Prediction of daily childhood asthma exacerbation from ambient meteorological, environmental risk factors and respiratory viruses, Philadelphia, PA, 2011 to 2016

IF 5.8 3区 环境科学与生态学 0 ENVIRONMENTAL SCIENCES Environmental Science and Pollution Research Pub Date : 2025-02-19 DOI:10.1007/s11356-025-36089-w
Wanyu Huang, Lucy F. Robinson, Amy H. Auchincloss, Leah H. Schinasi, Kari Moore, Steven Melly, Christopher B. Forrest, Chén C. Kenyon, Anneclaire J. De Roos
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Abstract

Childhood asthma exacerbation has multiple risk factors that occur concurrently in the environment — including extreme meteorological conditions, air pollution, aeroallergens, and respiratory virus infections. Few studies have predicted asthma exacerbation based on multiple time-varying environmental risk factors, together. In this study, we constructed an autoregressive integrated moving average (ARIMA) model to predict “high-risk” days for childhood asthma exacerbation in Philadelphia, PA from 2011 to 2016, during the aeroallergen season of each year, using a total of 28,540 asthma exacerbation case events identified from electronic health record (EHR) data. We selected predictors from quantile weighted sum regression (gQWS), incorporating temporal lags and season-stratification (early- vs. late-season), which were entered subsequently into multivariable ARIMA models. We found that daily nitrogen dioxide (NO2), as well as monthly rhinovirus and respiratory syncytial virus (RSV) infection levels, were higher on the predicted “high-risk” days, as compared to days with lower childhood asthma exacerbation risk. The model performed better for late-season asthma exacerbation (July to October) than for early season (March to June). Future work and continued research is needed to facilitate local health guidelines pertaining to childhood asthma exacerbation.

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环境气象、环境危险因素和呼吸道病毒对儿童哮喘日加重的预测,费城,2011 - 2016。
儿童哮喘加重有多种危险因素,这些因素在环境中同时发生,包括极端气象条件、空气污染、空气过敏原和呼吸道病毒感染。很少有研究基于多个时变环境风险因素共同预测哮喘恶化。在这项研究中,我们构建了一个自回归综合移动平均(ARIMA)模型来预测2011年至2016年,在每年的空气过敏原季节,宾夕法尼亚州费城儿童哮喘加重的“高风险”天数,使用从电子健康记录(EHR)数据中识别的28,540例哮喘加重病例事件。我们从分位数加权和回归(gQWS)中选择预测因子,结合时间滞后和季节分层(早季与晚季),随后将其输入多变量ARIMA模型。我们发现每日二氧化氮(NO2)以及每月鼻病毒和呼吸道合胞病毒(RSV)感染水平在预测的“高风险”日高于儿童哮喘恶化风险较低的日子。该模型对哮喘发作季末(7 - 10月)的表现优于对哮喘发作季初(3 - 6月)的表现。未来的工作和持续的研究需要促进有关儿童哮喘恶化的当地健康指南。
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来源期刊
CiteScore
8.70
自引率
17.20%
发文量
6549
审稿时长
3.8 months
期刊介绍: Environmental Science and Pollution Research (ESPR) serves the international community in all areas of Environmental Science and related subjects with emphasis on chemical compounds. This includes: - Terrestrial Biology and Ecology - Aquatic Biology and Ecology - Atmospheric Chemistry - Environmental Microbiology/Biobased Energy Sources - Phytoremediation and Ecosystem Restoration - Environmental Analyses and Monitoring - Assessment of Risks and Interactions of Pollutants in the Environment - Conservation Biology and Sustainable Agriculture - Impact of Chemicals/Pollutants on Human and Animal Health It reports from a broad interdisciplinary outlook.
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