Risk Analysis, Vol. 27, No. 2, 2007 DOI: 10.1111/j.1539-6924.2007.00893.x

A Meta-Analysis of Children’s Hand-to-Mouth Frequency

Data for Estimating Nondietary Ingestion Exposure

Jianping Xue,1 Valerie Zartarian,1∗ Jacqueline Moya,1 Natalie Freeman,2 Paloma Beamer,3

Kathy Black,4 Nicolle Tulve,1 and Stuart Shalat4

Because of their mouthing behaviors, children have a higher potential for exposure to available chemicals through the nondietary ingestion route; thus, frequency of hand-to-mouth

activity is an important variable for exposure assessments. Such data are limited and difficult to collect. Few published studies report such information, and the studies that have

been conducted used different data collection approaches (e.g., videography versus real-time

observation), data analysis and reporting methods, ages of children, locations, and even definitions of “mouthing.” For this article, hand-to-mouth frequency data were gathered from 9

available studies representing 429 subjects and more than 2,000 hours of behavior observation. A meta-analysis was conducted to study differences in hand-to-mouth frequency based

on study, age group, gender, and location (indoor vs. outdoor), to fit variability and uncertainty

distributions that can be used in probabilistic exposure assessments, and to identify any

data gaps. Results of this analysis indicate that age and location are important for hand-tomouth frequency, but study and gender are not. As age increases, both indoor and outdoor

hand-to-mouth frequencies decrease. Hand-to-mouth behavior is significantly greater indoors

than outdoors. For both indoor and outdoor hand-to-mouth frequencies, interpersonal, and

intra-personal variability are ∼60% and ∼30%, respectively. The variance difference among

different studies is much bigger than its mean, indicating that different studies with different

methodologies have similar central values. Weibull distributions best fit the observed data for

the different variables considered and are presented in this article by study, age group, and

location. Average indoor hand-to-mouth behavior ranged from 6.7 to 28.0 contacts/hour, with

the lowest value corresponding to the 6 to <11 year olds and the highest value corresponding

to the 3 to <6 month olds. Average outdoor hand-to-mouth frequency ranged from 2.9 to 14.5

contacts/hour, with the lowest value corresponding to the 6 to <11 year olds and the highest

value corresponding to the 6 to <12 month olds. The analysis highlights the need for additional

hand-to-mouth data for the <3 months, 3 to <6 months, and 3 to <6 year age groups using

standardized collection and analysis because of lack of data or high uncertainty in available

(cont.)

1 U.S. Environmental Protection Agency.

2 University of Florida.

3 Stanford University.

4 Environmental and Occupational Health Sciences Institute.

∗ Address correspondence to Valerie Zartarian, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, EPA Region 1, CAP, 1 Congress Street, Suite 1100, Boston, MA 02114, USA; tel: 617-918-1541; fax:

617-918-0541; zartarian.valerie@epa.gov

411 0272-4332/07/0100-0411$22.00/1 C 2007 Society for Risk Analysis
412 Xue et al.

data. This is the first publication to report Weibull distributions as the best fitting distribution

for hand-to-mouth frequency; using the best fitting exposure factor distribution will help improve estimates of exposure. The analyses also represent a first comprehensive effort to fit

hand-to-mouth frequency variability and uncertainty distributions by indoor/outdoor location

and by age groups, using the new standard set of age groups recommended by the U.S. Environmental Protection Agency for assessing childhood exposures. Thus, the data presented in

this article can be used to update the U.S. EPA’s Child-Specific Exposure Factors Handbook

and to improve estimates of nondietary ingestion in probabilistic exposure modeling.

KEY WORDS: Children’s exposure; hand-to-mouth frequency; nondietary ingestion

1. INTRODUCTION

Individuals have the potential for exposure to

toxic chemicals through the nondietary ingestion

route. Toxic chemicals can be transferred from contaminated surfaces or soil to the hand and then ingested via hand-to-mouth activity. Thus, frequency of

hand-to-mouth behaviors is an important variable for

estimating mass of chemical ingested by humans via

mouthing. This pathway is particularly important for

children because as part of their natural development,

children mouth their fingers and other objects (Hubal

et al., 2000). Data to assess this pathway are limited

and difficult to collect. Few published studies report

such information, and the studies that have been conducted used different data collection approaches, data

analysis, and reporting methods, ages of children, locations, and even definitions of “mouthing” (i.e., contact with lips, inside of mouth, tongue). Because this

difference in reporting of data makes it challenging to

compare results among individual studies, the available data have not been analyzed collectively for use

in probabilistic exposure models.

