title: Predictors of airborne endotoxin in the home.
abstract: We identified home characteristics associated with the level of airborne endotoxin in 111 Boston-area homes enrolled in a cohort study of home exposures and childhood asthma, and we developed a predictive model to estimate airborne endotoxin. We measured endotoxin in family-room air and in dust from the baby's bed, family room, bedroom, and kitchen floor. Level of airborne endotoxin was weakly correlated (r < 0.3) with level of endotoxin in each of the four types of dust samples and was significantly correlated with endotoxin in family-room dust (p < 0.05). Endotoxin in family-room dust accounted for < 6% of the variability of airborne endotoxin. In a multivariate model, certain home characteristics were positively (p < 0.05) associated with airborne endotoxin. These included current presence of dog (difference in level, dog vs. no dog = 72%, partial R(2 )= 12.8%), past presence of dog (partial R(2) = 5.5%), and endotoxin level in family-room dust (partial R(2) = 5.3%). Use of a dehumidifier (partial R(2) = 6.4%) was negatively associated (p = 0.02; difference = -31%) with airborne endotoxin. Other home characteristics were identified as important determinants of increased airborne endotoxin in this model, but individual coefficients were not statistically significant (alpha = 0.05): total amount of fine dust collected in the home (partial R(2 )= 3.8%), concrete floor in family room (3.7%), water damage (3.6%), and use of cool-mist humidifier in past year (2.7%). This multivariate model explained 42% of the variability of airborne endotoxin levels, a substantial improvement over that with dust endotoxin alone. Airborne endotoxin in Boston-area homes appears to be determined by the presence of dogs, moisture sources, and increased amounts of settled dust.
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
authors.forename: J H
authors.initials: JH
authors.lastname: Park
authors.forename: D L
authors.initials: DL
authors.lastname: Spiegelman
authors.forename: D R
authors.initials: DR
authors.lastname: Gold
authors.forename: H A
authors.initials: HA
authors.lastname: Burge
authors.forename: D K
authors.initials: DK
authors.lastname: Milton
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D016902:Air Pollution, Indoor / Q000032:analysis*; D000485:Allergens / Q000009:adverse effects / Q000032:analysis*; D000818:Animals; D000829:Animals, Domestic; D001249:Asthma / Q000453:epidemiology / Q000382:microbiology; D002415:Cats; D004285:Dogs; D004391:Dust / Q000032:analysis; D004731:Endotoxins / Q000009:adverse effects / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D062665:Epidemiological Monitoring; D006798:Housing; D006801:Humans; D006813:Humidity; D006967:Hypersensitivity / Q000453:epidemiology / Q000382:microbiology; D007223:Infant; D008137:Longitudinal Studies; D008404:Massachusetts / Q000453:epidemiology; D051379:Mice; D015233:Models, Statistical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012621:Seasons; D014028:Tobacco Smoke Pollution
chemical_list: D000393:Air Pollutants; D000485:Allergens; D004391:Dust; D004731:Endotoxins; D014028:Tobacco Smoke Pollution