Predictors of Endotoxin Levels in U.S. Housing

Article (PDF Available)inEnvironmental Health Perspectives 117(5):763-71 · June 2009with31 Reads
DOI: 10.1289/ehp.11759 · Source: PubMed
The relationship of domestic endotoxin exposure to allergy and asthma has been widely investigated. However, few studies have evaluated predictors of household endotoxin, and none have done so for multiple locations within homes and on a national scale. We assayed 2,552 house dust samples in a nationwide study to understand the predictors of household endotoxin in bedroom floors, family room floors, beds, kitchen floors, and family room sofas. Reservoir house dust from five locations within homes was assayed for endotoxin and demographic and housing information was assessed through questionnaire and onsite evaluation of 2,456 residents of 831 homes selected to represent national demographics. We performed repeated-measures analysis of variance (rANOVA) for 37 candidate variables to identify independent predictors of endotoxin. Meteorologic data were obtained for each primary sampling unit and tested as predictors of indoor endotoxin to determine if wetter or warmer microclimates were associated with higher endotoxin levels. Weighted geometric mean endotoxin concentration ranged from 18.7 to 80.5 endotoxin units (EU)/mg for the five sampling locations, and endotoxin load ranged from 4,160 to 19,500 EU/m(2). Bivariate analyses and rANOVA demonstrated that major predictors of endotoxin concentration were sampling location in the home, census division, educational attainment, presence of children, current dog ownership, resident-described problems with cockroaches, food debris, cockroach stains, and evidence of smoking observed by field staff. Low household income entered the model if educational attainment was removed. Increased endotoxin in household reservoir dust is principally associated with poverty, people, pets, household cleanliness, and geography.


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v o l u m e 117
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Household exposure to endotoxin has emerged
as an important factor in the development and
severity of nonatopic asthma (Michel et al.
1996; Thorne et al. 2005) while apparently
reducing the likelihood of allergic sensitization
and lessening the chance of developing eosino-
philic asthma (Braun-Fahrländer et al. 2002;
Ernst and Cormier 2000; Klintberg et al.
2001). However, there is strong evidence that
occupational endotoxin exposure is a potent
agent for the development and exacerbation
of neutrophilic asthma, asthmalike syndrome,
and organic dust toxic syndrome (orne and
Duchaine 2007).
Endotoxin is an amphiphilic outer-cell-
wall component of gram-negative bacteria that
is a potent inflammatory agent and asthma
trigger. As a microorganism-associated molecu-
lar pattern (MAMP), endotoxin is recognized
by the innate immune system through an
evolutionarily conserved pathway. Endotoxin
recognition and signal amplification occur
through a series of endotoxinprotein and
proteinprotein interactions leading to acti-
vation of toll-like receptor-4 (TLR4), with
resulting inflammation (Sigsgaard et al. 2008).
Key mole cules for the endotoxin recog nition
pathway include lipopolysaccharide-binding
protein, CD14, and MD-2 (Hađina et al.
2008). A number of polymorphisms have been
identified that affect expression of key mole-
cules in the inflammatory cascade and that
may play a role in responsiveness to endotoxin.
us, dose, coexposures to other MAMPs and
allergens, and genetic susceptibility may be
important predictors of response to indoor
Because of the importance of limiting
endotoxin exposures, particularly among asth-
matic individuals, several studies have evalu-
ated the predictors of endotoxin concentration
in house dust or endotoxin loading of surfaces
in homes (Bischof et al. 2002; Gehring et al.
2004; Park et al. 2001; Wickens et al. 2003).
In general, these studies have been confined
to a particular geographic area, demographic
group, or type of housing, and most have
been limited to either the family room floor
dust or bedding. Because of the targeted scope
of these studies and the focus on one or two
municipalities, some contradictory findings
have emerged, raising the question as to the
generalizability of the findings.
e National Survey of Lead and Allergens
in Housing (NSLAH) provided the opportu-
nity to investigate the predictors of endotoxin
contamination in housing in a nationwide sam-
ple designed to represent the U.S. population.
For this study, we sampled five locations within
each home and assessed a host of characteristics
of the homes and occupants, yielding a robust
data set. Prior reports from this survey explored
the relationships between allergen and endo-
toxin exposures and the preva lence of adverse
health outcomes. Our goal in this study was
to determine the factors related to increased
levels of endotoxin in homes to guide future
health studies and public health interventions
designed to reduce exposures.
Study design. is study used samples that we
collected for the NSLAH. e study design,
sampling, and assay methods for endotoxin
have been published (Vojta et al. 2002). e
associations of endotoxin concentrations with
allergy, asthma, and wheezing have also been
published (orne et al. 2005). We carried
out this study in 831 housing units repre-
sentative of the nation’s 96 million homes
that allow children. e parent study received
institutional review board approval, and study
subjects gave written informed consent before
their participation.
Exposure assessment. Two field staff visited
each participating household and adminis-
tered an extensive questionnaire, conducted a
home inspection, and collected samples from
five locations (bedroom floors, family room
floors, beds, kitchen floors, and family room
sofas). The questionnaire included informa-
tion on age, type and conditions of the home,
and demographics and health of the residents
(Vojta et al. 2002). Dust was vacuum-sampled
into an in-line filter using a standardized pro-
tocol and then sieved (425 µm), aliquoted into
lots of 100 mg, and frozen at 80°C. Samples
Address correspondence to P.S. orne, Department
of Occupational and Environmental Health,
University of Iowa, College of Public Health, 100
Oakdale Campus, 176 IREH, Iowa City, IA 52242-
5000 USA. Telephone: (319) 335-4216. Fax: (319)
335-4006. E-mail:
Supplemental Material is available online at http://
*Current address: Rho, Inc., Chapel Hill, NC.
We thank R. Jaramillo and P. Crockett for statistical
analyses and K. Kulhankova for endotoxin assays.
This work was supported by National Institute
of Environmental Health Sciences (NIEHS) grant
P30 ES05605, U.S. Department of Housing and
Urban Development’s Office of Healthy Homes and
Lead Hazard Control, and the Intramural Research
Program of the NIEHS, National Institutes of Health.
The authors declare they have no competing
financial interests.
Received 4 June 2008; accepted 14 October 2008.
Predictors of Endotoxin Levels in U.S. Housing
Peter S. Thorne,
Richard D. Cohn,
Deepak Mav,
Samuel J. Arbes Jr.,
and Darryl C. Zeldin
Environmental Health Sciences Research Center, College of Public Health, University of Iowa, Iowa City, Iowa, USA;
Group, LLC, Durham, North Carolina, USA;
Division of Intramural Research, National Institute of Environmental Health Sciences,
Research Triangle Park, North Carolina, USA
Ba c k g r o u n d : e relationship of domestic endotoxin exposure to allergy and asthma has been
widely investigated. However, few studies have evaluated predictors of household endotoxin, and
none have done so for multiple locations within homes and on a national scale.
oB j e c t i v e s : We assayed 2,552 house dust samples in a nationwide study to understand the predic-
tors of household endotoxin in bedroom floors, family room floors, beds, kitchen floors, and family
room sofas.
