Subject-Domain Approach to the Study of Air Pollution Effects on Schoolchildren's Illness Absence

doi:10.1093/aje/152.1.67

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Title:	Subject-Domain Approach to the Study of Air Pollution Effects on Schoolchildren's Illness Absence
Authors:	Hwang, Jing-shiang;陳怡如;Chen, Yi-ju;Wang, Jung-der;Lai, Yu-min;Yang, Chun-yuh;Chan, Chang-chuan
Contributors:	淡江大學統計學系
Keywords:	air pollution;epidemiologic methods;nitrogen dioxide;statistics;time-dependent covariate;time series
Date:	2000-08
Issue Date:	2011-10-23 16:42:35 (UTC+8)
Publisher:	The Johns Hopkins University School of Hygiene and Public Health
Abstract:	In this paper, the authors propose a new statistical modeling technique, the subject-domain approach, which is theoretically proven to be equivalent to the time-domain approach in detecting an association between exposure and response with time trends. The authors use an empirical data set from a school absence monitoring study conducted during the 1994–1995 school year in Taiwan to demonstrate this subject-domain approach's application to environmental epidemiologic studies. Because the subject-domain models can control the influential personal confounding factors in the models, they show greater statistical power than the traditional time-domain approaches in determining the relation between air pollution and illness absences. The authors' models found that the schoolchildren's risks of illness absence were significantly related to acute exposures to nitrogen dioxide and nitrogen oxides with a 1-day lag (p < 0.01) at levels below the World Health Organization's guidelines. By contrast, the authors could not detect significant associations between air pollution and schoolchildren's absenteeism using time-domain approaches. Such findings imply that the models built on subject domain may be a general solution to the problem of the ecologic fallacy, which is commonly encountered in environmental and social epidemiologic studies.
Relation:	American Journal of Epidemiology 152(1), pp.67-74
DOI:	10.1093/aje/152.1.67
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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