IJCA-18216; No of Pages 7 International Journal of Cardiology xxx (2014) xxx–xxx

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An evidence-based appraisal of global association between air pollution and risk of stroke Wan-Shui Yang ⁎,1, Xin Wang 1, Qin Deng 1, Wen-Yan Fan 1, Wei-Ye Wang 1 Department of Social Science and Public Health, School of Basic Medical Science, Jiujiang University, Jiujiang, China Jiangxi Province Key Laboratory of Systems Biomedicine, Jiujiang University, Jiujiang, China

a r t i c l e

i n f o

Article history: Received 7 February 2014 Received in revised form 11 May 2014 Accepted 12 May 2014 Available online xxxx Keywords: Air pollution Stroke Meta-analysis Case-crossover study Time series study

a b s t r a c t Background: The aim of this study was to evaluate the transient effects of air pollutants on stroke morbidity and mortality using the meta-analytic approach. Methods: Three databases were searched for case-crossover and time series studies assessing associations between daily increases in particles with diameter b 2.5 μm (PM2.5) and diameter b 10 μm (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), ozone, and risks of stroke hospitalizations and mortality. Risk estimates were combined using random-effects model. Results: A total of 34 studies were included in the meta-analysis. Stroke hospitalizations or mortality increased 1.20% (95%CI: 0.22–2.18) per 10 μg/m3 increase in PM2.5, 0.58% (95%CI: 0.31–0.86) per 10 μg/m3 increase in PM10, 1.53% (95%CI: 0.66–2.41) per 10 parts per billion (ppb) increase in SO2, 2.96% (95%CI: 0.70–5.27) per 1 ppm increase in CO, and 2.24% (95%CI: 1.16–3.33) per 10 ppb increase in NO2. These positive associations were the strongest on the same day of exposure, and appeared to be more apparent for ischemic stroke (for all 4 gaseous pollutants) and among Asian countries (for all 6 pollutants). In addition, an elevated risk (2.45% per 10 ppb; 95%CI: 0.35–4.60) of ischemic stroke associated with ozone was found, but not for hemorrhagic stroke. Conclusion: Our study indicates that air pollution may transiently increase the risk of stroke hospitalizations and stroke mortality. Although with a weak association, these findings if validated may be of both clinical and public health importance given the great global burden of stroke and air pollution. © 2014 Elsevier Ireland. Ltd

1. Introduction According to a 2004 report released by World Health Organization, stroke is a leading cause of death and disability globally, which accounts for approximately 5.5 million deaths every year representing nearly 10% of all deaths; while 44 million disability-adjusted life-years are lost annually due to stroke. Therefore, the primary prevention efforts toward stroke should be explored given the great stroke burden in terms of mortality and disability worldwide [1]. Currently, there is increasing evidence of an association between acute exposure to air pollution and elevated risk of cardiovascular disease morbidity as well as mortality [2,3]. In particular, American Heart Association (AHA) concluded that the evidence of fine particles (diameter b 2.5 μm, PM2.5) exposure as a causal risk factor for cardiovascular morbidity and mortality is sufficient in their 2010 scientific

⁎ Corresponding author at: Department of Social Science and Public Health, School of Basic Medical Science, Jiujiang University, Jiujiang 332000, China. Tel./fax: + 86 7928577050. E-mail address: [email protected] (W.-S. Yang). 1 This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

statement [4], in which the conclusion was drawn on the basis of a comprehensive review of current evidence. However, the AHA statement is specifically designed as hazard identification, and it does not quantify the magnitude of the stroke risk associated with particulate pollutants. In addition, results from observational studies assessing transient effects of other pollutants have been inconclusive [2], varying from a positive to a null association, which mainly hampered by the lack of power of any individual study [5–38]. Moreover, most previous air pollution studies only focused on stroke in general, few studies have distinguished between ischemic and hemorrhagic strokes and have yielded inconsistent results [7,12,17,23,26, 28,31,34], which also could be, at least in part, explained by the limited power for single study; however, this issue is important because there may be major differences in the underlying mechanisms that may trigger ischemic stroke compared to hemorrhagic stroke [12,15,39]. On the other hand, given the limited number of studies that evaluated the shape of concentration–response function between air pollution and stroke among different pollution settings [8,10,31,32], the open questions about the differences in ambient air pollution–stroke association between low and high pollution settings still remained. On a global scale, quantifying the evidence by different regions characterized by various pollution levels, for example, most Asian countries such as China and Korea (characterized as high pollution settings) vs. European

http://dx.doi.org/10.1016/j.ijcard.2014.05.044 0167-5273/© 2014 Elsevier Ireland. Ltd