Generally, children’s mouthing behavior is studied using both direct observation and videotaping

methodologies (Zartarian et al., 1997; Reed et al.,

1999; Freeman et al., 2001; Ferguson et al., 2005). Observations may be conducted by a parent, after being

given special instructions, or by a trained observer.

Videotaping to record the child’s behavior is done by

a trained technician. Videotape footage is then translated by a person who watches the videotapes and

records information by hand (e.g., number of handto-mouth contacts) or uses video translation software

(Zartarian et al., 1997; Ferguson et al., 2005). Data

analyses from these studies are reported as either a

frequency of contact (i.e., contacts/time) or as an exposure period (i.e., minutes). This article focuses on

frequency of hand-to-mouth contact; future research

could analyze of available data for hand-to-mouth duration data.

The general equation for estimating nondietary

ingestion of chemical residue via hand-to-mouth

contact involves the product of hand residue or

soil loading (ug/cm2 or ug/g), hand-to-mouth frequency (# contacts/hr), hand surface area mouthed

per mouthing event (cm2), and exposure duration

(hr/day). Thus, to enhance estimates of nondietary

ingestion in exposure assessments, reliable hand-tomouth frequency data are important. Currently, data

from four studies are typically used to assess exposure

through this pathway (U.S. EPA, 2001a, 2002). The

mean and 95th percentile values of hand-to-mouth

frequencies from one of the available studies (Reed

et al., 1999) are currently used by the U.S. EPA Office

of Pesticides Programs in its Standard Operating Procedures (U.S. EPA, 2001a) for estimating nondietary

ingestion exposures. Updated distributions reflecting

all available data can be used in probabilistic models

(e.g., CalendexTM, developed by Exponent, Inc.,

http://www.exponent.com/practices/foodchemical/cal

endex.html; CARES, developed by the International Life Sciences Institute, http://cares.ilsi.org/;

LifelineTM, developed by The Lifeline Group, The

Lifeline Group, Inc., 2006, http://www.thelifeline

group.org/; and SHEDS, developed by U.S. EPA’s

Office of Research and Development, Zartarian

et al., 2000, 2006) that are used to conduct children’s

exposure and risk assessments.

A workshop on Micro/Macro-Activity Data

Needs to Improve Multi-Media, Multi-Pathway

Exposure/Intake Dose conducted by the EPA in 2001

(U.S. EPA, 2001b) brought scientists together to discuss activity studies to date and different approaches

used (e.g., real-time hand recording and videotaping),

and to identify research needs. The analysis presented

here is an attempt to respond to the workshop recommendation to assess how similar or different activity

data are across studies, and determine any data gaps

for additional research. Although the studies used to

conduct this analysis collected data on other mouthing
Estimation of Nondietary Ingestion Exposure 413

behaviors (e.g., object-to-mouth), the analysis presented in this article focuses on hand-to-mouth behavior only.

This article is the first attempt to compile handto-mouth frequency data from all available studies,

and to conduct a meta-analysis with the following

objectives:

1. Examine differences across studies by age (using the new EPA recommended age groupings (U.S. EPA, 2005)), gender, and indoor/

outdoor location;

2. Fit variability distributions to the available

hand-to-mouth frequency data for use in 1-D

Monte Carlo exposure assessments;

3. Fit uncertainty distributions to the available

hand-to-mouth frequency data for use in 2-D

Monte Carlo exposure assessments; and

4. Assess hand-to-mouth frequency data needs

using the new EPA recommended age groupings (U.S. EPA, 2005).

2. METHODS

2.1. Approach for Examining Differences Across

Studies by Age, Gender, and

Indoor/Outdoor Location

Few published studies containing hand-to-mouth

frequency data are available, and those available

have collected and reported the data in different

ways. Those studies that were available in 2002 are

described in the U.S. EPA Child-Specific Exposure

Factors Handbook (U.S. EPA, 2002). Additional studies are being included in the updated version of

the handbook, expected to be released in 2007. A

summary of the 9 studies used in the meta-analysis

for this article are given in Table I. For each cited

Table I. Summary of Hand-to-Mouth Frequency Studies Used in Meta-Analysis

No. of Subjects’ Age

Study Study Location Subjects M:F Ratio (Min, Mean, Max) Yrs. Child Observation Period

