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<p>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 (NO<sub>2</sub>), 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&#xa0;to October) than for early season (March&#xa0;to June). Future work and continued research is needed to facilitate local health guidelines pertaining to childhood asthma exacerbation.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月19日

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  • 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

摘要

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.