International Journal of Cardiology 192 (2015) 56–60
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Letter to the Editor
Short-term effects of air pollution on out-of-hospital cardiac arrest in Shenzhen, China Xiayun Dai a,b,1, Xiaosheng He a,1, Zhiming Zhou c, Jingquan Chen d, Sheng Wei a, Renjie Chen e, Binyao Yang a, Wei Feng a, Aijun Shan f, Tangchun Wu a, Huan Guo a,⁎ a Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China b Wuhan Prevention and Treatment Center for Occupational Diseases, Wuhan, China c Baoan Center for Disease Control and Prevention of Shenzhen, Shenzhen, China d Shenzhen Center for Disease Control and Prevention, Shenzhen, China e Research Institute for the Changing Global Environment and Fudan Tyndall Centre, Fudan University, Shanghai, China f Shenzhen People's Hospital, Shenzhen, China
a r t i c l e
i n f o
Article history: Received 1 May 2015 Accepted 6 May 2015 Available online 8 May 2015 Keywords: Cardiac arrest Air pollution Time-series analysis
Out-of-hospital cardiac arrest (OHCA) is a major public health problem due to its high prevalence and fatality rate worldwide. The investigation of risk factors is imperative and may provide new insight on early prevention of OHCAs. Multiple studies investigating the associations between air pollutions and OHCAs were conducted in developed countries with low air pollution status. The results were inconsistent [1–3]. Although the effects of air pollutions on human health in China have recently attracted more and more concern, especially owning to the increasing number of days with very high levels of air pollutants, the data about the associations between air pollutants and OHCAs are quite limited. Thus, we conducted a time-series analysis to evaluate the acute effects of PM10 (of aerodynamic diameter ≤ 10 μm), O3, nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) on OHCAs of primary cardiac etiology during 2011–2013 in Shenzhen City. A total of 1975 OHCA cases were collected. Daily data of air pollutants and meteorology were from the database of Shenzhen Environmental Monitoring Center and the Meteorological Bureau of Shenzhen Municipality, respectively. The over-dispersed generalized linear Poisson models (quasi-likelihood) were used to analyze the effects of air pollutants on
⁎ Corresponding author. E-mail address: [email protected] (H. Guo). 1 Contribute equally.
http://dx.doi.org/10.1016/j.ijcard.2015.05.016 0167-5273/© 2015 Elsevier Ireland Ltd. All rights reserved.
OHCAs. All analyses were conducted in R 3.0.1. The detailed description for data collection and statistical analysis was shown in the Supplementary materials. The mean daily concentrations of PM10, SO2, NO2, CO and O3 were 57.85, 10.79, 47.53, 1.02 × 103 and 55.75 μg/m3, respectively. Lower levels of all pollutants (except O3) were in hot seasons than in warm seasons (Supplementary Table 1). These pollutants were moderately positively correlated (all P b 0.05, Supplementary Table 2). An IQR increase in NO2 increased OHCA risks by 7.44% (95% confidence interval (CI), 0.08%–14.81%), 8.01% (0.61%–15.42%), and 8.17% (0.26%–16.08%) at lag 0, lag 1, and lag 0–1 day, respectively (Table 1). And an IQR increase of SO2 was associated with an increase of 7.25% (0.34%–14.16%) and 7.87% (0.36%–15.37%) in OHCAs at lag 2 and lag 1–2 day, respectively. However, these associations only kept significant in hot seasons but not in warm seasons. Similar associations were found between PM10, O3 and OHCAs in hot seasons (Table 1). As the effects of pollutants on OHCAs focused on lag 0–lag 2 days, we used two-pollutant models to investigate the associations between pollutants and OHCAs during this period. And the interactions of pollutants were also evaluated. After adjustment for co-pollutants (PM10, SO2, CO or O3), the effects of NO2 on OHCAs remained statistically significant at lag 0 day. Similar associations with OHCAs were observed for lag 2 of SO2 in all years, lag 0 of O3, lag 1 and lag 2 of PM10, SO2 and NO2 in hot seasons, with adjusting for these co-pollutants (Table 2). The associations between effects of NO2 on OHCAs on lag 1 day was modified by O3 (Pinteraction = 0.045, Fig. 1b). Significant association between NO2 and OHCAs was only observed in the highest O3 tertile subgroups (T3), but not in the lowest and median tertile subgroups (T1 and T2, Supplementary Table 3). NO2 and SO2 are crucial constituents of traffic-related pollutants. The increased NO2 significantly raised the therapeutic intervention for lifethreatening arrhythmia [4]. Arrhythmia is the common cause for sudden cardiac deaths. However, among eight studies investigating the impact of NO2 on OHCAs [2,3,5–10], only one identified positive associations [2]. In our study, increased NO2 significantly raised the OHCA risk at lag 0 and lag 1 but not at lag 2–lag 6 days, indicating a rapid effect of short-term NO2 exposure on precipitating OHCAs.
