Environmental Health Perspectives • volume 117 | number 3 | March 2009 461

Research | Children’s Health

The U.S. Centers for Disease Control and 

Prevention (CDC) estimates that 310,000 

children between 1 and 6 years of age in the 

United States have blood lead (PbB) levels > 10 

micrograms per deciliter (CDC 2005). The 

health effects associated with PbB levels at or 

above this level of concern have been well documented, including learning and behavioral 

problems (National Research Council 1993). 

Evidence suggests that children with PbB < 10 

μg/dL also experience notable adverse effects 

and that no safe level of lead exposure exists 

(Canfield et al. 2003; CDC 1991; Lanphear 

et al. 2000, 2005b; Schwartz 1994). In this 

study we identified factors associated with 

childhood residential dust lead (PbD) exposure.

Lead exposure can occur through a variety 

of sources, including air, bare soil, home remedies, drinking water, toy jewelry, and others 

(Levin et al. 2008). However, the major pathway of exposure for children is from deteriorated lead-based paint and lead-contaminated 

dust in the home that is ingested during normal hand-to-mouth behavior (CDC 2007; 

Lanphear et al. 1998). The importance of 

PbD from lead paint was recognized very 

early (Gibson 1904), and work was done subsequently in an attempt to quantify its exposure contribution (Sayre et al. 1974). 

Although lead-based paint was banned 

from residential use in 1978, approximately 

38 million older housing units in the United 

States still contained lead-based paint, and an 

estimated 24 million housing units contain significant lead hazards as of 2000 (Jacobs et al. 

2002). Although intact paint does not generally result in significant immediate exposure, all 

paint eventually deteriorates; lead-based paint 

that is chipping, peeling, or flaking or otherwise separating from its substrate presents a 

hazard. In addition, lead-contaminated settled 

dust, which is often found in houses with deteriorated lead-based paint, is a significant lead 

hazard (Lanphear et al. 1998). PbD can also be 

generated from the friction and impact of leadpainted surfaces (Dixon et al. 2007) and during 

housing renovation and repair projects where 

lead-based paint is present and proper precautions are not in place (Lanphear et al. 2005a; 

Reissman et al. 2002). The use of leaded gasoline, which peaked in the early 1970s, has also 

contaminated soil around the home (Mielke 

1999). Many studies, employing a variety of 

research designs, have demonstrated that soillead concentrations are a significant contributor to PbD and children’s PbB (Bornschein 

et al. 1987; Clark et al. 2004; Dixon et al. 

2007; Lanphear et al. 1998). Numerous 

cross-sectional (Lanphear et al. 1998) and 

longitudinal studies [U.S. Department of 

Housing and Urban Development (HUD) 

2004] have firmly established the correlation 

of settled PbD and children’s PbB. In an effort 

to protect young children from adverse effects 

of lead, current federal health-based hazard 

standards indicate that floor and window PbD 

should not exceed 40 μg/ft2 and 250 μg/ft2, 

respectively [U.S. Environmental Protection 

Agency (EPA) 2001]. 

Through an interagency agreement with 

the CDC, the U.S. HUD Office of Healthy 

Homes and Lead Hazard Control sponsored 

the collection of PbD wipe samples and 

housing-related data through the National 

Health and Nutrition Examination Survey 

(NHANES) from 1999 through 2004, marking the first time that NHANES has collected 

both health and housing environmental data. 

Using these national survey data, we investigated PbD in homes to explore the feasibility 

of lowering PbD standards. Here we present 

the demographic and housing characteristics 

associated with floor and windowsill PbD. We 

used linear regression modeling to predict natural log-transformed floor and windowsill PbD 

and logistic regression modeling to predict the 

log odds of PbD at various levels. A companion article in this issue (Dixon et el. 2009) 

presents the analysis of NHANES data with 

respect to childhood PbB levels. Together these 

data identify the important risk factors and the 

relationship between PbD and children’s PbB 

in the United States in recent years.

Methods

Study population. We examined three waves 

of NHANES (1999–2000, 2001–2002, 

Address correspondence to S.L. Dixon, National 

Center for Healthy Housing, 10320 Little Patuxent 

Parkway, Suite 500, Columbia, MD 21044 USA. 

Telephone: (443) 539-4156. Fax: (443) 539-4150. 

E-mail: sdixon@nchh.org

We thank R. Kaufman, D. Brody, and M.J. Brown of 

the U.S. Centers for Disease Control and Prevention.

The U.S. Department of Housing and Urban 

Development funded this project under contract 

C-PHI-00931.

The opinions expressed in this paper do not necessarily represent those of the U.S. government.

D.E.J. is paid as director of research at the 

National Center for Healthy Housing, a nonprofit 

agency dedicated to decreasing children’s exposure to 

hazards in the home. The other authors declare they 

have no competing financial interests. 

Received 10 July 2008; accepted 13 November 2008.