Zartarian et al., 1998 CA 4 0.5 2.4, 3.3, 4.2 6–10 hrs

Reed et al., 1999 NJ 30 0.4 1.8, 4.2, 6.2 3–7 hrs

Leckie et al., 2000 CA 20 0.35 1, 3.3, 12 1–2 hrs

Freeman et al., 2001 MN 19 0.42 3, 7, 12 4 hrs

Greene, 2002 TX and IL 169 0.53 0.3, 1.5, 3.0 4 hrs on 2 different days

Tulve et al., 2002 WA 86 0.5 0.8, 2.8, 5 5 to 60 mins per day for 1–6 days

Hore, 2003 NJ 10 0.6 2, 3.4, 4.6 4 hrs

Black et al., 2005 TX 68 0.44 0.6, 2.3, 5 4 hrs

Beamer et al., in preparation CA 23 0.44 0.5, 1.3, 2.3 2–6 hours

study, all of the protocols and procedures related to

human subjects research were reviewed and approved

by an independent institutional review board (IRB)

and complied with all applicable requirements of the

Common Rule regarding additional protections for

children. While most of these involved videotaping

methods, several used parents and trained observers

(Tulve et al., 2002; Greene, 2002). Some of the videography studies (Black et al., 2005; Beamer et al., in

preparation; Leckie et al., 2000; Zartarian et al., 1997;

Hore, 2003) used video translation software developed by Stanford University (Ferguson et al., 2005;

Zartarian et al., 1997) and others (Freeman et al., 2001;

Reed et al., 1999) used manual recording from video

observations. As mentioned in AuYeung et al. (2004),

mouthing of hands can be defined differently across

studies (lips, inside of mouth, tongue).

Because the published studies reported summary

statistics and study results in different ways and for

different age groups, the authors of individual studies

were contacted to obtain hourly hand-to-mouth frequency data so that the data could be pooled and reanalyzed collectively in this meta-analysis, using the new

EPA age groupings. For the 3 to <6 months age group,

indoor hand-to-mouth frequency data were available

for 10 children, and no outdoor hand-to-mouth frequency data were available. For the 6 to <12 months

age group, there were 65 subjects with indoor handto-mouth frequency and 10 with outdoor. For the 1 to

<2 years age group, there were 118 subjects with indoor hand-to-mouth frequency and 28 with outdoor.

Forthe 2 to <3 years age group, there were 92 subjects

with indoor hand-to-mouth frequency and 41 with

outdoor. For the 3 to <6 years age group, there were

96 subjects with indoor hand-to-mouth frequency and

41 with outdoor. For the 6 to <11 years age group,

there were 14 subjects with indoor hand-to-mouth frequency and 15 with outdoor. For the 11 to <16 years
414 Xue et al.

Table II. Indoor Hand-to-Mouth Frequency (No. of Contacts/Hour) from Various Studies

Study No. of Observations Mean Std P50 P5 P25 P75 P95

Zartarian et al., 1998 4 3.9 2.1 3.4 2.1 2.4 5.4 6.9

Reed et al., 1999 30 9.5 6.4 8.5 2.0 5.0 11.0 25.0

Freeman et al., 2001 19 6.7 6.0 5.0 0.4 2.4 10.2 20.6

Tulve et al., 2002 238 15.9 19.5 10.0 0.0 2.0 24.0 59.0

Greene, 2002 342 18.6 19.1 12.0 0.0 5.0 25.0 57.0

Hore, 2003 10 19.9 14.1 16.0 1.0 10.3 26.1 47.8

Black et al., 2005 68 16.1 13.5 13.3 3.0 9.0 19.4 35.9

Beamer et al., in preparation 23 20.2 13.9 17.0 3.0 11.3 26.4 47.6

age group, there were 3 subjects with indoor hand-tomouth frequency and 2 with outdoor. Tables II and III

present the summary statistics of indoor and outdoor

hand-to-mouth frequency, respectively, from the data

in each of the individual studies.

No data were available in any of the studies for

infants from birth to <1 month or for 1 to <3 months

old. General linear models and mixed effects models

were used to test for any statistical difference between

studies, age category, gender, and location (indoor vs.

outdoor). Summary statistics were obtained by study,

age, and location. The data were merged if there was

no statistical difference among studies.

2.2. Approach for Fitting Variability Distributions

To fit variability distributions, the data from the 9

studies were compiled and put into comparable units

of frequency (hourly rate) as the ratio of number of

hand-to-mouth contacts divided by a time interval.