Table 1 Percent change (%) and 95% CI in risk of daily OHCA mortality associated with an IQR increase of pollutant concentrations in lag days. Seasons
PM10
SO2
NO2
CO
O3
L0
All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm
2.00 (−7.13, 11.12) 4.39 (−9.56, 18.33) 5.25 (−8.11, 18.6) 6.31 (−2.83, 15.45) 18.14 (4.52, 31.76) −0.87 (−14.29, 12.55) 2.56 (−6.63, 11.75) 16.20 (2.61, 29.78) −7.28 (−20.93, 6.36) −0.21 (−9.5, 9.07) 9.59 (−4.26, 23.44) −6.98 (−20.65, 6.69) −3.4 (−12.74, 5.95) 4.25 (−9.75, 18.25) −10.76 (−24.76, 3.25) −5.78 (−15.22, 3.66) −0.39 (−14.53, 13.76) −9.6 (−23.66, 4.47) −6.06 (−15.48, 3.36) 1.38 (−12.76, 15.52) −10.35 (−24.63, 3.92) 3.95 (−6.66, 14.56) 14.81 (−1.35, 30.97) −1.35 (−16.84, 14.14) 4.82 (−5.79, 15.43) 22.11 (6.23, 37.99) −5.75 (−21.54, 10.03) 1.36 (−9.36, 12.08) 18.11 (2.06, 34.16) −8.62 (−24.44, 7.19)
2.07 (−4.59, 8.74) 2.76 (−6.82, 12.34) 1.61 (−8.44, 11.65) 6.05 (−0.54, 12.64) 10.65 (1.45, 19.85) 0.83 (−9.35, 11) 7.24 (0.76, 13.73) 9.44 (0.24, 18.64) 3.60 (−6.39, 13.59) 1.31 (−5.5, 8.11) 5.82 (−3.65, 15.29) −7.07 (−17.68, 3.54) −0.34 (−7.2, 6.51) 4.72 (−4.87, 14.3) −10.36 (−21.25,0.53) −1.83 (−8.96, 5.31) −2.06 (−12.06, 7.95) −4.78 (−15.97, 6.42) −1.63 (−8.73, 5.48) −7.28 (−17.61, 3.04) 3.22 (−7.77, 14.2) 4.84 (−2.81, 12.48) 8.89 (−2.03, 19.81) 0.51 (−11.28, 12.29) 7.87 (0.36, 15.37) 12.54 (1.88, 23.2) 1.76 (−9.94, 13.46) 5.71 (−1.94, 13.36) 10.75 (−0.02, 21.53) −1.63 (−13.56, 10.3)
7.44 (0.56, 14.33) 7.65 (−2.74, 18.04) 5.96 (−3.99, 15.91) 8.01 (1.07, 14.95) 12.79 (2.59, 22.99) 1.24 (−8.81, 11.3) 1.49 (−5.62, 8.60) 7.35 (−2.99, 17.69) −7.86 (−18.46, 2.73) −3.53 (−10.73, 3.67) 2.39 (−8.14, 12.91) −10.89 (−21.52,−0.26) −0.49 (−7.63, 6.66) 2.75 (−7.82, 13.32) −4.92 (−15.72, 5.87) 1.06 (−6.07, 8.18) 2.62 (−7.92, 13.16) 0.75 (−9.88, 11.38) −0.93 (−8.06, 6.21) 0.51 (−10.16, 11.18) −2 (−12.61, 8.62) 8.17 (0.26, 16.08) 13.56 (1.51, 25.61) 1.15 (−10.14, 12.44) 3.75 (−4.29, 11.79) 11.64 (−0.40, 23.68) −6.55 (−18.44, 5.35) −2.73 (−10.91, 5.44) 7.27 (−4.90, 19.44) −13.19 (−25.3,−1.08)
0.15 (−10.62, 10.93) −4.36 (−21.55, 12.83) −1.47 (−18.80, 15.87) 3.50 (−7.36, 14.36) −2.08 (−19.38, 15.22) 1.17 (−16.17, 18.52) −1.03 (−11.85, 9.80) −8.07 (−25.34, 9.21) −6.40 (−23.66, 10.87) 0.16 (−10.71, 11.03) −11.41 (−28.74, 5.93) −1.63 (−18.93, 15.68) 4.06 (−6.87, 14.99) −4.49 (−21.84, 12.86) 0.12 (−17.16, 17.4) 4.00 (−6.97, 14.96) −6.33 (−23.58, 10.92) 2.23 (−15.25, 19.72) 1.02 (−9.95, 11.98) −4.73 (−21.97, 12.52) −5.32 (−22.97, 12.33) 2.3 (−9.54, 14.14) −5.31 (−24.15, 13.54) 1.01 (−18.87, 20.89) −0.82 (−12.64, 11.01) −10.93 (−29.75, 7.89) −3.49 (−23.5, 16.52) 0.65 (−11.19, 12.5) −10.30 (−29.14, 8.54) −3.23 (−23.15, 16.69)
7.49 (−2.18, 17.16) 12.85 (1.20, 24.50) 4.63 (−16.72, 25.99) 0.16 (−9.73, 10.05) 3.75 (−8.13, 15.63) −3.65 (−24.59, 17.3) −0.28 (−10.16, 9.6) −0.30 (−12.2, 11.60) 11.53 (−9.85, 32.92) −0.96 (−10.9, 8.99) 1.70 (−10.17, 13.57) −3.96 (−25.54, 17.62) −0.55 (−10.46, 9.35) 5.18 (−6.59, 16.96) −9.57 (−31.64, 12.51) −4.87 (−14.92, 5.18) −0.49 (−12.5, 11.52) −13.34 (−35.52, 8.84) 2.14 (−7.73, 12) 6.36 (−5.53, 18.26) −0.42 (−22.39, 21.55) 5.03 (−6.07, 16.13) 10.97 (−2.17, 24.11) −3.04 (−28.58, 22.51) −0.26 (−11.46, 10.93) 1.88 (−11.47, 15.24) 8.36 (−17.72, 34.44) 0.47 (−10.77, 11.72) 2.09 (−11.27, 15.45) 4.21 (−22.08, 30.49)
L1
L2
L3
L4
L5
L6
L0–1
L1–2
L2–3
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Daily lag
The bold means statistically significant (p b 0.05).
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Table 2 Excess risks of cardiac arrest mortality associated with an IQR increase on lag 0–lag 2 days of PM10, CO, O3, NO2, and SO2 under single- and two-pollutant models. Pollutants Lag 0 PM10
SO2
NO2
CO
O3
Lag 1 PM10
SO2
NO2
CO
O3
Lag 2 PM10
SO2
NO2
CO
Models
All year
Hot
Warm
– PM10 + CO PM10 + O3 PM10 + SO2 PM10 + NO2 – SO2 + CO SO2 + O3 SO2 + NO2 SO2 + PM10 – NO2 + CO NO2 + O3 NO2 + SO2 NO2 + PM10 – CO + O3 CO + SO2 CO + NO2 CO + PM10 – O3 + CO O3 + SO2 O3 + NO2 O3 + PM10
2.00 (−7.13, 11.12) 2.40 (−7.76, 12.55) −0.35 (−10.01, 9.32) 0.18 (−12.14, 12.5) −6.37 (−18.01, 5.27) 2.07 (−4.59, 8.74) 2.33 (−4.81, 9.47) 1.28 (−5.46, 8.03) −2.45 (−10.47, 5.57) 1.98 (−7.02, 10.99) 7.44 (0.56, 14.33) 9.03 (1.40, 16.65) 8.02 (1.13, 14.91) 8.80 (0.60, 16.99) 10.42 (1.64, 19.20) 0.15 (−10.62, 10.93) −0.42 (−11.19, 10.36) −1.18 (−12.7, 10.34) −5.76 (−17.61, 6.1) −1.08 (−13.05, 10.9) 7.49 (−2.18, 17.16) 7.52 (−2.19, 17.22) 7.19 (−2.61, 16.99) 8.57 (−1.12, 18.25) 7.61 (−2.65, 17.87)