Exposure of U.S. Children to Residential Dust Lead, 1999–2004: 

I. Housing and Demographic Factors

Joanna M. Gaitens,1 Sherry L. Dixon,2 David E. Jacobs,2 Jyothi Nagaraja,3 Warren Strauss,3 Jonathan W. Wilson,2

and Peter J. Ashley4

1Healthy Housing Solutions, Inc., Columbia, Maryland, USA; 2National Center for Healthy Housing, Columbia, Maryland, USA; 3Battelle 

Memorial Institute, Columbus, Ohio, USA; 4U.S. Department of Housing and Urban Development, Washington, DC, USA

Background: Lead-contaminated house dust is a major source of lead exposure for children in the 

United States. In 1999–2004, the National Health and Nutrition Examination Survey (NHANES) 

collected dust lead (PbD) loading samples from the homes of children 12–60 months of age.

Objectives: In this study we aimed to compare national PbD levels with existing health-based standards and to identify housing and demographic factors associated with floor and windowsill PbD.

Methods: We used NHANES PbD data (n = 2,065 from floors and n = 1,618 from windowsills) 

and covariates to construct linear and logistic regression models. 

Results: The population-weighted geometric mean floor and windowsill PbD were 0.5 μg/ft2

[geometric standard error (GSE) = 1.0] and 7.6 μg/ft2 (GSE = 1.0), respectively. Only 0.16% of 

the floors and 4.0% of the sills had PbD at or above current federal standards of 40 and 250 μg/ft2, 

respectively. Income, race/ethnicity, floor surface/condition, windowsill PbD, year of construction, 

recent renovation, smoking, and survey year were significant predictors of floor PbD [the proportion of variability in the dependent variable accounted for by the model (R2) = 35%]. A similar set 

of predictors plus the presence of large areas of exterior deteriorated paint in pre-1950 homes and 

the presence of interior deteriorated paint explained 20% of the variability in sill PbD. A companion article [Dixon et al. Environ Health Perspect 117:468–474 (2009)] describes the relationship 

between children’s blood lead and PbD.

Conclusion: Most houses with children have PbD levels that comply with federal standards but 

may put children at risk. Factors associated with PbD in our population-based models are primarily 

the same as factors identified in smaller at-risk cohorts. PbD on floors and windowsills should be 

kept as low as possible to protect children.

Key words: dust lead, housing, lead, National Health and Nutrition Examination Survey, 

NHANES. Environ Health Perspect 117:461–467 (2009). doi:10.1289/ehp.11917 available via 

http://dx.doi.org/ [Online 14 November 2008]
Gaitens et al.

462 volume 117 | number 3 | March 2009 • Environmental Health Perspectives

2003–2004) data for children 12 to 60 months 

of age with measured PbB. Included in this 

population were homes of 2,155 children, of 

which 2,065 had floor PbD data and 1,618 

had windowsill PbD data. NHANES is a 

nationally representative cross-sectional household survey that uses a complex, stratified, 

multistage probability sampling design to track 

the health of the noninstitutionalized civilian 

U.S. population. Details of the NHANES 

protocol, survey and analytical procedures, 

and handling of samples are available elsewhere [National Center for Health Statistics 

(NCHS) 2006a, 2006b, 2006c]. 

Demographic and housing characteristics.

NHANES interviewers collected demographic 

and housing information through a structured 

household interview. Characteristics included 

child’s race/ethnicity, household and family 

income, type of home (e.g., mobile home or 

trailer, one-family house detached, one-family 

house attached to one or more houses, apartment, or “other”), number of apartment units 

in building, year of construction, number of 

years the family had lived in the home, ownership status, and smoking in the home. Parents 

of participants reported the race/ethnicity 

of their child based on lists that included an 

open response. We used a composite race/ethnicity variable for this analysis: non-Hispanic 

white, non-Hispanic black, Hispanic (composed mostly of Mexican American because 

of oversampling), or other race or ethnicity. 

The poverty-to-income ratio (PIR) is the ratio 

of income to the family’s appropriate poverty threshold (Office of Management and 

Budget 1978). PIR values < 1.00 are below 

the poverty threshold, whereas PIR values 

of ≥ 1.00 indicate income above the poverty 

level. Variables on smoking behavior included 

the presence of smoking in the home, number of smokers, and the number of cigarettes 

smoked in the home per day. An adult member of the household reported the presence 

of peeling, chipping, or flaking paint (i.e., 

deteriorated) inside and outside the home. 

The household member categorized the paint 

condition inside the house as follows: no deteriorated paint, deteriorated paint but no large 

areas, large areas of deteriorated paint in one 

room, or large areas of deteriorated paint in 

more than one room. Similarly, the household member classified the exterior paint condition as no deteriorated paint, deteriorated 

paint but no large areas, or large areas of deteriorated paint. Large areas inside the home 

were defined as areas larger than one sheet of 

a newspaper and large areas on the outside of 

the home as areas larger than a door. 

The household member also reported 

whether the home had been repainted, whether 

they had scraped old paint or whether there 

had been renovations of windows, cabinets, 

and/or walls in the preceding 12 months.

