Knowledge-based and statistically modeled relationships between residential moisture damage and occupant reported health symptoms

被引:6
作者
Haverinen, U
Vahteristo, M
Moschandreas, D
Nevalainen, A
Husman, T
Pekkanen, J
机构
[1] Natl Publ Hlth Inst, Lab Environm Microbiol, FIN-70701 Kuopio, Finland
[2] IIT, Chicago, IL 60616 USA
[3] Natl Publ Hlth Inst, Epidemiol Unit, FIN-70701 Kuopio, Finland
关键词
indoor air; exposure indicators; modeling; mold; respiratory;
D O I
10.1016/S1352-2310(02)00822-1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study continues to develop a quantitative indicator of moisture damage induced exposure in relation to occupant health in residential buildings. Earlier, we developed a knowledge-based model that links moisture damage variables with health symptoms. This paper presents a statistical model in an effort to improve the knowledge-based model, and formulates a third, simplified model that combines aspects of the both two models. The database used includes detailed information on moisture damage from 164 houses and health questionnaire data from the occupants. Models were formulated using generalized linear model formulation procedures, with 10 moisture damage variables as possible covariates and a respiratory health symptom score as the dependent variable. An 80% random sample of the residences was used for the formulation of models and the remaining 20% were used to evaluate them. Risk ratios (RR) for the respiratory health symptom score among the 80% sample were between 1.32 (1.12-1.55) and 1.48 (1.19-1.83), calculated per 10 points index increase. For the 20% sample, RRs were between 1.71 (1.13-2.58) and 2.34 (1.69-3.23), respectively. Deviance values in relation to degrees of freedom were between 2.00-2.12 (80% sample) and 1.50-1.81 (20% sample). The models developed can be simulated as continuous variables and they all associated significantly with the symptom score, the association being verified with a subset of the database not employed in the model formulation. We concluded that the performance of all models was similar. Therefore, based on the knowledge-based and statistical models, we were able-to construct a simple model that can be used in estimating the severity of moisture damage. (C) 2002 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:577 / 585
页数:9
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