4.39 (−9.56, 18.33) 8.17 (−7.95, 24.29) −5.50 (−22.2, 11.21) 3.11 (−16.97, 23.2) −4.36 (−23.15, 14.42) 2.76 (−6.82, 12.34) 4.47 (−5.99, 14.94) −0.50 (−10.66, 9.65) −5.71 (−20.31, 8.9) 1.22 (−12.61, 15.05) 7.65 (−2.74, 18.04) 11.88 (−0.09, 23.85) 6.34 (−4.07, 16.75) 12.31 (−3.45, 28.07) 9.85 (−4.22, 23.93) −4.36 (−21.55, 12.83) −7.78 (−25.13, 9.56) −7.54 (−26.27, 11.19) −13.88 (−33.51, 5.76) −9.30 (−29.09, 10.49) 12.85 (1.20, 24.50) 13.80 (1.95, 25.65) 13.04 (0.77, 25.32) 12.00 (0.26, 23.74) 15.37 (1.44, 29.29)
5.25 (−8.11, 18.60) 8.27 (−7.64, 24.17) 5.17 (−8.21, 18.55) 6.41 (−10.68, 23.51) 1.29 (−14.77, 17.35) 1.61 (−8.44, 11.65) 2.23 (−8.60, 13.05) 1.58 (−8.47, 11.63) −0.02 (−10.63, 10.59) −1.41 (−14.35, 11.53) 5.96 (−3.99, 15.91) 8.23 (−3.16, 19.62) 8.29 (−2.66, 19.23) 5.96 (−4.41, 16.34) 5.43 (−6.51, 17.37) −1.47 (−18.8, 15.87) −0.43 (−18.53, 17.67) −2.89 (−21.6, 15.81) −8.21 (−27.93, 11.51) −7.24 (−27.87, 13.39) 4.63 (−16.72, 25.99) 4.48 (−17.84, 26.8) 4.60 (−16.78, 25.97) 12.01 (−11.52, 35.55) 4.39 (−16.94, 25.71)
– PM10 + CO PM10 + O3 PM10 + SO2 PM10 + NO2 – SO2 + CO SO2 + O3 SO2 + NO2 SO2 + PM10 – NO2 + CO NO2 + O3 NO2 + SO2 NO2 + PM10 – CO + O3 CO + SO2 CO + NO2 CO + PM10 – O3 + CO O3 + SO2 O3 + NO2 O3 + PM10
6.31 (−2.83, 15.45) 6.19 (−3.97, 16.36) 6.98 (−2.68, 16.64) 1.33 (−11.04, 13.69) 0.03 (−11.49, 11.55) 6.05 (−0.54, 12.64) 6.07 (−1.02, 13.16) 6.17 (−0.50, 12.85) 2.82 (−5.04, 10.68) 5.40 (−3.53, 14.34) 8.01 (1.07, 14.95) 8.60 (0.92, 16.28) 8.11 (1.14, 15.09) 6.45 (−1.75, 14.64) 8.00 (−0.73, 16.73) 3.50 (−7.36, 14.36) 3.50 (−7.39, 14.39) −0.08 (−11.7, 11.53) −2.13 (−14.07, 9.81) 0.32 (−11.71, 12.35) 0.16 (−9.73, 10.05) −0.05 (−9.96, 9.86) −1.22 (−11.18, 8.75) 1.37 (−8.54, 11.28) −2.25 (−12.65, 8.16)
18.14 (4.52, 31.76) 25.43 (9.56, 41.3) 22.10 (6.01, 38.20) 14.24 (−5.55, 34.04) 12.13 (−6.21, 30.47) 10.65 (1.45, 19.85) 13.34 (3.25, 23.43) 10.71 (1.06, 20.36) 4.46 (−9.51, 18.44) 3.66 (−9.79, 17.11) 12.79 (2.59, 22.99) 17.90 (6.09, 29.71) 12.57 (2.30, 22.84) 9.14 (−6.24, 24.51) 6.73 (−6.95, 20.41) −2.08 (−19.38, 15.22) −3.1 (−20.64, 14.44) −11.92 (−30.68, 6.84) −16.76 (−36.44, 2.91) −17.75 (−37.55, 2.05) 3.75 (−8.13, 15.63) 4.12 (−7.95, 16.19) −0.26 (−12.61, 12.1) 2.11 (−9.80, 14.03) −6.37 (−20.31, 7.58)
−0.87 (−14.29, 12.55) −1.85 (−17.59, 13.9) −0.82 (−14.25, 12.61) −2.63 (−20.06, 14.8) −2.52 (−18.6, 13.55) 0.83 (−9.35, 11.00) 0.66 (−10.35, 11.67) 0.80 (−9.39, 11.00) 0.49 (−10.24, 11.22) 2.09 (−11.09, 15.28) 1.24 (−8.81, 11.3) 1.19 (−10.29, 12.67) 0.60 (−10.53, 11.74) 1.09 (−9.48, 11.67) 2.28 (−9.77, 14.32) 1.17 (−16.17, 18.52) 0.29 (−17.92, 18.5) 0.75 (−18.01, 19.5) 0.19 (−19.61, 19.99) 2.42 (−17.93, 22.77) −3.65 (−24.59, 17.3) −3.54 (−25.54, 18.45) 3.63 (−17.34, 24.59) −3.11 (−26.3, 20.08) −3.62 (−24.61, 17.37)
– PM10 + CO PM10 + O3 PM10 + SO2 PM10 + NO2 – SO2 + CO SO2 + O3 SO2 + NO2 SO2 + PM10 – NO2 + CO NO2 + O3 NO2 + SO2 NO2 + PM10 – CO + O3 CO + SO2 CO + NO2 CO + PM10
2.56 (−6.63, 11.75) 3.64 (−6.59, 13.86) 2.94 (−6.76, 12.65) −7.89 (−20.4, 4.63) 2.19 (−9.26, 13.64) 7.24 (0.76, 13.73) 8.65 (1.66, 15.63) 7.44 (0.87, 14.01) 8.99 (1.42, 16.56) 11.06 (2.22, 19.9) 1.49 (−5.62, 8.6) 2.16 (−5.69, 10.01) 1.48 (−5.67, 8.64) −3.63 (−11.91, 4.66) 0.48 (−8.37, 9.33) −1.03 (−11.85, 9.8) −1.01 (−11.86, 9.84) −6.21 (−17.77, 5.36) −2.41 (−14.34, 9.53) −2.89 (−14.9, 9.13)
16.20 (2.61, 29.78) 25.89 (10.11, 41.67) 22.74 (6.80, 38.68) 12.88 (−6.85, 32.61) 17.24 (−0.87, 35.36) 9.44 (0.24, 18.64) 13.44 (3.39, 23.49) 10.38 (0.79, 19.98) 10.09 (−3.82, 24.01) 3.12 (−10.29, 16.54) 7.35 (−2.99, 17.69) 12.96 (1.04, 24.89) 7.46 (−2.95, 17.88) −0.97 (−16.51, 14.57) −1.19 (−14.83, 12.45) −8.07 (−25.34, 9.21) −8.20 (−25.7, 9.03) −17.87 (−36.58, 0.84) −18.51 (−38.22, 1.19) −23.86 (−43.57,−4.15)
−7.28 (−20.93, 6.36) −6.39 (−22.41, 9.62) −7.59 (−21.28, 6.1) −18.52 (−36.71,−0.33) −2.55 (−18.61, 13.52) 3.60 (−6.39, 13.59) 6.00 (−4.81, 16.81) 3.7 (−6.28, 13.67) 6.54 (−3.69, 16.78) 12.5 (−0.62, 25.62) −7.86 (−18.46, 2.73) −7.78 (−19.83, 4.28) −6.64 (−18.29, 5.00) −10.02 (−21.07, 1.03) −6.81 (−19.35, 5.74) −6.40 (−23.66, 10.87) −4.00 (−22.08, 14.08) −10.50 (−29.31, 8.31) −0.30 (−19.96, 19.36) −2.16 (−22.42, 18.1)
X. Dai et al. / International Journal of Cardiology 192 (2015) 56–60
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Table 2 (continued) Pollutants
Models
All year
Hot
O Lag 3 2
– O3 + CO O3 + SO2 O3 + NO2 O3 + PM10
−0.28 (−10.16, 9.60) −0.22 (−10.13, 9.69) −1.93 (−11.87, 8.01) −0.05 (−10.00, 9.89) −1.29 (−11.70, 9.13)
−0.30 (−12.2, 11.60) 0.60 (−11.48, 12.67) −4.11 (−16.48, 8.25) −1.19 (−13.16, 10.78) −10.64 (−24.54, 3.25)
Warm 11.53 (−9.85, 32.92) 10.07 (−12.33, 32.48) 11.67 (−9.71, 33.06) 5.94 (−17.48, 29.36) 12.05 (−9.43, 33.52)
Note: Values were percentage increase (%) and 95% CI in risk of OHCAs. The bold means statistically significant (p b 0.05).