PbD measurements. Interviewers collected 

separate single-surface floor and windowsill 

PbD samples from the room where the family member reported that children spent most 

of their time while awake, typically the living 

room or play room. Floor PbD wipe samples 

were collected from a measured 1 ft2 area 

in 2,065 homes using a standard procedure 

for moist wipes (ASTM E-1728-03; ASTM 

International 2003). Floor PbD was measured 

using graphite furnace atomic absorption 

spectroscopy and reported in micrograms per 

square foot. Windowsill PbD in 1,618 homes 

was also reported as micrograms per square 

foot using information on the length and 

width dimensions of the windowsill wiped 

area. The laboratory detection limits (DLs) 

for the moist wipe samples were 0.16 μg for 

floors and 2 μg for windowsills. Blank samples 

were collected in 10% of the sampled homes. 

Robust laboratory quality control procedures 

were followed (NCHS 2006d, 2006e, 2006f).

Floor and sill PbD values that were below 

the DLs were assigned the value of DL/√

–

2 in 

the NHANES dust analysis data set. Forty-four 

percent of the sill loadings and 12.5% of the 

floor loadings were below the DLs. Although 

sill loadings are generally higher than floor 

PbD, the surface area of windowsills is typically 

less than the 1 ft2 sampled on floors, resulting 

in more sill loadings below the DL. The effects 

of the high percentage of sill loadings below 

the DL in the linear model are limited because 

the surface areas of sills sampled varied and 

consequently the loadings varied. 

We categorized the floor’s surface and 

condition as uncarpeted smooth and cleanable, uncarpeted not smooth or not cleanable, low-pile carpet, or high-pile carpet. 

Windowsill conditions were characterized 

as either smooth and cleanable or not. The 

modeling presented here also examined room 

cleanliness, presence of clutter, and room 

location as reported by the individual collecting the wipe sample. 

Statistical methods. We analyzed data 

using SAS (SAS System for Windows, version 9.1.3; SAS Institute Inc., Cary, NC) 

and SUDAAN software (version 9.0.0; RTI 

International, Research Triangle Park, NC). 

We used linear regression models to predict natural log-transformed PbD and logistic 

regression models to predict the probability 

that a home’s PbD exceeds various thresholds 

(10 μg lead/ft2 for floors and 100 and 250 μg/

ft2 for windowsills). Models for 10 μg/ft2 for 

floors and 100 μg/ft2 for windowsills were 

selected because analyses in the companion 

article indicate that 95% of children 1–5 years 

of age would have PbB ≤ 10 μg/dL at these 

dust lead levels. Because only three homes in 

the data set had floor PbD exceeding the federal hazard standard of 40 μg/ft2, we could 

not use logistic regression modeling to predict 

the probability that floor PbD exceeded the 

standard. The models adjusted the parameter 

estimates for the clustering and unequal survey weights within NHANES. The modeling 

employed Taylor series expansion theory without degrees of freedom adjustments. Backward 

elimination of insignificant independent variables (p > 0.10) was followed by additional 

steps to allow addition and/or removal of variables. For certain variables, there was a high 

percentage of missing values (e.g., 28% of the 

study sample did not have year of construction 

documented). We fit an intercept term for the 

study participants who had a missing value 

so that we could include these homes in the 

analysis. For categorical variables, we reported 

the p-value for the test for a significant difference in the dependent variable between the 

category of interest and the comparison category, where the comparison category is the 

category with parameter estimate of zero. The 

overall p-value is the type 3 F-test that captures the overall statistical significance of each 

variable included in the model. For categorical 

variables with missing values, the missing level 

was not included in this hypothesis test.

Table 1. Descriptive statistics for continuous housing and demographic variables, NHANES 1999–2004.

Weighted

Variable Levels No. of units GM (GSE) AM (SE)

Floor PbD by floor Missing 90 — —

surface/condition Smooth and cleanable 453 0.99 (1.11) 3.16 (0.56)

Not smooth and cleanable 25 1.70 (1.47) 4.92 (2.11)

Carpeted, low pile 1,381 0.46 (1.06) 0.91 (0.11)

Carpeted, high pile 206 0.35 (1.10) 0.62 (0.08)

All nonmissing 2,065 0.52 (1.05) 1.34 (0.14)

Windowsill lead loading by Missing 537 —

window surface condition All nonmissing 1,618 7.64 (1.07) 57.79 (9.42)

Smooth and cleanable 1,453 6.98 (1.07) 47.57 (5.30)

Not smooth and cleanable 165 16.9 (1.24) 146.6 (69.35)

PIRa Missing 136 — —

Nonmissing 2,019 — 2.07 (0.05)

Years lived in homea Missing 23 — —

Nonmissing 2,132 — 2.64 (0.04)

Abbreviations: AM, arithmetic mean; GSE, geometric standard error. 

aGM and GSE are undefined because of zero values.
Dust lead in homes of U.S. children

Environmental Health Perspectives • volume 117 | number 3 | March 2009 463

Because the surface area and concentration of lead paint is higher in pre-1978 and 

particularly pre-1950 housing (Jacobs et al. 

2002), our models allowed the effects of paint 

deterioration, renovation, repainting, and 

paint scraping in the preceding 12 months 

to be modified by the year of construction. 

This allowed paint deterioration, renovation, 

repainting, and paint scraping to have effects 

on PbD only in homes constructed before 

1978 or before 1950.

Model diagnosis is complex for the analysis of data from a clustered multiframe survey 

with unequal weights, such as NHANES. 

