INTRODUCTION

In the Republic of Korea (hereafter Korea) , asthma was the fifth leading cause of disease burden in 2007 [1]. The hospital admission rate for asthma in 2011 was two times greater than the overall average for Organization for Economic Cooperation and Development countries [2]. Pediatric asthma corresponds to the largest burden of disease in children, and the prevalence of asthma has steadily increased from 1998 to the present [3]. Furthermore, the incidence of adult asthma has rapidly increased, and the burden of this disease seems to have continually increased in Korea [4].

Clark et al. [5] reported that recent increases in prevalence are thought to be caused not only by genetic factors, but by exposure to environmental factors. Evidence indicates that air pollution exacerbates preexisting asthma and contributes to the development of asthma [6-10]. According to studies of associations between air pollutants and asthma exacerbation, increases in mortality and emergency department visits can be triggered by 10-μm particulate matter, nitrogen dioxide (NO₂), and ozone (O₃) [11-13].

The findings of a recent study suggested that eczema and rhinitis are strongly associated with symptomatic atopic asthma in adults in middle age [14]. However, until recently, few studies have explored the effect modification by prior allergic disease, including atopic dermatitis (AD), allergic rhinitis (AR), and asthma, on the association between asthma exacerbation and air pollutants. Furthermore, the asthma index forecast system, which was developed as a preventive strategy and has been in operation since 2009, does not consider prior allergic diseases [15].

In this study, we assessed the short-term effect of ambient air pollution on emergency department (ED) visits due to asthma in Seoul, after stratification by demographic characteristics, prior allergic disease, and season, in order to analyze the effect modification of these factors on the association between air pollutant levels and asthma exacerbation.

METHODS

Using data collected by the Health Insurance Review and Assessment Service (HIRA), we constructed a study population of individuals who visited an ED in Seoul at least once from January 1, 2005 to December 31, 2009. ED visits were identified using the International Classification of Diseases, 10th revision codes for asthma (J45-46), and the number of daily patient visits was calculated. For each participant, the anonymized secondary data comprised all individualized claims data, including participants’ age and gender. The first visits of individual patients who had multiple ED encounters during the study period were classified as the subgroup without a history of asthma. The presence of a history of asthma history and prior allergic disease among asthma patients was defined as any outpatient visits or admissions with a discharge diagnosis code for asthma, AR (J30), or AD (L20) from 2000 until the ED visit. Four seasons were identified: spring (March to May), summer (June to August), fall (September to November), and winter (December to February).

Air pollution data for O₃ (parts per billion [ppb]), carbon monoxide (CO, parts per million [ppm]), NO₂ (ppb), sulfur dioxide (SO₂, ppb), and particulate matter of median aerometric diameter less than 10 μm (PM₁₀, μg/m³) were obtained from 27 ambient automated fixed-site monitoring stations at 25 districts in Seoul maintained by the Research Institute of Public Health and Environment of the Seoul metropolitan government. Daily mean concentrations of CO, NO₂, SO₂, and PM₁₀ and 8-hour maximum O₃ were calculated as the average of hourly measured data from 2005 to 2009. The Korea Meteorological Administration provided daily average data, including temperature (°C), relative humidity (%), and vapor pressure (hPa) measured by a station at Jongno-gu in Seoul from 2005 to 2009. The total number of measurements was 1826, and the interquartile ranges (IQRs) used in our analyses were O₃, 22.6 ppb; CO, 0.3 ppm; NO₂, 18.3 ppb; SO₂, 2.8 ppb; PM₁₀, 35.5 μg/m³; temperature, 17.7°C; relative humidity, 21.9%; and vapor pressure, 12.6 hPa.

In the present study, associations between ED visits for asthma and ambient air pollution concentrations were estimated using generalized additive models with a Poisson distribution [16]. A generalized additive model is a semi-parametric model, in which a linear predictor depends on the sum of smooth functions of covariates to control for potential confounding [17].

The models contained the daily number of ED visits for asthma as the dependent variable, ambient air pollution concentrations as independent variables, and covariates, including the day of the week, holidays, time trends, and daily mean meteorological measurements. The day of the week, holidays, and pollutant concentration were modeled as linear effects and others as a cubic spline variable to adjust for seasonal and temporal effects; time trends were measured with 6 degrees of freedom (df) per year, temperature with 6 df, and relative humidity and vapor pressure with 3 df, which were chosen a priori based on previous studies [18,19].