Please cite this article as: Yang W-S, et al, An evidence-based appraisal of global association between air pollution and risk of stroke, Int J Cardiol (2014), http://dx.doi.org/10.1016/j.ijcard.2014.05.044

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W.-S. Yang et al. / International Journal of Cardiology xxx (2014) xxx–xxx

countries and USA (characterized as low pollution settings), using the meta-analytic technique, would help us to better understand this issue. We therefore conduct a systematic review and meta-analysis of case-crossover and time series studies to quantitatively assess the transient acute effects of air pollutants including PM2.5, inhalable particles (diameter b 10 μm, PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3) on stroke hospitalization and stroke mortality. Here, the “transient acute effect” means the immediate change in the risk of acute-onset stroke hospitalization or mortality due to short-term exposure to air pollution [40]. We also conducted a secondary analysis by stroke subtypes (ischemic vs. hemorrhagic strokes), geographic locations (Asian countries vs. European countries and USA), and other characteristics of interests. 2. Methods 2.1. Search strategy We searched Medline (PubMed), Embase, and Web of Science from their inception to October 2013 and systematically identified case-crossover and time series studies that evaluated the transient effect of air pollution on the risk of stroke hospitalization and mortality. No language restriction was applied. The search strategy included terms for outcome (stroke, cerebrovascular diseases, ischemic stroke, and hemorrhagic stroke), exposure (air pollution, particulate matter, sulfur dioxide, carbon monoxide, nitrogen dioxide, and ozone), and study design (case-crossover studies and time series studies). The reference lists of the retrieved original peer-review articles as well as pertinent review articles were also scanned to identify any additional relevant studies. 2.2. Study selection and data extraction A published article was included if it 1) had a case-crossover or time series design, 2) evaluated the transient acute association between gaseous (carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone) or particulate (PM2.5 or PM10) air pollutants and stroke hospitalization or mortality, and 3) presented odds ratio (OR), relative risk (RR) with its 95% confidence interval (CI) or standard error. If an article was duplicated, or derived from the same population as previously published and presented risk estimates for the same pollutants, the most recent publication was included. Using a unified data form, two investigators (W.S.Y. and W.Y. W.) independently evaluated study eligibility and conducted data extraction; discrepancies were settled by consensus or by involving a third reviewer (W.Y.F.) for adjudication. Relevant variables included in the data form were as follows: study name (together with the first author's name and year of publication), study region, study periods, study design, number of cases, outcome measurement, and adjustments. If any of the above-mentioned data was not available in the articles, the first or corresponding authors were contacted by email for additional information. 2.3. Quality assessment There is, to our knowledge, no validated scale to evaluate methodological quality for studies with case-crossover or time series design, we thus adapted a quality scale from validated scales for other types of observational studies (e.g. cohort and case-control designs) and particularly selected several items from the Newcastle–Ottawa Scale (NOS) [41] and the Cochrane risk of bias tool [42], and this method was also suggested by Mustafic et al. [43]. We created a 6-point scoring system, in which a study was judged on 4 broad perspectives as follows: 1) the quality of air pollutant assessments, 2) the validation of stroke data, 3) the extent of adjustment for potential confounders, and 4) the generalizability of the findings. For the quality of air pollutant assessments [43], studies received 1 point if measurements were performed at least daily with b25% missing data; whereas those with ≥25% missing data and/or with measurement frequency b1 time per day received no point. For the validation of stroke data, studies were assigned one point if the outcome of interest was coded based on the International Classification of Diseases or according to medical records, while no point was given for the absence of the above 2 criteria. For the extent of adjustment for potential confounders [43], studies received no point if no adjustment has been made for long term trends, seasonality, or temperature; studies can be given one point if the above 3 adjustments had been done; those that also adjusted for humidity and/or day of week received an additional 1 point; those that adjusted for holidays and/or influenza epidemics together with the adjustments corresponding with a score of 2, can be assigned a full score of 3. For the generalizability of the findings [41,42], we considered the results to be applicable and assigned one point if the stroke cases in the study should be all eligible stroke cases over a defined period of time, and in a defined catchment area or in a defined hospital or clinic, group of hospitals, health maintenance organization, or an appropriate sample of those cases (e.g. randomly selected). No point was given if not satisfying the above requirements in part, or not stated. Studies were judged to be of good quality if they obtained the full score for all the 4 components; studies were considered to be of low quality if any component from the

