Open Access, Peer-reviewed
eISSN 2052-4374
    Ann Occup Environ Med. 2022;34(1):e21. English.
    Published online Aug 22, 2022.
    https://doi.org/10.35371/aoem.2022.34.e21
    Copyright © 2022 Korean Society of Occupational & Environmental Medicine
    Original Article

    Association between exposure to particulate matter and school absences in Korean asthmatic adolescents

    Seongmin Jo,1 Kiook Baek,1 Joon Sakong,1,2 and Chulyong Park1,2
      • 1Department of Occupational and Environmental Medicine, Yeungnam University Hospital, Daegu, Korea.
      • 2Department of Preventive Medicine and Public Health, Yeungnam University College of Medicine, Daegu, Korea.
    • Correspondence: Chulyong Park. Department of Occupational and Environmental Medicine, Yeungnam University Hospital, 170 Hyeonchung-ro, Nam-gu, Daegu 42415, Korea. Email: ironyong@gmail.com
    Received April 12, 2022; Revised June 28, 2022; Accepted July 28, 2022.

    This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Abstract

    Background

    Because particulate matter (PM) and asthma are closely related, the prevalence of school absence among adolescents with asthma can be affected by the concentration of PM. We aimed to investigate the relationship between school absences due to asthma and the total number of days that the PM concentration exceeded the standard.

    Methods

    We used the data from the 16th Korea Youth Risk Behavior Survey and the PM levels of 17 metropolitan cities and provinces gathered from the AirKorea. Information on the characteristics of asthmatic adolescents and the prevalence of school absence was obtained using a questionnaire, while the PM levels based on the total number of days with poor and very poor PM grades were collected from the AirKorea website. Both χ2 test and logistic regression analysis were performed using the weights presented in the original dataset.

    Results

    In the case of particulate matter of 10 microns in diameter or smaller (PM10), the odds ratio (OR) after adjusting for confounders (sex, school year, body mass index, smoking history, diagnosis of allergic rhinitis, diagnosis of atopic dermatitis and city size) was 1.07 (95% confidence interval [CI]: 1.01–1.13) for absents due to asthma when the total days of poor and very poor grades of PM10 (81 μg/m3 or higher) increased by 1 day. In the analysis of particulate matter of 2.5 microns in diameter or smaller (PM2.5), the OR after adjusting for confounders was 1.01 (95% CI: 1.00–1.03) for absents due to asthma when the total number of days with poor and very poor PM2.5 grades (36 μg/m3 or higher) increased by 1 day.

    Conclusions

    A significant association was observed between the total number of days of poor and very poor PM10 and PM2.5 grades and school absence due to asthma; PM can cause asthma exacerbation and affect the academic life.

    Keywords
    Adolescent; Air pollution; Asthma; Asthma exacerbation; Particulate matter

    BACKGROUND

    The annual average concentration of particulate matter (PM) in major cities in Korea is decreasing overall, but is still higher than that of major cities in other Organization for Economic Co-operation and Development (OECD) countries, such as New York and London.1 Several studies have reported that particulate matter of 10 microns in diameter or smaller (PM10) and particulate matter of 2.5 microns in diameter or smaller (PM2.5) can affect the cardiovascular system and cause ischemic heart disease,2, 3 cancer,4, 5 and increasing mortality.6 PMs are particles suspended in the air and can affect the respiratory system7, 8, 9 and cause asthma10, 11 when inhaled. According to the 2015 Global Burden of Disease Study, the number of asthma patients worldwide is estimated at 358.2 million, an increase of about 12.6% over 1990. Through this, it can be seen that the prevalence and burden of asthma are increasing worldwide. The risk of asthma occurrence and exacerbation can increase due to various environmental risk factors and can affect patients’ daily lives. Several studies have reported an association between asthma and air pollution. Epidemiologically, several studies have reported an association between PM exposure and asthma incidence.12, 13 In a previous study, the occurrence of asthma symptoms among children and adolescents was related to exposure to PM,14, 15 and a significant positive correlation was found between national or regional air pollutant data and hospitalization due to worsening of asthma in children and adolescents in Denmark, Greece, and Seoul.16, 17, 18 Beyond the primary health effects of PM, some studies have reported on the burden of life among adolescents due to asthma and PM exposure, such as increasing prevalence of school absence due to worsening of asthma symptoms19, 20 or exceeding concentrations of air pollutants such as PM.21, 22