We also found that exposure to SO2 increased OHCA risk at lag 2 days but not at other days, suggesting the difference in time scales of action after the peak exposure. Unlike ours, a total of seven studies explored the effect of SO2 on OHCAs, but all findings were null [2,3,6–8,10]. Our positive results might partly contribute to the higher median concentrations of NO2 and SO2 in Shenzhen (43.56 and 9.48 μg/m3) than that in Houston (10.46 and 4.35 μg/m3), Melbourne (21.71 and 0.81 μg/m3), Helsinki (mean: 24.1 and 3.8 μg/m3), Perth Metropolitan (5.64 and 1.05 μg/m3), Stockholm (mean of NO2: 15.7 μg/m3), and Rome (SO2: 4.6 μg/m3), where the levels of NO2 and SO2 were too low to cause an effect. In addition, the null results in New York and Rome (15.5 °C) [5] might partly due to the lower temperature than that in Shenzhen City (22.95 °C). The potential patho-biologic mechanisms include systemic inflammation and dysregulation of autonomic nervous system. The effects of PM10 and O3 on OHCAs were widely evaluated and the inconclusive results might be partly due to the difference in PM10 constituents, high correlations among pollutants, the temperature, characteristics, sample sizes, measurement methods, study designs, and so on. In our study, the positive associations of PM10 and O3 with OHCAs were only found in hot seasons but not in warm seasons, consistent with the previous study [7]. Mostly importantly, we found that the levels of O3 can significantly modify the effects of NO2 on OHCAs. The potential mechanisms remained unclear. It is assumed that NO2 and O3 might act through similar biologic pathways and increase OHCA risks. There are several limitations in this study. First, as the exact death cause of cases dead on arrival couldn't be confirmed, the exclusion of these cases may lead to selection bias. Secondly, because of the absence of stratification by age, sex, disease history, and personal risk factors owing to the lack of this information, the potential confounding factors might disturb these results. However, our study exactly fills the gap in the literature by providing the evidence that the impact of short-term exposure to air pollutants on OHCAs is acute and severe in China, a developing country where the concentration of air pollutants exceeds those in developed countries. The difference among our results from the reported studies suggested that there was region-specific health effect of air pollutants. In summary, the present study identified the adverse effects of NO2, SO2, PM10 and O3 on OHCAs, especially in hot seasons, suggesting that residents living under hot weather are susceptible to OHCAs caused by air pollutants. Furthermore, O3 can significantly modified the effects of NO2 on OHCAs since individuals co-exposed to high levels of O3 and NO2 render them a higher OHCA risk. Substantial public health attention should be paid to air pollutants and their joint effects on OHCAs in China.
Conflict of interest None. Fig. 1. Excess risks (%) and 95% CI of cardiac arrests associated with an IQR increase in NO2 by the tertile of O3 on lag 0 (a), lag 1 (b), and lag 2 (c) days. The horizontal axis shows the tertile subgroups of O3. The cutoffs of O3 concentrations are b40.75, 40.75–64.19, and ≥64.19 μg/ m3. The vertical axis shows the increased risk of OHCAs due to an IQR increase in NO2 concentrations. Pinteraction values are obtained by introducing the tertile of O3 × NO2 into the statistical analysis model, where O3 is a categorical variable and NO2 is a continuous variable.
Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijcard.2015.05.016.
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References [1] T.H. Teng, T.A. Williams, A. Bremner, H. Tohira, P. Franklin, A. Tonkin, et al., A systematic review of air pollution and incidence of out-of-hospital cardiac arrest, J. Epidemiol. Community Health 68 (2014) 37–43. [2] J. Wichmann, F. Folke, C. Torp-Pedersen, F. Lippert, M. Ketzel, T. Ellermann, et al., Out-of-hospital cardiac arrests and outdoor air pollution exposure in Copenhagen, Denmark, PLoS One 8 (2013) e53684. [3] K.B. Ensor, L.H. Raun, D. Persse, A case-crossover analysis of out-of-hospital cardiac arrest and air pollution, Circulation 127 (2013) 1192–1199. [4] A. Peters, E. Liu, R.L. Verrier, J. Schwartz, D.R. Gold, M. Mittleman, et al., Air pollution and incidence of cardiac arrhythmia, Epidemiology 11 (2000) 11–17. [5] F. Forastiere, M. Stafoggia, S. Picciotto, T. Bellander, D. D'Ippoliti, T. Lanki, et al., A case-crossover analysis of out-of-hospital coronary deaths and air pollution in Rome, Italy, Am. J. Respir. Crit. Care Med. 172 (2005) 1549–1555. [6] M. Dennekamp, M. Akram, M.J. Abramson, A. Tonkin, M.R. Sim, M. Fridman, et al., Outdoor air pollution as a trigger for out-of-hospital cardiac arrests, Epidemiology 21 (2010) 494–500.
[7] R.A. Silverman, K. Ito, J. Freese, B.J. Kaufman, D. De Claro, J. Braun, et al., Association of ambient fine particles with out-of-hospital cardiac arrests in New York City, Am. J. Epidemiol. 172 (2010) 917–923. [8] F.S. Rosenthal, M. Kuisma, T. Lanki, T. Hussein, J. Boyd, J.I. Halonen, et al., Association of ozone and particulate air pollution with out-of-hospital cardiac arrest in Helsinki, Finland: evidence for two different etiologies, J. Expo. Sci. Environ. Epidemiol. 23 (2013) 281–288. [9] A. Raza, T. Bellander, G. Bero-Bedada, M. Dahlquist, J. Hollenberg, M. Jonsson, et al., Short-term effects of air pollution on out-of-hospital cardiac arrest in Stockholm, Eur. Heart J. 35 (2014) 861–868. [10] L. Straney, J. Finn, M. Dennekamp, A. Bremner, A. Tonkin, I. Jacobs, Evaluating the impact of air pollution on the incidence of out-of-hospital cardiac arrest in the Perth Metropolitan Region: 2000–2010, J. Epidemiol. Community Health 68 (2014) 6–12.