Thus for the linear models, residual analysis 

to assess the validity of the assumption of normality of the error was based on models with 

the same predictors but ignoring the clustering and survey weights.

For the logistic model, we used analysis 

of deviance (McCullagh and Nelder 1989) 

to assess the goodness of fit of the model. 

Although this measure accounts for the survey 

weights, it does not account for the effects of 

clustering. 

Results

Characteristics of the study population. Tables 

1 and 2 provide descriptive statistics for the 

continuous and categoric variables that were 

significant in predicting floor and windowsill PbD in the linear and logistic regression 

models. The geometric means (GMs) for floor 

and windowsill PbD were 0.52 μg/ft2 and 

7.64 μg/ft2, respectively. Only 0.16% of the 

weighted floor dust samples and 4.00% of the 

windowsill dust samples were at or above the 

current federal hazard standards. Most floor 

and sill samples (84.3%) were collected in 

family rooms, living rooms, or dens. Nearly 

10% of the samples were collected in bedrooms, 1.7% from kitchens, 1.4% from dining rooms, and 3.10% from another room. 

Floor dust samples were primarily from 

carpeted areas (80.12%). Only 1% of floor 

dust samples and 10% of windowsill samples were from nonsmooth or noncleanable 

hard surfaces. However, uncarpeted floor surfaces that were not smooth or not cleanable 

had higher GM PbD than did smooth and 

cleanable surfaces (1.7 μg/ft2 vs. 1.0 μg/ft2). 

Floor PbD from smooth and cleanable surfaces were more than double PbD on low-pile

and high-pile carpeted surfaces (1.0 μg/ft2

vs. 0.46 μg/ft2 and 0.35 μg/ft2, respectively). 

Approximately 21% of homes had smoking 

occurring inside the home, 22% of homes 

had reported areas of deteriorated paint inside 

the home, 52% of homes were built before 

1978, and 4% of homes were constructed 

before 1950 and had recent renovations. Only 

1.7% of homes reported exterior deteriorated 

paint, possibly because of the large surface 

area required for this category.

Table 2. Descriptive statistics for categorical housing and demographic variables, NHANES 1999–2004.

Weighted percent

No. Missing Missing

Variable Levels of units included excluded

Floor PbD loading ≥ 40 μg/ft2 Missing 90 3.68 —

 No 2,062 96.17 99.84

Yes 3 0.15 0.16

Floor PbD loading ≥ 10 μg/ft2 Missing 90 3.68 —

No 2,006 94.40 98.00

Yes 59 1.93 2.00

Floor surface/condition Missing 90 3.68 —

Smooth and cleanable 453 18.18 18.87

Not smooth and cleanable 25 0.96 1.00

Carpeted, low pile 1,381 66.08 68.60

Carpeted, high pile 206 11.1 11.52

Windowsill PbD Missing 537 24.17 —

loading > 100 μg/ft2 No 1,465 69.07 96.00

Yes 153 6.75 4.00

Windowsill PbD Missing 537 24.17 —

loading ≥ 250 μg/ft2 No 1,538 72.8 91.10

Yes 80 3.03 8.90

Window surface condition Missing 537 24.17 —

Smooth and cleanable 1,453 68.00 89.67

Not smooth and cleanable 165 7.83 10.33

Room dust sampled Missing 90 3.68 —

Living room, family room, or den 1,700 81.20 84.30

Dining room 29 1.31 1.36

Kitchen 33 1.66 1.73

Bedroom 250 9.17 9.52

Another room 53 2.98 3.10

Year of construction Missing 840 28.1 —

1990–present 287 19.61 27.28

1978–1989 265 14.84 20.64

1960–1977 304 14.35 19.96

1950–1959 168 7.43 10.34

1940–1949 82 4.27 5.94

Before 1940 209 11.39 15.84

Window, cabinet, or wall Missing 174 5.97 —

renovation in pre-1950 home Yes 70 3.98 4.23

No 1,911 90.05 95.77

Anyone smoke Missing 23 1.50 —

inside the home Yes 430 20.78 21.09

No 1,702 77.73 78.91

Year of survey 1999–2000 624 30.23 30.23

2001–2002 765 34.08 34.08

2003–2004 766 35.69 35.69

Extent of peeling, flaking, or Missing 392 15.88 —

chipping paint outside No deteriorated paint 1,376 64.57 76.75

Deteriorated paint but no large areas 309 15.98 18.99

Large areas of deteriorated paint 78 3.58 4.26

Presence of large area of deteriorated Missing 283 10.42 —

paint outside in pre-1950 home Yes 27 1.55 1.73

No 1,845 88.03 98.27

Extent of paint deterioration inside Missing 28 1.45 —

No deteriorated paint 1,596 76.73 77.86

Deteriorated paint but no large areas 439 18.75 19.03

Large areas of deteriorated paint 65 2.33 2.36

in one room

Large areas of deteriorated paint 27 0.74 0.75

in two or more rooms

Presence of paint deterioration inside Missing 28 1.45 —

Yes 531 21.82 22.14

No 1,596 76.73 77.86

Paint scraped when home Missing 1,423 59.59 —

repainted in preceding 12 months Yes 197 10.63 26.44

No 535 29.57 73.56

Race/ethnicity Non-Hispanic white 618 57.09 57.09

Non-Hispanic black 634 15.32 15.32

Hispanica 837 23.82 23.82

Other 66 3.77 3.77

aSixty-six percent of Hispanics are Mexican Americans.
Gaitens et al.