Me t h o d s : Reservoir house dust from five locations within homes was assayed for endotoxin and
demographic and housing information was assessed through questionnaire and onsite evaluation of
2,456 residents of 831 homes selected to represent national demographics. We performed repeated-
measures analysis of variance (rANOVA) for 37 candidate variables to identify independent predic-
tors of endotoxin. Meteorologic data were obtained for each primary sampling unit and tested as
predictors of indoor endotoxin to determine if wetter or warmer microclimates were associated with
higher endotoxin levels.
re s u l t s : Weighted geometric mean endotoxin concentration ranged from 18.7 to 80.5 endotoxin
units (EU)/mg for the five sampling locations, and endotoxin load ranged from 4,160 to 19,500
. Bivariate analyses and rANOVA demonstrated that major predictors of endotoxin concen-
tration were sampling location in the home, census division, educational attainment, presence of
children, current dog ownership, resident-described problems with cockroaches, food debris, cock-
roach stains, and evidence of smoking observed by field staff. Low household income entered the
model if educational attainment was removed.
co n c l u s i o n : Increased endotoxin in household reservoir dust is principally associated with poverty,
people, pets, household cleanliness, and geography.
ke y w o r d s : allergens, asthma triggers, endotoxin, house dust, housing characteristics, indoor air,
lipopolysaccharide, microorganism-associated molecular pattern, predictive model, reservoir dust.
Environ Health Perspect 117:763–771 (2009). doi:10.1289/ehp.11759 available via http://dx.doi.
org/ [Online 16 October 2008]
Thorne et al.
v o l u m e 117
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Environmental Health Perspectives
were then assayed for endotoxin and com-
mon allergens (Vojta et al. 2002). A 50-mg
subsample of each dust sample was extracted
with 1.0 mL pyrogen-free water containing
0.05% Tween-20 and analyzed for endotoxin
using the kinetic chromogenic Limulus ame-
bocyte lysate assay (Thorne 2000). In total,
2,512 endotoxin determinations were linked
with complete housing data and were available
for statistical analysis. We excluded 43 sam-
ples collected from basements from statistical
analy ses (because of limited power), leaving
2,469 endotoxin values from 790 households.
Meteorologic data. We obtained meteoro-
logic data for study locations specified by lon-
gitude and latitude (to three decimal degrees)
from the Oregon Climate Service PRISM data
explorer for monthly high-resolution precipi-
tation and temperature climate data (Oregon
Climate Service 2008). Annual precipitation
and annual maximum and minimum tem-
peratures were obtained for the years in which
samples were collected and applied each as
indicators of local climatic conditions in the
regression modeling as prediction variables.
Statistical analysis. We performed bivari-
ate analyses and repeated-measures analyses of
variance (rANOVAs) to assess the relationship
between each housing or occupant charac-
teris tic and the level of endotoxin concentra-
tion [endotoxin units (EU) per milligram]
and endotoxin load (EU per square meter).
Endotoxin was evaluated as a continuous vari-
able with logarithmic transformation. In the
bivariate analyses, endotoxin levels were sum-
marized using geometric means (GMs) and
comparisons were made using ANOVAs.
For the rANOVA, we preliminarily identi-
fied 37 possible predictors of log-transformed
endotoxin concentrations or loads measured
at five different locations for each household,
based on knowledge gleaned through previ-
ous research and the bivariate analysis results.
Set 1 consisted of demographic factors, set 2
consisted of characteristics of the home, set 3
included questionnaire data on pets and ver-
min, set 4 included field-staff–observed evi-
dence of household characteristics, and set 5
consisted of factors specific to bedrooms. We
determined the optimal subset of these predic-
tors using an rANOVA-based model selec-
tion process, with sampling locations treated
as repeated measures and each household
treated as an individual observation. In effect,
the rANOVA approach characterizes relation-
ships between predictors and the distribution
of multiple related endotoxin measure ments in
a household.
Estimation and rANOVA model optimi-
zation were based on a maximum-likelihood
procedure using the Akaike information
criterion (AIC) statistic. We implemented
a hierarchical model selection procedure in
which we partitioned predictor variables of
interest into five logical sets and sequentially
selected the best subset of predictor variables
from each set using an exhaustive search.
We repeated the process using all possible
orderings of the variable sets to obtain the
optimal set of predictors. e best subset of
bedroom-specific predictors was obtained
by fitting models using only bedroom floor
and bedroom bed endotoxin levels. Further
details are described in Supplemental Material
(available online at http://www.ehponline.
We applied sample weights in all analyses
to account for housing unit selection prob-
abilities, nonresponse, and poststratification.
Taylor series linearization methods were used
to obtain variance estimates adjusted for clus-
tering associated with the multistage com-
plex survey design, with the exception of the
AIC-based rANOVA. Statistical analyses were
conducted in SAS-callable SUDAAN (version
9.0; Research Triangle Institute, Research
Triangle Park, NC) and SAS (version 9.1;
SAS Institute Inc., Cary, NC).
This study is the first to evaluate domes-
tic endotoxin levels over a wide geographic
region and across demographic groups repre-
senting urban, suburban, rural; wealthy and
poor; African American (black), Hispanic,
and white; apartment dwellers and people
living in multifamily or single family homes;
children and adults; with or without pets;
with and without allergy or asthma. This
allowed us to develop an understanding of
the predictors of domestic endotoxin for the
entire United States. Figure 1 shows the GM
concentrations of 2,469 surface samples col-
lected from the kitchen floor, family room
floor, family room sofa, bedroom floor, and
bedding. Endotoxin concentrations in sam-
ples from the kitchen and family room floors
were about 4-fold higher than concentrations
in the bedding, and family room sofa and
bedroom floor concentrations were approxi-
mately twice those in the bedding. Endotoxin
load values demonstrated that bedroom floors
were substantially less contaminated than
family room floors, sofas, and kitchens but
more than twice as contaminated as bedding.
Although family room floors and sofas had
lower endotoxin concentration than kitchen
floors, the amount of dust was higher, so the
endotoxin loads were comparable.
Tables 1–3 show potential predictors of
endotoxin concentrations assessed in this
study for bedroom floor, family room floor,
and bedding samples. Table 1 lists household
factors and their endotoxin concentrations
(GM and p-values) compared with the refer-
ent subpopulation (the referent is the sub-
population with no p-value listed). A number
of household factors showed consistency as
predictors of endotoxin across sampling loca-
tions. e West census region (illustrated in
Figure 2) had higher endotoxin levels than the
Northeast, South, or Midwest regions. When
we analyzed this further using the nine U.S.
census divisions, we found that the Pacific
division (California, Oregon, Washington)
was the highest for all sampling locations and
New England (Connecticut, Massachusetts,
Maine, New Hampshire, Rhode Island,
Vermont) was the lowest. e Pacific division
spans 2,000 km from north to south and rep-
resents both warm, dry (e.g., San Diego, CA)
and cool, wet climates (e.g., Portland, OR).
In Figure 2 we have plotted quartiles of the
GM endotoxin concentrations for all house-
holds and all household sampling sites within
geographic primary sampling units (PSUs)
[i.e., metropolitan statistical areas (MSAs)
or rural counties]. On this map, for exam-
ple, the orange square over Boulder County,
Colorado, represents the unadjusted GM of
52 samples collected in the cities of Boulder
and Longmont (population, 225,339; PSU
weight, 20.357). The red circle in western
Figure 1. Endotoxin concentration (left) and endotoxin load (right) in the dust samples shown as GM and
95% confidence limits (error bars). We adjusted values for survey design information and sample weighting.
Endotoxin load as EU per sample rather than EU per square meter.
Dust endotoxin concentration (EU/mg)
Dust endotoxin load (EU/m
n = 454
Concentration Load
room floor
room sofa
Bedding Kitchen
room floor
room sofa
Predictors of household endotoxin
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Kansas represents 81 samples collected in
five adjoining counties (combined popula-
tion, 23,293; PSU weight, 91.333). Figure 2
illustrates that the high endotoxin values for
the Pacific census division were primarily
in Southern California. The New England
and Middle Atlantic divisions plus Delaware,
Maryland, Virginia, and the District of
Columbia had no PSUs in the highest quartile
and had 71% in the lowest quartile.
