Numerous epidemiologic studies have demonstrated short-term effects of ambient air pollution on morbidity in North America and Europe.1–4 Previous studies identified increased risk for hospital admissions, including for stroke,5 congestive heart failure,6 pneumonia and chronic obstructive pulmonary disease,3 and cardiovascular and respiratory causes.2,7 Some studies showed that air pollution effects for hospitalizations varied regionally.1,4 However, studies of the associations between air pollution and morbidity are limited in Asia. Studies from other regions are used to estimate the health impacts of air pollution in Asia, owing to lack of local studies.8 Local studies in Asia that involve different air pollution characteristics and sources, housing structures, activity patterns, or populations can provide relevant estimates of the local impacts of air pollution exposure for policy makers.
Although scientific evidence of air pollution and health in Asia is far less than in North America or Europe, a growing number of studies based in Asia have observed associations between air pollution and hospital admissions. Recent studies indicate increased risk of hospitalizations for asthma among children in Taiwan9 and an increase in the total cardiovascular hospital admissions in China.10 However, as the characteristics of ambient air pollution and other community characteristics (such as age structure, disease patterns, and socioeconomic characteristics) may differ by location, studies are still needed in other regions. Further, few multicity studies have been conducted in Asia. The Public Health and Air Pollution in Asia project is the first Asian multicity study on mortality using a common protocol and could provide more robust effect estimates for the region.11,12 This project did not include Korea, and analysis focused on mortality, rather than hospital admissions.
Korea, like many Asian countries, has experienced rapid economic development, accompanied by increased industrialization and air pollution. There is, nonetheless, considerable heterogeneity in the population, economic, cultural, and pollution characteristics across Asian regions. Studies from less developed Asian regions may not be applicable to more developed nations, such as South Korea or Japan. Online supplemental eTable 1 (http://links.lww.com/EDE/A685) compares several health, economic, and environmental indicators for South Korea with several other Asian countries (Thailand, China, India, Japan) and several Western countries (United States, United Kingdom, Canada, France). South Korea has one of the highest life expectancies of the Asian countries (84 for women, 77 for men), which is more comparable with those of Japan or Western countries (eg, in United States; 81 for woman, 76 for men) than Asian countries, such as India or China. South Korea also has a more urban population (83%) than do some other Asian countries (eg, 31% for India, 34% for Thailand). Other characteristics for Korea lie between those of other Asian countries (except Japan) and the Western countries (eg, gross domestic product per capita, motor vehicles per population). Clearly, these generalizations obscure within-country differences; however, they serve to demonstrate that quickly developing Asian countries, such as South Korea, are likely to have different air pollution, social, and health patterns than less industrialized Asian nations or Western nations that developed earlier.
Studies have been conducted for Korea on air pollution and hospital admissions for cardiovascular and respiratory diseases in Seoul,13 asthma in Busan14 and Seoul,15 emergency hospital visits for asthma in Seoul,16 and ischemic heart disease in Seoul.17 However, single-city studies of risk of hospitalizations are more susceptible to publication bias.18 Numerous multicity studies have been conducted in North America and Europe,1,3,4 but no previous multicity study on effects of air pollution on hospital admissions or any morbidity outcome has been conducted for Korea. Multicity results are not subject to publication bias of results for a single city and have the advantage of synthesizing evidence across a broader region. Several meta-analyses found evidence of publication bias in single-city studies of risk of hospitalizations.18
We investigated the associations between ambient air pollution and hospital admissions in eight cities in Korea for the period 2003–2008, and estimated city-specific effects and overall effects across the cities. To examine whether some populations are particularly vulnerable, we evaluated effect modifiers (such as sex and age) on the relation between hospital admissions and ambient air pollution.