Log[E(Y)]=β0 +β1 ambient air pollution+β2day of week+β3holiday+S(time trend, df = 6)+S(temperature, df = 6)+S(relative humidity, df = 3) + S(vapor pressure, df = 3)

The percent increases in ED visits and their 95% confidence intervals (CIs) were reported for the short-term effects of each IQR increment for each pollutant concentration. In order explore lag effects, we used a single-lag model and a cumulative-effect model. The effect of an ambient air pollutant on a given day was estimated using the concentration on the same day or on all previous 3 days (lag 0, lag 1, lag 2, or lag 3, respectively) The cumulative effect of mean pollutant concentrations during the 1 to 7 previous days prior to the ED visits was used to estimate the effect (e.g., lags 0-3 used the mean concentrations of lag 0, lag 1, lag 2, and lag 3).

To explore the effect modification among subgroups, participants were stratified by gender, age in years (<2, 2-5, 6-14, 15-39, 40-64, and ≥65), prior allergic disease (with no history of allergic disease, with any history of allergic disease, with a history of asthma, or with a history of AD or AR), and season. The presence of statistically significant differences between effect estimates across the strata of a potential effect modifier was determined by calculating 95% CIs [20]. All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) or R version 3.2.1 (Comprehensive R Archive Network: http://cran.r-project.org).

RESULTS

Table 1 summarizes the characteristics of the ED visits. The study population consisted of 27 146 patients who made 33 751 ED visits due to asthma. Table 2 lists the 24-hour mean concentrations of ambient air pollutants and meteorological variables. The temporal variation of asthma ED visits, daily mean concentrations of air pollutants, temperature, relative humidity, and vapor pressure is shown in Supplemental Figure 1. The Pearson correlation coefficient between pollutant concentrations and meteorological variables was shown in Supplemental Table 1.

Figure 1 shows the estimated percent increase in asthma ED visits for each IQR change in air pollutants using single lags from the same day to day 3 and cumulative lags from the given day to day 7. The strongest association per IQR increase in air pollutant exposure was observed for O₃ exposure; a 5.6% increase (95% CI, 3.6% to 7.5%) was found in the lag 1 model, and a 9.6% increase (95% CI, 6.9% to 12.3%) was found in the in cumulative lag 1 model. O₃ exhibited a positive association with asthma ED visits for all lag periods. An IQR change in the PM₁₀ concentration was significantly associated with ED visits in cumulative lag models 5, 6, and 7 (2.3%; 95% CI, 0.5% to 4.1%). NO₂ was also significantly associated with asthma ED visits in the lag 3 model (2.7%; 95% CI, 1.0% to 4.5%) and the cumulative lag 7 model (3.4%; 95% CI, 0.4% to 6.6%). However, for CO, NO₂, and SO₂ in the lag 0, lag 1, and cumulative lag 1 models, an increase in air pollutants was negatively associated with the risk of ED visits.

