A respiratory alert model for the Shenandoah Valley, Virginia, USA

Hondula, David M., Davis, Robert E., Knight, David B., Sitka, Luke J., Enfield, Kyle, Gawtry, Stephen B., Stenger, Phillip J., Deaton, Michael L., Normile, Caroline P. and Lee, Temple R. (2012) A respiratory alert model for the Shenandoah Valley, Virginia, USA. International Journal of Biometeorology, 57 1: 91-105. doi:10.1007/s00484-012-0537-7

Author Hondula, David M.
Davis, Robert E.
Knight, David B.
Sitka, Luke J.
Enfield, Kyle
Gawtry, Stephen B.
Stenger, Phillip J.
Deaton, Michael L.
Normile, Caroline P.
Lee, Temple R.
Title A respiratory alert model for the Shenandoah Valley, Virginia, USA
Journal name International Journal of Biometeorology   Check publisher's open access policy
ISSN 0020-7128
Publication date 2012-03
Sub-type Article (original research)
DOI 10.1007/s00484-012-0537-7
Volume 57
Issue 1
Start page 91
End page 105
Total pages 15
Place of publication Heidelberg, Germany
Publisher Springer
Collection year 2013
Language eng
Abstract Respiratory morbidity (particularly COPD and asthma) can be influenced by short-term weather fluctuations that affect air quality and lung function. We developed a model to evaluate meteorological conditions associated with respiratory hospital admissions in the Shenandoah Valley of Virginia, USA. We generated ensembles of classification trees based on six years of respiratory-related hospital admissions (64,620 cases) and a suite of 83 potential environmental predictor variables. As our goal was to identify short-term weather linkages to high admission periods, the dependent variable was formulated as a binary classification of five-day moving average respiratory admission departures from the seasonal mean value. Accounting for seasonality removed the long-term apparent inverse relationship between temperature and admissions. We generated eight total models specific to the northern and southern portions of the valley for each season. All eight models demonstrate predictive skill (mean odds ratio = 3.635) when evaluated using a randomization procedure. The predictor variables selected by the ensembling algorithm vary across models, and both meteorological and air quality variables are included. In general, the models indicate complex linkages between respiratory health and environmental conditions that may be difficult to identify using more traditional approaches.
Keyword Air quality
Classification tree
Respiratory health
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Online First - 22 March 2012

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Chemical Engineering Publications
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Created: Mon, 23 Jul 2012, 15:38:27 EST by David B. Knight on behalf of School of Civil Engineering