The study was carried out in seven major Korean cities (Seoul, Busan, Incheon, Daegu, Daejeon, Gwangju, and Ulsan) and the city of Jeju, which is on Jeju Island off the southern coast. We obtained hospital admission data from 1 January 2003 to 31 December 2008, from the National Health Insurance Corporation, Republic of Korea. These eight cities account for 48% of the total population of the Republic of Korea. eFigure 1 (http://links.lww.com/EDE/A685) shows the cities’ locations. We collected data on cause of hospitalization, sex, and age from morbidity records. We considered hospitalization causes of allergic disease (International Classification of Diseases, ICD-10; World Health Organization 2007, J30, J45, L20), asthma (ICD-10, J45, J46), selected respiratory disease (ICD-10, J05, J18, J20, J21, J40-J42, J44-J46, J67), and cardiovascular disease (ICD-10, I00-I99). The data on selected respiratory disease hospitalizations included croup, pneumonia, bronchiolitis, respiratory infection including bronchitis, chronic obstructive pulmonary disease, asthma, and pneumonitis.
The National Meteorological Administration, Republic of Korea, provided hourly measurements of ambient temperature, relative humidity, and barometric pressure for each city during the study period. We converted weather data into 24-hour (ie, daily) values.
We obtained hourly ambient air pollution levels for each city from the Ministry of Environment, Republic of Korea, during the whole study period. Each monitor has routine measurements for particulate matter 10 µm or less in aerodynamic diameter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO), using standardized reference methods. We used 24-hour averages as the exposure index, except for O3 and CO, for which we calculated maximum daily 8-hour moving averages (based on previous research).19
We applied a two-stage Bayesian hierarchical model. As the first stage, we estimated the increase in morbidity risk, using generalized linear models for each city, separately. We controlled for daily mean temperature, relative humidity, barometric pressure, day of the week, and time trends, to account for seasonal and long-term trends. We excluded the first day of each month to control for the tendency of provider organizations to submit claims to the Health Insurance Review Agency on the first day of each month and interaction between this tendency and year. The model structure is:
where E(Ytc) = expected number of hospitalization, assumed to follow an overdispersed Poisson distribution, for city c on day t; βc0 = model intercept for city c; ac = vector of regression coefficients for day of the week for city c; DOWt = categorical variable for day of the week on day t; ns(timet) = natural cubic spline of a variable representing time to adjust for long-term trends, with 7 degrees of freedom (df) per year; ns(temperaturetc) = natural cubic spline of temperature for city c for day t, with 3 df; ns(humiditytc) = natural cubic spline of humidity for city c on day t, with 4 df; and ns(pressuretc) = natural cubic spline of pressure for city c on day t, with 4 df. We estimated a separate effect for each city, using Equation 1. We then generated an overall effect estimate across the eight cities by combining city-specific effects, accounting for the estimates’ statistical uncertainty, using Bayesian hierarchical modeling through two-level normal independent sampling estimation.20
We examined the effect of air pollutants with single-day lags of the same day (lag 0) and previous days (lag 1, lag 2, and lag 3) and cumulative lags (lag 0–1, lag 0–2, and lag 0–3). For subsequent analyses, we selected the lag with the most certain effect estimates (largest t-statistics) by cause and pollutant, regardless of whether this lag had the largest central estimate. Analyses were stratified by cause, sex, and age (0–14, 15–64, 65–74, and ≥75 years). All analyses were conducted using R 2.10.1 (R Foundation for Statistical Computing, Vienna, Austria). Results were expressed as the percent change in morbidity per interquartile range (IQR) increase of each pollutant, based on an average of the city-specific IQR values.
Table 1 shows summary statistics of hospital admission and meteorologic and air pollution variables for each city. The mean number of daily hospital admissions for asthma ranged from 5.7 in Jeju to 36.3 in Seoul. Average daily mean temperature was highest in Jeju and the lowest in Incheon. Jeju had the lowest average levels of all air pollutants except O3. Average O3 concentration was higher in Jeju and Daegu, which also had higher overall temperatures, than most other cities. Generally, air pollutant concentrations for each city were not highly correlated with each other, except for some pollutant pairs (eg, NO2 and SO2) in some cities (eTable 2; http://links.lww.com/EDE/A685). On average, across the cities, the highest correlations were for CO and NO2 (average correlation 0.7), CO and SO2 (0.6), and SO2 and NO2 (0.6).
