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Short-Term Effects of Multiple Outdoor Environmental Factors on Risk of Asthma Exacerbations: Age-Stratified Time-Series Analysi

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Short-Term Effects of Multiple Outdoor Environmental Factors on Risk of Asthma Exacerbations: Age-Stratified Time-Series Analysi Short-term effects of multiple outdoor environmental factors on risk of asthma exacerbations: Age-stratified time-series analysis Seung Won Lee, MD, PhD,a,b* Dong Keon Yon, MD,c,d* Chase C. James, MSc,e Shinhae Lee, MD,c Hyun Yong Koh, MD, PhD,f Youn Ho Sheen, MD, PhD,g Jae-Won Oh, MD, PhD,h Man Yong Han, MD,c and George Sugihara, PhDe Seongnam, Seoul, and Guri, Korea, San Diego, Calif, and Boston, Mass GRAPHICAL ABSTRACT Background: Although the different age groups had differences Methods: We performed an age-stratified analysis of the short- in sensitivity of asthma exacerbations (AEs) to environmental term effects of 4 groups of outdoor environmental factors on factors, no comprehensive study has examined the age-stratified AEs in Seoul Metropolitan City (Korea) from 2008 and 2012. effects of environmental factors on AEs. The statistical analysis used a Poisson generalized linear Objective: We sought to examine the short-term effects in age- regression model, with a distributed lag nonlinear model for stratified groups (infants, preschool children, school-aged identification of lagged and nonlinear effects and convergent children, adults, and the elderly) of outdoor environmental cross-mapping for identification of causal associations. factors (air pollutants, weather conditions, aeroallergens, and Results: Analysis of the total population (n 5 10,233,519) respiratory viral epidemics) on AEs. indicated there were 28,824 AE events requiring admission to an From aGraduate School of Medicine, CHA University, Seongnam; bthe Department of Disclosure of potential conflict of interest: The authors declare that they have no relevant Data Science, Sejong University College of Software Convergence, Seoul; cthe conflicts of interest. Department of Pediatrics, CHA Bundang Medical Center, CHA University Received for publication November 8, 2018; revised August 13, 2019; accepted for pub- School of Medicine, Seongnam; dthe Medical Corps, Republic of Korea Army, lication August 21, 2019. Seongnam; eScripps Institution of Oceanography, University of California San Diego; Available online September 16, 2019. fF.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical Corresponding author: Man Yong Han, MD, PhD, Department of Pediatrics, CHA Uni- School, Boston; gthe Department of Pediatrics, CHA Gangnam Medical Center, versity School of Medicine, 351 Yatap-dong, Bundang-gu, Seongnam, Gyeonggi-do, CHA University School of Medicine, Seoul; and hthe Department of Pediatrics, 463-712, Republic of Korea. E-mail: [email protected]. Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri. The CrossMark symbol notifies online readers when updates have been made to the *These authors contributed equally to this work. article such as errata or minor corrections Supported by CHA University School of Medicine, Seongnam, Republic of Korea. This 0091-6749/$36.00 Ó work was partially supported by the Influenza and Respiratory Viruses Surveillance 2019 American Academy of Allergy, Asthma & Immunology Project of the Korea Centers for Disease Control and Prevention, Republic of Korea. https://doi.org/10.1016/j.jaci.2019.08.037 1542 J ALLERGY CLIN IMMUNOL LEE ET AL 1543 VOLUME 144, NUMBER 6 emergency department during the study period. Diurnal temperature range had significant effects in pediatric (infants, Abbreviations used preschool children, and school-aged children) and elderly AE: Asthma exacerbation (relative risk [RR], 1.056-1.078 and 1.016, respectively) subjects. CO: Carbon monoxide Tree and weed pollen, human rhinovirus, and influenza virus DLNM: Distributed lag nonlinear model had significant effects in school-aged children (RR, 1.014, 1.040, DTR: Diurnal temperature range HRV: Human rhinovirus 1.042, and 1.038, respectively). Tree pollen and influenza virus ICD-10: International Classification of Diseases, 10th revision had significant effects in adults (RR, 1.026 and 1.044, IFV: Influenza virus respectively). Outdoor air pollutants (particulate matter of NO : Nitrogen dioxide _ m 2 <10 m in diameter, nitrogen dioxide, ozone, carbon monoxide, O3: Ozone and sulfur dioxide) had significant short-term effects in all age PM10: Particulate matter of 10 mm or less in diameter groups (except for carbon monoxide and sulfur dioxide in RR: Relative risk infants). RSV: Respiratory syncytial virus Conclusion: These findings provide a need for the development SO2: Sulfur dioxide of tailored strategies to prevent AE events in different age SS: Solar sunshine hours groups. (J Allergy Clin Immunol 2019;144:1542-50.) Key words: Asthma exacerbation, air pollutant, weather condition, aeroallergen, human