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Letter to the Editor
Short-term effects of air pollution on out-of-hospital cardiac arrest in Shenzhen, China Xiayun Dai a,b,1, Xiaosheng He a,1, Zhiming Zhou c, Jingquan Chen d, Sheng Wei a, Renjie Chen e, Binyao Yang a, Wei Feng a, Aijun Shan f, Tangchun Wu a, Huan Guo a,⁎ a Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China b Wuhan Prevention and Treatment Center for Occupational Diseases, Wuhan, China c Baoan Center for Disease Control and Prevention of Shenzhen, Shenzhen, China d Shenzhen Center for Disease Control and Prevention, Shenzhen, China e Research Institute for the Changing Global Environment and Fudan Tyndall Centre, Fudan University, Shanghai, China f Shenzhen People's Hospital, Shenzhen, China
a r t i c l e
i n f o
Article history: Received 1 May 2015 Accepted 6 May 2015 Available online 8 May 2015 Keywords: Cardiac arrest Air pollution Time-series analysis
Out-of-hospital cardiac arrest (OHCA) is a major public health problem due to its high prevalence and fatality rate worldwide. The investigation of risk factors is imperative and may provide new insight on early prevention of OHCAs. Multiple studies investigating the associations between air pollutions and OHCAs were conducted in developed countries with low air pollution status. The results were inconsistent [1–3]. Although the effects of air pollutions on human health in China have recently attracted more and more concern, especially owning to the increasing number of days with very high levels of air pollutants, the data about the associations between air pollutants and OHCAs are quite limited. Thus, we conducted a time-series analysis to evaluate the acute effects of PM10 (of aerodynamic diameter ≤ 10 μm), O3, nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) on OHCAs of primary cardiac etiology during 2011–2013 in Shenzhen City. A total of 1975 OHCA cases were collected. Daily data of air pollutants and meteorology were from the database of Shenzhen Environmental Monitoring Center and the Meteorological Bureau of Shenzhen Municipality, respectively. The over-dispersed generalized linear Poisson models (quasi-likelihood) were used to analyze the effects of air pollutants on
⁎ Corresponding author. E-mail address: [email protected] (H. Guo). 1 Contribute equally.
http://dx.doi.org/10.1016/j.ijcard.2015.05.016 0167-5273/© 2015 Elsevier Ireland Ltd. All rights reserved.
OHCAs. All analyses were conducted in R 3.0.1. The detailed description for data collection and statistical analysis was shown in the Supplementary materials. The mean daily concentrations of PM10, SO2, NO2, CO and O3 were 57.85, 10.79, 47.53, 1.02 × 103 and 55.75 μg/m3, respectively. Lower levels of all pollutants (except O3) were in hot seasons than in warm seasons (Supplementary Table 1). These pollutants were moderately positively correlated (all P b 0.05, Supplementary Table 2). An IQR increase in NO2 increased OHCA risks by 7.44% (95% confidence interval (CI), 0.08%–14.81%), 8.01% (0.61%–15.42%), and 8.17% (0.26%–16.08%) at lag 0, lag 1, and lag 0–1 day, respectively (Table 1). And an IQR increase of SO2 was associated with an increase of 7.25% (0.34%–14.16%) and 7.87% (0.36%–15.37%) in OHCAs at lag 2 and lag 1–2 day, respectively. However, these associations only kept significant in hot seasons but not in warm seasons. Similar associations were found between PM10, O3 and OHCAs in hot seasons (Table 1). As the effects of pollutants on OHCAs focused on lag 0–lag 2 days, we used two-pollutant models to investigate the associations between pollutants and OHCAs during this period. And the interactions of pollutants were also evaluated. After adjustment for co-pollutants (PM10, SO2, CO or O3), the effects of NO2 on OHCAs remained statistically significant at lag 0 day. Similar associations with OHCAs were observed for lag 2 of SO2 in all years, lag 0 of O3, lag 1 and lag 2 of PM10, SO2 and NO2 in hot seasons, with adjusting for these co-pollutants (Table 2). The associations between effects of NO2 on OHCAs on lag 1 day was modified by O3 (Pinteraction = 0.045, Fig. 1b). Significant association between NO2 and OHCAs was only observed in the highest O3 tertile subgroups (T3), but not in the lowest and median tertile subgroups (T1 and T2, Supplementary Table 3). NO2 and SO2 are crucial constituents of traffic-related pollutants. The increased NO2 significantly raised the therapeutic intervention for lifethreatening arrhythmia [4]. Arrhythmia is the common cause for sudden cardiac deaths. However, among eight studies investigating the impact of NO2 on OHCAs [2,3,5–10], only one identified positive associations [2]. In our study, increased NO2 significantly raised the OHCA risk at lag 0 and lag 1 but not at lag 2–lag 6 days, indicating a rapid effect of short-term NO2 exposure on precipitating OHCAs.
