Association between air pollution and daily mortality and hospital admission due to ischaemic heart diseases in Hong Kong

Atmospheric Environment 120 (2015) 360e368

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Association between air pollution and daily mortality and hospital admission due to ischaemic heart diseases in Hong Kong Wilson Wai San Tam a, Tze Wai Wong b, *, 1, Andromeda H.S. Wong b a b

Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, China

h i g h l i g h t s
We found significant associations between ischemic heart disease and air pollution.
All five air pollutants were associated with IHD hospital admissions and deaths.
An exposureeresponse relation existed with five air pollutants and IHD mortality.
Similar relations existed with hospital admissions for IHD except for SO2 and O3.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 April 2015 Received in revised form 20 August 2015 Accepted 22 August 2015 Available online 28 August 2015

Ischaemic heart disease (IHD) is one of the leading causes of death worldwide. The effects of air pollution on IHD mortalities have been widely reported. Fewer studies focus on IHD morbidities and PM2.5, especially in Asia. To explore the associations between short-term exposure to air pollution and morbidities and mortalities from IHD, we conducted a time series study using a generalized additive model that regressed the daily numbers of IHD mortalities and hospital admissions on daily mean concentrations of the following air pollutants: nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter less than 10 mm (PM10), particulate matter with an aerodynamic diameter less than 2.5 mm (PM2.5), ozone (O3), and sulfur dioxide (SO2). The relative risks (RR) of IHD deaths and hospital admissions per 10 mg/m3 increase in the concentration of each air pollutant were derived in single pollutant models. Multipollutant models were also constructed to estimate their RRs controlling for other pollutants. Significant RRs were observed for all five air pollutants, ranging from 1.008 to 1.032 per 10 mg/m3 increase in air pollutant concentrations for IHD mortality and from 1.006 to 1.021 per 10 mg/m3 for hospital admissions for IHD. In the multipollutant model, only NO2 remained significant for IHD mortality while SO2 and PM2.5 was significantly associated with hospital admissions. This study provides additional evidence that mortalities and hospital admissions for IHD are significantly associated with air pollution. However, we cannot attribute these health effects to a specific air pollutant, owing to high collinearity between some air pollutants. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Ischaemic heart disease Air pollution PM10 PM2.5 Hospital admissions Mortality

1. Background The association between air pollution and mortality, hospital admissions or general practice consultations has been reported in many epidemiological studies over the decades (Atkinson et al.,

* Corresponding author. 4/F School of Public Health, Prince of Wales Hospital, Shatin, Hong Kong, China. E-mail address: [email protected] (T.W. Wong). 1 This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. http://dx.doi.org/10.1016/j.atmosenv.2015.08.068 1352-2310/© 2015 Elsevier Ltd. All rights reserved.

2001; Hajat et al., 2002; Samet et al., 2000; Schwartz, 1999; Wong et al., 2006). Earlier studies focused mainly on nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter less than 10 mm (PM10), sulfur dioxide (SO2) or ozone (O3). Particulate matter with an aerodynamic diameter less than 2.5 mm (PM2.5), has become the focus in recent studies because of its small size and ability to penetrate deep down the respiratory tract (Cao et al., 2012; Garrett and Casimiro, 2011; Ito et al., 2011). Ischaemic heart disease (IHD) is one of the leading causes of death worldwide and is projected to be the top cause of death at 2030 (Finegold et al., 2013; World Health Organization, 2008). IHD

