3]. Stroke is a leading cause of death and disability globally, which accounts for five million deaths each year representing nearly 10% of all deaths, and 44 million disability-adjusted life-years are lost annually due to stroke [4]. In China, stroke prevalence estimates in 2013 were statistically greater than those reported in China three decades ago. The age-standardized prevalence, incidence and mortality rates were 1114.8 per 100,000 people, 246.8 and 114.8 per 100,000 person-years, respectively [5]. Therefore, effects of air pollution on stroke should be explored given the great stroke burden in terms of mortality and disability worldwide for primary prevention efforts. Although exposure to air pollution has been associated with an increase in mortality and morbidity of stroke, these studies were mainly focused on particulate matters (PM) and air pollutants from
Int. J. Environ. Res. Public Health 2017, 14, 189; doi:10.3390/ijerph14020189
www.mdpi.com/journal/ijerph
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fossil fuel combustion [6–9]. For carbon monoxide (CO) and ozone, studies reported inconsistent results [10–13]. For example, Tian et al. (2015) found that low environmental CO was associated with reduced risk of daily stroke admissions [10] and Männistö et al. (2015) observed decreased odds of stroke events with exposure to ozone [12]. These findings suggest that there may be a non-linear exposure-response relationship which was not well researched. Besides, it remains unclear how air pollution influences stroke in different seasons with varied concentrations. It is important to understand the characteristics of individuals who are at increased risk of adverse stroke events due to air pollution. Some subpopulations may be particularly sensitive to PM exposure, such as elderly subjects, diabetic patients, and individuals with known coronary artery disease [14,15]. However, prior findings about the modifying effects of characteristics of individuals for gaseous air pollution remain inconsistent. For example, Kan et al. (2008) found that females and the elderly were more vulnerable to nitrogen dioxide (NO2 ), sulfur dioxide (SO2 ), and ozone [16]. Zheng et al. (2013) also found elderly people had higher estimates for cerebrovascular admissions for NO2 and SO2 than the younger [17]. However, other studies suggest opposite results or no modifying effects of age and sex [8,18]. The aim of this study was to examine the associations of gaseous air pollutants with stroke admissions and their exposure-response relationships, using a case-crossover design, and to determine whether the associations differed in season, sex, and age, in order to capture the susceptible subpopulations. 2. Materials and Methods 2.1. Air Pollution and Health Data NO2 , SO2 , CO, ozone, and particulate matter that is 2.5 µm or less in diameter (PM2.5 ) measurements in Beijing, China during 1 January 2013 to 31 December 2014 were retrieved from the Centre of City Environmental Protection Monitoring Website Platform of Beijing. The District-level 24-h mean concentrations of air pollutants were used as metrics of exposures. Meteorological data in 16 districts of Beijing were obtained from the Chinese Meteorological Bureau. We obtained disease data from the medical record database for cardiovascular and cerebrovascular diseases in Beijing. The medical record database contains all the hospitals that have the capability to diagnose and treat cardiovascular and cerebrovascular disease in Beijing. Hospital admissions for stroke (International Classification of Diseases, 10th revision, ICD10: I61-63) and demographic characteristics, including sex and age, between 1 January 2013 to 31 December 2014 were retrieved from the database. The protocol of this study was approved by the School of Public Health, Capital Medical University (SPHCMU) Institutional Review Board (IRB00009511). Informed consent was not required for this study because all health data were analyzed at the aggregated level, no individual record information for patients was involved and no patients were contacted. 2.2. Statistical Analysis A bidirectional case-crossover design was used to examine the associations of gaseous air pollutants and stroke admissions. The case-crossover design is a variant of the matched case control study and consists of only cases, which serve as their own controls in the analysis. This design can control the effects of day of week, season, and slowly varying confounders. In this study, case period was defined as the day of hospital admission. Control periods were chosen in a two-week window before and after the case period for the same days of the week as the hospital admission [19]. Conditional logistic regression was used to perform the analyses. Temperature, relative humidity, and holidays were controlled as previous studies have shown that these variables are the potential confounding factors [20,21]. We used natural cubic splines with three degrees of freedom (df ) to include the effects of temperature and relative humidity on stroke on
Int. J. Environ. Res. Public Health 2017, 14, 189