Table 1 Percent change (%) and 95% CI in risk of daily OHCA mortality associated with an IQR increase of pollutant concentrations in lag days. Seasons
PM10
SO2
NO2
CO
O3
L0
All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm All year Hot Warm
2.00 (−7.13, 11.12) 4.39 (−9.56, 18.33) 5.25 (−8.11, 18.6) 6.31 (−2.83, 15.45) 18.14 (4.52, 31.76) −0.87 (−14.29, 12.55) 2.56 (−6.63, 11.75) 16.20 (2.61, 29.78) −7.28 (−20.93, 6.36) −0.21 (−9.5, 9.07) 9.59 (−4.26, 23.44) −6.98 (−20.65, 6.69) −3.4 (−12.74, 5.95) 4.25 (−9.75, 18.25) −10.76 (−24.76, 3.25) −5.78 (−15.22, 3.66) −0.39 (−14.53, 13.76) −9.6 (−23.66, 4.47) −6.06 (−15.48, 3.36) 1.38 (−12.76, 15.52) −10.35 (−24.63, 3.92) 3.95 (−6.66, 14.56) 14.81 (−1.35, 30.97) −1.35 (−16.84, 14.14) 4.82 (−5.79, 15.43) 22.11 (6.23, 37.99) −5.75 (−21.54, 10.03) 1.36 (−9.36, 12.08) 18.11 (2.06, 34.16) −8.62 (−24.44, 7.19)
2.07 (−4.59, 8.74) 2.76 (−6.82, 12.34) 1.61 (−8.44, 11.65) 6.05 (−0.54, 12.64) 10.65 (1.45, 19.85) 0.83 (−9.35, 11) 7.24 (0.76, 13.73) 9.44 (0.24, 18.64) 3.60 (−6.39, 13.59) 1.31 (−5.5, 8.11) 5.82 (−3.65, 15.29) −7.07 (−17.68, 3.54) −0.34 (−7.2, 6.51) 4.72 (−4.87, 14.3) −10.36 (−21.25,0.53) −1.83 (−8.96, 5.31) −2.06 (−12.06, 7.95) −4.78 (−15.97, 6.42) −1.63 (−8.73, 5.48) −7.28 (−17.61, 3.04) 3.22 (−7.77, 14.2) 4.84 (−2.81, 12.48) 8.89 (−2.03, 19.81) 0.51 (−11.28, 12.29) 7.87 (0.36, 15.37) 12.54 (1.88, 23.2) 1.76 (−9.94, 13.46) 5.71 (−1.94, 13.36) 10.75 (−0.02, 21.53) −1.63 (−13.56, 10.3)
7.44 (0.56, 14.33) 7.65 (−2.74, 18.04) 5.96 (−3.99, 15.91) 8.01 (1.07, 14.95) 12.79 (2.59, 22.99) 1.24 (−8.81, 11.3) 1.49 (−5.62, 8.60) 7.35 (−2.99, 17.69) −7.86 (−18.46, 2.73) −3.53 (−10.73, 3.67) 2.39 (−8.14, 12.91) −10.89 (−21.52,−0.26) −0.49 (−7.63, 6.66) 2.75 (−7.82, 13.32) −4.92 (−15.72, 5.87) 1.06 (−6.07, 8.18) 2.62 (−7.92, 13.16) 0.75 (−9.88, 11.38) −0.93 (−8.06, 6.21) 0.51 (−10.16, 11.18) −2 (−12.61, 8.62) 8.17 (0.26, 16.08) 13.56 (1.51, 25.61) 1.15 (−10.14, 12.44) 3.75 (−4.29, 11.79) 11.64 (−0.40, 23.68) −6.55 (−18.44, 5.35) −2.73 (−10.91, 5.44) 7.27 (−4.90, 19.44) −13.19 (−25.3,−1.08)
0.15 (−10.62, 10.93) −4.36 (−21.55, 12.83) −1.47 (−18.80, 15.87) 3.50 (−7.36, 14.36) −2.08 (−19.38, 15.22) 1.17 (−16.17, 18.52) −1.03 (−11.85, 9.80) −8.07 (−25.34, 9.21) −6.40 (−23.66, 10.87) 0.16 (−10.71, 11.03) −11.41 (−28.74, 5.93) −1.63 (−18.93, 15.68) 4.06 (−6.87, 14.99) −4.49 (−21.84, 12.86) 0.12 (−17.16, 17.4) 4.00 (−6.97, 14.96) −6.33 (−23.58, 10.92) 2.23 (−15.25, 19.72) 1.02 (−9.95, 11.98) −4.73 (−21.97, 12.52) −5.32 (−22.97, 12.33) 2.3 (−9.54, 14.14) −5.31 (−24.15, 13.54) 1.01 (−18.87, 20.89) −0.82 (−12.64, 11.01) −10.93 (−29.75, 7.89) −3.49 (−23.5, 16.52) 0.65 (−11.19, 12.5) −10.30 (−29.14, 8.54) −3.23 (−23.15, 16.69)
7.49 (−2.18, 17.16) 12.85 (1.20, 24.50) 4.63 (−16.72, 25.99) 0.16 (−9.73, 10.05) 3.75 (−8.13, 15.63) −3.65 (−24.59, 17.3) −0.28 (−10.16, 9.6) −0.30 (−12.2, 11.60) 11.53 (−9.85, 32.92) −0.96 (−10.9, 8.99) 1.70 (−10.17, 13.57) −3.96 (−25.54, 17.62) −0.55 (−10.46, 9.35) 5.18 (−6.59, 16.96) −9.57 (−31.64, 12.51) −4.87 (−14.92, 5.18) −0.49 (−12.5, 11.52) −13.34 (−35.52, 8.84) 2.14 (−7.73, 12) 6.36 (−5.53, 18.26) −0.42 (−22.39, 21.55) 5.03 (−6.07, 16.13) 10.97 (−2.17, 24.11) −3.04 (−28.58, 22.51) −0.26 (−11.46, 10.93) 1.88 (−11.47, 15.24) 8.36 (−17.72, 34.44) 0.47 (−10.77, 11.72) 2.09 (−11.27, 15.45) 4.21 (−22.08, 30.49)
L1
L2
L3
L4
L5
L6
L0–1
L1–2
L2–3
X. Dai et al. / International Journal of Cardiology 192 (2015) 56–60
Daily lag
The bold means statistically significant (p b 0.05).
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Table 2 Excess risks of cardiac arrest mortality associated with an IQR increase on lag 0–lag 2 days of PM10, CO, O3, NO2, and SO2 under single- and two-pollutant models. Pollutants Lag 0 PM10
SO2
NO2
CO
O3
Lag 1 PM10
SO2
NO2
CO
O3
Lag 2 PM10
SO2
NO2
CO
Models
All year
Hot
Warm
– PM10 + CO PM10 + O3 PM10 + SO2 PM10 + NO2 – SO2 + CO SO2 + O3 SO2 + NO2 SO2 + PM10 – NO2 + CO NO2 + O3 NO2 + SO2 NO2 + PM10 – CO + O3 CO + SO2 CO + NO2 CO + PM10 – O3 + CO O3 + SO2 O3 + NO2 O3 + PM10
2.00 (−7.13, 11.12) 2.40 (−7.76, 12.55) −0.35 (−10.01, 9.32) 0.18 (−12.14, 12.5) −6.37 (−18.01, 5.27) 2.07 (−4.59, 8.74) 2.33 (−4.81, 9.47) 1.28 (−5.46, 8.03) −2.45 (−10.47, 5.57) 1.98 (−7.02, 10.99) 7.44 (0.56, 14.33) 9.03 (1.40, 16.65) 8.02 (1.13, 14.91) 8.80 (0.60, 16.99) 10.42 (1.64, 19.20) 0.15 (−10.62, 10.93) −0.42 (−11.19, 10.36) −1.18 (−12.7, 10.34) −5.76 (−17.61, 6.1) −1.08 (−13.05, 10.9) 7.49 (−2.18, 17.16) 7.52 (−2.19, 17.22) 7.19 (−2.61, 16.99) 8.57 (−1.12, 18.25) 7.61 (−2.65, 17.87)