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has also shifted from the 4th leading cause of burden of diseases in 1990 (Mathers et al., 2008) to the top in 2010 (Murray et al., 2012), imposing a major economic and resource burden on health and public health systems. Experimental studies on animals suggest PM exposure results in myocardial ischaemia and infarction (Wellenius et al., 2003, 2002). A recent study reported that PM2.5 exposure could cause inflammation, endothelial dysfunction and autonomic nervous system injuries in rats (Wang et al., 2013). A review by a committee of the American Heart Association in 2004 expressed concern of a causal link between PM and adverse cardiovascular outcomes (Brook et al., 2004). Recently, several panel studies reported the harmful effects of PM2.5 exposure. Bartell et al. reported that exposure to PM may increase the risk of ventricular tachycardia for elderly people with coronary artery disease (Bartell et al., 2013) while Xu et al. reported that ambient PM2.5 could affect cardiac autonomic function of elderly patients with heart disease, and subjects with hypertension appeared to be more susceptive to the autonomic dysfunction induced by PM2.5 (Xu et al., 2013). Many studies have reported the association between air pollution and IHD mortalities (Mathers et al., 2008; Dai et al., 2014; Lin et al., 2013; Pascal et al., 2014; Wong et al., 2015) Fewer studies examined specifically the associations between IHD morbidities and air pollution (Dai et al., 2014; Xie et al., 2015; Mann et al., 2002; Pun et al., 2014). Time series studies on air pollution and health in Hong Kong have been conducted since the late 1990s, before PM2.5 was regularly monitored (Wong et al., 1999, 2002). Several studies that focused on specific respiratory diseases (Ko et al., 2007a,b) have been reported. However, the association between PM2.5 and IHD morbidities has not been investigated locally and in most Asian cities. The aim of this study is to explore the short-term associations of air pollution with morbidities and mortalities caused by IHD in Hong Kong, based hospital and census data from 2001 to 2010. 2. Materials and methods 2.1. Data Daily numbers of mortalities between 2001 and 2010 were obtained through the Known Death Microdata Set from the Census and Statistics Department, HKSAR Government. The Known Death Microdata Set covered all deaths reported in Hong Kong, and were coded according to the 10th revision of the international classification of diseases (ICD-10). All mortality data were systematically checked for errors by the Department of Health and the Census and Statistics Department. There was a small percentage (about 1%) of missing data owing to uncertain cause of death. For the purpose of this study, the numbers of daily deaths due to IHD (ICD-10 code: I25) were extracted. Daily numbers of emergency hospital admissions due to IHD (ICD-9 code: 410e414) in Hong Kong from 2001 to December 2010 were obtained from all 17 ‘acute hospitals’ (those that provide 24-h accident and emergency services) under the Hospital Authority of Hong Kong (HA), a government funded body that provides hospital services to Hong Kong residents. The HA provides 89.6% of hospital beds in Hong Kong (Legislative Council Minutes, 2007), and about 98% of emergency hospital beds as no private hospital provided accident and emergency services until 2007. The hospital discharge data are systematically checked by the Hospital Authority for quality, and no missing data were recorded (Cheung et al., 2007). The concentrations of air pollutants, including NO2, SO2, O3, and PM10 were regularly monitored in all of the 13 monitoring stations while PM2.5 was monitored in 3 monitoring stations. Data regarding hourly concentrations of these pollutants were obtained for the corresponding 10-year period from the Environmental

361

Protection Department (www.epd.gov.hk). The daily means were computed for NO2, SO2, PM10, and PM2.5 while the daily day-time 8h average was computed for O3. The daily means were then averaged across the stations. The percentage of missing data by station ranged from a minimum of 0.2% for NO2 to a maximum 12.4% for PM2.5. The median percentage of missing data by station ranged from 2.1% (for SO2) to 6.6% (for PM2.5). When there were more than 25% of missing data in any station in one day, they were predicted by a regression model using corresponding data from the nearest station (Wong et al., 2006). The daily mean temperatures and relative humidity at different districts were acquired from the Hong Kong Observatory (www.hko.gov.hk). No missing data were recorded. 2.2. Statistical methods A generalised additive model, using the Poisson distribution with a log-link function, was used to construct core models for daily mortalities and admissions individually (Hastie and Tibshirani, 1995). In the core model on mortality, we regressed the daily numbers of deaths on the following variables: time, day of the week, mean temperature, mean humidity, a holiday indicator and an influenza indicator (Qiu et al., 2012). Penalised smoothing splines were used to adjust for seasonal pattern and long term trends, temperature and humidity (Qiu et al., 2012; Host et al., 2008). The degrees of freedoms were chosen a priori as 4 df per year for time, 6 df for the temperature on the same day, 6 df for the moving average of the temperature of lag day 1 to day 3, and 3 df for humidity (Yang et al., 2014). The equation of the initial core model for mortality is given by:

log½EðMortality at day tÞ ¼ a þ sðt; df ¼ 4=yearÞ   þ s Templag 0 day ; df ¼ 6   þ s Tempave: lag 0e3 days ; df ¼ 6   þ s Humiditylag 0 day ; df ¼ 3 þ b1  Day of the week indicator þ b2  Holiday indicator þ b3  Influenza indicator A similar model was fitted for daily numbers of hospital admissions for IHD, but the degrees of freedoms were chosen a priori as 7 df per year for time (Qiu et al., 2012). The equation of the core model for hospital admissions is given by:

log½EðMortality at day tÞ ¼ a þ sðt; df ¼ 7=yearÞ   þ s Templag 0 day ; df ¼ 6   þ s Tempave: lag 0e3 days ; df ¼ 6   þ s Humiditylag 0 day ; df ¼ 3 þ b1  Day of the week indicator þ b2  Holiday indicator þ b3  Influenza indicator In both models, the quasi-likelihood method was used to correct for over-dispersion (Hastie and Tibshirani, 1995). To account for potential autocorrelation, the partial autocorrelation were computed for the core models and autoregressive terms were added until no significant partial auto-correlation function (pacf) was observed (Wong et al., 2002; Ko et al., 2007a). Linear effects of all the air pollutants for the same day (lag 0) up to 5 lag days (lag 5) and cumulative lag days, from lag day 0 to day 1, up to lag day

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0eday 5 were explored. The lag day with the largest t-statistic for each pollutant was then identified as the best lag day of the pollutant. This method has been used elsewhere (Zanobetti and Schwartz, 2009). The analyses were conducted using the function “gam” in R (Wood, 2006), and the results were expressed as the relative risks (RRs) of death or hospital admissions for IHD for every 10 mg/m3 increase in the concentrations of each pollutant. The exposureeresponse relationships between the log-relative risk of mortality or hospital admissions and the air pollutants' concentrations were examined graphically, by replacing the linear term of the pollutant concentration with a smoothing function. The predicted values of the smoothing term, i.e. log-RR, were then plotted against the pollutant concentrations (Fig. 1). Subgroup analyses were conducted for hospital admissions for those aged below 65 and those aged 65 or above. To control for the effects of individual pollutants, we also construct multiple pollutant models (for mortality and hospital admissions respectively) by including all three gaseous air pollutants and PM10 or PM2.5 into the model and eliminating non-significant variables (p > 0.05) by backward elimination. Ethical approval is not required as only anonymous, grouped data were sought. 3. Results Table 1 shows the descriptive statistics for the mean daily number of deaths and admissions, air pollutant concentrations, temperature and humidity. The mean daily number of deaths and hospital admissions due to IHD during the study period were 8.1 (SD ¼ 3.3) and 32.2 (SD ¼ 8.2) respectively. The mean temperature and humidity were 23.5  C (SD ¼ 5.0  C) and 78.2% (SD ¼ 10.2%) respectively. The mean concentration (in mg/m3) of NO2, PM10, PM2.5, O3 and SO2 were 57.1 (SD ¼ 20.9), 53.2 (SD ¼ 29.4), 37.8 (SD ¼ 22.5), 32,9 (SD ¼ 17.7), 19.8 (SD ¼ 13.6). For PM10, there was an extremely high value of 572.9 mg/m3 recorded on 22 Mar 2010. The concentrations in all other days were below 200 mg/m3 (Guang et al., 2011) Table 2 shows the correlation matrix between the air pollutants, temperature and humidity. The correlations between PM10 and PM2.5, NO2 and PM10, and NO2 and PM2.5 were 0.926 (p < 0.001), 0.722 (p < 0.001) and 0.785 (p < 0.001) respectively. Table 3 shows the relative risks (RR) of mortality and hospital admissions from IHD for the pollutants on the statistically ‘best’ lag days. Significant RR of mortality were observed for all 5 pollutants, ranging from 1.008 (O3 lag day 5) to 1.032 (SO2 lag day 0e5). The ‘best’ lag was day 0e5, except O3 (at lag day 5). For hospital admissions, significant RR were also observed for all 5 pollutants, ranging from 1.006 (O3 on lag day 0e5) to 1.021 (NO2 on lag day 0e4). The best lag day for PM10, PM2.5 and O3 was day 0e5, while that for SO2 was day 0e3. Fig. 1aee shows the exposureeresponse relationship between the pollutants and the health outcomes. For IHD mortality, all pollutants show an increasing exposureeresponse relationship. As for hospital admissions, the linear trends for O3 and SO2 were reversed at high concentrations, probably affected by a few observations with very higher concentrations. As around 93% of the subjects died at aged 65 or above, subgroup analyses were conducted for hospital admissions data only. The RRs for NO2 and SO2 were higher among those aged 65 and above, compared to those aged below 65. The reverse was true for PM10 and PM2.5. The RR for O3 was the identical for the two age groups. None of the differences in RRs between the subgroups were statistically significant (Table 4). As a sensitivity analysis, we used a generalized linear mixed model with the R function “glmmPQL” to derive RRs and compare them with those derived our GAM model. The RRs from the two models were similar in magnitude (±0.006