    Previous studies of PM and asthma in adolescents have been limited to schools or cities and studies analyzed by combining surveys of adolescents nationwide and atmospheric data measured nationwide were also insufficient. In addition, the emphasis was on clinical approaches, such as hospitalization or emergency room visits due to worsening asthma. This study aimed to identify the relationship on a national scale by analyzing the relationship between school absences due to asthma and PM using the Korea Youth Risk Behavior Survey (KYRBS) data of 57,925 students, and to infer the deterioration of asthma, which is difficult to understand outside the current medical system, through school absences due to asthma. Furthermore, this study aimed to investigate the effect of PM on the daily and academic lives of adolescents diagnosed with asthma.

    METHODS

    Study population and participants

    KYRBS is an annual survey conducted in Korean middle and high school students by the Korea Disease Control and Prevention Agency (formerly Korea Centers for Disease Control and Prevention) in 2005. In this survey, various questionnaires were administered to evaluate the Korean adolescents’ health behavior and calculate the health indicators necessary for planning and evaluating the youth health promotion programs, including management of asthma-related symptoms or promotion of lifestyle changes.23

    This study was based on the 16th Korea Youth Risk Behavior Survey 2020. Multi-stage cluster sampling of 57,925 students from 800 schools (400 middle schools and 400 high schools) in 17 provinces in Korea was conducted online from August to November 2020. The sampling process can be divided into population stratification, sampling allocation, and sampling stages. In the stratification stage, the population was divided into 117 layers using 39 regional groups and school levels as stratification variables to minimize sample errors. In the sample distribution stage, the sample size was set to 400 middle schools and 400 high schools, and five middle and high schools were allocated first by the 17 cities and provinces. The proportional allocation method was applied to match the population and sample composition ratios for each stratified variable. For sampling, a stratified colony extraction method was used. The primary extraction unit was a school selected by the permanent random number extraction method, and the secondary extraction unit was a class randomly extracted for each grade. Students who were absent for a long time, unable to participate in the survey by themselves, and students with difficulty deciphering text were excluded from the sample. The details are described elsewhere.23, 24

    School absence due to asthma

    In this study, those who answered “Yes” to the question “Have you ever been diagnosed with asthma by a doctor since you were born?” were selected as the study participants. Those who responded “1–3 days”, “4–6 days”, and “7 days or more” to the question “In the last 12 months, how many days have you been absent due to asthma?” were classified as having experienced school absence due to asthma.

    Participant characteristics

    The characteristics of the participants were sex, school year, and body mass index (BMI) based on the self-reported height and weight, smoking history, diagnosis of allergic rhinitis, diagnosis of atopic dermatitis, and city size. School year was divided into middle school and high school. Patients with a BMI of 25 kg/m2 were regarded as obese. If the participants answered “Yes” to any of the three questions (“Have you ever smoked one or two sips of regular cigarettes?”, “Have you ever used liquid e-cigarettes containing nicotine?”, and “Have you ever used heat-not-burn tobacco products?”), they were considered to have a smoking history. The presence of allergic rhinitis and atopic dermatitis was evaluated using the questions “Have you ever been diagnosed with allergic rhinitis by a doctor since you were born?” and “Have you ever been diagnosed with atopic dermatitis by a doctor since you were born?” If the study participants had missing values in the aforementioned variables, they were excluded from the analysis. Cities were divided into large cities including metropolises, medium-to small cities, and counties including regions which did not fit the other classifications.