4.39 (−9.56, 18.33) 8.17 (−7.95, 24.29) −5.50 (−22.2, 11.21) 3.11 (−16.97, 23.2) −4.36 (−23.15, 14.42) 2.76 (−6.82, 12.34) 4.47 (−5.99, 14.94) −0.50 (−10.66, 9.65) −5.71 (−20.31, 8.9) 1.22 (−12.61, 15.05) 7.65 (−2.74, 18.04) 11.88 (−0.09, 23.85) 6.34 (−4.07, 16.75) 12.31 (−3.45, 28.07) 9.85 (−4.22, 23.93) −4.36 (−21.55, 12.83) −7.78 (−25.13, 9.56) −7.54 (−26.27, 11.19) −13.88 (−33.51, 5.76) −9.30 (−29.09, 10.49) 12.85 (1.20, 24.50) 13.80 (1.95, 25.65) 13.04 (0.77, 25.32) 12.00 (0.26, 23.74) 15.37 (1.44, 29.29)
5.25 (−8.11, 18.60) 8.27 (−7.64, 24.17) 5.17 (−8.21, 18.55) 6.41 (−10.68, 23.51) 1.29 (−14.77, 17.35) 1.61 (−8.44, 11.65) 2.23 (−8.60, 13.05) 1.58 (−8.47, 11.63) −0.02 (−10.63, 10.59) −1.41 (−14.35, 11.53) 5.96 (−3.99, 15.91) 8.23 (−3.16, 19.62) 8.29 (−2.66, 19.23) 5.96 (−4.41, 16.34) 5.43 (−6.51, 17.37) −1.47 (−18.8, 15.87) −0.43 (−18.53, 17.67) −2.89 (−21.6, 15.81) −8.21 (−27.93, 11.51) −7.24 (−27.87, 13.39) 4.63 (−16.72, 25.99) 4.48 (−17.84, 26.8) 4.60 (−16.78, 25.97) 12.01 (−11.52, 35.55) 4.39 (−16.94, 25.71)
– PM10 + CO PM10 + O3 PM10 + SO2 PM10 + NO2 – SO2 + CO SO2 + O3 SO2 + NO2 SO2 + PM10 – NO2 + CO NO2 + O3 NO2 + SO2 NO2 + PM10 – CO + O3 CO + SO2 CO + NO2 CO + PM10 – O3 + CO O3 + SO2 O3 + NO2 O3 + PM10
6.31 (−2.83, 15.45) 6.19 (−3.97, 16.36) 6.98 (−2.68, 16.64) 1.33 (−11.04, 13.69) 0.03 (−11.49, 11.55) 6.05 (−0.54, 12.64) 6.07 (−1.02, 13.16) 6.17 (−0.50, 12.85) 2.82 (−5.04, 10.68) 5.40 (−3.53, 14.34) 8.01 (1.07, 14.95) 8.60 (0.92, 16.28) 8.11 (1.14, 15.09) 6.45 (−1.75, 14.64) 8.00 (−0.73, 16.73) 3.50 (−7.36, 14.36) 3.50 (−7.39, 14.39) −0.08 (−11.7, 11.53) −2.13 (−14.07, 9.81) 0.32 (−11.71, 12.35) 0.16 (−9.73, 10.05) −0.05 (−9.96, 9.86) −1.22 (−11.18, 8.75) 1.37 (−8.54, 11.28) −2.25 (−12.65, 8.16)
18.14 (4.52, 31.76) 25.43 (9.56, 41.3) 22.10 (6.01, 38.20) 14.24 (−5.55, 34.04) 12.13 (−6.21, 30.47) 10.65 (1.45, 19.85) 13.34 (3.25, 23.43) 10.71 (1.06, 20.36) 4.46 (−9.51, 18.44) 3.66 (−9.79, 17.11) 12.79 (2.59, 22.99) 17.90 (6.09, 29.71) 12.57 (2.30, 22.84) 9.14 (−6.24, 24.51) 6.73 (−6.95, 20.41) −2.08 (−19.38, 15.22) −3.1 (−20.64, 14.44) −11.92 (−30.68, 6.84) −16.76 (−36.44, 2.91) −17.75 (−37.55, 2.05) 3.75 (−8.13, 15.63) 4.12 (−7.95, 16.19) −0.26 (−12.61, 12.1) 2.11 (−9.80, 14.03) −6.37 (−20.31, 7.58)
−0.87 (−14.29, 12.55) −1.85 (−17.59, 13.9) −0.82 (−14.25, 12.61) −2.63 (−20.06, 14.8) −2.52 (−18.6, 13.55) 0.83 (−9.35, 11.00) 0.66 (−10.35, 11.67) 0.80 (−9.39, 11.00) 0.49 (−10.24, 11.22) 2.09 (−11.09, 15.28) 1.24 (−8.81, 11.3) 1.19 (−10.29, 12.67) 0.60 (−10.53, 11.74) 1.09 (−9.48, 11.67) 2.28 (−9.77, 14.32) 1.17 (−16.17, 18.52) 0.29 (−17.92, 18.5) 0.75 (−18.01, 19.5) 0.19 (−19.61, 19.99) 2.42 (−17.93, 22.77) −3.65 (−24.59, 17.3) −3.54 (−25.54, 18.45) 3.63 (−17.34, 24.59) −3.11 (−26.3, 20.08) −3.62 (−24.61, 17.37)
– PM10 + CO PM10 + O3 PM10 + SO2 PM10 + NO2 – SO2 + CO SO2 + O3 SO2 + NO2 SO2 + PM10 – NO2 + CO NO2 + O3 NO2 + SO2 NO2 + PM10 – CO + O3 CO + SO2 CO + NO2 CO + PM10
2.56 (−6.63, 11.75) 3.64 (−6.59, 13.86) 2.94 (−6.76, 12.65) −7.89 (−20.4, 4.63) 2.19 (−9.26, 13.64) 7.24 (0.76, 13.73) 8.65 (1.66, 15.63) 7.44 (0.87, 14.01) 8.99 (1.42, 16.56) 11.06 (2.22, 19.9) 1.49 (−5.62, 8.6) 2.16 (−5.69, 10.01) 1.48 (−5.67, 8.64) −3.63 (−11.91, 4.66) 0.48 (−8.37, 9.33) −1.03 (−11.85, 9.8) −1.01 (−11.86, 9.84) −6.21 (−17.77, 5.36) −2.41 (−14.34, 9.53) −2.89 (−14.9, 9.13)
16.20 (2.61, 29.78) 25.89 (10.11, 41.67) 22.74 (6.80, 38.68) 12.88 (−6.85, 32.61) 17.24 (−0.87, 35.36) 9.44 (0.24, 18.64) 13.44 (3.39, 23.49) 10.38 (0.79, 19.98) 10.09 (−3.82, 24.01) 3.12 (−10.29, 16.54) 7.35 (−2.99, 17.69) 12.96 (1.04, 24.89) 7.46 (−2.95, 17.88) −0.97 (−16.51, 14.57) −1.19 (−14.83, 12.45) −8.07 (−25.34, 9.21) −8.20 (−25.7, 9.03) −17.87 (−36.58, 0.84) −18.51 (−38.22, 1.19) −23.86 (−43.57,−4.15)
−7.28 (−20.93, 6.36) −6.39 (−22.41, 9.62) −7.59 (−21.28, 6.1) −18.52 (−36.71,−0.33) −2.55 (−18.61, 13.52) 3.60 (−6.39, 13.59) 6.00 (−4.81, 16.81) 3.7 (−6.28, 13.67) 6.54 (−3.69, 16.78) 12.5 (−0.62, 25.62) −7.86 (−18.46, 2.73) −7.78 (−19.83, 4.28) −6.64 (−18.29, 5.00) −10.02 (−21.07, 1.03) −6.81 (−19.35, 5.74) −6.40 (−23.66, 10.87) −4.00 (−22.08, 14.08) −10.50 (−29.31, 8.31) −0.30 (−19.96, 19.36) −2.16 (−22.42, 18.1)
X. Dai et al. / International Journal of Cardiology 192 (2015) 56–60
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Table 2 (continued) Pollutants
Models
All year
Hot
O Lag 3 2
– O3 + CO O3 + SO2 O3 + NO2 O3 + PM10
−0.28 (−10.16, 9.60) −0.22 (−10.13, 9.69) −1.93 (−11.87, 8.01) −0.05 (−10.00, 9.89) −1.29 (−11.70, 9.13)
−0.30 (−12.2, 11.60) 0.60 (−11.48, 12.67) −4.11 (−16.48, 8.25) −1.19 (−13.16, 10.78) −10.64 (−24.54, 3.25)
Warm 11.53 (−9.85, 32.92) 10.07 (−12.33, 32.48) 11.67 (−9.71, 33.06) 5.94 (−17.48, 29.36) 12.05 (−9.43, 33.52)
Note: Values were percentage increase (%) and 95% CI in risk of OHCAs. The bold means statistically significant (p b 0.05).