464 volume 117 | number 3 | March 2009 • Environmental Health Perspectives

Floor PbD linear model. Table 3 provides 

the parameter estimates and associated SEs

for the linear model that predicts natural logtransformed floor PbD. 

Floor PbD was not significantly different 

between housing built from 1978 to 1989 

and that from 1990 to present, although the 

difference between pre- and post-1978 was 

significant (p < 0.001). The difference in PbD 

by race/ethnicity was also significant, with 

non-Hispanic blacks having significantly 

higher levels than non-Hispanic whites (p

< 0.001). PbD in Hispanic homes was not 

significantly different than non-Hispanic 

white homes (p = 0.864). A higher PIR was 

significantly associated with lower PbD (p

= 0.021). Higher windowsill PbD was significantly associated with higher floor PbD 

(p < 0.001). Floor PbD in the 1999–2000 

NHANES was significantly higher than floor 

PbD in the 2001–2002 or 2003–2004 waves 

(both p < 0.001). The presence of a smoker 

in the home was associated with significantly 

higher floor PbD (p < 0.006), as was window, 

cabinet, or wall renovation in pre-1950 homes 

(p = 0.056).

Although floor PbD on uncarpeted nonsmooth or noncleanable floors was higher than 

on smooth and cleanable floors, this difference 

was not statistically significant (p = 0.170), perhaps because only 25 homes had uncarpeted 

floors that were not smooth and cleanable. 

This multivariate statistical model explains 

35% of the variability in floor PbD of a 

home. If a variable was significant in either 

the linear or logistic model but not the other 

model, the cells for that variable in the model 

that did not contain the variable in Table 3 

contain a dash (—). 

Floor PbD logistic model. Only 1.92% of 

homes had floor PbD ≥ 10 μg/ft2 (Table 2). 

The parameter estimates for the log-odds that 

floor PbD is ≥ 10 μg/ft2 are shown in Table 3. 

Our results indicate that PbD from high-pile

or low-pile carpet is much less likely to exceed 

10 μg/ft2 than PbD from smooth and cleanable floors (p < 0.001 and 0.024, respectively). 

Floor PbD in homes of non-Hispanic blacks 

was higher than in homes of non-Hispanic 

whites (p = 0.088). Year of construction, type 

of home or apartment, and sill PbD were also 

significant predictors of floor PbD ≥ 10 μg/

ft2. Several variables, including PIR, smoking in the home, renovation, and survey year,

were significant in the linear regression model 

but were not significant in the logistic model. 

Windowsill PbD linear model. Table 4 

provides the parameter estimates and associated SEs for the linear model that predicts 

natural log-transformed windowsill PbD. 

Homes built after 1950 had lower windowsill PbD compared with those built 

before 1950 (p < 0.001). The presence of 

Table 3. Model results for floor PbD.

Linear model for log PbD (R2 = 35%) Logistic model PbD ≥ 10 μg/ft2 (R2 = 7%a)

Variables Overall p-value Estimate (SE) p-Value Overall p-value Estimate (SE) p-Value