    PM analysis

    PM exposure was assessed based on the data obtained from the AirKorea website,1 which has released data on outdoor air quality levels nationwide on a real-time basis since 2002. A total of 794 monitoring stations are operated by the Ministry of Environment and local governments nationwide. The β-ray absorption method is used to measure PM10 and PM2.5. The mass concentration of particulate matter was measured using β-ray intensity which is measured as they are emitted from a source, and again after passing through particulate matter filtered from the air over a set period of time. The difference in intensity is used to calculate the mass concentration of the particulate matter.25 Only statistical data satisfying an effective processing ratio of 75% were obtained.26

    Use of air quality index to evaluate participants’ PM exposure

    A modified air quality index, known as the comprehensive air quality index (CAI), was used. The CAI is used to describe the ambient air quality about SO2, CO, O3, NO2 and PM based on the health risk of air pollution. In this study, only PM10 and PM2.5 were targeted, the explanation of CAI was written based only on PM. The CAI of PM was classified into four grades (good, moderate, poor, and very poor). With regard to the PM10 levels, the daily average of 0–30 μg/m3 was classified as good, 31–80 μg/m3 as moderate, 81–150 μg/m3 as poor, and 151 μg/m3 or higher as very poor. In terms of PM2.5 levels, the daily average of 0–15 μg/m3 was classified as good, 16–35 μg/m3 as moderate, 36–75 μg/m3 as poor, and 76 μg/m3 or higher as very poor. In this study, the number of days with poor and very poor PM grades in 17 metropolitan cities and provinces from August 2019 to July 2020, before the 16th KYRBS was conducted in August 2020, were summed to evaluate the air quality level. Subsequently, the annual PM exposure of each participant was estimated by the total number of days with poor and very poor PM grades based on the address of each participant’s cities and provinces.

    Statistical analysis

    The χ2 test was used to analyze the relationship between school absence due to asthma and the general characteristics of the study participants and the total number of days with poor and very poor PM grades. The calculated data of the dependent variables were expressed as numbers, estimated numbers, estimated standard errors, and estimated percentages for each categorical variable. The estimated values for the dependent variable were obtained using complex sample analysis that uses an estimation formula that applies strata, clusters, and weights. In the sampling process, the population was stratified to minimize sample error, and the regional group and school level were divided into stratification variables and used as the strata. The weight was obtained by multiplying the reciprocal extraction rate by the reciprocal response rate and the weight post-correction rate. The sampling rate was calculated reflecting the sampling process of the sample design, and the response rate was calculated using the response rate by grade of the sample school and was calculated to be the same as the number of middle and high school students nationwide as of April 2020. An estimation formula reflecting the stratification, clustering, and weight information of the complex sampling was used. The details have been described elsewhere.23

    Among 3.238 adolescents diagnosed asthma, a logistic regression model was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) between absence from school due to asthma and total number of days with poor and very poor PM grades. Statistical analyses of the data were conducted in 2 ways. First, the OR of school absence due to asthma was calculated when the total number of days with poor and very poor PM grades increased by 1 day. After that, the median values of the exposure frequency of poor and very poor PM grades and OR of school absence due to asthma were calculated by setting the median as a reference point for dividing the independent variable in the 2 groups. The results of both methods mentioned above were presented before and after adjusting for confounding factors. The covariates that might confound the association between the total number of days with poor and very poor PM grades and school absence due to asthma included sex,27 BMI,28, 29 school year (age),15 smoking history,30, 31, 32 diagnosis of allergic rhinitis,33 diagnosis of atopic dermatitis34 and city size.35

    As an additional analysis, subgroup analysis was performed on patients who received treatment within the last 12 months using complex logistic regression. In response to the question, “Have you taken or inhaled medicine to treat asthma in the last 12 months?” Students who responded that “I was treated regularly even if I had no symptoms” and “I was treated only when I had symptoms” were classified into one group and “I was not treated” into another group. In addition, complex ordinary logistical regression analysis was conducted by placing dependent variables mentioned above as ordinal variables (“None,” “1–3 days,” “4–6 days,” and “7 days or more”) presented by raw data of KYRBS.

    The χ2 test and logistic regression analysis were performed using the weights presented in the original dataset. IBM SPSS version 27.0 for Windows (IBM Corp., Armonk, NY, USA) was used to perform all statistical analyses, and a p-value of < 0.05 was considered significant.

    Ethics statement

    This study was approved by the Institutional Review Board (IRB) of Yeungnam University Hospital (IRB No. 2022-01-052) and waived the requirement for informed consent.