Table 2 shows some summary statistics of hospital admissions by cause, sex, and age. Those less than 15 years of age had a higher number of hospital admissions for allergy, asthma, or selected respiratory disease than other age groups. Most hospital admissions from cardiovascular disease were for those 15–64 years. Rates of hospital admissions were similar for men and women, with slightly higher rates for men for all hospitalizations.
We estimated city-specific risks and generated overall effects across cities combining each city-specific estimate of air pollution on morbidity by cause and pollutant (eTable 3 [http://links.lww.com/EDE/A685], Figure 1). O3 was linked to selected respiratory hospitalizations in most cities, with central estimates ranging from a 2.0% to 6.3% increase per IQR of O3 (19.4 ppb). Some cities showed lower levels of cardiovascular hospital admissions with higher O3 levels. O3 was associated with higher rates of allergic disease or asthma hospitalizations in almost all cities (eTable 3; http://links.lww.com/EDE/A685). Lag structures were based on the most certain effect estimates (largest t-statistic) from analysis investigating multiple lag structures (lags 0, 1, 2, 3, 0–1, 0–2, or 0–3) (eTable 4; http://links.lww.com/EDE/A685).
Figure 1 shows overall effect estimates across all cities for the association between air pollution and morbidity by cause. PM10, O3, and NO2 were associated with increased allergic diseases, asthma, and selected respiratory hospital admissions. PM10 and NO2 were associated with all causes of hospital admissions; hospital admission for asthma was most strongly associated for PM10, and hospital admission for allergic disease with NO2. For an IQR increase in PM10 (30.7 µg/m3), we observed an overall increase of 2.2% (95% posterior interval = 0.5%–3.9%), 2.8% (1.3%–4.4%), 1.7% (0.9%–2.6%), and 0.7% (0.0%–1.4%) in allergic, asthma, selected respiratory, and cardiovascular admissions, respectively. For an IQR increase in NO2 (12.2 ppb), the corresponding increases were 2.3% (0.6%–4.0%), 2.2% (0.3%–4.1%), 2.2% (0.6%–3.7%), and 2.2% (1.1%–3.4%). O3 was positively associated with all the hospitalization causes studied, except cardiovascular disease, for which the association was negative. SO2 was positively associated with selected respiratory and cardiovascular admissions, and CO was associated with increased cardiovascular hospitalization.
We performed sensitivity analyses for, (1) the degrees of freedom for time trend, (2) the degrees of freedom for meteorologic variables, (3) the lag structure for weather terms, (4) season-specific patterns, and (5) weekday-only analysis to examine other residual confounding factors. eFigure 2 (http://links.lww.com/EDE/A685) shows results for sensitivity analysis to the degree of freedom for time trend. Overall effect estimates were fairly robust for all causes and pollutants. eTable 5 (http://links.lww.com/EDE/A685) shows results for sensitivity analysis to the degree of freedom for meteorological variables, such as temperature, relative humidity, and pressure. Overall estimates were very robust for all causes and pollutants (eg, central estimates ranged from a 7.4–7.5% increase in hospitalizations from allergic disease per IQR increase in O3). Overall estimates using another lag structure (lag 0–3) for weather variables showed similar magnitude and direction of central estimates with original estimates, using current day lag (eTable 6; http://links.lww.com/EDE/A685).
We also conducted additional analysis to show the seasonal variation of the pollutants (eTable 7; http://links.lww.com/EDE/A685). Figure 2 and eTable 8 (http://links.lww.com/EDE/A685) show results for the season-specific analysis overall and by each city. For all air pollutants, except O3, hospitalizations for allergic disease, asthma, and selected respiratory disease showed higher positive associations in summer and mostly lower associations in winter. The effects of all air pollutants, except O3, on cardiovascular hospitalizations were higher in the fall. For O3, negative associations with cardiovascular admissions based on year-round estimates (Figure 1) weakened but remained negative in summer-only analysis. eTable 9 (http://links.lww.com/EDE/A685) shows the result of the weekday-only analysis for O3, in which the association between O3 and cardiovascular hospital admissions remained negative (eTable 9; http://links.lww.com/EDE/A685).