After stratification of ED visits by gender, age, season, and prior history of allergic disease, effect modification was observed (Figures 2-4). Among men, the highest risk for asthma ED visits was observed for O₃ in the cumulative lag 3 model (9.0%; 95% CI, 5.5% to 12.6%). Among women, the association between ED visits and O₃ exposure in the cumulative lag 7 model was the strongest (15.0; 95% CI, 10.5% to 19.6%). Women had a statistically significantly higher risk than men after an IQR change in O₃ in the cumulative lag 5 and cumulative lag 7 models (p<0.05 and p<0.01, respectively). IQR increases of all pollutants were found to have the strongest associations with ED visits during spring. The risk of ED visits for asthma with exposure to O₃, CO, SO₂, and PM₁₀ was strongest in the cumulative lag 7 model, and that for SO₂ and PM₁₀ was statistically significantly higher in spring than in summer and winter, while the risk for O₃ was higher in spring than in fall. For NO₂ exposure, the highest risk was observed in the cumulative lag 5 model, while the risk in the cumulative lag 7 model was statistically significantly higher in spring than in summer. In the summer, ED visits were significantly associated with O₃ (lag 0 and cumulative lag 1), and in the lag 0 model, the risk associated with O₃ exposure was higher than in spring. In the fall, the risk of ED visits increased after exposure to IQR increases in CO, NO₂, SO₂ and PM₁₀ in the lag 3 model. In winter, in the lag 1, cumulative lag 1, and cumulative lag 5 models, IQR changes in O₃ concentration increased the risk of asthma ED visits; the risk in the lag 1 model was significantly higher than in summer, and the risk in the cumulative lag 5 model was statistically higher than in fall. Among all age groups, the strongest effect for O₃ exposure was observed in the cumulative lag 3 model (26.9%; 95% CI, 11.1% to 18.8%) among individuals 6-19 years of age, and the discrepancy with other age groups was statistically significant. Similar findings were found for the risk of asthma ED visits for NO₂ exposure in the cumulative lag 7 model (16.5%; 95% CI, 2.2% to 9.1%) among individuals two to five years of age, for SO₂ exposure in the cumulative lag 5 model (7.3%, 95% CI, 3.1% to 11.7%) and PM₁₀ exposure in the cumulative lag 7 model (5.0%; 95% CI, 1.0% to 9.3%) among those ≥65 years age, and for CO exposure in the cumulative lag 7 model (6.2%; 95% CI, 1.0% to 11.6%) among those aged 40-64 years. Although the difference between subgroups was statistically non-significant, the largest risk of ED visits for asthma per IQR increase of O₃ concentration was observed among patients with a history of AD or AR in the cumulative lag 3 model (13.1%; 95% CI, 5.8% to 21.0%). The risk for each IQR increase of PM₁₀ concentration was statistically significantly higher in the cumulative lag 1 model (3.9%; 95% CI, 1.2% to 6.7%) and in the lag 1 model (3.1%; 95% CI, 1.0% to 5.3%) among participants with a previous history of AR or AD than in participants without any history of allergic disease or with a history of asthma (p<0.05 for both). Among the participants with a prior history of asthma, the association between ED visits for asthma and NO₂, CO, and SO₂ was greatest in the lag 3 model (p<0.01 for all).

DISCUSSION

In our study, IQR increases in the concentration of ambient air pollutants were associated with increased ED visits for asthma. In particular, among subjects with a prior history of AD or AR, the risk was higher than for those without such histories. Among pollutants, the strongest association per IQR increase in air pollutants exposure was observed for O₃ in the cumulative lag 1 model. IQR changes in PM₁₀ and NO₂ concentrations were significantly associated with ED visits in the cumulative lag 7 model. The findings of our results are consistent with those of previous studies [10,21-24]. Implausible negative associations of certain pollutants with asthma ED visits were observed. One possible explanation of the negative associations is that CO, NO₂, and SO₂ were highly negatively correlated with O₃; thus, relatively high levels of CO, NO₂, and SO₂ could be indicators of low levels of O₃, resulting in less asthma ED visits.

In our study, after stratification of subjects by demographic characteristics, seasons, and prior allergic disease, the highest risk for O₃ exposure was observed in the cumulative lag 3 model (26.9%; 95% CI, 11.1% to 18.8%) among individuals 6-19 years of age; this was a statistically significant effect in comparison with other age groups. Women patients had a statistically significantly higher risk than men after O₃ exposure, with a lagged effect. Much epidemiological evidence regarding different patterns of associations between air pollution and respiratory health for men and women has been reported; however, the results have not been consistent with regard to gender discrepancies [25]. Gender differences in airway behavior are influenced by three biological determinants: dimensional (structural and functional) relationships, hormonal determinants, and immunological determinants [26]. In the current study, the risk associated with O₃ exposure was higher for women than men, although the risks associated with exposure to other ambient pollutants was statistically similar for both genders. These findings might be attributed to dimensional determinants; that is, that women have, on average, a shorter airway and smaller lungs than men. In our study, for all ambient air pollutants, the risk showed a bimodal distribution, with peaks in the age groups of 2-19 years and ≥65 years. Of all age groups, the largest association between ₃ exposure and ED visits was observed among patients aged 6-19 years, while that for NO₂ exposure was found among participants 2-5 years of age. It is likely that children are likely to engage in more outdoor activities and to be more affected by exposure to ambient pollutants. The highest risks per IQR change in SO₂ and PM₁₀ concentrations were observed in participants ≥65 years of age, and the highest risk per IQR change in CO concentration was observed in individuals between 40-64 years of age. The younger age group (2-19 years old) tended to have the strongest association between ED visits for asthma and IQR increases in all pollutants. A plausible interpretation of those results might be that children are still in the process of lung growth and the development of defense mechanisms. The results of the present study are similar to those of the most recent systematic review and meta-analysis [27]. Among the subgroup aged <6 years, the associations were statistically significant; however, the patterns of asthma ED visits among those age groups (<2 and 2-5 years) might be biased due to misclassification. Asthma is difficult to diagnose, and for this age group, a clinical diagnosis is necessary because it is not possible to perform a pulmonary function test and many other medical conditions induce cough and wheezing. For example, respiratory virus infections, not asthma, are another common cause of wheezing in children aged <2 years, and the number of ED visits for asthma might therefore be overestimated [3]. Children (<2 years) also spend more than half the day indoors, such that the association is slightly weaker than for other groups. In those over 45 years of age, although the differential diagnosis is relatively easier than children, the diagnosis of asthma can also be problematic as the condition can be confused with chronic obstructive pulmonary disease (chronic bronchitis) or heart failure (left ventricle). In terms of seasonal effects, associations with air pollutants had a greater magnitude during spring (March to May). In spring, all pollutants were found to have the largest association with ED visits for asthma. Stieb et al. [28] reported that a greater association was observed during the warm season (April to September). In summer, IQR increases in O₃ concentration elevated the risk of asthma significantly more than in the spring. Several studies have reported worse effects on ED visits due to asthma during the summer season [29,30]. In fall, ED visits for asthma were associated with CO, NO₂, SO₂, and PM₁₀ exposure. In winter, except patients exposed to O₃ in the lag 1 model and in the cumulative lag 5 model, the risk for asthma ED visits was not significant regardless of air pollutants. This seasonal difference in risk may be explained by differences in exposure patterns; that is, in winter many people tend remain indoors, while in the warm season (spring and summer), people are more likely to engage in outdoor activities. However, because individual exposure level and susceptibility were not evaluated in this study, further studies are needed to better understand this issue [31].