    RESULTS

    Participants’ characteristics

    Among the 54,948 respondents of the 16th KYRBS, 3,238 were selected as participants of this study. Table 1 shows the comparison between the general characteristics and school absences due to asthma. Among the adolescents diagnosed with asthma, 1,834 (56.8%) were men and 1,404 (43.2%) were women. A total of 129 (7.2%) men and 85 (6.3%) women were absent due to asthma, showing no significant difference between the sexes (p = 0.306). When classified according to BMI, 2,565 patients (79.9%) had normal weight, while 674 (20.1%) were obese. No significant difference was observed in the prevalence of school absence due to asthma between 169 (6.8%) patients with normal weight and 45 (6.7%) patients with obesity (p = 0.863). A total of 1,504 middle school students (43.2%) and 1,734 (56.8%) high school students were found to have asthma. When middle and high school students were compared, 125 (8.6%) and 89 (5.5%), respectively, were absent due to asthma, with the prevalence of school absence significantly higher among middle school students (p = 0.001). Among the students diagnosed with asthma, 2,834 (87.5%) had no smoking experience, while 404 (12.5%) had smoking experience. No significant differences were observed in the prevalence of school absences due to asthma between 180 (6.6%) students in the non-smoking group and 34 (8.4%) students in the smoking group (p = 0.162). Of the 2,004 patients (62.9%) diagnosed with allergic rhinitis, 143 (7.3%) were absent due to asthma. This ratio was higher than that of 71 (6.0%) students who were absent due to asthma among the 1,234 (37.1%) participants who were not diagnosed with allergic rhinitis, but the difference was not significant (p = 0.191). Of the 2,000 patients (61.5%) who had not been diagnosed with atopic dermatitis in their lifetime, 136 (7.1%) were absent from school due to asthma exacerbation. Of the 1,238 patients (38.5%) who had been diagnosed with atopic dermatitis in their lifetime, 78 (6.4%) were absent from school due to asthma exacerbation. However, no significant difference was observed between the two groups (p = 0.434). Of the students diagnosed with asthma, 1,388 (42.3%) lived in large cities, 1,612 (52.2%) lived in medium-to small cities, and 238 (5.5%) lived in counties. No significant differences were observed in the prevalence of school absence due to asthma among 79 (5.8%) students in the large city group, 122 (7.6%) students in the medium-to small city group, and 13 (7.0%) in the county group (p = 0.163).

    Table 1
    General characteristics of the study participants and absence from school due to asthma

    PM results by region

    Table 2 shows the comparison between the total number days with poor and very poor PM grades within 1 year and prevalence of school absence due to asthma in 17 metropolitan cities and provinces nationwide. The total number of days with poor and very poor PM10 grades was the lowest in Gwangju and Jeollanam-do (1 day), followed by Daejeon, Jeollabuk-do, and Gyeongsangnam-do (2 days). Meanwhile, Gyeonggi-do had the highest total number of days with poor and very poor PM10 grades (9 days), followed by Chungcheongnam-do (8 days), and Seoul Metropolitan City (7 days). In terms of PM2.5, Gyeongsangnam-do had the lowest total number of days with poor and very poor PM grades (6 days), followed by Ulsan Metropolitan City and Jeollanam-do (8 days). By contrast, Chungcheongbuk-do had the highest total number of days with poor and very poor PM grades (45 days), followed by Gyeonggi-do and Chungcheongnam-do (43 days). Of the 17 metropolitan cities and provinces, the prevalence of asthma was highest in Chungcheongnam-do (7.6%), followed by Chungcheongbuk-do (6.9%), Daegu (6.8%), and Jeju-do (3.6%). The ratio of school absences due to asthma was highest in Chungcheongnam-do (9.4%), followed by Jeollanam-do (9.3%) and Busan and Gyeonggi-do (8.7%). On the contrary, Daegu and Sejong had the lowest ratio of school absences due to asthma (2.2%).

    Table 2
    Total number of days with poor and very poor PM grades in 17 metropolitan cities and provinces in Korea and school absence due to asthma

    Association of PM exposure and asthma exacerbation-related absence

    Table 3 shows the OR of school absence due to asthma when the total number of days with poor and very poor PM grades increased by 1 day. In terms of PM10, the OR before adjusting for confounders was 1.07 (95% CI: 1.01–1.14, p = 0.024) when the total days with poor and very poor PM10 grades increased by 1 day. After adjusting for confounding factors, the OR remained significant at 1.06 (95% CI: 1.00–1.13, p = 0.045). In the analysis of PM2.5, the OR before adjusting for confounders was 1.01 (95% CI: 1.00–1.03, p = 0.017) when the total number of days with poor and very poor PM2.5 grades increased by 1 day. After adjusting for confounding factors, the OR remained significant at 1.01 (95% CI: 1.00–1.02, p = 0.041).