We further examined effect modification by sex and age (eTables 10 and 11; http://links.lww.com/EDE/A685). Figure 3 shows overall effects of air pollution on hospital admissions by sex. Although the central effect estimate was higher for one sex than the other for some pollutants and causes (eg, NO2 effect on allergic disease hospitalizations was higher in men and PM10 effect on selected respiratory hospitalizations was higher in women), these interactions were not significant for any pollutant or cause. There were no consistent trends of excess risk in one sex, although allergic disease or asthma hospital admissions were higher for men than women.
Figure 4 shows effects by age group (eTable 11; http://links.lww.com/EDE/A685). For allergic and asthma hospitalizations, the largest effects were found in the youngest age group (less than 15 years) for O3 and NO2. PM10 showed an overall trend of higher effects on allergic or asthma admissions for the youngest or oldest age groups, with higher effects on selected respiratory or cardiovascular admissions for the oldest group. Effects were generally stronger in the oldest groups compared with the youngest for cardiovascular admissions and all pollutants, except O3.
In a multicity study of ambient air pollution and hospital admissions in Korea, we found evidence of associations between morbidity and short-term exposure to air pollution. PM10, O3, and NO2 were associated with hospital admission for allergic disease, asthma, selected respiratory disease, and cardiovascular disease. SO2 was associated with selected respiratory and cardiovascular disease hospitalizations, and CO2 was associated with cardiovascular disease hospitalizations.
There were negative associations between O3 and cardiovascular disease (Figure 1). These negative associations remained in two sensitivity analyses, including season-specific and weekday-only analysis, although the negative associations were weaker in summer, when O3 levels are high. Hong et al21 also found a weak negative association between O3 and mortality in Incheon, Korea, but these findings are generally not consistent with most of the previous studies of O3 and morbidity.17 Factors such as weather, local and regional pollutant concentrations, or population characteristics may affect the direction or magnitude of effects, as could unidentified confounders, although these associations remained in stratified analysis by sex and age.
Taking all hospital admissions for allergic disease, asthma, selected respiratory disease, and cardiovascular disease, we found overall increases of 0.7–2.8% with PM10 and 2.2–2.3% with NO2, per IQR increase in exposure. Single-city Korean studies have also reported hospital admissions effects from these pollutants. eTable 12 (http://links.lww.com/EDE/A685) compares our findings with previous studies of hospital admissions and air pollution in Asia, including Korea. To compare effect estimates across the studies, we calculated estimates from other studies based on our IQRs. Although there was variation in the studies, exposure to ambient air pollution was associated with cause-specific hospital admissions. A 30.7-µg/m3 increase in PM10 in Seoul was associated with increase in hospitalization from cardiovascular and respiratory causes by 2.4% and 3.7%, respectively.13 Also in Seoul, Lee et al15 found that an IQR increase in PM10 (30.7 µg/m3) or NO2 (12.2 ppb) was associated with a 5.3% (95% confidence interval = 3.0–7.6%) or 12.4% (8.3–16.6%) increase in hospitalization for asthma among children under 15 years of age, respectively. Another study in Seoul reported that the effects of IQR increases for PM10 and NO2 increased hospitalization from ischemic cardiovascular diseases among the elderly population by 3.8% (95% confidence interval = 0.8%–6.9%) and 6.6% (2.5%–11%), respectively.17
We found seasonal patterns in the effects of air pollutants (eg, PM10, NO2). For example, PM10 effects were higher for hospital admissions from allergic disease, asthma, and selected respiratory disease in summer. Peng et al22 examined data for 100 US cities and found an association between PM10 and mortality in spring and summer. Another study in Korea found higher PM10 effects for nonaccidental mortality and hospital admissions attributable to cardiovascular and respiratory disease during the summer.13 The higher summer season effects could be explained by different exposure patterns to air pollutants (eg, higher levels of outdoor activities in summer) or different particulate matter composition by season (eg, maximum levels of more toxic particles in spring or summer).13,22 However, Bell et al23 found stronger associations in winter for fine particles and respiratory and cardiovascular hospitalizations. Lower effects in summer could relate to differences in ventilation from air conditioning systems.