Intercept 0.235 –0.239 (0.199) 0.235 < 0.001 –6.179 (0.834) < 0.001

Floor surface/condition

Smooth and cleanable < 0.001 0.000 < 0.001 0.000

Not smooth and cleanable 0.354 (0.254) 0.170 0.449 (0.740) 0.547

Carpeted, low pile –0.634 (0.094) < 0.001 –2.147 (0.401) < 0.001

Carpeted, high pile –0.868 (0.110) < 0.001 –2.868 (1.229) 0.024

Log windowsill PbD loading

Intercept for missing < 0.001 0.409 (0.090) < 0.001 < 0.001 0.000

Slope 0.172 (0.027) < 0.001 0.732 (0.106) < 0.001

Race/ethnicity

Non-Hispanic white < 0.001 0.000 — 0.009 0.000 —

Non-Hispanic black 0.373 (0.086) < 0.001 0.900 (0.516) 0.088

Hispanic –0.015 (0.087) 0.864 — — —

Otherb –0.194 (0.129) 0.140 — –0.492 (0.581) 0.402

PIR

Intercept for missing 0.028 0.036 (0.120) 0.768 — — —

Slope –0.047 (0.020) 0.021 — — —

Year of construction

Intercept for missing < 0.001 –0.136 (0.141) 0.338 < 0.040 –0.032 (0.665) 0.962

1990–present –0.795 (0.128) < 0.001 — —

1978–1989 –0.714 (0.149) < 0.001 — —

1960–1977 –0.410 (0.137) 0.004 — —

1950–1959 –0.366 (0.177) 0.044 0.872 (0.723) 0.234

1940–1949 –0.393 (0.242) 0.118 1.284 (1.015) 0.213

Before 1940 0.000 — 0.000 —

1960–present — — –1.519 (0.775) 0.056

Anyone smoke inside the home

Intercept for missing 0.006 –0.352 (0.344) 0.312 — — —

Yes 0.253 (0.087) 0.006 — —

No 0.000 — —

Window, cabinet, or wall renovation in a pre-1950 home

Intercept for missing 0.056 –0.113 (0.116) 0.334 — — —

Yes 0.355 (0.181) 0.056 — —

No 0.000 — —

Year of survey

1999–2000 < 0.001 0.429 (0.093) < 0.001 — — —

2001–2002 0.067 (0.091) 0.470 — —

2003–2004 0.000 — —

Home-apartment type

One-family house, detached — — 0.042 1.032 (0.550) 0.067

One-family house, attached — — 1.397 (0.739) 0.066

Apartment (1–9 units) — — 1.964 (0.604) 0.002

Apartment (≥ 10 units) — — 0.000

aEstimated using Cox–Snell methodology. b “Other” includes Hispanics for the logistic model.
Dust lead in homes of U.S. children

Environmental Health Perspectives • volume 117 | number 3 | March 2009 465

deteriorated interior paint was associated with 

higher sill PbD (p = 0.028). Pre-1950 homes 

that had large areas of deteriorated paint on 

the outside of the home also had higher sill 

PbD than other homes (p = 0.076). Homes of 

non-Hispanic blacks had significantly higher 

sill PbD than homes of non-Hispanic whites 

(p < 0.001). Sill PbD in homes of Hispanics 

were not significantly different from that in 

homes of non-Hispanic whites (p = 0.298). 

Smoking inside the home was also positively 

associated with sill PbD (p = 0.001). Samples 

taken from surfaces that were not smooth or 

not cleanable had significantly higher PbD 

compared with samples taken from smooth 

and cleanable surfaces (p = 0.009), as was the 

case for floors. Similar to the linear floor PbD 

model, the year of the survey was statistically 

significant. The sill PbD in 1999–2000 was 

higher than in the 2001–2002 or 2003–2004 

waves (p = 0.006).

The multivariate statistical model explains 

20% of the variability in natural-log transformed windowsill PbD. 

Windowsill PbD logistic regression models. Only 8.90% of the homes had windowsill PbD ≥ 100 μg/ft2, and 4.00% of homes 

yielded windowsill PbD ≥ 250 μg/ft2 (Table 

2). The parameter estimates for the log-odds 

that a windowsill PbD is ≥ 100 μg/ft2 and 

≥ 250 μg/ft2 are shown in Table 5. 

In both logistic models, smoking in the 

home and year of construction were statistically significant predictors. If someone 

smoked inside the home, the odds that the sill 

PbD was ≥ 100 or ≥ 250 μg/ft2 were nearly 

90% higher than if no one smoked inside the 

home.

Interestingly, in the logistic model for 

PbD ≥ 100 μg/ft2, the odds that sill PbD was 

≥ 100 μg/ft2 for homes with large areas of 

exterior deteriorated paint was about three 

times higher than for homes with no exterior 

deteriorated paint. In the logistic model for 

Table 4. Linear model results for log windowsill PbD (R2 = 20%).

Variable Levels Overall p-value Estimate (SE) p-Value

Intercept < 0.001 2.670 (0.190) < 0.001

Race/ethnicity Non-Hispanic white 0.001 0.000 —

Non-Hispanic black 0.521 (0.114) < 0.001

Hispanic 0.145 (0.138) 0.298

Other –0.234 (0.241) 0.338

Year of construction Intercept for missing < 0.001 –0.777 (0.234) 0.002

1990–present –1.616 (0.249) < 0.001

1978–1989 –1.442 (0.216) < 0.001

1960–1977 –1.332 (0.221) < 0.001

1950– 1959 –1.072 (0.315) 0.001

1940–1949 –0.715 (0.410) 0.088

Before 1940 0.000 —

Window surface condition Smooth and cleanable 0.001 0.000 —

Not smooth and cleanable 0.759 (0.213) 0.001

Anyone smoke inside the home Intercept for missing 0.001 0.664 (0.824) 0.425

Yes 0.460 (0.130) 0.001

No 0.000

Presence of large area of Intercept for missing 0.076 –0.422 (0.200) 0.040

deteriorated paint outside Yes 0.992 (0.545) 0.076

in pre-1950 home No 0.000 —

Presence of paint Intercept for missing 0.028 –0.413 (0.649) 0.528

deterioration inside Yes 0.361 (0.159) 0.028

No 0.000 —

Year of survey 1999–2000 0.020 0.330 (0.144) 0.027

2001–2002 –0.100 (0.114) 0.382

2003–2004 0.000 —

Table 5. Logistic model results for windowsill PbD.a

PbD ≥ 100 μg/ft2 (R2 = 7%a) PbD ≥ 250 μg/ft2 (R2 = 4%a)

Variable Levels Overall p-Value Estimate (SE) p-Value Overall p-Value Estimate (SE) p-Value