    Table 3
    OR of school absence due to asthma when the total number of days with poor and very poor PM grades increased by 1 day

    Sensitivity analysis

    Table 4 shows the OR of absence from school due to asthma by setting the median exposure frequency of poor and very poor PM concentrations. First, the median values of the total number days with poor or very poor PM10 concentrations divided into 1–6 days and 7–9 days were 48.6% and 51.3%, respectively. The OR for 1–6 days was 1.69 (95% CI: 1.28–2.23, p < 0.001) before adjusting for confounders. After adjusting for confounding factors, the OR remained significant at 1.66 (95% CI: 1.25–2.19, p < 0.001). Second, the median value of the total number of days with poor or very poor PM2.5 grades divided into 6–31 days and 36–45 days were 44.3% and 55.6%, respectively. The OR for 6–31 days was 1.66 (95% CI: 1.25–2.21, p = 0.001) before adjusting for the confounding factors. After adjusting for the confounders, the OR was still significant at 1.60 (95% CI: 1.19–2.15, p = 0.002).

    Table 4
    OR of school absence due to asthma by setting the median exposure frequency of poor and very poor PM concentrations

    Subgroup analysis

    As an additional analysis, subgroup analysis of the group treated within 12 months and ordinal logistic regression was performed with the number of days of school absence due to asthma as the dependent variable. Supplementary Table 1 shows that 1,993 (62.5%) students received regular or irregular asthma treatment within 12 months, and 1,245 (37.5%) students did not receive treatment. A total of 137 students were absent for 1–3 days, 31 students for 4–6 days, and 46 students for ≤ 7 days. Supplementary Table 2 shows the group that received regular or irregular asthma treatment within 12 months. School absences increased significantly as the total number of days with poor and very poor PM increased. (adjusted OR of PM10: 1.11, 95% CI: 1.03–1.18, p = 0.004, adjusted OR of PM2.5: 1.01, 95% CI: 1.00–1.03, p = 0.032). Supplementary Table 3 shows the group that received regular or irregular asthma treatment within 12 months, school absences increased significantly in the group with 7–9 days of poor and very poor PM10 grades and 36–45 days of poor and very poor PM2.5 grades (adjusted OR of PM10: 1.99, 95% CI: 1.44–2.77, p < 0.001, adjusted OR of PM2.5: 1.78, 95% CI: 1.27–2.49, p = 0.001). Supplementary Table 4 shows a significant increase in the total number of days of school absence when the total number of days with poor and very poor PM grades increased by 1 d. (adjusted OR of PM10: 1.06, 95% CI: 1.00–1.13, p = 0.039, adjusted OR of PM2.5: 1.01, 95% CI: 1.00–1.02, p = 0.038). Supplementary Table 5 shows significantly increased total days of school absence in the group with 7–9 days of poor and very poor PM10 grades, and 36–45 days of poor and very poor PM2.5. grades (adjusted OR of PM10: 1.66, 95% CI: 1.26–2.20, p < 0.001, adjusted OR of PM2.5: 1.60, 95% CI: 1.20–2.15, p = 0.002).

    DISCUSSION

    This study investigated whether adolescents with asthma were absent from school due to worsening of asthma symptoms using the AirKorea data and the 16th KYRBS. A significant positive was found association between the total number of days with poor and very poor PM grades and school absences due to asthma.