rhinovirus, influenza virus Outdoor air pollutants, weather conditions, outdoor aeroallergens, and burden of viral respiratory tract infections Asthma exacerbations (AEs) are especially common in Table I lists the multiple items in 4 major categories that could affect AE: young and elderly subjects1 and are associated with seasonal outdoor air pollutants, weather conditions, outdoor aeroallergens, and burden variations of environmental factors, such as weather condi- of viral respiratory tract infections. Data on air pollutants were from the Min- tions, aeroallergens, and ambient air pollution.2 There is an istry of the Environment (Korean government); daily mean concentrations were recorded at 34 observatory stations in Seoul, and daily average values autumnal peak of AEs in children caused by human rhinovirus were calculated for each air pollutant. Data on meteorological factors in Seoul (HRV) and a winter peak in elderly subjects from the United 8 3 4,5 (latitude, 37.57 N) were from the Korea Meteorological Administration. Out- States and Korea caused by the influenza virus (IFV). door aeroallergen concentrations were from the Asian-Pacific Pollen Allergy 6-8 9 Ambient air pollutants, aeroallergens, and certain weather Network; daily counts of tree, grass, and weed pollens were recorded from 3 5,10,11 conditions. can exacerbate the effects of these viral respi- pollen-collecting station in Seoul18 using 7-day recording volumetric spore ratory tract infections.2 Furthermore, AEs are more common in traps (Burkard Manufacturing, Rickmansworth, United Kingdom). Weekly infants11-13 and the elderly14 after exposure to air pollutants nationwide viral respiratory tract detection rates were calculated from the re- and changes in weather. sults of respiratory virus PCR from the Korea Centers for Disease Control and 4 Although previous studies have identified individual factors Prevention. associated with the risk of AEs,2,5-11,15 no comprehensive study has examined the effects of all these factors on AEs. Thus we Statistical analysis comprehensively examined the short-term effects of multiple A Poisson generalized linear regression model combined with a distrib- environmental factors (air pollutants, weather conditions, aeroal- uted lag nonlinear model (DLNM) was used to determine lagged and lergens, and burden of viral respiratory tract infections) on AEs in nonlinear effects and to quantify the effect of environmental variables (daily an age-stratified population from Seoul Metropolitan City in an outdoor air pollutants, daily weather conditions, daily outdoor aeroallergens, effort to develop effective personalized strategies for prevention and 7-day moving average of nationwide viral respiratory tract detection rate) of AEs. on AE.19 For all variables, 0 to 4 lag days had the maximum relative risk (RR); after lag day 6, the RR was approximately 1, and the RR was not sta- tistically significant after lag day 7. Therefore the multivariable DLNM for pollutants is as follows: METHODS ð Þ 5 +7 ½b 1 1 1 ð ; 4 = Þ Study population log E Yt l 5 1 lZt2l DOW PubH ns Time df year This time-series analysis was a retrospective population-based cohort study 1nsðHM; 5 df Þ1nsðSS; 8 df Þ1nsðDTR; 7 df Þ in Seoul Metropolitan City (Republic of Korea) from January 2008 to December 1 ð ; 8 Þ1 ð ; 3 Þ1 ð ; 8 Þ 2012. Data are from the registries of 257 hospitals in Seoul and were extracted ns tree df ns grass df ns weed df from the National Health Insurance System of the Health Insurance Review and 1nsðHRV; 9 df Þ1nsðIFV; 6 df Þ1nsðRSV; 7 df Þ1Lagðres; 1Þ; Assessment Service, which covers approximately 98% of the Korean popula- 16 tion. These records have patient information (age and sex), date of visit, and where E(Yt) is the number of daily AE events expected on day t; bl is the log disease diagnosis based on the International Classification of Diseases, 10th relative rate coefficients for lag l; Zt-l is each independent variable for day t at revision (ICD-10). Detailed descriptions of these data were provided lag l; day of the week (DOW) and public holidays (PubH) are controlled cat- previously.4 egorical variables; ns(Time, 4 df/year) is the natural cubic spline function (ns) The database included all AE events requiring admission to an emergency for time, with 4 df/y selected by calculating the minimum of the residuals us- department that were coded by ICD-10 as J45 (asthma) or J46 (status ing the partial autocorrelation function13 (df was selected based on the lowest asthmaticus).17 Subjects were classified into 5 age groups: younger than Akaike information criterion10); and Lag(res, 1) is the DLNM model residual 2 years (infants), 2 to 5 years (preschool children), 6 to 17 years (school- error of the first-order lagged variable to control the autocorrelation function aged children), 18 to 59 years (adults), and 60 years or older (elderly). This and control for seasonality
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