The denominator of that ratio was reported differently across studies: the average was ∼1 hour for

outdoors and ∼3 hours for indoors. Thus, an “observation” in Tables 2 through 8 is defined as the

number of reported hand-to-mouth frequency ratios,

which could have different denominators. As shown

in Table I, the study periods and data collection intervals also varied across studies. A unique ID was

assigned to each individual, and the data were sorted

Table III. Outdoor Hand-to-Mouth Frequency (No. Contacts/Hour) from Various Studies

Study No. of Observations Mean Std P50 P5 P25 P75 P95

Leckie et al., 2000 20 7.2 13.4 1.8 0.0 0.3 7.6 40.1

Freeman et al., 2001 17 2.0 3.8 0.0 0.0 0.0 2.3 11.9

Tulve et al., 2002 60 9.3 10.1 7.0 0.0 0.0 14.0 35.5

Black et al., 2005 46 9.7 12.7 4.9 0.0 1.3 10.1 40.9

Beamer in preparation, 2006 17 9.2 8.1 7.1 0.0 2.1 14.0 26.0

into the new EPA age groupings for risk assessment

(U.S. EPA, 2005) (3 to <6 months, 6 to <12 months,

1 to <2 years, 2 to <3 years, 3 to <6 years) according

to the age of the child. Method of moments and maximum likelihood estimation methods were used along

with visual inspection of the data, and then goodnessof-fit tests (Kolmogorov-Smirnov, Cramer-von-Mises,

Anderson Darling, chi-square) were applied to verify the selection among a number of distributions, including lognormal, Weibull, and normal distributions.

Analyses were also conducted on the inter- and intrapersonal variability of indoor and outdoor hand-tomouth frequency.

2.3. Approach for Fitting Uncertainty Distributions

Some exposure models (e.g., Zartarian et al., 2000,

2006; Xue et al., 2006; Burke et al., 2001) have the

option of conducting single-stage Monte Carlo sampling to assess the variability in exposure or dose for

a population of interest (expressed as population percentiles), or two-stage Monte Carlo sampling to evaluate uncertainty along with variability (expressed as

a range of values for each percentile). Generally, an

exposure model run in two-stage Monte Carlo sampling consists of generating N simulations each comprised of M iterations, which produces a family of

N predicted distributions of population exposures.

The entire process of a single sampling of uncertain
Estimation of Nondietary Ingestion Exposure 415

parameters, followed by repeated sampling from the

variability parameters, is referred to as a simulation

(MacIntosh et al., 1995).

To generate uncertainty distributions presented

in this article, the bootstrap approach described

in Xue et al., 2006 was used as follows. For

two-parameter distributions like the Weibull, the

parameter pairs are chosen from a list generated by

the following steps:

1. Fit a variability distribution (the “parent distribution”), estimating parameters v1 and v2

(e.g., geometric mean and geometric standard

deviation), to all data from the original S studies using the method of moments.

2. Fit a variability distribution (using the shape

of the parent distribution) to data in each of

the original S studies using the method of moments, and examine the scatter plot of the S

v1 and v2 values to get a sense of the scale of

uncertainty.

3. Sample B data points from the parent distribution K different times (B is the bootstrap

sample size; K is the number of samples of parameter pairs to be saved for uncertainty runs,

typically 200).

4. For each of those K sets of B data points, fit

the parent distribution and compute the parameter values of interest. This gives K (v1,v2)

pairs.

5. Overlay the scatter plot of the K (v1,v2) pairs

with the S (v1,v2) pairs obtained in Step 2.

6. Repeat Steps 3–5 with different values of B,

until the scatter plot from Step 4 satisfactorily matches the spread seen in the scatter plot

from Step 2.

Steps 5 and 6 require one to compare the input variable uncertainty with that seen in the original studies, leading to a choice for B. The size of B

depends on the sample size of the original data and

how well the bootstrap scatter plots fit over the data

points among the various data sources (Step 5). Lower

B values correspond to higher uncertainties.