    PM, can be naturally generated, such as dust, pollen, soil particles, and forest fires, or artificially created, such as industrial, construction, mining, smoking, and fossil fuels of urban transportation and power plants, is a representative air pollutant of which the human body is frequently exposed to.7, 36 Depending on the size of the PM, the degree of invasion and health effects on the respiratory system differ. PM10, which is 2.510 μm in size, acts on the nasal cavity, pharynx, and primary bronchi. PM2.5, which is 0.1–2.5 μm in size, acts on the peripheral bronchus and alveoli.7, 8, 9 PM affects the human respiratory system through several mechanisms, including innate/acquired immunity, oxidative stress, and bronchial remodeling.10 Diesel exhaust particles, known as PM10, can worsen the allergic symptoms by increasing the levels of proinflammatory cytokines in epithelial cells and inducing the release of neutrophils and eosinophils.37, 38 Furthermore, PM10 induces antigen-presenting cell mediated responses that modulate macrophages to contribute to innate immunity39, 40 and T helper (Th) cell-mediated responses to secrete cytokines, such as interleukin (IL)-10.41 In terms of PM2.5, high concentrations of PM2.5 increases the production of IL-13 and IL-17 and cause an imbalance between Th1 and Th2 cells by regulating their cytokines.42, 43 Airway damage and inflammation due to oxidative stress also occur. PM produces oxidants and free radicals. They oxidize the airway cells and damage the DNA, thereby causing airway damage.44 Through these mechanisms, it has been hypothesized that PM is associated with the development and exacerbation of asthma and other allergic diseases.

    In this study, as the total number of days with poor and very poor PM grades increased, the correlation between school absence due to asthmatic symptoms significantly increased. These results are consistent with those of previous studies. Iskandar et al.16 reported that exposure to higher concentrations of PM could trigger hospitalization due to asthma in the Danish 0–18-year-old study group. Every time the interquartile range of the average concentration of PM10 and PM2.5 for 5 days increased by 1 quartile, the ORs of hospitalization due to asthma were 1.07 (95% CI: 1.03–1.12) for PM10 and 1.09 (95% CI: 1.04–1.13) for PM2.5. Samoli et al.18 reported that air pollution in Greece affects the rate of pediatric hospitalization due to acute exacerbation of asthma. When the concentration of PM10 increased by 10 μg/m3, the number of pediatric asthma hospital admissions increased by 2.54% (95% CI: 0.06%–5.08%). Lee et al.17 reported a relationship between air pollution and asthma exacerbation in children with asthma in Seoul. The estimated relative risk of hospitalization due to asthma was 1.07 (95% CI: 1.04–1.11) for PM ≤ 10 μm, while every interquartile range increased (interquartile range = 40.4 μg/m3). Meng et al.45 reported that the increased ORs of experiencing daily or weekly asthma symptoms were 1.29 (95% CI: 1.05–1.57) for PM10 and 1.82 (95% CI: 1.11–2.98) for PM2.5 when the PM concentration increased by 10 μg/m3 after adjusting for age, sex, race/ethnicity, poverty level, and insurance status.

    Several studies have reported that respiratory diseases including asthma may affect academic productivity and increase the frequency of school absenteeism. Zhang et al.22 reported that the moving average of PM2.5 for 3 days was significantly associated with school absence, and the OR of school absence was 1.37 (95% CI: 1.07–1.74). Geng et al.21 reported that the ORs of respiratory-related school absence were 1.021 (95% CI: 1.019–1.024) and 1.015 (95% CI: 1.014–1.017) for each 10 μg/m3 increase in PM2.5 and PM10, respectively, in Qingdao. Kim et al.46 reported school absence due to asthma and other allergic diseases based on the previous KYRBS. This study showed that asthma was negatively correlated with better school performance. School performance was divided into five groups, and the ORs for school absence due to asthma from the highest to the lowest grade were 0.74 (95% CI: 0.66–0.83), 0.87 (95% CI: 0.79–0.96), 0.83 (95% CI: 0.75–0.91), and 0.93 (95% CI: 0.85–1.02), respectively (p < 0.001).

