Knowledge-based and statistically modeled relationships between residential moisture damage and occupant reported health symptoms
Haverinen, U., Vahteristo, M., Moschandreas, D., Nevalainen, A., Husman, T. and Pekkanen, J.
2003
Atmospheric Environment, 37(4), 577-585
Indoor air; Exposure indicators; Modeling; Mold; Respiratory
Haverinen, U., Vahteristo, M., Moschandreas, D., Nevalainen, A., Husman, T. and Pekkanen, J., (2003), "Knowledge-based and statistically modeled relationships between residential moisture damage and occupant reported health symptoms", Atmospheric Environment, 37(4), 577-585.
| Abstract: |
| 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.
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