for each pair for the same pollutant). In the multi-pollutant model for IHD mortality, only NO2 remained statistically significant and was retained in the model. For hospital admissions, when the three gaseous pollutants and PM10 were included, both NO2 and SO2 remained significant. When we replaced PM10 with PM2.5, both PM2.5 and SO2 were retained in the model. 4. Discussion Our study shows that short-term exposure to ambient NO2, PM10, SO2, O3 and PM2.5 are significantly associated with mortality and morbidity due to IHD. Compared with the results in previous local studies, all the RR are slightly smaller for mortality (Wong et al., 2002) but are similar for hospital admissions (Wong et al., 1999). The mean concentrations of NO2, PM10 and SO2 are slightly higher than their corresponding concentrations in the mid 90s while that of O3 is slightly lower (Wong et al., 1999, 2002). The US National Morbidity, Mortality and Air Pollution Study (NMMAPS) concluded that each 10 mg/m3 elevation in PM10 was associated with a 0.31% increase in risk of cardiopulmonary mortality (Brook et al., 2004; Dominici et al., 2005). By contrast, the APHEA-2 study, which summarized the results from 29 European countries, concluded there was an increase of 0.6% daily all-cause mortality and 0.69% cardiovascular mortality for each 10 mg/m3 increase in PM10 (Katsouyanni et al., 2001). Another pooled analysis of hospital admissions studies showed significant increases in admission rates of 0.7% for ischemic heart disease for each 10 mg/m3 elevation in PM10 (Brook et al., 2004; Morris, 2001). The excess risk of mortality from IHD in our study was higher, at 1.2% for PM10. This is not unexpected, as in our experience, the RR of a specific outcome such as IHD is generally higher than that for a disease group, like cardiovascular diseases (Xie et al., 2015). The magnitude of the RRs of cardiovascular mortality reported in the NMMAPS and APHEA-2 studies (Dominici et al., 2005; Katsouyanni et al., 2001) and the RR of cardiovascular hospital admissions by Morris (Morris, 2001) are within the 95% confidence interval of the corresponding RRs in our study. Besides PM10, PM2.5 was also reported to be significantly associated with cardiovascular mortality and hospital admission (Cao et al., 2012; Ito et al., 2011). Xie reported a significant association between PM2.5 and mortalities as well as hospital admissions due to IHD (Xie et al., 2015). Our RRs are significantly higher than Xie's results and might be related to our lower concentrations of PM2.5 compared to that in Beijing, as the exposure response curves show a smaller gradient at higher concentrations of PM2.5. For PM10 and PM2.5, a linear exposureeresponse relationship with no obvious threshold limit was observed in our curves. This is in agreement with the World Health Organization air quality guidelines (ebrary Inc.World Health Organization, 2006) However, the range of concentrations in our series was narrow, with few “clean” days. This limits our ability to detect thresholds using these curves. We found that the RRs of mortality and hospital admissions were consistently higher for PM2.5 than for PM10. This is in agreement with the suggestion that PM2.5 is more toxic than PM10 e the former has a much smaller aerodynamic diameter, and penetrates deeply into the lung parenchyma. It is derived mainly from combustion and contains a mixture of toxic metals and organic carbon including polycyclic aromatic hydrocarbons. PM10 is partly made up of non-toxic crustal elements. Nevertheless, PM2.5 comprises a large proportion of PM10. In Hong Kong, the ratio of PM2.5 to PM10 is about 0.7 to one. Franchini and Mannucci (2011) summarized the potential mechanisms for PM to cardiovascular diseases including the activation by particulate matter of inflammatory pathways and hemostasis factors (Simkhovich et al., 2008), production of reactive

