Association of airborne moisture-indicating microorganisms withbuilding-related symptoms and water damage in 100 U.S. office buildings:Analyses of the U.S. EPA BASE data
Metrics of culturable airborne microorganisms for either total organisms or suspected harmful subgroups have generally not been associated with symptoms among building occupants. However, the visible presence of moisture damage or mold in residences and other buildings has consistently been associated with respiratory symptoms and other health effects. This relationship is presumably caused by adverse but uncharacterized exposures to moisture-related microbiological growth. In order to assess this hypothesis, we studied relationships in U.S. office buildings between the prevalence of respiratory and irritant symptoms, the concentrations of airborne microorganisms that require moist surfaces on which to grow, and the presence of visible water damage. For these analyses we used data on buildings, indoor environments, and occupants collected from a representative sample of 100 U.S. office buildings in the U.S. Environmental Protection Agency's Building Assessment Survey and Evaluation (EPA BASE) study. We created 19 alternate metrics, using scales ranging from 3-10 units, that summarized the concentrations of airborne moisture-indicating microorganisms (AMIMOs) as indicators of moisture in buildings. Two were constructed to resemble a metric previously reported to be associated with lung function changes in building occupants; the others were based on another metric from the same group of Finnish researchers, concentration cutpoints from other studies, and professional judgment. We assessed three types of associations: between AMIMO metrics and symptoms in office workers, between evidence of water damage and symptoms, and between water damage and AMIMO metrics. We estimated (as odds ratios (ORs) with 95% confidence intervals) the unadjusted and adjusted associations between the 19 metrics and two types of weekly, work-related symptoms--lower respiratory and mucous membrane--using logistic regression models. Analyses used the original AMIMO metrics and were repeated with simplified dichotomized metrics. The multivariate models adjusted for other potential confounding variables associated with respondents, occupied spaces, buildings, or ventilation systems. Models excluded covariates for moisture-related risks hypothesized to increase AMIMO levels. We also estimated the association of water damage (using variables for specific locations in the study space or building, or summary variables) with the two symptom outcomes. Finally, using selected AMIMO metrics as outcomes, we constructed logistic regression models with observations at the building level to estimate unadjusted and adjusted associations of evident water damage with AMIMO metrics. All original AMIMO metrics showed little overall pattern of unadjusted or adjusted association with either symptom outcome. The 3-category metric resembling that previously used by others, which of all constructed metrics had the largest number of buildings in its top category, was not associated with symptoms in these buildings. However, most metrics with few buildings in their highest category showed increased risk for both symptoms in that category, especially metrics using cutpoints of >100 but <500 colony-forming units (CFU)/m{sup 3} for concentration of total culturable fungi. With AMIMO metrics dichotomized to compare the highest category with all lower categories combined, four metrics had unadjusted ORs between 1.4 and 1.6 for both symptom outcomes. The same four metrics had adjusted ORs of 1.7-2.1 for both symptom outcomes. In models of water damage and symptoms, several specific locations of past water damage had significant associations with outcomes, with ORs ranging from 1.4-1.6. In bivariate models of water damage and selected AMIMO metrics, a number of specific types of water damage and several summary variables for water damage were very strongly associated with AMIMO metrics (significant ORs ranging above 15). Multivariate modeling with the dichotomous AMIMO metrics was not possible due to limited numbers of observations.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE. Assistant Secretary for Energy Efficiency andRenewable Energy. Office of the Building Technologies Program; Environmental Protection Agency Agreement DW89939365-01-0 and RequisitionPR-DC-0100105/BB3223, Base Analysis Contract 1W-2348-NANX; CaliforniaDepartment of Health Services
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 862117
- Report Number(s):
- LBNL-53908; R&D Project: 474508; BnR: BT0201000; TRN: US200602%%61
- Country of Publication:
- United States
- Language:
- English
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