above 4 components received zero point; all other studies were deemed to be of intermediate quality [43]. 2.4. Data synthesis The RRs were used as the common measure of association across studies, and the ORs from case-crossover studies were considered equivalent to RRs in time series studies [44, 45]. Time series analysis is the most commonly used technique to assess what fraction of the daily variations in counts of hospital admissions/deaths due to the daily variations in air pollution of the preceding days through relative risk regression analysis accounting for variables that varied in time such as meteorological parameters, but are less effective control for secular trends such as seasonal effects [46]. Because the unit of observation in time series studies is the day but not the individual, usual risk factors for stroke (e.g. smoking, diabetes, or hypertension) do not vary in the short-term time window analyzed with air pollution daily variations, can thus be excluded as confounders. The case-crossover design is considered to be an alternative to time series analysis, in which cases serve as their own controls, and risk estimates are based on comparisons of exposure in a case period when the event occurred with exposure in specified control periods through matched case–control methods [40]. The case-crossover design can thus control for individual characteristics such as age, sex, socioeconomic status, smoking, and comorbidity fixed. Also, through choosing the control period within a few weeks of outcome, this approach decreased any potential confounding role of the long-term time trends, seasonality, and day of week. Overall, both time series and case-crossover designs can provide reasonable estimates of transient effect (i.e. an immediate change in risk) of short-term exposure to elevated concentrations of ambient air pollutants on an acuteonset disease outcome [45], although risk estimates that obtained from case-crossover approach is less precise (with wider confidence intervals) than those in time series design [45]. Because most of the included studies used generalized linear models and assumed a linear relationship between air pollution and outcome, and the current available studies with exposure-response analysis also supported a linear shape for stroke [8,10,31,32], we therefore firstly created a standardized increment in pollutant concentration as follows: 10 μg/m3 for PM2.5 and PM10, 10 parts per billion (ppb) for NO2, SO2, and O3, and 1 part per million (ppm) for CO. The reason for choosing the above values as the standardized increments to present risk estimates is that these levels were most frequently used in previous air pollution studies. Secondly, we recalculated the risk estimates for the standardized increment for each pollutant for every included study using the following formula:  RRstandardized ¼ e

ð

Þ

Ln RRorigin Incrementorigin Incrementstandardized



where RR is the relative risk, and Ln is the log to base e. In the third stage, we combined the recalculated study-specific RR using random-effects model [47]. Heterogeneity within the studies was evaluated using Cochran's Q and I2 statistics, and the null hypothesis that the studies are homogeneous was rejected if the p value for heterogeneity was less than 0.10 [48] or the I2 value was N50%. Publication bias was evaluated using Begg's rank correlation method [49]. We also performed a meta-regression analysis to investigate the sources of heterogeneity according to study level characteristics, including sex, study population, study design, stroke subtype, and adjustment for confounding factors. Subgroup analysis was conducted by study design (time series vs. case-crossover studies), geographical location (Asia vs. Europe and North America), and stroke subtype (ischemic vs. hemorrhagic stroke). Population-attributable risks (PARs) per pollutant were also estimated using our overall risk estimates and the following equation: PAR% = 100 × Pe(RR − 1) / (Pe[RR − 1] + 1), for which Pe is the prevalence of the exposure (air pollution) in the population and is assumed to be 100%. We combined adjusted risk estimates that controlled for meteorological, temporal, and seasonal parameters for every included study. Most of the included studies provided multiple estimates for single lags (e.g. lag 0, lag 1, and lag 2). In this case, the shortest lag was used in our overall analysis. We also combined the risk estimates according to different lags including lag 0, lag 1, and lag 2 for each pollutant. Some studies separated risk estimates according to season (cold vs. warm season) [20,21] or temperature [28], study region [14], and stroke subtype [17,20,22,23,26], and did not report overall risk estimates. In this case, the stratified estimates were included in our analysis. Several studies [8,9,16, 27–30,35] only provided cumulative lags such as lag 0-1, lag 0-2, and lag 0-3. In this case, we only included these in the overall analysis, but not for the single lag analysis. All data analyses were carried out using R 2.15.3 (meta 3.1-2) (R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria).

3. Results 3.1. Literature search and study characteristics A flow diagram of the literature search strategy employed in the present study is shown in Fig. 1. A total of 34 studies that consisted of 20 time series [8,9,11,15,18,19,21,24,25,27,29–38] and 14 casecrossover [5–7,10,12–14,16,17,20,22,23,26,28] studies were included in the final analysis.