Fig. 1. 1a: Log-RR of IHD hospital admissions (left) and mortality (right) against the concentration of NO2. 1b: Log-RR of IHD hospital admissions (left) and mortality (right) against the concentration of PM10. 1c: Log-RR of IHD hospital admissions (left) and mortality (right) against the concentration of PM2.5. 1d: Log-RR of IHD hospital admissions (left) and mortality (right) against the concentration of O3. 1e: Log-RR of IHD hospital admissions (left) and mortality (right) against the concentration of SO2.

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Fig. 1. (continued).

oxygen species through the oxidative stress pathway (Nemmar et al., 2007), and decreased heart rate variability (Franchini and Mannucci, 2011). Time series studies with individual chemical components of PM have been reported to elucidate the toxicology of PM2.5 (Pun et al., 2014). Besides PM, the three gaseous pollutants, NO2, O3 and SO2 were also significant risk factors for IHD morbidity and mortality. These findings are in agreement with several other reports (Beckerman et al., 2012; Nuvolone et al., 2013; Sunyer et al., 2003). Beckerman et al. (2012) reported that NO2 was significantly associated with increased IHD risk, with an RR of 1.33 (95% CI: 1.20, 1.47) while the APHEA-II study suggested that SO2 may play an independent role in triggering ischemic cardiac events (Sunyer et al., 2003). The multi-cities study in Italy revealed that O3 would increase coronary mortality (Nuvolone et al., 2013). In the APHENA study in Europe (Katsouyanni et al., 2009), cardiovascular (but not respiratory) mortality was significantly associated with 1 h O3 in single pollutant models, while both cardiovascular and respiratory

Table 1 Descriptive statistics.

IHD daily death IHD daily admission NO2 (mg/m3) PM10 (mg/m3) O3 (mg/m3) SO2 (mg/m3) PM2.5 (mg/m3) Temperature ( C) Humidity (%)

Mean

SD

Min.

25%-ile

Median

75%-ile

Max.

8.1 32.2 57.1 53.2 32.9 19.8 37.8 23.5 78.2

3.3 8.2 20.9 29.4 17.7 13.6 22.5 5.0 10.2

1 9 13.4 12.1 4.2 3.0 5.6 8.2 31.0

6 27 42.0 30.1 17.4 10.8 19.6 19.5 73.0

8 31 53.9 47.5 29.4 16.5 33.0 24.7 79.0

10 37 68.4 70.5 46.7 24.3 50.4 27.7 85.0

25 79 168.7 572.9a 83.4 135.8 179.7 31.8 98

a There was one day with extremely high concentrations of PM10 (on 22 Mar 2010).

hospital admissions were significantly associated with 1 h O3 after adjusted for PM10. Srebot et al. (2009) reviewed the biological effect of O3 on the cardiovascular system, including the influence on macro-vascular diameter and tone, increased peripheral blood

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Table 2 Correlations between pollution and meteorological variables. (*p < 0.05; **p < 0.01).

Temperature Humidity NO2 PM10 O3 SO2 PM2.5

Temperature

Humidity

NO2

PM10

O3

SO2

PM2.5

1

0.236** 1

0.366** 0.365** 1

0.352** 0.481** 0.722** 1

0.113** 0.446** 0.289** 0.505** 1

0.100** 0.207** 0.556** 0.427** 0.104** 1

0.348** 0.440** 0.785** 0.926** 0.469** 0.489** 1

Table 3 Relative risks (95% confidence limits) of IHD mortality and hospital admissions per 10 mg/m3 increase in concentrations of air pollutants. Ischaemic heart disease