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the day of hospital admission. The public holidays were controlled through binary variable (coded as public holiday = 1 and no public holiday = 0) in the model. All results were from models including temperature, relative humidity, and holidays. We graphically examined the exposure-response relationship of air pollutants with stroke admissions. We applied natural cubic splines with 3 df for NO2 , SO2 and ozone, and 4 df for CO to model the relationship. Two-pollutant models adjusting the other gaseous air pollutants were fitted to check the robustness of our results. Percent change and 95% confidence interval (CI) in stroke admissions associated with a 10.0 µg/m3 increase in daily concentration of NO2 , SO2 and ozone, and a 1.0 mg/m3 increase in daily concentration of CO were estimated on the same day (lag0), the previous day (lag1), the two preceding days (lag2), and three days moving average (lag0–2). Stratified analyses were performed to examine whether the associations differed in season (warm season: May to October and cold season: November to April), sex (men and women) and age (>65 years and ≤65 years). Two-pollutant models adjusting PM2.5 were fitted to check the modifying effect of PM2.5 on associations of gaseous air pollutants and stroke admissions. We tested the statistical significance of subgroup differences through Z test [10]. Sensitivity analyses were performed to check the robustness of the results. We used a different method to choose controls, i.e., time-stratified case-crossover design. Degrees of freedom were changed for meteorological variables. We also used temperature and relative humidity lagged by up to two weeks to control the potential lagged effects of meteorological variables. The conditional logistic regression was performed using the PHREG procedure in SAS statistical software (Version 9.2; SAS Institute, Inc., Cary, NC, USA). We used the Akaike’s Information Criterion to choose the df for the splines and determine the goodness of the model fit. All statistical tests were two-sided, and p-values less than 0.05 were considered statistically significant. 3. Results There were 147,624 stroke admissions in 2013–2014 that formed the basis for this study. On average there were 205.1 hospital admissions per day for stroke, including 125.7 for men and 119.2 for patients >65 years (Table 1). Table 1. Summary statistics of stroke admissions in Beijing, China, 2013–2014. Count/Day
Mean
SD
Median
IQR
Minimum
Maximum
All days All Men Women Age > 65 Age ≤ 65
205.1 125.7 79.4 119.2 85.8
62.8 40.1 24.5 37.9 26.7
212.0 128.0 82.0 126.0 85.0
113.0 70.0 42.0 69.0 43.0
81.0 42.0 28.0 41.0 27.0
375.0 225.0 150.0 215.0 179.0
Warm days All Men Women Age > 65 Age ≤ 65
206.6 127.2 79.4 120.5 86.1
64.4 41.5 24.7 39.5 26.7
210.0 129.0 81.5 126.5 86.0
118.5 75.0 42.5 72.0 44.5
81.0 51.0 28.0 45.0 36.0
375.0 225.0 150.0 208.0 179.0
Cold days All Men Women Age > 65 Age ≤ 65
203.6 124.1 79.5 118.0 85.6
61.2 38.6 24.4 36.3 26.8
213.5 128.0 82.5 126.0 85.0
108.0 65.0 41.0 64.0 43.0
81.0 42.0 30.0 41.0 27.0
340.0 218.0 134.0 215.0 152.0
SD, standard deviation; IQR, interquartile range.
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Summary statistics of air pollutants and meteorological variables for all days in Beijing, China, 2013–2014 are presented in Table 2. The means (standard deviation, SD) of air pollutants and meteorological variables were 52.5 (28.1) µg/m3 for NO2 , 24.5 (24.8) µg/m3 for SO2 , 1.7 (1.2) mg/m3 for CO, 117.5 (73.2) µg/m3 for ozone, 89.8 (73.2) µg/m3 for PM2.5 , 11.9 (11.2) ◦ C for temperature, and 56.1 (17.5) % for relative humidity, respectively. On warm days, the means (SD) of NO2 , SO2 , CO, and ozone were 44.9 (21.5) µg/m3 , 11.4 (11.0) µg/m3 , 1.2 (0.6) mg/m3 , and 163.7 (69.1) µg/m3 , respectively. On cold days, the corresponding values were 60.1 (31.2) µg/m3 , 43.7 (38.7) µg/m3 , 2.2 (1.4) mg/m3 , and 70.6 (39.9) µg/m3 , respectively. Table 2. Summary statistics of air pollutants and meteorological variables in Beijing, China, 2013–2014. Variables
Mean
SD
Med
IQR
Min
Max
All days NO2 (µg/m3 ) SO2 (µg/m3 ) CO (mg/m3 ) Ozone (µg/m3 ) PM2.5 (µg/m3 ) Temperature (◦ C) Relative humidity (%)
52.5 24.5 1.7 117.5 89.8 11.9 56.1
28.1 24.8 1.2 73.2 73.2 11.2 17.5
48.3 14.8 1.3 99.0 71.4 13.5 56.5
36.3 27.1 1.0 102.5 82.0 20.7 27.2
1.0 1.0 0.0 0.0 4.0 −13.3 12.8
196.8 197.1 11.5 703.0 685.5 31.1 96.0
Warm season NO2 (µg/m3 ) SO2 (µg/m3 ) CO (mg/m3 ) Ozone (µg/m3 ) PM2.5 (µg/m3 ) Temperature (◦ C) Relative humidity (%)
44.9 11.4 1.2 163.7 79.8 20.9 64.1
21.5 11.0 0.6 69.1 57.7 5.5 15.2
42.4 8.0 1.0 160.0 66.6 22.0 65.3
58.0 14.0 1.5 101.1 70.3 7.7 21.1
1.0 1.0 0.0 2.0 4.0 5.0 18.4
159.9 197.1 9.0 703.0 373.0 31.1 94.3
Cold season NO2 (µg/m3 ) SO2 (µg/m3 ) CO (mg/m3 ) Ozone (µg/m3 ) PM2.5 (µg/m3 ) Temperature (◦ C) Relative humidity (%)
60.1 43.7 2.2 70.6 100.6 2.5 47.8
31.2 38.7 1.4 39.9 84.8 7.1 15.7
55.7 34.0 2.0 67.0 80.0 1.3 46.8
79.0 57.5 3.0 44.2 92.9 10.5 22.0
1.0 2.0 0.1 0.0 4.3 −13.3 12.8
196.8 537.0 11.5 331.0 685.5 20.9 96.0
SD, standard deviation; Med, median; IQR, interquartile range; Min, minimum value; Max, maximum value; NO2 , nitrogen dioxide; SO2 , sulfur dioxide; CO, carbon monoxide; PM2.5 , particulate matter that is 2.5 µm or less in diameter.