Please cite this article as: Yang W-S, et al, An evidence-based appraisal of global association between air pollution and risk of stroke, Int J Cardiol (2014), http://dx.doi.org/10.1016/j.ijcard.2014.05.044

W.-S. Yang et al. / International Journal of Cardiology xxx (2014) xxx–xxx

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Fig. 1. Flow chart of references selection in the meta-analysis.

The characteristics of 34 included articles are given in Table 1. All the studies were published between 1996 and 2013 and were conducted in Asia [China [6–8,11,25,27,28,35], Japan [13,15,20], and Korea [31,32]], Europe [9,14,17,21,22,29,30,33,37,38], and North America [USA [5,10, 19,24,26,34,36] and Canada [12,16,18,23]]. Of all selected studies, 24 studies used stroke hospital admissions (HA) as an outcome [5,6,9,10, 12–14,16–19,22–26,28–30,33–35,37,38], while 9 studies reported stroke mortality [7,8,11,15,20,21,27,31,32], and one for both stroke mortality and stroke hospitalizations [36]. Although the study populations overlapped in the two articles by Hong et al. [31,32], these two studies were included in the meta-analysis because they reported risk estimates by different air pollutants. The median daily concentrations of pollutants reported in some included studies are given in Table 2, and we did not present all individuals because the relevant data were not available among few studies. Most of the included studies (27 of all 34 studies) were judged to be of good or intermediate quality according to the 6-point scoring system (Table 1). 3.2. Overall analysis and subgroup analysis There was a positive association between stroke hospitalization or stroke mortality, and all gaseous and particulate air pollutants except ozone (Tables 3 & 4). Specifically, stroke hospitalization or mortality increased 1.20% (95%CI: 0.22–2.18) per 10 μg/m3 increase in PM2.5, 0.58% (95%CI: 0.31–0.86) per 10 μg/m3 increase in PM10, 1.53% (95%CI: 0.66– 2.41) per 10 parts per billion (ppb) increase in SO2, 2.96% (95%CI: 0.70–5.27) per 1 ppm increase in CO, and 2.24% (95%CI: 1.16–3.33) per 10 ppb increase in NO2. The strongest associations were found on the same day of exposure (lag 0), with this effect diminishing at longer lag days. Significant between-study heterogeneity was detected for all pollutants except PM2.5. We found that the air pollution–stroke associations significantly differed by study population and stroke subtype (for all pollutants except PM2.5); whereas the meta-regression analyses did

not provide any evidence of a substantial effect of differences by sex, adjustment for confounding factors, or by study design (data not shown). Begg's rank correlation test provided no evidence of substantial publication bias in our meta-analysis. In the subgroup analysis, the positive associations were more apparent and remained significant for ischemic stroke for all 4 gaseous pollutants (Table 4). Although there is a null association of ozone with the risk of overall stroke, the summary RR of ischemic stroke for an increase in 10 ppb of ozone was 1.0245 (95% CI: 1.0035–1.0460). When stratified by geographical locations, the positive associations were more evident among Asian countries compared to those among Europe and North America for all 6 pollutants. The positive relations between air pollution and stroke remained but with narrower confidence intervals in the time series study than those in the case-crossover study. Assuming linear concentration–response relationships without a threshold between air pollution and risk for stroke, we estimated that a total of 8.34%, 15.35%, and 1.54% of acute stroke hospital admissions or stroke mortality could be attributable to major air pollutant (PM2.5, PM10, SO2, CO, and NO2) exposure in the World, Asia, and Europe and North America, respectively (Tables 3 & 4). 4. Discussion Overall, relying on current evidence, we found an increased risk of stroke hospitalizations and mortality with a transient increase in major ambient air pollutants (PM2.5, PM10, SO2, CO, and NO2). We also found a positive association between exposure to ozone and ischemic stroke. Interestingly, these positive associations were more apparent for ischemic stroke (for all 4 gaseous pollutants) compared to hemorrhagic stroke, and were more evident among Asian countries than those in Europe and North America (for all pollutants). Assuming linear concentration–response relationships without a threshold between air pollution and risk for stroke, daily exposure to ambient air pollution

Please cite this article as: Yang W-S, et al, An evidence-based appraisal of global association between air pollution and risk of stroke, Int J Cardiol (2014), http://dx.doi.org/10.1016/j.ijcard.2014.05.044

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W.-S. Yang et al. / International Journal of Cardiology xxx (2014) xxx–xxx

Table 1 Contextual details of studies included in the meta-analysis. Study

Region

Design Period

Population Outcome

Potential confounders includeda

Adjustments Generalizability Study Outcome Exposure quality measurements measurements (0–3 points) (0–1 point) (0–1 point) (0–1 point)

