دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: A respiratory alert model for the Shenandoah Valley, Virginia, USA.
المؤلفون: Hondula DM; Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22904, USA. dmh4j@virginia.edu, Davis RE, Knight DB, Sitka LJ, Enfield K, Gawtry SB, Stenger PJ, Deaton ML, Normile CP, Lee TR
المصدر: International journal of biometeorology [Int J Biometeorol] 2013 Jan; Vol. 57 (1), pp. 91-105. Date of Electronic Publication: 2012 Mar 22.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Springer Verlag Country of Publication: United States NLM ID: 0374716 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1254 (Electronic) Linking ISSN: 00207128 NLM ISO Abbreviation: Int J Biometeorol Subsets: MEDLINE
أسماء مطبوعة: Publication: New York, NY : Springer Verlag
Original Publication: Leiden.
مواضيع طبية MeSH: Models, Theoretical*, Hospitalization/*statistics & numerical data , Respiratory Tract Diseases/*epidemiology, Humans ; Respiratory Tract Diseases/prevention & control ; Seasons ; Virginia/epidemiology ; Weather
مستخلص: 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.
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تواريخ الأحداث: Date Created: 20120323 Date Completed: 20131112 Latest Revision: 20211021
رمز التحديث: 20240628
DOI: 10.1007/s00484-012-0537-7
PMID: 22438053
قاعدة البيانات: MEDLINE
الوصف
تدمد:1432-1254
DOI:10.1007/s00484-012-0537-7