Intercept < 0.001 –1.289 (0.385) 0.002 < 0.001 –2.502 (0.352) < 0.001

Race/ethnicity Non-Hispanic white — — — 0.002 0.000 —

Non-Hispanic black — — — 1.127 (0.297) < 0.001

Otherb — — — 0.049 (0.569) 0.932

Year of construction Intercept for missing 0.005 –1.162 (0.354) 0.002 < 0.001 –1.161 (0.386) 0.004

1990–present –2.194 (0.805) 0.009 –3.201 (0.812) < 0.001

1978–1989 –1.852 (0.542) 0.001 –2.038 (0.895) 0.028

1960–1977 –1.603 (0.365) < 0.001 –1.705 (0.464) 0.001

1950–1959 –1.045 (0.507) 0.045 –1.009 (0.624) 0.113

1940–1949 –0.193 (0.521) 0.713 –0.121 (0.576) 0.834

Before 1940 0.000 — 0.000 —

Anyone smoke Intercept for missing 0.041 –0.336 (1.046) 0.749 0.059 10.472 (0.895) < 0.001

inside the home Yes 0.623 (0.296) 0.041 0.625 (0.323) 0.059

No 0.000 0.000 —

Extent of paint Missing — — — 0.005 –10.017 (0.385) < 0.001

deterioration inside No deteriorated paint — — 0.000 —

Deteriorated paint but no large areas — — –0.044 (0.417) 0.916

Large areas in one room — — –1.402 (1.058) 0.192

Large areas in two or more rooms — — 1.458 (0.669) 0.035

Extent of peeling, Intercept for missing 0.005 –0.530 (0.456) 0.251 — — —

flaking, or chipping No deteriorated paint 0.000 — — —

paint outside Deteriorated paint but no large areas –0.317 (0.364) 0.389 — —

Large areas of deteriorated paint 1.303 (0.586) 0.031 — —

Paint scraped when home Intercept for missing 0.053 0.433 (0.293) 0.146 — — —

repainted in last 12 months Yes 0.899 (0.451) 0.053 — —

No 0.000 — — —

Years lived in the home Intercept for missing 0.076 0.000 — — — —

Slope –0.227 (0.124) 0.076 — —

Year of survey 1999–2000 0.050 0.534 (0.229) 0.024 — — —

2001–2002 –0.087 (0.306) 0.776 — — —

2003–2004 0.000 — — — —

aEstimated using Cox–Snell methodology. b“Other” includes Hispanics.
Gaitens et al.

466 volume 117 | number 3 | March 2009 • Environmental Health Perspectives

PbD ≥ 250 μg/ft2, if the interior paint deterioration was large in two or more rooms, 

the odds that sill PbD was ≥ 250 μg/ft2 were 

about three times higher than if there was no 

interior paint deterioration. 

Most variables that achieved statistical 

significance did so in more than one of the 

three sill PbD models, making these findings 

robust. For example, smoking and year of 

building construction were significant in all 

three models. Paint scraping in the preceding 

12 months and the years lived in the home 

were only significant in the logistic model for 

PbD ≥ 100 μg/ft2. Window surface condition 

was only significant in the linear model.

Discussion 

Consistent with other national data (Jacobs 

et al. 2002), we confirm that the year of construction is a strong predictor of PbD and 

that post-1978 housing has significantly lower 

PbD than older housing. Housing units built 

after 1950 have significantly lower floor PbD 

than older housing. Furthermore, floor PbD 

in houses built between 1940 and 1949 are 

not significantly different than in the pre1940 houses. This is consistent with the 

concentration of lead in paint. Before 1940, 

this concentration typically ranged from 10

to 50% (Rabin 1989; U.S. HUD 1995). 

However, in 1955, a voluntary paint industry 

standard limited the concentration to 1%, 

although the degree of compliance with this 

standard is unknown (American National 

Standards Institute 1955). In 1978, the 

Consumer Product Safety Commission and 

Congress banned the use of lead paint for residential use, limiting lead in new house paint 

to 0.06% by weight (Consumer Products 

Safety Commission 1977). 

Pre-1950 homes with window, cabinet,

or wall renovation within the preceding 12 

months had higher floor PbD than other 

homes. Renovation activities completed without using lead-safe work practices can generate significant amounts of PbD (Jacobs et al. 

2003). The U.S. EPA recently promulgated 

a final regulation in an attempt to control 

exposures from renovation (U.S. EPA 2008). 

Replacing windows in a lead-safe manner can 

help control PbD and may have large economic benefits (Nevin et al. 2008). Compared 

with other housing components, windows are 

known to have the highest levels of lead-based 

paint and lead-contaminated dust (Jacobs 

et al. 2002). 

We found that dust wipe samples taken 

from homes with chipping, peeling, or flaking 

(i.e., deteriorated) paint had higher windowsill 

PbD than homes without deteriorated paint, 

which is consistent with other research (U.S. 

HUD 2004; Wilson et al. 2006). Although 

we expected to see similar findings for floors, 

we did not. This could be attributable to other 

factors included in the model. For example, 

PIR and recent renovation were significant 

in the floor model, but not in the windowsill 

model, which could have masked the effect 

of deteriorated paint. In addition, it is more 

likely that floors are more regularly cleaned 

than windowsills. 