Another household factor relating to endo-
toxin was living in poverty, for which mean
bedroom floor and bedding endotoxin levels
were 56% (p = 0.003) and 58% (p = 0.021)
higher than in nonimpoverished households,
respectively. Households occupying two- or
three-story homes including a basement (if
present) had significantly lower bedroom floor
(p = 0.002) and family room floor (p = 0.006)
endotoxin. Homes on a single level or in
multilevel apartment buildings had higher
endotoxin. Having air conditioning, a stove
exhaust fan, or an air filtration system were
not significant predictors. Having electric heat
as the main heating source was associated with
higher bedroom (p = 0.012) and family room
floor (p = 0.009) endotoxin than the other/
none category. Also, whether the occupants
lived in a single or multifamily dwelling or
owned their home was not related to endo-
toxin in the homes.
Metropolitan status demonstrated higher
values for MSAs with populations of > 1 mil-
lion than for those with < 1 million that were
significant for bed endotoxin (p = 0.035) and
showed a trend for bedroom floor (p = 0.073)
and kitchen floor (p = 0.080). Homes built
before 1978 had higher endotoxin levels in
family room floors (p = 0.040) but not in
other locations.
Table 2 shows the GM and p-values
for a variety of endotoxin source factors in
domestic environments for bedding, bed-
room floor, and family room floor endotoxin.
Increasing numbers of people living in the
household showed a very strong relationship
with increasing endotoxin concentration, as
did having children residing in the home.
For family room floor endotoxin, the GM
was 42.7 EU/mg for households with a single
resident, 58.1 for two-member households
(p =
0.019), between 76.8 and 79.0 for three
or four residents (p < 0.005), and 87.0 for
households with > four residents (p < 0.001).
We also observed this trend for bedroom
floor and bedding endotoxin but it was less
dramatic. Having a child or children in the
Table 1. Household predictors of endotoxin concentration in bedroom floors, family room floors, and bedding.
Bedroom floor Family room floor Bedding
Predictor Subpopulation No. GM (EU/mg) p-Value
No. GM (EU/mg) p-Value
No. GM (EU/mg) p-Value
Census region Northeast 96 29.1 72 51.4 82 16.4
South 210 33.6 0.407 158 62.0 0.402 161 16.9 0.885
Midwest 137 37.4 0.174 139 67.6 0.152 114 18.9 0.542
West 145 44.3 0.035* 120 75.6 0.068 113 25.0 0.046*
Census division New England 30 24.7 21 31.1 29 13.7
South Atlantic 80 28.2 0.538 63 53.2 0.012* 61 15.3 0.750
Middle Atlantic 66 33.4 0.175 51 75.8 0.000** 53 19.4 0.355
West South Central 80 33.5 0.133 61 62.0 0.002** 56 19.8 0.252
West North Central 60 35.7 0.078 59 64.5 0.000** 59 25.0 0.062
East North Central 77 38.7 0.031* 80 69.9 0.000** 55 14.6 0.887
East South Central 50 40.8 0.017* 34 74.3 0.042* 44 15.6 0.671
Mountain 60 42.0 0.019* 43 67.2 0.000** 53 21.6 0.165
Pacific 85 47.2 0.002** 77 83.4 0.000** 60 31.0 0.011*
Metro status MSA < 1 million 302 32.6 249 61.2 228 16.6
Non-MSA 105 34.4 0.589 83 69.8 0.410 86 19.0 0.396
MSA ≥ 1 million 181 42.1 0.073 157 64.3 0.744 156 22.6 0.035*
Housing unit type Multifamily 88 27.2 75 61.2 71 16.3
Single family 500 36.8 0.103 414 64.3 0.786 399 19.1 0.447
Housing unit year category 1978 or newer 156 34.9 128 52.8 125 18.3
Older than 1978 432 35.5 0.910 361 69.9 0.040* 345 18.8 0.868
Race Black 90 26.5 79 73.7 67 19.2
Other 54 30.5 0.519 40 81.2 0.719 43 19.2 1.000
White 437 37.4 0.021* 363 61.8 0.260 351 18.8 0.915
Ethnicity Non-Hispanic 520 34.9 443 63.0 414 18.0
Hispanic 62 39.5 0.567 42 73.1 0.517 51 27.2 0.095
Household income ≥ $30,000 327 31.8 266 63.4 255 18.2
< $30,000 235 41.2 0.045* 195 64.8 0.864 186 19.7 0.564
Living in poverty No 450 32.9 378 62.6 355 17.6
Yes 106 51.5 0.003** 83 78.4 0.171 81 27.8 0.021*
Own or rent home Rent 209 34.0 172 63.1 172 20.2
Own 377 35.9 0.648 315 64.1 0.914 296 17.9 0.360
Education after high school Some 398 31.9 326 61.9 307 17.4
None 190 44.5 0.005** 163 68.6 0.357 163 21.6 0.183
No. of stories, including basement 23 307 30.7 0.002** 251 54.8 0.006** 262 17.4 0.164
≥ 4 36 34.2 0.353 40 75.1 0.929 32 16.5 0.346
1 243 42.8 196 76.5 174 21.4
Main heating source Other/none 111 28.5 89 50.9 93 18.2
Gas 302 35.8 0.058 252 63.2 0.174 247 19.8 0.543
Electric 173 40.4 0.012* 146 78.1 0.009** 129 17.1 0.737
Air conditioning in home Yes 463 35.0 378 64.5 368 17.6
No 124 36.1 0.737 110 61.3 0.722 101 22.9 0.105
Fan that exhausts stove to outside No 133 30.8 110 62.8 118 17.7
Yes 128 34.7 0.445 114 71.8 0.418 92 24.4 0.084
Air filtration system in home No 502 34.8 421 63.9 399 18.7
Yes 73 37.6 0.621 55 57.7 0.517 60 17.7 0.807
MSA, metropolitan staitistical area.
Based on t-statistics using log-transformed endotoxin concentration. *p < 0.05. **p < 0.01.
Thorne et al.
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Environmental Health Perspectives
home was significantly associated with higher
endotoxin for bedroom floors (p < 0.001),
family room floors (p = 0.028), and bedding
(p < 0.001).
Several other potential source factors were
significantly associated with bedroom floor
endotoxin. Current pets or pets in the house-
hold in the past 6 months and current or past
dogs or cats were significant (all p 0.001;
Table 2). Also significant were cockroach
problems in the past year (p = 0.026) and,
for family room floors, cigarette smoking
(p = 0.004). We found no effect on endotoxin
of dehumidifier use or season in which we
sampled the household.
During household visits, our field staff con-
ducted a walk-through survey noting specific
factors relating to characteristics of the home.
Table 3 lists staff-observed factors and their
relationship with endotoxin concentrations. For
both bedroom floors and family room floors,
evidence of smoking (p = 0.012; p < 0.001),
cockroach stains (p = 0.041; p = 0.009), and
food debris (p = 0.044; p < 0.001) were sig-
nificant predictors of endotoxin. Observed
mold or mildew in the room was associated
with higher bedroom endotoxin but was rarely
observed (21 of 581). Carpeted floor, room air
conditioner, and room air cleaning device were
not significant predictors. Extreme room tem-
peratures on the day of the survey [i.e., < 18°C
(65°F) or > 29°C (84°F)] were associated with
higher endotoxin concentration for bedroom
floors (p = 0.008) and family room floors
(p = 0.033). Relative humidity in the room
on the survey day was not a factor for family
room floor or bedding endotoxin. However,
for bedroom floor endotoxin, relative humidity
< 40% was associated with higher endotoxin
than the other four humidity ranges from 40%
to > 69%. Field staff recorded whether or not
the bed in the sampled bedroom was equipped
with an impermeable cover for the mattress,
box spring, or pillow. Interestingly, all three
covers were significantly associated with higher
bedroom floor endotoxin concentration (Table
3). Having a stuffed animal (e.g., teddy bear)
in the bed also increased bedding endotoxin
(p = 0.024).