Xu et al. (2013) Xiang et al. (2013) Qian et al. (2013) Chen et al. (2013) Carlsen et al. (2013) Wellenius et al. (2012) Wang et al. (2012) Villeneuve et al. (2012) Turin et al. (2012) Bedada et al. (2012) Yorifuji et al. (2011) O'Donnell et al. (2011) Andersen et al. (2010) Szyszkowicz et al. (2008) Lisabeth et al. (2008) Kettunen et al. (2007) Henrotin et al. (2007) Yamazaki et al. (2007) Villeneuve et al. (2006) Low et al. (2006) Chan (2006) Wellenius et al. (2005) Tsai et al. (2003) Kan et al. (2004) Sunyer et al. (2003) Hong et al. (2002) Hong et al. (2002) Le Tertre et al. (2002) Ballester et al. (2001) Linn et al. (2000) Wong et al. (1999) Wordley et al. (1997) Poloniecki et al. (1997) Ponka et al. (1996)

USA China China China Iceland USA China Canada Japan UK Japan Canada Denmark Canada USA Finland France Japan Canada USA Taiwan USA Taiwan China Europe Korea Korea Europe Spain USA China USA England Finland

CC CC CC TS TS CC TS CC CC CC TS CC CC TS TS TS CC CC CC TS TS CC CC TS TS TS TS TS TS TS TS TS TS TS

≥65 y All ≥65 y All All All All ≥20 y All 27–93y All All All All All ≥65 y All ≥65 y ≥65 y All ≥50 y ≥65 y All All All All All All All All All All All All

A,B,C,D A,B,C,D A,B,C,D A,B,C,D A,B,C,D,E,F A,B,C,O C,D,E,O A,B,C,D A,B,C,D,O A,B,C,D A,B,C,D,E,F,G,O C A,B,C,D,O A,B,C,D A,B,C A,B,C,D,E,F A,B,C,D,F,G A,B,C,D A,B,C,D C,D,F,G A,B,C,E A,B,C,O A,B,C,D A,B,C,E,O A,B,C,D,F,G A,B,C,D,E,O A,B,C,D,E,O A,B,C,D,E,F,G NAb A,B,C A,B,C,D,E,F A,B,C,D A,B,C,D,F,G A,C,D

1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1994–2000 2006–2008 2003–2008 1996–2008 2003–2009 1999–2008 2001–2009 2003–2009 1988–2004 2003–2007 2003–2008 2003–2008 2003–2006 1992–2002 2001–2005 1998–2004 1994–2004 1990–1994 1992–2002 1995–2003 1997–2002 NAb 1997–2000 2001–2002 1990–1996 1991–1997 1995–1998 1989–1996 1994–1996 1992–1995 1994–1995 1992–1994 1987–1994 1987–1989

HA HA M M HA HA M HA HA HA M HA HA HA HA M HA M HA HA HA HA HA M HA M M HA HA HA HA HA and M HA HA

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 2 2 2 3 1 0 2 2 2 3 0 2 2 1 3 3 2 2 0 2 1 2 2 3 2 2 3 0 1 3 2 3 0

1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Intermediate Intermediate Intermediate Intermediate Good Intermediate Low Intermediate Low Low Good Low Intermediate Intermediate Intermediate Good Good Intermediate Intermediate Low Intermediate Intermediate Intermediate Intermediate Good Intermediate Intermediate Good Low Intermediate Good Intermediate Good Low

Abbreviations: CC = case-crossover study; HA = hospital admission; M = mortality; TS = time series study. a A = long term trends; B = seasonality; C = temperature; D = humidity; E = day of week; F = holidays; G = influenza epidemics; O = others. b The information is not available in the original article, and we attempted to contact the authors but without any response.