Spearman correlation coefficients for correlations among the exposure variables are presented in Table 3. NO2 , SO2 , and CO were positively correlated with each other (correlation coefficient, r = 0.58 to 0.68, p < 0.001) and negatively correlated with ozone (r = −0.43 to –0.29, p < 0.001). Correlations between temperature and NO2 , SO2 , and CO were negative (r = −0.29 to –0.42, p < 0.001). Correlation between temperature and ozone was positive (r = 0.79, p < 0.001). Exposure-response relationships of stroke admissions with air pollutants in different pollutant models are shown in Figure 1. The exposure-response curves suggest an approximately linear rise in stroke risk with daily changes in NO2 , SO2 , and ozone concentrations in both single- and two-pollutant models. For CO, there was a linear increase in stroke risk at higher CO concentrations, with a threshold of around 5 mg/m3 for a 24-h average exposure.
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Table 3. Spearman correlation coefficients among the exposure variables. Table 3. Spearman correlation coefficients among the exposure variables.
Variable NO2 SO2 *2 NO 2 1.00NO2 0.58SO Variable SO2 NO - 1.00 1.00 0.58 * 2 CO SO2 - - 1.00 OzoneCO - - Ozone PM2.5 - PM2.5 T T RH RH - - -
CO 0.61 CO * 0.68 0.61 ** 1.00* 0.68 1.00 ---
Ozone PM2.5 T RH −0.29 * PM0.61 Ozone T−0.24 * RH0.13 * 2.5 * −0.46 * 0.48 * −0.60 * −0.29 * 0.61 * −0.24 * * 0.13−0.21 * −0.43 * 0.68 * −0.42 * 0.18 * −0.46 * 0.48 * −0.60 * −0.21 * −0.43 * 0.68 * *−0.42 * * 0.18 *0.15 * −0.09 0.79 1.00 * 0.79 * 1.00−0.091.00 −0.05 * 0.15 *0.40 * 1.00 −0.05 * 0.40 * 0.36 * 1.00 1.00 0.36 * - 1.00 1.00 - - -
NO, nitrogen 2, nitrogen dioxide; SO2, sulfur dioxide; CO, carbon monoxide; PM2.5, particulate matter that is 2.5 NO dioxide; SO2 , sulfur dioxide; CO, carbon monoxide; PM2.5 , particulate matter that is 2.5 µm or less in 2 µ m or less in diameter; T, relative temperature; RH,* prelative diameter; T, temperature; RH, humidity. < 0.001.humidity. * p < 0.001.
Figure 1. Exposure-response relationships of stroke admissions with air pollutants on the concurrent Figure 1. Exposure-response relationships of stroke admissions with air pollutants on the concurrent day in different pollutant models. day in different pollutant models. 3 increase Percent (95% CI) CI) in hospital admissions for stroke associated with a 10.0 Percentchanges changes (95% in hospital admissions for stroke associated withµg/m a 10.0 μg/m3 3 3 inincrease NO2 , SO and2, ozone, and a 1.0 and mg/m in CO are Tablein4.Table 4. in2 ,NO SO2, and ozone, a 1.0increase mg/m increase in shown CO are in shown
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Table 4. Percent change (95% CI) in hospital admissions for stroke associated with a 10.0 µg/m3 increase in NO2 , SO2 , and ozone, and 1.0 mg/m3 increase in CO stratified by season.
Variables
Season
All Days
Test for Difference between Seasons
Warm Season
Cold Season
Z Value
p Value
NO2 Lag0 Lag1 Lag2 3 days average
0.82 (0.52 to 1.13) # 0.39 (0.11 to 0.67) * −0.14 (−0.40 to 0.12) 0.22 (−0.18 to 0.61)
2.35 (1.76 to 2.94) # 1.80 (1.22 to 2.37) # 0.34 (−0.22 to 0.90) 2.24 (1.44 to 3.05) #
0.40 (0.02 to 0.78) * −0.13 (−0.46 to 0.20) −0.08 (−0.38 to 0.23) −0.37 (−0.84 to 0.10)
5.426 5.735 1.281 5.516