Table 4 lists data for significant predictors
of kitchen floor endotoxin, which show that the
kitchen floor had a distinct profile of endotoxin
predictors. As with the other household sam-
pling locations, kitchen endotoxin levels were
significantly lower for the Northeast census
region and the New England census division.
Kitchen endotoxin was higher for those living
Table 2. Endotoxin source as predictors of endotoxin concentration in bedroom floors, family room floors, and bedding.
Bedroom floor Family room floor Bedding
Endotoxin source Subpopulation No. GM (EU/mg) p-Value No. GM (EU/mg) p-Value No. GM (EU/mg) p-Value
No. of people living in the home 1 90 30.7 84 42.7 72 16.7
2 183 28.5 0.668 145 58.1 0.019* 152 13.3 0.296
3 119 37.7 0.316 97 79.0 0.004** 85 23.6 0.067
4 113 47.0 0.103 98 76.8 0.000** 93 25.5 0.073
> 4 83 50.0 0.012* 65 87.0 0.000** 68 32.8 0.003**
Children < 6 years of age living in the home No 465 33.1 397 62.3 377 16.4
Yes 121 49.3 0.001** 91 74.4 0.363 90 38.5 0.000**
Children < 18 years of age living in the home No 313 29.6 267 57.8 246 14.3
Yes 274 47.0 0.000** 221 75.8 0.028* 222 28.7 0.000**
Pets in home in the last 6 months No 258 27.2 221 63.5 220 16.2
Yes 328 43.0 0.000** 267 64.1 0.927 249 21.2 0.083
Pets currently in the home No 286 27.7 242 60.9 244 15.8
Yes 299 44.3 0.000** 245 66.7 0.401 223 22.5 0.019*
Dogs in home in the last 6 months No 365 29.4 304 62.0 308 17.3
Yes 218 46.2 0.000** 181 66.9 0.561 159 21.4 0.221
Dogs currently in the home No 391 30.8 327 59.5 329 17.6
Yes 194 46.3 0.001** 160 73.2 0.051 138 21.3 0.209
Cats in home in the last 6 months No 426 31.7 364 64.8 349 16.5
Yes 157 45.8 0.001** 121 61.4 0.679 119 26.1 0.012*
Cats currently in the home No 443 31.9 376 63.8 358 16.6
Yes 142 47.9 0.000** 111 64.0 0.974 109 27.2 0.010*
Season home was sampled Summer 184 32.2 161 71.3 156 16.7
Fall 268 34.3 0.614 231 62.8 0.443 206 18.8 0.528
Winter 136 41.7 0.069 97 56.1 0.178 108 21.5 0.134
Problems with cockroaches in the past 12 months No 461 33.0 375 60.1 372 17.8
Yes 126 49.4 0.026* 113 82.1 0.046* 97 23.5 0.051
No. of cockroaches seen per day on average < 5 69 44.8 61 83.7 53 25.2
5–50 17 111.6 0.016* 15 111.3 0.328 15 23.9 0.907
> 50 7 62.3 0.211 6 175.3 0.222 7 39.8 0.373
Cockroaches controlled by an exterminator Yes 35 40.5 29 81.2 28 23.6
No 90 53.7 0.262 83 81.4 0.994 68 23.9 0.957
Any insecticides, bug sprays, or roach motels used No 23 43.7 23 92.3 18 18.2
Yes 102 50.4 0.546 90 79.7 0.537 79 24.8 0.432
Cigarette smokers in household No 340 32.7 283 56.1 268 17.2
Yes 245 39.1 0.119 204 76.7 0.004** 200 20.9 0.070
Frequency of cigarettes smoked inside per day Never 51 26.8 39 88.7 40 18.4
< Once 15 35.9 0.570 11 59.7 0.145 10 26.9 0.445
1–3 times 21 34.7 0.430 18 62.9 0.277 16 18.3 0.990
4–10 times 55 33.7 0.319 41 52.6 0.079 51 21.1 0.643
> 10 times 97 53.5 0.001** 89 92.4 0.850 76 21.9 0.489
Cigar, pipe, etc., smokers in household No 537 35.8 441 63.4 429 18.4
Yes 48 29.7 0.251 45 64.2 0.946 38 21.9 0.524
Use of dehumidifier in the home Yes 85 33.9 69 67.3 79 16.3
No 492 35.8 0.682 412 62.9 0.676 386 19.1 0.306
Last time floor or carpet was cleaned ≥ 1 week ago 278 31.3 208 61.0 203 16.8
< 1 week ago 274 39.7 0.020* 270 65.9 0.562 235 20.9 0.183
Based on t-statistics using log-transformed endotoxin concentration. *p < 0.05. **p < 0.01.
Predictors of household endotoxin
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in poverty (130 vs. 75 EU/mg; p = 0.001), with
lower household income (p = 0.001), and with
lower educational attainment (p = 0.021).
Problems with cockroaches, live or dead cock-
roaches in the kitchen, and cockroach stains
were all strong predictors of endotoxin levels
(p < 0.001). Households reporting problems
with cockroaches in the past 12 months had
2-fold higher endotoxin than did those with-
out cockroaches. Within the subpopulation of
those with cockroach problems, households
where the residents sighted > 50 cockroaches
per day (n = 7) had a mean kitchen floor endo-
toxin level of 838 EU/mg, 10-fold higher than
the overall mean of 80.5 EU/mg. In addition,
evidence of rodents (p = 0.002), cigarette smok-
ing (p < 0.001), and mold or mildew (p = 0.02)
were highly significant predictors of increased
kitchen endotoxin concentration. In contrast
to other locations in the homes, people of
black race had significantly higher endotoxin
in kitchen floor dust samples than did whites or
other races (p = 0.005).
Next we sought to identify the optimal set
of candidate predictors of household endo-
toxin using rANOVA with household as sub-
ject and the five sampling locations as repeated
Table 3. Field-staff–observed predictors of endotoxin concentration in bedroom floors, family room floors, and bedding.