may contribute to approximately 8% of acute stroke hospitalizations or stroke mortality worldwide. Given the great global burden of stroke and air pollution, our findings if validated may be of both clinical and public health importance given the great global burden of stroke and air pollution. The mechanism by which air pollution leads to stroke is poorly understood [4]. Hypothesized mechanisms for air pollution-related stroke include systemic inflammation, thrombosis, and vascular endothelial dysfunction [50,51]. Air pollutants could induce an acute systemic inflammatory response with an increased number of circulating fibrinogen, C-reactive protein and white blood cell [50,51], which could be a trigger for inflammation and increase blood coagulation [52]. In addition, air pollution, may harmfully influence measures of cardiovascular physiology including blood pressure and heart rate, which could be via sympathoexcitation and/or impairing vasomotor function [50,51,53]. Considering the potential etiological heterogeneity in stroke [39], we differentiated the subtypes (ischemic and hemorrhagic) and found that the adverse effects of air pollution were more evident for ischemic stroke for all 4 gaseous pollutants. These differences, if true, may reflect the heterogeneous etiology between ischemic and hemorrhagic stroke and need immediate hospital attention; the underlying mechanisms warrant further elucidation in future studies. However, because of a smaller number of cases of hemorrhagic stroke and a consequence of a wider confidence interval for risk estimate as compared with those for ischemic stroke in our analysis, the discrepancies would have been accidental and the play of chance or the lack of power could not be ruled out, which need replication. The results according to different study design showed that time series study had similar risk estimates

but with narrower confidence intervals compared with the casecrossover study, which was in line with a previous methodological study [45]. We noted that the increased risk of stroke in Asian countries were almost 2–9 times as high as those in Europe and North America, where ambient air concentrations are generally higher (see Table 2). The reasons for such regional differences in the association remain unclear. We hypothesize that the differences may reflect the potential effect modifications by disproportionate risk factors for stroke such as comparatively lower income and socioeconomic status, less education [54] and access to care that are borne by low to middle income countries, because most of the included studies conducted in Asia (n = 13) are from China (n = 7) and Korea (n = 3). Besides, these regional differences were more apparent when we excluded data from Japan (data not shown). Given the increasing trend of stroke incidence and mortality [1,55] and the higher air pollutant levels in those regions (Table 2), key targets for control stroke among less developed countries such as China are urgently introduced. These observed geographic differences may also reflect that there could be a difference in the shape of concentration–response function for stroke between low and high pollution settings. To our knowledge, only 2 studies that were conducted in countries with high air pollution concentrations including China [8] and Korea [31], have evaluated the exposure–response relationships between air pollution and risk for stroke, and have demonstrated a liner shape without a threshold. Given the limited evidence, more epidemiologic studies at both low and high pollution settings are needed to confirm or refute our findings. Strengths of present study include larger sample size with increased statistical power compared to each individual study, and a high

Please cite this article as: Yang W-S, et al, An evidence-based appraisal of global association between air pollution and risk of stroke, Int J Cardiol (2014), http://dx.doi.org/10.1016/j.ijcard.2014.05.044

W.-S. Yang et al. / International Journal of Cardiology xxx (2014) xxx–xxx Table 2 Median daily concentrations of particulate and gaseous air pollutants by geographical locations.a Ambient air pollutants PM2.5 (μg/m3) Asian countries Europe North America PM10 (μg/m3) Asian countries Europe North America SO2 (ppb) Asian countries Europe North America CO (ppm) Asian countries Europe North America NO2 (ppb) Asian countries Europe b North America Ozone (ppb) Asian countries Europe North America

Median

Median range

Q1

Table 3 Summary risk estimates, heterogeneity, population-attributable risk, and assessment for publication bias stratified by particulate air pollutants.a

Q3 Number of studies

26.6 8.0 7.3

21.6–36.5 – 6.9–8.7

NA 5.5 4.6

NA 11.7 10.6

71.6 20.6 23.1

45.0–110.0 23.6–32.1 28.0–19.4

49.7 14.1 16.3

106.9 26.2 34.1

11.4 5.2 4.2

3.9–18.3 0.8–1.0 1.5–9.0

8.4 0.9 2.6

18.6 2.3 7.6

1.0 0.5 0.7

0.8–1.2 0.2–0.9 0.3–1.0

0.8 0.3 0.5

1.3 0.5 1.1

28.1 21.5 19.3

16.0–34.0 11.7–35.0 12.4–23.5

20.6 8.9 13.8

35.2 17.0 25.8

22.0 24.8 21.8

20.1–23.8 11.2–64.8 3.0–35.7

13.3 23.3 16.9

32.0 39.2 30.7

Abbreviations: CO = carbon monoxide; NO2 = nitrogen dioxide; PM2.5 = particles with size b2.5 μm; PM10 = particles with size b10 μm; Q1 = first quartile value; Q3 = third quartile value; SO2 = sulfur dioxide. a Median pollutant concentration together with Q1 and Q3 derived from the average daily pollutant concentrations reported per study. Range of the median pollutant concentrations across the studies from minimum to maximum. b As some studies only reported the median concentration but not the values of the first and third quartiles, the interquartile range may not include the median value.