The homes of non-Hispanic blacks had 

significantly higher PbD than the homes of 

non-Hispanic whites, even after controlling for 

other factors. Many prior studies have shown 

that African-American children are at higher 

risk compared with white children (U.S. CDC 

2005). For example, similar to our findings, 

other studies (Lanphear et al. 1996, 2002) 

found that African-American children were 

exposed to higher PbD loadings and worse 

housing conditions than white children.

Previously published studies suggest that 

rental units were more likely to have lead-based 

paint hazards than owner-occupied housing 

(Jacobs et al. 2002; Lanphear et al. 2005a). 

Although we found that homeownership status was a significant predictor of floor PbD in 

bivariate analysis, after controlling for other factors such as PIR, renovation, and the presence 

of deteriorating paint, it was not significant in 

the final models. Because low-income families are more likely to rent, the fact that home 

ownership was not significant in the model 

(but poverty was) is not surprising. Moreover, 

the type of home had a significant association 

with PbD in bivariate analysis, but not in the 

final models, again probably because of the 

confounding influence of poverty.

A prior study found a relationship between 

exposure to environmental tobacco smoke 

exposure and the PbB levels of young children who were included in the NHANES III 

(1988–1994) (Mannino et al. 2003). Because 

lead is a component of tobacco smoke, such a 

relationship between PbB levels and tobacco 

smoke exposure is not surprising. That smoking in the home was a significant predictor of 

floor and windowsill PbD, even when controlling other factors, suggests that lead in secondhand smoke is a significant contributor to lead 

on interior surfaces, at least at the relatively 

low surface loadings documented in this study. 

We found that the surface condition from 

which the PbD samples were taken significantly influences the reported PbD. Other 

studies have found that uncarpeted smooth 

and cleanable surfaces have significantly 

lower PbD after cleaning compared with 

rough uncarpeted surfaces (Dixon et al. 1999; 

Ettinger et al. 2002). Although our results 

indicated that floor PbD on uncarpeted nonsmooth or noncleanable surfaces was not significantly higher than floor PbD on smooth 

and cleanable surfaces, the trend was in the 

expected direction. It is possible that our findings were not statistically significant because 

of the small number of dust samples taken 

from nonsmooth or noncleanable floor surfaces in this data set. 

The presence of carpeting also influenced reported PbD. We found that PbD 

on carpeted floors was significantly lower 

than lead loadings on hard-surfaced floors. 

This observed difference in lead loading by 

flooring type is likely attributable to the fact 

that wipe sampling was the methodology 

used in this study. Wipe sampling captures 

only dust adhering to the tops of the carpet 

fibers, whereas most of the dust in the carpet 

matrix is located deeper in the pile. A study 

that reported much higher PbD in vacuum 

samples from carpet compared with adjacent wipe samples (Bai et al. 2003) supports 

this idea. The significance of this observation 

with respect to children’s lead exposure is not 

well understood. Our findings do not clarify 

whether or not carpet contributes to higher or 

lower exposures. 

The NHANES survey year was significantly associated with PbD. Although PbD 

should decline with time as the ratio of post1978 to pre-1978 homes increases, the magnitude of the decline (14%) for the floor PbD 

between the first and second waves was much 

larger than expected. Similarly, the magnitude 

of the decline (43%) between the first and 

second waves for windowsill PbD is unlikely 

to be explained only by temporal changes 

in national PbD. Although many housing 

characteristics were included in the model, 

other significant factors that we were unable 

to control for may exist (e.g., different waves 

of NHANES sampling may occur in different 

geographic regions). Thus, the year of survey 

variable may reflect geographic differences 

in the three study wave locations that could 

account for the observed trend in floor PbD. 

We used data from a large national survey that combined housing and environmental data, which is a strength of our study. 

However, the housing data included in this 

study may not be representative of the national 

housing stock. The sampling and weighting 

methodology used in NHANES is population 

based, not geography based; thus, the survey 

includes a nationally representative sample 

of the U.S. population but not a representative sample of U.S. housing. Integrated health 

and housing surveys that are representative of 

both the population and the housing stock are 

needed in the United States; such surveys were 

completed recently in eight European cities 

(Bonnefoy et al. 2003). Finally, a limitation of 

the NHANES PbD data is that they are based 

on a single floor and a single windowsill PbD 

measurement in a given home. The HUD 

Guidelines for the Evaluation and Control of 

Lead-Based Paint Hazard in Housing recommend that six to eight floor and sill samples be 

taken to help reduce spatial variability, which 

could not be assessed here (U.S. HUD 1995). 
Dust lead in homes of U.S. children

Environmental Health Perspectives • volume 117 | number 3 | March 2009 467

Despite these potential limitations, the study 

results presented are largely consistent with 

earlier findings.

Levels of PbD on floors and windowsills 

should be kept as low as possible to protect 

children from lead exposure. The current standards for floor and windowsill PbD were set in 

1999–2001 to protect 95% of children from 

developing a PbB level > 15 μg/dL [the environmental intervention level established by the 

CDC (2005)], in light of feasibility and measurement limitations. These findings show that 

in most children’s homes, the average level of 

PbD is well below the current standards, making it feasible to lower the current standards 

and thus afford more protection for children. 

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