Some age groups may be more susceptible, such as the young for allergic and asthma hospitalizations or the elderly for cardiovascular admissions, although our results did not conclusively identify susceptibility by age. Previous studies suggest that age was an important effect modifier for air pollution effects on hospitalization.24 However, studies of age-related susceptibility are limited in that most studies have examined the effect of air pollution in all ages combined, or in children or adults alone. Ko et al25 described effects of air pollution on asthma hospitalization rates in three age groups (0–14, 15–65, and >65 years) in Hong Kong and found stronger associations in the younger group, although such patterns were not consistent across pollutants. A study in Germany found that traffic-related outdoor air pollution was associated with atopy in children.26 Moorman et al27 reported that children are disproportionately affected by asthma, with the highest hospitalization in individuals under age 18 years. Silverman and Ito24 reported that asthma morbidity on higher-pollution days was consistently highest among children age 6 to 18 years.
Children may be more vulnerable to harmful effects of air pollution owing to their immature immune and defense systems and developing respiratory and other systems.28 Also, children can receive a higher exposure to ambient air pollutants than adults because they are physically more active, and spend more time outside.29
The elderly may be more vulnerable owing to biological reductions in lung or cardiac function or reduced resistance to infection.30 Susceptibility patterns by age may differ by location in relation to cultural differences (eg, children’s daily activities, social structure of the elderly living alone or with families). Barnett et al31 reported that CO, NO2, and PM were more strongly associated with adult cardiovascular hospital admissions in the elderly in Australia and New Zealand.
Schwartz and Morris32 found that PM10 was associated with daily admissions for ischemic heart disease for people age 65 years or older in Detroit. Fung et al33 reported that short-term effects of SO2 were associated with daily cardiac hospital admissions for people age 65 years or more in Ontario.
We found generally similar effects for men and women. Although several studies have reported different associations between air pollution and morbidity for women and men, evidence of effect modification by sex is not consistent. Luginaah et al34 examined risk of respiratory hospitalization among adults in Canada and reported associations among women. Granados-Canal et al7 found stronger short-term associations between air pollution and respiratory hospital admissions in men in greater Paris. In an analysis of PM10 and hospital admissions owing to respiratory disease, Yi et al13 reported that women had greater effects than men during the entire period, whereas men showed stronger effects during summer. Lin et al35 reported respiratory hospitalizations associated with PM10 among boys, and with NO2 among girls. Possible explanations for differences by sex are biologic differences (eg, hormonal status, lung size and growth, PM deposition, gas absorption, and inflammation) between men and women, exposure differences such as occupational exposures, and differences in activity patterns (eg, healthcare management).36 Also, in some regions, including our study area, women tend to be poorer or have lower socioeconomic status.37
We found that PM10 was associated with hospital admission for allergic disease, asthma, selected respiratory disease, and cardiovascular disease. PM2.5 is not routinely measured in Korea, although efforts are underway to expand the monitoring network for PM2.5 total mass.38 We previously estimated cause-specific mortality effects of PM2.5 mass and constituents in Seoul, using our own pollution measurements and found positive associations between PM2.5 mass and total, respiratory, and cardiovascular mortality. Additional work is needed on other size fractions of PM, such as PM2.5, and on other health outcomes, such as hospital admissions.
Most studies in Asia (including Korea) of air pollution effects on hospital admissions are based on analysis from a single city. One strength of this study is that we included several cities across Korea to avoid publication bias and generate an overall estimate representing a broader region. Results from single-city studies may not be generalizable beyond the city under study, and combining effect estimates across single-city studies can be challenging owing to the use of different modeling approaches.26 Our findings provide evidence that ambient air pollution is associated with risk of hospital admissions and support the hypothesis of increased susceptibility among the young or the elderly to pollution effects on specific diseases.
We thank the Korea Centers for Disease Control and Prevention, National Health Insurance Corporation for providing health insurance data.
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