proportion of studies with moderate-to-high quality included in the meta-analysis. There are, however, several possible sources of bias in our systematic review and meta-analysis. First, the use of regional monitoring sites to determine the personal air pollution levels may lead to misclassification bias of exposure. Second, due to the high correlation among pollution components, it is difficult to separate the independent effect for each pollutant, which may confound the observed associations. In outdoor air, for example, NO2 is often highly correlated with other combustion products notably PM2.5. Thus, in most cases, NO2 may serve as a surrogate for all traffic-related combustion products [56]. However, the results for ozone could be less influenced given the weak correlation between ozone and other pollutant concentrations in ambient air [56]. Although particulate pollutants are considered to be responsible for a large number of adverse cardiovascular outcomes [4] and have received by far the most attention, we also noted a greater strength of associations for all gaseous pollutants. Third, without consideration the relevance of cumulative effects may limit the ability for causal inference. Because of a few studies focusing on long-term exposure to air pollution in relation to stroke, we were unable to evaluate the long-term effects for each pollutant, which may underestimate the current associations. Fourth, our findings should be interpreted with cautions given an important limitation that substantial heterogeneity was detected within all selected studies except those for PM2.5. The meta-regression analysis indicated that the differences in study populations and stroke subtypes could partly explain such high heterogeneity. Fifth, although publication bias has not been detected in our analysis, publication bias cannot be ruled out given a low power among current standard detection methods for publication bias [57]. Additionally, as outcomes in most of the included studies were from the vital statistics department, the time of stroke symptom onset was not available for most stroke cases, and thereby the misclassifications of time of event onset due to the assignment of the exposure to air pollution based on

5

Percent increase in risk (95% CI) per 10 μg/m3 increase for each particulate air pollutantb Overall analysis Subgroup analysis Lag 0 to 2 Lag 0 Lag 1 Lag 2 Stroke type Ischemic stroke Hemorrhagic stroke Outcome Hospital admissions Mortality Study region Europe and North America Asia Study design Case–crossover study Time series study I2 (p for heterogeneity) Publication bias (p value) PAR% (95% CI)c World Europe and North America Asia

PM2.5

PM10

8

21

1.20 (0.22–2.18)⁎

0.58 (0.31–0.86)⁎

1.27 (0.28–2.27)⁎ 0.13 (−0.82–1.08) −0.17 (−1.68–1.37)

0.62 (0.54–0.70)⁎ 0.45 (0.24–0.66)⁎ 0.27 (0.01–0.54)⁎

1.04 (−0.25–2.34) 1.22 (−0.55–3.02)

0.72 (−0.06–1.50) 0.68 (−0.91–2.29)

0.50 (−0.19–2.93) 1.34 (0.27–2.42)⁎

0.71 (0.10–1.33)⁎ 0.65 (0.54–0.77)⁎

1.62 (−0.73–4.03) 1.11 (0.04–2.19)⁎

0.20 (−0.17–0.57) 0.66 (0.37–0.96)⁎

1.26 (0.23–2.30)⁎ 1.23 (0.20–2.27)⁎ 38.2 (0.09) 0.59

0.66 (0.32–1.01)⁎ 0.46 (0.27–0.66)⁎ 76.2 (b0.01) 0.67

1.19 (0.22–2.14)⁎ 1.59 (−0.74–3.87) 1.10 (0.04–2.14)⁎

0.58 (0.31–0.70)⁎ 0.20 (−0.17–0.57) 0.66 (0.37–0.95)⁎

Abbreviations: PM2.5 = particles with size b2.5 μm; PM10 = particles with size b10 μm. a The * symbol indicates p b 0.05. b Percent increase in risk = (1 − RR) × 100%. c PAR% = 100 × Pe(RR − 1) / (Pe[RR − 1] + 1), where Pe is the prevalence of the exposure in the population and is assumed to be 100%.

hospital admission date instead of time of symptom onset may bias the results toward the null [58]. Finally, given the aforementioned uncertainty for the shape of the concentration–response functions and the observed geographic differences, the estimated PARs, which were calculated on the basis of the assumption that the air pollutant levels is linearly associated with the risk of stroke, may thereby be problematic and should be interpreted with cautions if the shape of the concentration–response function is nonlinear, although most included studies assumed linear relationships, and few available studies with exposure–response analysis also demonstrated a linear shape for stroke without a threshold [8,31]. The variability of pollutant concentrations, however, does not influence the calculations because PARs were calculated for specific increments if the linear assumption is true. In conclusion, air pollution has a close temporal association with stroke hospitalizations and mortality. Future air pollution studies are warranted to clarify the life-time course of cumulative effects, the vulnerable populations, the shape of concentration–response function, and the responsible pollution constituents as well as their potential synergisms for stroke. Although the causality and physiological relevance remain for further elucidation, air pollution has now become a global public health issue with major cardiovascular consequences, especially in the Asian countries such as China, which calls for urgent cooperative actions of this disease at many levels from local to national to global.