NO2

PM10

PM2.5

O3

SO2

Mortality ‘Best’ lag day Hospital admissions ‘Best’ lag day

1.024 (1.016, 1.033)** 0e5 1.021 (1.016, 1.025)** 0e4

1.012 (1.006, 1.019)** 0e5 1.008 (1.005, 1.011)** 0e5

1.018 (1.010, 1.025)** 0e5 1.015 (1.011, 1.019)** 0e5

1.008 (1.001, 1.014)* 5 1.006 (1.001, 1.011)* 0e5

1.032 (1.018, 1.047)** 0e5 1.019 (1.012, 1.025)** 0e3

Remark: *: p < 0.05; **: p < 0.01.

Table 4 Relative risks (95% confidence intervals) of hospital admissions for IHD for every 10 mg/m3 increase in pollutant concentration by age groups. Age group

NO2 Lag 0e4 days

Lag 0e5 days

Lag 0e5 days

Lag 0e3 days

Lag 0e5 days

>¼65 years <65 years

1.021 (1.016, 1.026) 1.018 (1.010, 1.027)

1.008 (1.004, 1.011) 1.011 (1.005, 1.017)

1.006 (1.000, 1.011) 1.006 (0.997, 1.016)

1.020 (1.012, 1.027) 1.014 (1.002, 1.027)

1.014 (1.010, 1.019) 1.017 (1.009, 1.025)

PM10

pressure, short-term autonomic imbalance, effects on inflammation response, and increased oxidative stress in the cardiovascular system (Nuvolone et al., 2013; Srebot et al., 2009). Many researchers considered NO2 to be a proxy for PM. However, the associations between NO2 and short-term health effects remain after adjustment for other pollutants, including PM10 and PM2.5. The WHO considers “it is reasonable to infer that NO2 has some direct effects” (World Health Organization, 2013). The potential pathways of NO2 inducing cardiovascular diseases include the increase of plasma fibrinogen (Pekkanen et al., 2000), ventricular arrhythmia and ventricular tachycardia (Peters et al., 2000) leading to hemodynamic disturbances (Chang et al., 2005) while the pathways for SO2 include a change in heart rate variability attributed to stimulation of receptors in the upper respiratory tract (Sunyer et al., 2003; Tunnicliffe et al., 2001). The above studies provide a plausible pathophysiological basis for the association between PM and the gaseous pollutants and IHD. The elderly are more vulnerable to IHD and are generally considered to be more susceptible to air pollution. Differences in susceptibility have been shown in a meta-analysis of 108 papers (Bell et al., 2013). However, the differences in RRs by age groups in our study are small, non-uniform in direction (reversed for PM10 and PM2.5) and not statistically significant. Results from multipollutant models are difficult to interpret, owing to high collinearity between pollutants (in particular, NO2 and PM). The retention of only NO2 in the mortality model and either NO2 and SO2 or PM2.5 and SO2 in the hospital admissions models illustrates this problem. The role of PM as a risk factor for IHD has been supported in toxicological studies, and the retention of NO2 in the model suggests the latter might be a proxy for PM. Previous studies suggested a modification of the PM effect by NO2 (Katsouyanni et al., 2001; Samoli et al., 2008) and a local study also revealed a synergistic effect between PM10 and NO2 on emergency cardiac hospitalizations (Yu et al., 2013). From evidence of the health effects of PM in animal models, it appears more plausible