Bedroom floor Family room floor Bedding
Predictor Subpopulation No. GM (EU/mg) p-Value
No. GM (EU/mg) p-Value
No. GM (EU/mg) p-Value
Evidence of smoking in the room No 504 32.6 374 58.1 407 18.7
Yes 77 55.9 0.012* 109 88.7 0.001** 59 17.5 0.575
Cockroach stains in the room No 566 34.1 471 62.6 454 18.4
Yes 12 70.6 0.041* 11 142.2 0.009** 11 29.4 0.201
Live/dead cockroaches in the room No 570 34.4 465 63.3 458 18.4
Yes 10 65.4 0.122 18 78.5 0.367 8 31.1 0.263
Evidence of rodents in the room No 566 35.2 476 63.4 456 18.4
Yes 13 22.7 0.567 6 106.9 0.179 10 30.0 0.173
Food debris in the room No 495 33.2 386 57.7 404 18.0
Yes 85 50.1 0.044* 97 95.5 0.000** 61 23.2 0.126
Mold/mildew observed in the room No 560 34.3 461 62.9 446 18.3
Yes 21 61.9 0.058 22 93.2 0.151 20 25.6 0.048*
Other moisture evidence in the room No 542 34.4 457 62.6 430 18.3
Yes 39 45.8 0.174 26 95.9 0.109 36 22.2 0.203
Floor surface carpeted No 75 35.4 60 57.6 88 18.4
Yes 490 34.7 0.883 415 65.0 0.500 364 18.5 0.969
Temperature in room (°C) < 18 27 57.6 28 85.9 20 21.1
18–23 233 37.7 0.068 202 56.4 0.033* 186 19.5 0.746
24–29 278 30.4 0.008** 215 66.9 0.275 223 17.2 0.385
> 29 39 50.6 0.656 38 79.0 0.744 31 20.9 0.982
Relative humidity in room (%) < 40 116 46.9 105 64.9 88 19.9
40–49 188 34.5 0.030* 140 60.3 0.666 156 17.6 0.449
50–59 128 31.3 0.016* 123 74.8 0.401 106 19.8 0.992
60–69 98 31.0 0.008** 74 50.5 0.260 84 16.8 0.467
> 69 49 30.6 0.014* 44 77.5 0.472 28 18.5 0.785
Room air conditioner No 521 34.6 400 62.7 423 18.5
Yes 56 34.9 0.964 83 69.9 0.474 42 18.9 0.920
Room air cleaning device Yes 7 24.3 11 75.6 7 13.6
No 570 34.8 0.127 471 63.5 0.132 458 18.7 0.441
Mattress cover on bed No 417 31.1 370 17.4
Yes 143 46.0 0.001** 85 26.9 0.051
Box spring cover on bed No 452 32.7 392 17.7
Yes 109 44.0 0.036* 65 26.6 0.082
Pillow cover on bed No 433 32.2 377 17.6
Yes 128 44.0 0.018* 80 25.4 0.111
Stuffed animal(s) in bed No 431 34.1 357 17.3
Yes 130 35.0 0.822 101 23.8 0.024*
Based on t-statistics using log-transformed endotoxin concentration. *p < 0.05. **p < 0.01.
Figure 2. U.S. map showing the census regions, census divisions, and quartiles of the GM endotoxin con-
centration for all five sampling locations within homes, aggregated by PSUs of the survey.
0100 200Miles
0Miles200 400
0 200 400 Miles
1st quartile, < 35.1 EU/mg
2nd quartile, ≥ 35.1 and < 43.0 EU/mg
3rd quartile, ≥ 43.0 and < 57.5 EU/mg
4th quartile, ≥ 57.5 EU/mg
Thorne et al.
v o l u m e 117
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Environmental Health Perspectives
measures. To streamline the analy sis, 37 candi-
date predictor variables were partitioned into
five logical sets (S1–S5) shown in Table 5.
After all permutations were explored, the model
shown in Table 6 yielded high predictive values
with strong statistical significance. Coefficients
for sampling locations mirror the data shown
in Figure 1, with bedding lowest and kitchen
floor highest in endotoxin concentration. With
the New England census division as the ref-
erent, Mountain, West North Central, and
Pacific were 7391% higher (p < 0.001) in
household endotoxin concentration. Higher
endotoxin concentration was associated with
lower educational attainment (p = 0.014), chil-
dren in the home (p = 0.035), currently having
a dog in the household (p < 0.0001), prob-
lems with cockroaches in the past 12 months
(p = 0.0022), field-staffobserved food debris
(p = 0.029), cockroach stains (p < 0.0001),
and evidence of smoking (p = 0.0087). When
we ran the analysis for bedroom bedding and
included floor endotoxin alone and S5 vari-
ables, the only additional variable from S5 that
emerged was having an encapsulating mat-
tress case on the sampled bed (p = 0.048). e
rANOVA analysis for endotoxin load (Table 6)
revealed that sampling location, census divi-
sion, education, dog in home, problems with
cockroaches, food debris, and cigarette smok-
ing were significant predictors (p < 0.0001
for all). Additional predictors for endotoxin
load were cat in home (p = 0.0035), mold/mil-
dew observed (p = 0.0012), and lower relative
humidity (p < 0.0001). e rank ordering of
endotoxin load by census division was some-
what different than for endotoxin concentra-
tion, although Mountain, West North Central,
and Pacific were the highest for both measures
of endotoxin and New England was the lowest
or second lowest.
The finding of a geographic trend for
higher endotoxin and data suggesting an
effect of poor indoor temperature control, low
humidity, and type of heating led us to con-
sider if the local temperature range or amount
Table 4. Predictors of endotoxin concentration in kitchen floors.
Kitchen floor
Predictor Subpopulation No. GM (EU/mg) p-Value
Census region Northeast 86 54.3
West 111 81.3 0.024*
Midwest 106 89.0 0.005**
South 151 94.4 0.004**
Census division New England 28 43.5
Middle Atlantic 58 65.9 0.022*
East North Central 55 76.2 0.003**
Mountain 40 77.2 0.000**
East South Central 38 81.5 0.162
Pacific 71 85.7 0.018*
South Atlantic 52 92.0 0.005**
West South Central 61 104.6 0.000**
West North Central 51 107.0 0.000**
Metro status MSA < 1 million 218 69.2
Non-MSA 92 89.8 0.102
MSA ≥ 1 million 144 93.1 0.080
Housing unit type Single family 393 75.9
Multifamily 61 126.1 0.011*
Race White 343 75.8
Other 37 76.6 0.957
Black 68 118.3 0.005**
Household income ($) ≥ 30,000 260 66.1
< 30,000 171 114.8 0.001**
Living in poverty No 354 75.2
Yes 75 130.0 0.001**
Own or rent home Own 305 72.4
Rent 146 104.9 0.017*
Education after high school Some 313 73.4
None 141 100.0 0.021*
No.of stories, including basement 23 238 72.8 0.064
≥ 4 42 84.9 0.708
1 173 92.9
Main heating source Other/none 102 70.2
Gas 237 77.0 0.557
Electric 113 101.5 0.067
Cats in home in the last 6 months Yes 119 66.9
No 331 85.9 0.062
Problems with cockroaches in the past 12 months No 356 70.4
Yes 98 144.4 0.000**
No. of cockroaches seen per day on average < 5 54 136.4
5–50 13 140.1 0.939
> 50 7 838.4 0.000**
Cigarette smokers in household No 265 68.9
Yes 187 101.1 0.007**
Evidence of smoking in the room No 348 70.3
Yes 105 123.4 0.000**
Cockroach stains in the room No 398 73.8
Yes 52 170.7 0.000**
Live/dead cockroaches in the room No 413 74.9
Yes 39 204.7 0.000**
Evidence of rodents in the room No 430 77.8
Yes 23 152.4 0.002**
Mold/mildew observed in the room No 379 76.9
Yes 74 103.5 0.020*
Floor surface carpeted No 364 73.8
Yes 76 100.8 0.068
Based on t-statistics using log-transformed endotoxin concentration. Only predictors with p-values 0.10 are shown.
*p < 0.05. **p < 0.01.
Table 5. Variables entered into the repeated
meas ures ANOVA.