Funding sources This work was supported by Jiangxi Provincial Health Department of China (grant number 20083168). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Please cite this article as: Yang W-S, et al, An evidence-based appraisal of global association between air pollution and risk of stroke, Int J Cardiol (2014), http://dx.doi.org/10.1016/j.ijcard.2014.05.044

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W.-S. Yang et al. / International Journal of Cardiology xxx (2014) xxx–xxx

Table 4 Summary risk estimates, heterogeneity, population-attributable risk, and assessment for publication bias stratified by gaseous air pollutants.a

Number of studies Percent increase in risk (95% CI) per 10 ppb increase for SO2, NO2, and O3, and per 1 ppm increase for COb Overall analysis Subgroup analysis Lag 0 to 2 Lag 0 Lag 1 Lag 2 Stroke type Ischemic stroke Hemorrhagic stroke Outcome Hospital admissions Mortality Study region Europe and North America Asia Study design Case-crossover study Time series study I2 (p for heterogeneity) Publication bias (p value) PAR% (95% CI)c World Europe and North America Asia

SO2

CO

NO2

O3

22

16

24

20

1.53 (0.66–2.41)⁎

2.96 (0.70–5.27)⁎

2.24 (1.16–3.33)⁎

0.48 (–0.04–1.01)

2.06 (0.98–3.16)⁎ 1.56 (0.22–2.91)⁎ 0.85 (−0.56–2.27)

1.94 (0.54–3.37)⁎ 0.55 (−1.64–2.80) 1.14 (−2.87–5.32)

1.52 (0.67–1.38)⁎ 0.79 (−0.029–1.89) 0.69 (−0.77–2.16)

0.16 (−0.25–0.57) 0.47 (−0.43–1.39) −0.01 (−0.17–0.14)

1.83 (0.53–3.15)⁎ 1.27 (−0.57–3.15)

5.36 (3.42–7.34)⁎ 4.00 (−7.65–17.12)

3.30 (1.30–5.34)⁎ 1.16 (−0.81–3.16)

2.45 (0.35–4.60)⁎ 1.77 (−2.65–6.38)

0.60 (−0.24–1.45) 2.45 (1.83–3.07)⁎

2.35 (−0.18–4.93) 7.78 (4.49–11.60)⁎

3.58 (1.61–5.60)⁎ 1.50 (0.37–2.63)⁎

0.57 (−0.36–1.51) 1.35 (−0.49–3.22)

0.75 (−0.15–1.65) 2.13 (1.20–3.17)⁎

0.73 (−0.60–2.08) 6.57 (3.96–9.24)⁎

1.56 (0.24–2.89)⁎ 3.58 (1.61–5.60)⁎

0.19 (−0.70–1.09) 1.61 (0.21–3.03)⁎

2.08 (1.23–2.94)⁎ 1.32 (0.31–2.34)⁎ 74.0 (b0.01) 0.07

5.01 (0.03–10.23)⁎ 2.14 (−0.08–4.41) 76.0 (b0.01) 0.69

3.02 (1.14–4.43)⁎ 1.88 (0.47–3.30)⁎ 82.6 (b0.01) 0.43

1.09 (−1.02–3.24) 0.61 (−0.24–2.47) 66.2 (b0.01) 0.55

1.51 (0.97–2.35)⁎ 0.74 (−0.15–1.62) 2.39 (1.19–3.07)⁎

2.87 (0.70–5.01)⁎ 0.72 (−0.60–2.04) 6.16 (3.81–8.46)⁎

2.19 (1.15–3.22)⁎ 1.54 (0.24–2.81)⁎ 3.46 (1.58–5.30)⁎

0.48 (−0.04–0.57) 0.19 (−0.70–1.08) 1.58 (0.21–2.94)⁎

Abbreviations: CO = carbon monoxide; NO2 = nitrogen dioxide; O3 = ozone; SO2 = sulfur dioxide. a The * symbol indicates p b 0.05. b Percent increase in risk = (1 − RR) × 100%. c PAR% = 100 × Pe(RR − 1) / (Pe[RR – 1] + 1), where Pe is the prevalence of the exposure in the population and is assumed to be 100%.

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