O3

SO2

PM2.5

that short-term exposure to PM2.5, instead of NO2 may be causally associated with a higher risk of IHD hospital admissions and mortality. Studies elsewhere of the toxicity of PM in animal models suggest PM might play a causal role in IHD (Gurgueira et al., 2002; Rhoden et al., 2005). Rhoden et al. (2005) suggested PM exposure would increase cardiac oxidants via autonomic signals and the resulting oxidative stress would be associated with significant functional alterations in the heart, while Gurgueira (Gurgueira et al., 2002) reported that concentrated ambient particles inhalation led to tissue-specific increases in the activities of the antioxidant enzymes superoxide dismutase and catalase, suggesting that episodes of increased particulate air pollution not only have potential for oxidant injurious effects but may also trigger adaptive responses. It is difficult to determine whether SO2 exerts an independent effect from PM, as their correlation is moderate. To study the delayed effects of air pollution on mortality and morbidity, we used different combinations of lag time up to 5 days. For most pollutants, the cumulative lag day (0e5) was the statistically most significant choice compared to other lag days. It appears therefore, that air pollutants may exert both an immediate and delayed effect. However, we have not investigated a longer effect than 5 days. As a sensitivity analysis, we have compared the RRs of the pollutants in the model with the influenza indicator term with those without. There was little change in the magnitude of the RRs, which remained statistically significant in either model. It is well-known that temperature is strongly associated with IHD (Chan et al., 2011, 2012, 2013). We have detected a strong and significant effect of temperature on IHD mortality and morbidity by using temperature as a linear term. While this is a well-researched topic (Chan et al., 2011, 2012, 2013), the objective of our study is to assess the effect of air pollutants on IHD. Hence, we treated temperature as a confounder and adjusted for its effects using smoothing spline function. Diseases such as chronic obstructive pulmonary diseases (COPD), pneumonia and influenza commonly co-exist with IHD

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events. However, our mortality dataset only provides the underlying cause of death, while the hospital dataset only provides the principal diagnosis on discharge. Hence, we could not further investigate the role of these conditions. Respiratory diseases such as COPD and asthma have been shown to be associated with air pollution (Wong et al., 1999, 2002, 2008). Respiratory diseases (Wong et al., 1999, 2008) can be considered as risk factors for IHD, but they do not fit the definition of a confounder in our study of the association between air pollution and IHD, as they co-vary with IHD. One can argued that IHD may act as proxys for respiratory diseases, the “true” effect of exposure to air pollutants. However, many studies on air pollution and cardiovascular diseases such as congestive heart failure and IHD exist in the literature (Wong et al., 1999, 2008; Goldberg et al., 2003; Lee et al., 2003; Pope et al., 2008; Yang, 2008). It is therefore logical to conclude that air pollution increases the morbidity and mortality risks of both groups of diseases. 4.1. Strengths and limitations This study included all registered deaths and all emergency hospital admissions at all Hong Kong public hospitals, which covered 98% of all emergency services in Hong Kong. The data are reliable, up-to-date and cover a 10-year-period (2001e2010). There were few missing data for mortality and none for hospital admissions. Changes in coding practice may affect the diagnosis of IHD, especially in time series studies involving long periods. However, we found no evidence of any changes in coding practice in the diagnosis of or cause of death from IHD. Four out of five pollutants were measured in 13 monitoring stations located in widespread areas in Hong Kong and hence the mean concentrations are representative compared with other studies that rely on a single monitoring station as a proxy for exposure of population over a much larger area (Xie et al., 2015). Missing data on air pollutants are caused by the need for maintenance work and calibration at different time periods in individual stations. This problem was minimized by imputing data using regression methods. There are several limitations in our study. First, ambient air pollution levels from outdoor monitoring sites are used as proxies of personal exposure. This inevitably leads to some misclassification of exposure created from as it ignored the population mobility and airconditioning effects in residences and workplaces. Nevertheless, it is commonly observed that a moderate correlation between individual exposure and ambient air pollution concentration exists. Secondly, PM2.5 was only measured in 3 stations and the measurement of PM2.5 may be under-representative. Unlike PM10, the concentrations of PM2.5 in different districts are fairly evenly distributed. Hence, the mean concentration is a good approximation of the overall picture. A common weakness in time series studies is the difficulty in attributing the association with health outcomes to a specific pollutant, even using a multipollutant model. The well-known collinearity between air pollutants is an ever-present problem. We also have not investigated into the delayed effects of air pollution on mortality than 5 days. Finally, we could not ascertain the effects of concomitant respiratory morbidity and mortality, owing to the lack of data on comorbidities or associated cause of deaths in our datasets. 4.2. Conclusion We have demonstrated that the risks of mortality and hospital admissions from IHD are associated with PM2.5, PM10 and the gaseous pollutants NO2, O3 and SO2. It is not possible in time series studies to attribute the health effects to a specific air pollutant, owing to the problem of collinearity between some pollutants.

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