A stratified analysis of prior allergic histories on ED visits for asthma showed an effect modification of pollutants on ED visits. With O₃ or PM₁₀ exposure, the risk of ED visits for asthma among those with a history of AD or AR was significantly higher than in participants without any history or participants with a history of asthma. The association between ED visits and IQR increases in NO₂, CO, and SO₂ concentrations among individuals with a history of asthma was also higher than among those without any such history. In terms of the risk for asthma ED visits per IQR change in O₃ concentration, no effect modification by prior allergic history was observed. These findings are consistent with those of another study that found that adult eczema was associated with more persistent asthma and asthma attacks [32]. The increased asthma exacerbation observed in the present study may suggest that atopic dermatitis could play a role in the mechanism of asthma attacks. Future studies are warranted to elucidate these points. A prospective population cohort study over four decades documented that 20% to 30% of cases of adult asthma with atopic characteristics were attributed to a history of eczema and rhinitis [33]. This result suggests that previous studies on the effects of atopic dermatitis and rhinitis on adult asthma may have underreported their findings due to regarding adult asthma as one condition rather than individual allergic characteristics [14].

The strengths of the present study include the stratification of the prior allergic histories of asthma patients, which could be a proxy for an analysis of the subtype of asthma (atopic or non-atopic). Additionally, we used data from national records, covering almost the entire Korean population, and the data on air pollutants and meteorological data had no missing values over the entire study period. Furthermore, subgroup analysis providing further insights into effect modifications may help improve the clinical applicability of our results. The results of the present study are supported by those of the most recent systematic review and meta-analysis [27]. In Korea, many studies have also documented the short-term exposure effects of daily pollutants on daily counts of ED visits in a given population, using a case-crossover study design [34,35] or a generalized additive model with a Poisson distribution [36,37]. However, few studies have evaluated the effect modification of allergic history on the association between ambient air pollution and asthma ED visits. The result might contribute to predictive models of ED visits in the context of precision medicine.

The goal of the present study was to estimate the shortterm effect of air pollutants, and to specify the type of asthma with a higher risk of exacerbation, but our analysis does have some limitations. Without the residential addresses of patients, we averaged the exposure data into a daily mean concentration on the district level, which may not account for individual variation in exposure due to local sources of exposure, air conditioning systems, ventilation, or time spent outdoors. The HIRA data also incorporated no individual data regarding smoking or drinking history, socioeconomic status, or psychological stress, which has been associated with AD and AR as well as asthma in adults. Subgroup analysis was performed to verify the robustness of the main findings. Furthermore, the current study design used single-pollutant models. Multiple-pollutant models would elucidate the joint effect of pollutants on adverse health outcomes [38]. This study also has a limitation stemming from the potential misclassification of asthma ED visits, since they were defined only by the discharge diagnosis code without pulmonary functional test results. For example, multiple ED visits could include asthma patients who sought care to obtain prescriptions for asthma but did not show symptoms.