We also found that exposure to SO2 increased OHCA risk at lag 2 days but not at other days, suggesting the difference in time scales of action after the peak exposure. Unlike ours, a total of seven studies explored the effect of SO2 on OHCAs, but all findings were null [2,3,6–8,10]. Our positive results might partly contribute to the higher median concentrations of NO2 and SO2 in Shenzhen (43.56 and 9.48 μg/m3) than that in Houston (10.46 and 4.35 μg/m3), Melbourne (21.71 and 0.81 μg/m3), Helsinki (mean: 24.1 and 3.8 μg/m3), Perth Metropolitan (5.64 and 1.05 μg/m3), Stockholm (mean of NO2: 15.7 μg/m3), and Rome (SO2: 4.6 μg/m3), where the levels of NO2 and SO2 were too low to cause an effect. In addition, the null results in New York and Rome (15.5 °C) [5] might partly due to the lower temperature than that in Shenzhen City (22.95 °C). The potential patho-biologic mechanisms include systemic inflammation and dysregulation of autonomic nervous system. The effects of PM10 and O3 on OHCAs were widely evaluated and the inconclusive results might be partly due to the difference in PM10 constituents, high correlations among pollutants, the temperature, characteristics, sample sizes, measurement methods, study designs, and so on. In our study, the positive associations of PM10 and O3 with OHCAs were only found in hot seasons but not in warm seasons, consistent with the previous study [7]. Mostly importantly, we found that the levels of O3 can significantly modify the effects of NO2 on OHCAs. The potential mechanisms remained unclear. It is assumed that NO2 and O3 might act through similar biologic pathways and increase OHCA risks. There are several limitations in this study. First, as the exact death cause of cases dead on arrival couldn't be confirmed, the exclusion of these cases may lead to selection bias. Secondly, because of the absence of stratification by age, sex, disease history, and personal risk factors owing to the lack of this information, the potential confounding factors might disturb these results. However, our study exactly fills the gap in the literature by providing the evidence that the impact of short-term exposure to air pollutants on OHCAs is acute and severe in China, a developing country where the concentration of air pollutants exceeds those in developed countries. The difference among our results from the reported studies suggested that there was region-specific health effect of air pollutants. In summary, the present study identified the adverse effects of NO2, SO2, PM10 and O3 on OHCAs, especially in hot seasons, suggesting that residents living under hot weather are susceptible to OHCAs caused by air pollutants. Furthermore, O3 can significantly modified the effects of NO2 on OHCAs since individuals co-exposed to high levels of O3 and NO2 render them a higher OHCA risk. Substantial public health attention should be paid to air pollutants and their joint effects on OHCAs in China.
Conflict of interest None. Fig. 1. Excess risks (%) and 95% CI of cardiac arrests associated with an IQR increase in NO2 by the tertile of O3 on lag 0 (a), lag 1 (b), and lag 2 (c) days. The horizontal axis shows the tertile subgroups of O3. The cutoffs of O3 concentrations are b40.75, 40.75–64.19, and ≥64.19 μg/ m3. The vertical axis shows the increased risk of OHCAs due to an IQR increase in NO2 concentrations. Pinteraction values are obtained by introducing the tertile of O3 × NO2 into the statistical analysis model, where O3 is a categorical variable and NO2 is a continuous variable.
Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijcard.2015.05.016.
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References [1] T.H. Teng, T.A. Williams, A. Bremner, H. Tohira, P. Franklin, A. Tonkin, et al., A systematic review of air pollution and incidence of out-of-hospital cardiac arrest, J. Epidemiol. Community Health 68 (2014) 37–43. [2] J. Wichmann, F. Folke, C. Torp-Pedersen, F. Lippert, M. Ketzel, T. Ellermann, et al., Out-of-hospital cardiac arrests and outdoor air pollution exposure in Copenhagen, Denmark, PLoS One 8 (2013) e53684. [3] K.B. Ensor, L.H. Raun, D. Persse, A case-crossover analysis of out-of-hospital cardiac arrest and air pollution, Circulation 127 (2013) 1192–1199. [4] A. Peters, E. Liu, R.L. Verrier, J. Schwartz, D.R. Gold, M. Mittleman, et al., Air pollution and incidence of cardiac arrhythmia, Epidemiology 11 (2000) 11–17. [5] F. Forastiere, M. Stafoggia, S. Picciotto, T. Bellander, D. D'Ippoliti, T. Lanki, et al., A case-crossover analysis of out-of-hospital coronary deaths and air pollution in Rome, Italy, Am. J. Respir. Crit. Care Med. 172 (2005) 1549–1555. [6] M. Dennekamp, M. Akram, M.J. Abramson, A. Tonkin, M.R. Sim, M. Fridman, et al., Outdoor air pollution as a trigger for out-of-hospital cardiac arrests, Epidemiology 21 (2010) 494–500.
[7] R.A. Silverman, K. Ito, J. Freese, B.J. Kaufman, D. De Claro, J. Braun, et al., Association of ambient fine particles with out-of-hospital cardiac arrests in New York City, Am. J. Epidemiol. 172 (2010) 917–923. [8] F.S. Rosenthal, M. Kuisma, T. Lanki, T. Hussein, J. Boyd, J.I. Halonen, et al., Association of ozone and particulate air pollution with out-of-hospital cardiac arrest in Helsinki, Finland: evidence for two different etiologies, J. Expo. Sci. Environ. Epidemiol. 23 (2013) 281–288. [9] A. Raza, T. Bellander, G. Bero-Bedada, M. Dahlquist, J. Hollenberg, M. Jonsson, et al., Short-term effects of air pollution on out-of-hospital cardiac arrest in Stockholm, Eur. Heart J. 35 (2014) 861–868. [10] L. Straney, J. Finn, M. Dennekamp, A. Bremner, A. Tonkin, I. Jacobs, Evaluating the impact of air pollution on the incidence of out-of-hospital cardiac arrest in the Perth Metropolitan Region: 2000–2010, J. Epidemiol. Community Health 68 (2014) 6–12.