Set Variable
S1 Census division (nine levels)
Metro status (certainty MSA, MSA, non-MSA)
Own or rent home
Household income < $30,000/year
Living in poverty
Race (white, black, other)
Education after high school (some, none)
S2 Housing unit type (single family, multifamily)
Housing unit age (1978 or newer, older than 1978)
No. of stories, including basement
Main heating source (gas, electric, other/none)
Air conditioning in home
Fan that exhausts stove to outside
Air filtration device in home
Water or dampness in home in past 12 months
Home often have mildewy or musty odor
Dehumidifier used in home
No. of people living in the home
Household has children < 18 years of age
S3 Pets currently in the home
Dogs currently in the home
Cats currently in the home
Problems with cockroaches in past 12 months
Cigarette smokers in the home
S4 Carpet in room
Temperature in room
Relative humidity in room
Mold/mildew observed
Food debris observed
Evidence of smoking
Cockroach stains observed
Live/dead cockroaches observed
Evidence of rodents
S5 Encapsulating mattress case observed
Encapsulating box spring case observed
Encapsulating pillow case observed
Stuffed animals in bed
Predictors of household endotoxin
Environmental Health Perspectives
v o l u m e 117
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May 2009
of precipitation during the study year were
related to endotoxin concentration in homes.
We reasoned that measurement of tempera-
ture and humidity on a single day could pro-
duce misclassification and be a poor measure
of typical local climate or usual indoor condi-
tions. Using spatial coordinates for each of
the study households, we queried the Prism
data explorer for annual precipitation and
maximumminimum temperatures for the
year in which we sampled the home. Linear
regression analysis of these factors with endo-
toxin concentration in each sampling loca-
tion revealed no relationship of these factors
for bedroom or family room floor endotoxin
(Table 7). However, precipitation during the
study year was a significant predictor of bed-
ding endotoxin (p = 0.033). Temperature
maxima and minima were related to kitchen
floor endotoxin (p = 0.001 and p = 0.013,
respectively) but showed no relation with
endotoxin for other sampling locations.
NSLAH has provided valuable information
on the levels of allergens and endotoxin in
the U.S. housing stock and the relationships
between exposures to these agents and disease
(Arbes et al. 2003, 2004; Cohn et al. 2004,
2006; Elliott et al. 2007; Salo et al. 2005,
2006; orne et al. 2005). NSLAH charac-
terized how exposures to indoor allergens
vary in U.S. homes. Alternaria, cat, and dog
allergens were most often detected and were
the allergens found at elevated levels in most
homes. Although each allergen appeared to
have a distinct set of predictors, levels were
strongly associated with regional, ethnic, and
socio economic factors.
We previously reported from NSLAH
that increasing concentration of endotoxin in
homes was a risk factor for increased preva-
lence of diagnosed asthma, asthma symptoms
in the past year, current use of asthma medica-
tions, and wheezing (orne et al. 2005). e
Table 6. Major predictors of endotoxin concentration and endotoxin load from rANOVAs.
Endotoxin concentration Endotoxin load
Predictor Category p-Value
SE e
SE e
Sampling location Bedding < 0.0001 2.40 0.12 11.1 < 0.0001 0.61 0.076 1.84
Bedroom floor 3.00 0.12 20.1 1.00 0.075 2.72
Family room sofa 3.23 0.12 25.4 1.05 0.077 2.86
Family room floor 3.56 0.12 35.3 1.21 0.076 3.35
Kitchen floor 3.73 0.12 41.8 0.92 0.079 2.50
Census division New England < 0.0001 0.00 < 0.0001 0.007 0.065 1.01
East South Central 0.22 0.13 1.25 0.072 0.065 1.08
South Atlantic 0.28 0.13 1.32 0.033 0.063 1.03
West South Central 0.39 0.12 1.48 0.077 0.059 1.08
Middle Atlantic 0.45 0.13 1.57 0.000
East North Central 0.46 0.12 1.58 0.039 0.058 1.04
Mountain 0.55 0.14 1.73 0.126 0.065 1.13
West North Central 0.65 0.13 1.91 0.301 0.063 1.35
Pacific 0.65 0.13 1.91 0.124 0.063 1.13
Education None after high school 0.014 0.00 < 0.0001 0.00
Some after high school –0.16 0.06 0.85 –0.14 0.032 0.87
Dog currently in the home No < 0.0001 0.00 < 0.0001 0.00
Yes 0.28 0.06 1.33 0.16 0.031 1.17
Problems with cockroaches in the past 12 months No 0.0022 0.00 < 0.0001 0.00
Yes 0.26 0.08 1.29 0.18 0.041 1.19
Food debris observed No 0.029 0.00 < 0.0001 0.00
Yes 0.15 0.07 1.16 0.17 0.035 1.19
Cockroach stains observed No < 0.0001 0.00 0.0027 0.00
Yes 0.60 0.14 1.81 0.24 0.081 1.28
Evidence of smoking
Cigarette smokers in the home No 0.0087 0.00 0.0007 0.00
Yes 0.19 0.07 1.21 0.10 0.030 1.11
Household has children < 18 years of age No 0.035 0.00 NS
Yes 0.13 0.06 1.14
Housing unit year category Older than 1978 NS < 0.0001 0.00
1978 or newer –0.13 0.032 0.88
Cat currently in the home No NS 0.0035 0.00
Yes 0.10 0.034 1.11
Mold/mildew observed No NS 0.0012 0.00
Yes 0.20 0.063 1.23
Relative humidity in home (%) < 40 NS < 0.0001 0.234 0.062 1.26
40–49 0.043 0.058 1.04
50–59 0.115 0.058 1.12
60–69 0.002 0.060 1.00
> 70 0.000
NS, not significant (α = 0.05).
Based on F-statistics for type-3 tests of overall significance of each factor.
Coefficient estimates for the sampling locations represent the mean log-transformed endotoxin concentra-
tion (EU/mg) at each location, at the reference level of all other factors in the model. Coefficients for other factors represent the estimated additional effect associated with the indicated
level of each factor.
Table 7. Consideration of potential role of local meteorologic data (p-values) during the study year on
endotoxin concentration indoors.
Location Maximum temperature (°C) Minimum temperature (°C) Precipitation (mm)
Bedroom floor NS NS NS
Family room floor NS NS NS
Bedding NS NS 0.033
Kitchen floor 0.001 0.013 NS
Family room sofa NS NS 0.081
NS, not significant (α = 0.05). We considered meteorologic factors separately to predict endotoxin concentration by location
based on longitude and latitude of the household.
Thorne et al.
v o l u m e 117
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Environmental Health Perspectives
joint effect of exposure to > 19.6 EU/mg bed-
room floor and bedding endotoxin on recent
symptomatic asthma yielded an adjusted
odds ratio of 2.83 compared with exposures
below this level. In our previous study, we also
demonstrated that there was a relatively weak
correlation between endotoxin values across
sampling locations within homes, with cor-
relation coefficients between 0.12 and 0.44,
demonstrating the importance of sampling
multiple locations within homes.
Several previous studies have investigated
predictors of endotoxin in residences. Gehring
et al. (2004) analyzed bedding dust endo-
toxin data from a birth cohort study of allergy
[the ongoing birth cohort study Influences
of Lifestyle-Related Factors on the Immune
System and the Development of Allergies in
Childhood (LISA)] conducted in Munich and
Leipzig, Germany. In their study, 28% of the
households were single-family homes, whereas
85% of U.S. households are single-family
homes, reflecting the high degree of home
ownership in the United States. Gehring et al.
(2004) found that dog, but not cat, owner-
ship was a significant predictor of endotoxin
concentration. Endotoxin in bedding dust
increased with increasing numbers of house-
hold occupants (< 4 vs. 4). Another study of
endotoxin predictors was conducted in Erfurt
and Hamburg, Germany (Bischof et al. 2002).
is casecontrol study of adult asthma and
allergy was conducted in 405 homes with sam-
ples collected from living room floors (95%
with carpets). Predictors of higher endotoxin
were old buildings, lower-story residence, lon-
ger occupancy, infrequent vacuum cleaning,
dog and cat ownership, and mouse infestation.