In conclusion, IQR increases in ambient air pollutants were positively associated with ED visits for asthma. After stratification by prior allergic disease, the strongest statistically significant risk was observed among subjects with a prior history of AD or AR who were exposed to O₃ or PM₁₀.

Notes

CONFLICT OF INTEREST

The authors have no conflicts of interest associated with the material presented in this paper.

SUPPLEMENTAL MATERIAL

Figure 1.

Changes in asthma emergency department visits with interquartile range (IQR) increases in ambient pollutants. (A) Single-lag model. (B) Cumulative-effect model (lags 0-3 used the mean concentrations of lag 0, lag 1, lag 2, and lag 3). O₃, ozone; CO, carbon monoxide; NO₂, nitrogen dioxide; SO₂, sulfur dioxide; PM₁₀, particulate matter with an aerodynamic diameter <10 μm.

Figure 2.

Percent increases in asthma emergency department (ED) visits with an interquartile range (IQR) increases in ambient pollutants after stratification for gender and season. For all strata, single-lag exposure for the day of the visit and 1, 2, and 3 days prior to the day of the visit were described as the first four digits (0, 1, 2, and 3, respectively). Cumulative lagged exposure, the mean concentrations from the day of visit to all subsequent days, were described as the last four digits (1, 3, 5, and 7, referring to cumulative lag 1, 3, 5, and 7, respectively). The IQR was 22.6 ppb for O₃, 0.3 ppm for CO, 18.3 ppb for NO₂, 2.8 ppb for SO₂, and 35.5 μg/m³ for PM₁₀. Blue dots indicate the percent increase in asthma ED visits, and bars indicate 95% confidence intervals. Spring, March to May; Summer, June to August; Fall, September to November; Winter, December to February; O₃, ozone; PM₁₀, particulate matter with an aerodynamic diameter <10 μm; CO, carbon monoxide; NO₂, nitrogen dioxide; SO₂, sulfur dioxide.

Figure 3.

Percent increase in asthma emergency department (ED) visits with interquartile range (IQR) increases in ambient pollutants (A: O₃, B: PM₁₀, C: CO, D: NO₂, and E: SO₂) after stratification for age group. For all strata, single-lag exposure for the day of the visit and 1, 2, and 3 days prior to the day of the visit were described as the first four digits (0, 1, 2, and 3, respectively). Cumulative lagged exposure, the mean concentrations from the day of visit to all subsequent days, were described as the last four digits (1, 3, 5, and 7, referring to cumulative lag 1, 3, 5, and 7, respectively). The interquartile IQR was 22.6 ppb for O₃, 0.3 ppm for CO, 18.3 ppb for NO₂, 2.8 ppb for SO₂, and 35.5 μg/m³ for PM₁₀. Blue dots indicate the percent increase in asthma ED visits, and bars indicate 95% confidence intervals. O₃, ozone; PM₁₀, particulate matter with an aerodynamic diameter <10 μm; CO, carbon monoxide; NO₂, nitrogen dioxide; SO₂, sulfur dioxide.

Figure 4.

Percent increases in asthma emergency department visits with interquartile range (IQR) increases in ambient pollutants (A: O₃, B: PM₁₀, C: CO, D: NO₂, and E: SO₂) after stratification for prior allergic history. For all strata, single-lag exposure for the day of the visit and 1, 2, and 3 days prior to the day of the visit were described as the first four digits (0, 1, 2, and 3, respectively). Cumulative lagged exposure, the mean concentrations from the day of visit to all subsequent days, were described as the last four digits (1, 3, 5, and 7, referring to cumulative lag 1, 3, 5, and 7, respectively). The IQR was 22.6 ppb for O₃, 0.3 ppm for CO, 18.3 ppb for NO₂, 2.8 ppb for SO₂, and 35.5 μg/m³ for PM₁₀. Blue dots indicate the percent increase in asthma ED visits, and bars indicate 95% confidence intervals. AD, atopic dermatitis; AR, allergic rhinitis; O₃, ozone; PM₁₀, particulate matter with an aerodynamic diameter <10 μm; CO, carbon monoxide; NO₂, nitrogen dioxide; SO₂, sulfur dioxide.

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Figure & Data

References

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