    This study has several limitations. First, although the association between the total number of days with poor and very poor PM grades and absence from school due to asthma was significant, it was difficult to determine causality because the KYRBS is a nationwide cross-sectional study. Second, rather than measuring the exposure level individually, exposure was assessed using an ecological approach, assuming the CAI in the cities and provinces as personal exposure. Therefore, the evaluation of each individual’s exposure to PM was crude. Although this exposure assessment was not ideal, it was necessary for this study because KYRBS sampling was created by integrating several cities and counties and it is difficult to match the AirKorea data to a single city, county, or district. However, previous studies have been conducted in a similar method, which covered a broad area and used an air quality index47, 48 and CAI is an indicator designed to easily inform the public of their daily exposure to PM, so it is thought to be a reasonable substitute for personal exposure. Third, asthma symptoms were assessed indirectly using a self-reported questionnaire without any clinical data or hospitalization records. Therefore, it was difficult to objectively distinguish the grade of asthma exacerbation and could not evaluate the correlation between the total number of days with poor and very poor PM grades and the severity of asthma symptoms. Lastly, this study could not reflect the individual factors, such as previous medical history, family history, and living environment because it was conducted using secondary data.

    Despite its limitations, this study has some strengths; it was conducted based on the KYRBS and the AirKorea data. KYRBS was aggregated from a large study population in Korea, and the AirKorea data were collected from different stations for PM measurement installed nationwide. In addition, it has the advantage of being able to observe health behaviors due to their worsening asthma while objective medical access to asthma symptoms is difficult. Therefore, this study could confirm the significant relationship between absence due to worsening of asthma and period with exceeding PM concentration based on the standard for Korean adolescent population, compared with other studies that were conducted in a limited area. The approach used in this study could help promote the health of adolescents with asthma.

    CONCLUSIONS

    In this study, a significant correlation between total number of days with poor and very poor PM10 and PM2.5 grades and school absence due to asthma was observed, which showed that the PM in the atmosphere increases the likelihood of asthma exacerbation among Korean adolescents diagnosed with asthma and can affect their academic life. However, further studies are needed to determine the possible effects of PM on worsening asthma in children and adolescent patients and their lifestyle and academic performance.

    SUPPLEMENTARY MATERIALS

    Supplementary Table 1

    Detailed characteristics of the asthma questionnaire among the study participants

    Click here to view.(42K, xls)

    Supplementary Table 2

    Odds ratio of school absence due to asthma when the total number of days with poor and very poor PM grades increased by 1 day, grouped by asthma treatment within 12 months

    Click here to view.(40K, xls)

    Supplementary Table 3

    Odds ratio of school absence due to asthma by setting the median exposure frequency of poor and very poor PM concentrationsa, grouped by asthma treatment within 12 months

    Click here to view.(39K, xls)

    Supplementary Table 4

    Odds ratio of school absence due to asthma when the total number of days with poor and very poor PM grades increased by 1 day, using complex ordinary logistic analysisa

    Click here to view.(38K, xls)

    Supplementary Table 5

    Odds ratio of school absence due to asthma by setting the median exposure frequency of poor and very poor PM concentrations using complex ordinary logistic analysisa

    Click here to view.(39K, xls)

    Notes

    Competing interests:The authors declare that they have no competing interests.

    Author Contributions:

    • Conceptualization: Jo S, Park C.

    • Data curation: Jo S.

    • Formal analysis: Jo S.

    • Investigation: Jo S.

    • Methodology: Jo S, Baek K.

    • Project administration: Park C.

    • Supervision: Baek K, Sakong J, Park C.

    • Writing - original draft: Jo S.

    • Writing - review & editing: Baek K, Sakong J, Park C.

    Abbreviations

    BMI body mass index
    CAI comprehensive air quality index
    CI confidence interval
    IL interleukin
    KYRBS Korea Youth Risk Behavior Survey
    OECD Organization for Economic Co-operation and Development
    OR odds ratio
    PM particulate matter
    PM10 particulate matter of 10 microns in diameter or smaller
    PM2.5 particulate matter of 2.5 microns in diameter or smaller
    SE standard error
    Th T helper

    ACKNOWLEDGEMENTS

    The authors wish to thank the Ministry of Education, Ministry of Health and Welfare, and Korea Centers for Disease Control and Prevention, which provided the raw data.

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    MeSH Terms
    Adolescent*
    Air Pollution
    Asthma
    Body Mass Index
    Dataset
    Dermatitis, Atopic
    Diagnosis
    Humans
    Korea
    Logistic Models
    Odds Ratio
    Particulate Matter*
    Prevalence
    Rhinitis, Allergic
    Risk-Taking
    Smoke
    Smoking
    Weights and Measures
    Metrics
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    Tables
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