No seasonal effect was observed, and no asso-
ciation of endotoxin with indoor temperature
or relative humidity was found.
In the LISA study, infants’ beds averaged
5.8 EU/mg endotoxin and mothers’ beds aver-
aged 3.0 EU/mg, both much lower than the
18.7 EU/mg measured in beds in our study.
Bischof et al. (2002) found mean endotoxin
levels of 33.0 EU/mg, also considerably less
than our value of 63.9 EU/mg for family room
floors. Differences in sampling and analy sis
methodologies could potentially account for
some of the increase in U.S. values over those
in Germany. Endotoxin analyses for these
studies were run somewhat differently than
our methodology. Our dust samples were
extracted using pyrogen-free water with 0.05%
Tween-20, whereas their extraction was in
pyrogen-free water alone. ey ran duplicate
assays at a single dilution, whereas we ran four
2-fold dilutions.
A third study analyzed data from liv-
ing room carpets in 77 suburban homes in
Wellington, New Zealand (Wickens et al.
2003). Important predictors of floor endo-
toxin concentration in the adjusted model
were total occupants (2–4 vs. 5), maximum
relative humidity (> 70.8% vs. < 70.8%),
age of vacuum cleaner (older vs. newer than
1 year), and steam cleaning or shampoo-
ing the carpet. Factors not related to endo-
toxin concentration included having a cat,
visible dampness or mold, and carpet type.
at study was not able to assess differences
in geography, housing type, poverty, or race.
Park et al. (2001) studied a cohort of chil-
dren of parents with allergies or asthma liv-
ing in the Boston area and evaluated factors
associated with recurrent wheezing. Higher
endotoxin concentration in family room floor
dust was associated with having a dog, whereas
being of black race was associated with sig-
nificantly lower family room floor endotoxin.
Family income was not a predictor of endo-
toxin in their multivariate analysis. Consistent
with our study, their mixed-models analysis
demonstrated that kitchen floors were higher
and bedroom floors lower in endotoxin con-
centration compared with family room floors
(Abraham et al. 2005). is is likely because
bedrooms typically are not trafficked by all
family members as are family rooms, whereas
kitchens have more potential sources of endo-
toxin. In contrast to our nationwide study,
Abraham et al. (2005) found that fall and
winter sampling was associated with lower
endotoxin. e lack of a seasonal effect in our
study likely reflects the wide variation of cli-
mate in the United States. Although winter in
Boston may produce dryer and colder indoor
air, indoor winter conditions may be wetter
(more rain) and warmer (air conditioning off)
in U.S. population centers of the Southwest.
Consistent with these prior studies, we
found that a higher number of occupants and
dog ownership were important predictors of
higher endotoxin. Age of the building was a
significant factor, but only for family room
endotoxin. In contrast to these studies, we
found that geographic location, children in the
home, poverty, cockroach infestation, smoking
in the home, and, for some sampling locations,
cat ownership were important factors. Several
of these factors could not be investigated in the
prior studies due to study design limitations
(e.g., limited geography, single sampling loca-
tion within homes, lack of diversity of popula-
tion or home type, affluent population).
Gram-negative bacteria grow in ecologic
niches that provide sufficient water, nutrients,
oxygen, and heat. Dead or quiescent bacteria
and cell-wall fragments composed of endo-
toxin can be transported in air or tracked in
with dust and soil. Humans and pets harbor
these organisms in the gut and on the skin,
from which they are shed. us, larger fami-
lies, children in the home, and dog ownership
contribute to household endotoxin. Spoiling
food and cockroach carcasses and feces are
additional sources of endotoxin. Although
cigarettes have a small amount of endotoxin,
the association in this study with evidence
of cigarette smoking is likely related to gen-
eral home hygiene rather than dissemina-
tion of endotoxin through smoking. Lower
educational attainment and living in poverty
are predictors of endotoxin likely because of
their association with poorer-quality housing,
introduction of endotoxin via work clothes
brought into the home, and a deficiency of
home hygiene.
A significant strength of NSLAH is
the characterization of predictors of endo-
toxin over a wide range of geography and
population demographics in multiple loca-
tions within homes. The bivariate analyses
(Tables 1–4) and the rANOVA (Tables 6
and 7) showed that the New England census
division had the lowest levels of endotoxin
for all five sampling locations in the homes
and that the Pacific census division had the
highest for four of the five. Nationwide, the
highest combined endotoxin was measured
in St. Louis, and the second and third highest
were Los Angeles and Santa Clara counties
in California. Figure 2 illustrates that New
England, the Middle Atlantic, and the north-
ern states of the South Atlantic census divi-
sions had lower endotoxin. e southwestern
United States, including California, Nevada,
and Arizona, had higher levels. is is perhaps
counterintuitive given the warm and often
dry climate of this region. It is commonly
assumed that because endotoxin arises from
bacteria, and bacteria thrive in water, higher
endotoxin will be associated with more humid
climates. is has been found to be the case
with molds and house dust mites. However,
although typical indoor molds require water
activities of only 0.8, bacteria require water
activities of 0.97 and therefore grow on
damp to wet substrates. Elevated humidity in
the absence of wet surfaces or stagnant water
in HVAC systems will not achieve water
activity levels sufficient to provide an eco-
logic niche to support the growth of bacteria.
Evaporative coolers, or swamp coolers, are a
type of air conditioning found mostly in the
Southwest that draws dry outside air through
wetted pads to lower air temperature by evap-
orative cooling. is type of air conditioning
was used in 14 of the households evaluated
and was associated with significantly higher
endotoxin in the bedding (p = 0.023) but was
not significantly different for other sampling
Main heating source and temperature con-
trol were important factors for family room
and bedroom floor endotoxin. In bivariate
analyses, having temperatures in the family
room between 18°C and 23°C or in the bed-
room between 24°C and 29°C was associated
with lower endotoxin compared with more
extreme temperatures (> 29°C or < 18°C).
Predictors of household endotoxin
Environmental Health Perspectives
v o l u m e 117
n u m b e r 5
May 2009
Electric heating was associated with higher
endotoxin concentrations compared with the
other/none category, and gas heating fell in
between. Temperature control and heating
systems vary regionally. Homes in areas with
cold winters more often rely on gas heat-
ing, whereas homes in warmer climates may
have only electric space heaters or no heat-
ing systems. We retained neither temperature
in room nor heating source in the rANOVA
models, likely due to their strong correla-
tion with census division (chi-square test of
independence for census division and heating
source, p < 0.0001).
We performed the rANOVA in an attempt
to determine which factors independently best
predict endotoxin in the five sampling loca-
tions. e resulting model explained 30% and
52% of the variation in the log-transformed
endotoxin concentration and load, respec-
tively, beyond that explained by differences
among the sampling locations themselves.
is suggests the possibility of population sub-
sampling and use of modeling to impute values
for endotoxin. e rANOVA confirmed dif-
ferences between sampling locations within
homes and the distribution by census divisions.
The rANOVA also demonstrated that lower
educational attainment and presence of food
debris and cockroaches are important predic-
tors of endotoxin in homes. The additional
factors of children and dogs in the household
suggest that poor housing conditions and high
occupancy are important factors leading to
higher endotoxin exposures. Indeed, we tested
other models and demonstrated that number
of people in the household and living in pov-
erty were important factors strongly correlated
with children in the home and lower educa-
tional attainment, respectively. Pairwise tests of
independence demonstrated strong covariance
of lower educational attainment with both liv-
ing in poverty and lower household income
(chi-square test, p < 0.0001 for both).
Our study has several limitations regarding
prediction of factors associated with endotoxin
exposure. First, sampling was performed on a
single day for each household. us, the dust
sample and environmental data collected on
that day were assumed to be representative
of that household. Second, as is frequently
done, we used measurements of reservoir dust
endotoxin as a proxy for personal inhaled
endotoxin exposure. Repeated measures of
breathing zone endotoxin while subjects are
awake and sleeping are difficult to obtain in
a large study. Reservoir dust sampling likely
reflects exposures sustained over a long period
of time and has been shown to be associated
with a variety of respiratory health outcomes
(orne et al. 2005). In addition, it is likely a
more stable estimate of exposure than a single-
time-point air sample. ird, some of the data
were based on interviews with the adult house-
hold resident. It is possible that responses to
potentially sensitive questions such as indoor
smoking or cockroach infestation were subject
to reporting bias. However, this is partially
mitigated by household observation data sys-
tematically reported by field staff. is study
was strengthened by the fact that the weighted
characteristics of the survey sample produced
results indicative of the nation as a whole. e
national scope of the study allowed us to inves-
tigate region and climate for their influence on
indoor endotoxin concentrations.
is nationwide study, representative of the
U.S. housing stock, demonstrated that the
concentration of endotoxin in house dust
depends on the location sampled within the
home and region of the country. Endotoxin
concentrations increased with children or
more occupants in the home, dogs present in
the home, lower educational attainment, liv-
ing in poverty, observed food debris, evidence
of cockroach infestation, and evidence of ciga-
rette smoking. e presence of stuffed animals
in the bed and having an impermeable mat-
tress cover were associated with higher bed
endotoxin. In contrast to indoor molds and
mite allergens, endotoxin was not associated
with having air conditioning, dehumidifier
use, or stove fans that exhaust outside. Neither
race nor ethnicity emerged as independent
predictors of household endotoxin. is study
shows that the burden of domestic endotoxin
exposure is disproportionately borne by fami-
lies living with poor home hygiene. Public
health interventions to reduce exposure to
endotoxin should include improving hous-
ing conditions, eliminating cockroach infes-
tations, reducing cigarette smoking indoors,
and removing mold and mildew in homes.
In addition to lowering endotoxin exposure,
these interventions would reduce exposures to
allergens and other asthma triggers.
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    • "The only parameters that we found significant were the number of exterior windows and the number of hours with internal doors opened — see Eq. (3). On the other hand, when endotoxin contents in household dust were considered, several studies have found that household features were significant predictors: lower educational or socioeconomic status, presence of dog or cat, total fine dust, food debris, smoking, concrete floor, observed mold/mildew, relative humidity, water damage (Park et al., 2001; Horick et al., 2006; Thorne et al., 2009). Why do dust endotoxins correlate more with household features than airborne endotoxins do? "
    [Show abstract] [Hide abstract] ABSTRACT: Indoor and outdoor endotoxin in PM2.5 was measured for the very first time in Santiago, Chile, in spring 2012. Average endotoxin concentrations were 0.099 and 0.094 [EU/m(3)] for indoor (N=44) and outdoor (N=41) samples, respectively; the indoor-outdoor correlation (log-transformed concentrations) was low: R=-0.06, 95% CI: (-0.35 to 0.24), likely owing to outdoor spatial variability. A linear regression model explained 68% of variability in outdoor endotoxins, using as predictors elemental carbon (a proxy of traffic emissions), chlorine (a tracer of marine air masses reaching the city) and relative humidity (a modulator of surface emissions of dust, vegetation and garbage debris). In this study, for the first time a potential source contribution function (PSCF) was applied to outdoor endotoxin measurements. Wind trajectory analysis identified upwind agricultural sources as contributors to the short-term, outdoor endotoxin variability. Our results confirm an association between combustion particles from traffic and outdoor endotoxin concentrations. For indoor endotoxins, a predictive model was developed but it only explained 44% of endotoxin variability; the significant predictors were tracers of indoor PM2.5 dust (Si, Ca), number of external windows and number of hours with internal doors open. Results suggest that short-term indoor endotoxin variability may be driven by household dust/garbage production and handling. This would explain the modest predictive performance of published models that use answers to household surveys as predictors. One feasible alternative is to increase the sampling period so that household features would arise as significant predictors of long-term airborne endotoxin levels.
    Article · Jul 2016
    • "So far, region, which involves climatic factors, building characteristics, building use, occupants behavior, etc., has been identified as the strongest determinant of the indoor microbial environment [13, 14] . Other potential determinants are crowdedness, pet ownership, indoor smoking, frequency of cleaning, room ventilation, age of the house, gas cooking, moisture damage, and farming [14][15][16][17][18][19][20][21][22] . In epidemiological studies, information on the determinants of the indoor microbial environment is usually obtained through questionnaires administered to the occupants. "
    [Show abstract] [Hide abstract] ABSTRACT: The global increase in the prevalence of asthma has been related to several risk factors; many of them linked to the “westernization” process and the characteristics of the indoor microbial environment during early life may play an important role. Living in moisture damaged homes contributes to the exacerbation and development of asthma. However, living in homes with a rich variety and high levels of microbes (e.g., traditional farming environments) may confer protection. While the results of previous research are rather consistent when it comes to observation/report of indoor moisture damage or when comparing farming versus non-farming homes, when actual measures targeting indoor microbial exposure are included, the picture becomes less clear and the associations appear inconsistent. This may partly be due to limitations of sampling and measurement techniques that make comparisons difficult and provide an incomplete picture of the indoor microbial environment and in particular also human exposure. In this regard, new generation sequencing techniques represent a potential revolution in better understanding the impact of the indoor microbiome on human health.
    Article · May 2016
    • "Overall, concentrations of endotoxin within house dust in Baltimore homes in this study (median 134 EU/mg) were similar to published reports in other urban communities, such as those found in New York City (75 EU/mg) (Perzanowski et al. 2006), as well as, specifically, COPD homes (96 EU/mg) (Osman et al. 2007). Consistent with national data (Thorne et al. 2009), concentrations of endotoxin in the dust derived from living areas were significantly higher than those collected from the bedroom, which may be reflective of the greater time spent by these participants within the living room, and has important implications for future exposure assessments of COPD homes. In addition, airborne endotoxin concentrations in our study (median 0.06 EU/m 3 ) were very close to other Baltimore homes (0.13 EU/m 3 ) (Mazique et al. 2011). "
    [Show abstract] [Hide abstract] ABSTRACT: Indoor air pollution has been linked to adverse COPD health, but specific causative agents have not yet been identified. We evaluated the role of indoor endotoxin exposure upon respiratory health in former smokers with COPD. Eighty-four adults with moderate to severe COPD were followed longitudinally and indoor air and dust samples collected at baseline, 3 and 6 months. Respiratory outcomes were repeatedly assessed at each time point. The associations between endotoxin exposure in air and settled dust and health outcomes were explored using generalizing estimating equations in multivariate models accounting for confounders. Dust endotoxin concentrations in the main living area were highest in spring and lowest in fall, while airborne endotoxins remained steady across seasons. Airborne and dust endotoxin concentrations were weakly correlated with one another (rs =+0.24, p = 0.005). Endotoxin concentrations were not significantly associated with respiratory symptoms, rescue medication use, quality of life, or severe exacerbations. In-vitro whole blood assays of the pro-inflammatory capacity of PM10 filters with and without endotoxin depletion demonstrated that the endotoxin component of indoor air pollution was not the primary trigger for IL-1β release. Our findings support that endotoxin is not the major driver in the adverse effects of indoor PM upon COPD morbidity. This article is protected by copyright. All rights reserved.
    Full-text · Article · Nov 2015
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