Using those B values, 200 data pairs of Weibull

distribution parameters were generated for each age

group both for indoor and outdoor frequencies. Each

pair was used for one variability run with 100 (M)

variability simulations to generate 200 (N) sets of

distributions (cumulative density functions) for conducting uncertainty analyses. After ranking the medians of those 200 runs, the 50th and 95th percentile

variability distributions were selected (i.e., those distributions with the 50th and 95th percentile ranked

medians). The 5th and 95th percentiles were calculated to get the ratio of the 95th vs. 5th for each variability run. This ratio indicates the variability (shown

in the first two columns of data in Table VII). Next,

the 50th and 95th percentiles of each of the 200 variability runs were selected, and the 5th and 95th percentiles were calculated for those 200 50th and 95th

percentiles. The ratio of the 95th to the 5th percentile

indicates the uncertainty from the selected various

pairs of 200 Weibull distribution parameters (results

shown in last two columns of the data in Table VII).

This process for variability and uncertainty analyses

was conducted for each age group and for the 2 locations (indoor/outdoor).

2.4. Approach for Assessing Hand-to-Mouth

Frequency Data Needs

Determining where additional data would help

improve the probability distributions presented was

based on the sample size for each age group, study, and

indoor/outdoor location and also on the uncertainty

analysis.

3. RESULTS

Combining all those hours of behavior observation data for the 429 children ages 0.3 to 12 years

across all 9 studies, it was found that age and location (indoor vs. outdoor) were important for handto-mouth frequency, but study and gender were not.

The overall general linear model and mixed model

show that results across studies were not statistically

significantly different. Hand-to-mouth frequency was

also not statistically different between genders. However, location (indoor vs. outdoor) and age groups are

statistically significant for hand-to-mouth frequency.

Summary statistics are different among studies for

both indoor and outdoor hand-to-mouth frequency,

mainly from age differences among those studies.

As age increases, both indoor and outdoor handto-mouth frequencies decrease. For both indoor and

outdoor hand-to-mouth frequency, interpersonal and

intra-personal variability are ∼60% and ∼30%, respectively. Also, the variance difference among different studies is much bigger than its mean (averaged

mean relative error in %).

Weibull distributions were found to best fit the

observed data for all analyses conducted. Fig. 1 illustrates this for two of the age groups as examples.

Tables IV and V present the Weibull distribution
416 Xue et al.

Fig. 1. Fits of alternative variability distributions for the indoor

hand-to-mouth frequency variable.

parameters and summary statistics for indoor and outdoor hand-to-mouth frequency, respectively, by age

group for each of the studies. To fit Weibull distributions, a minimum sample size of 9 was used, so some

Table IV. Indoor Hand-to-Mouth Frequency (No. of Contacts/Hour) Weibull Distributions by Age Group from Various Studies

Weibull Weibull

Shape Scale No. of

Study Age Group Distribution Parameter Parameter Chi-Square Observations Mean Std p50 p5 p25 p75 p95

Greene, 2002 3 to <6 months Weibull 1.28 30.19 Fail 23 28.0 21.7 23.0 3.0 8.0 48.0 65.0

Greene, 2002 6 to <12 months Weibull 1.12 20.67 Pass 88 19.8 17.8 14.5 2.0 7.0 29.0 50.0

Tulve et al., 2002 6 to <12 months Weibull 0.40 5.37 Fail 9 14.4 23.6 5.0 0.1 0.1 15.0 71.0

Black et al., 2005 6 to <12 months Weibull 1.30 20.92 Fail 11 19.1 17.1 13.7 3.3 9.3 19.7 56.8

Beamer et al., in 6 to <12 months Weibull 1.93 16.34 Pass 11 14.6 7.9 17.0 2.0 9.6 20.8 26.4

preparation

Greene, 2002 1 to <2 years Weibull 0.90 19.21 Pass 132 20.1 21.0 14.0 0.1 5.5 26.0 65.0

Tulve et al., 2002 1 to <2 years Weibull 0.82 16.98 Fail 84 18.5 19.2 14.0 0.1 5.0 27.0 54.0

Black et al., 2005 1 to <2 years Weibull 1.94 19.35 Pass 20 17.1 9.6 14.6 4.7 9.4 23.6 35.4

Greene, 2002 2 to <3 years Weibull 0.75 11.61 Pass 87 13.6 16.3 8.0 0.1 2.0 20.0 39.0

Tulve et al., 2002 2 to <3 years Weibull 0.58 8.66 Pass 41 12.1 13.6 8.0 0.1 2.0 15.0 36.0

Black et al., 2005 2 to <3 years Weibull 1.24 12.28 Pass 24 11.6 8.7 10.5 1.6 3.8 16.2 31.5

Reed et al., 1999 3 to <6 years Weibull 1.64 11.32 Pass 25 10.1 6.6 9.0 2.0 5.0 13.0 25.0

Greene, 2002 3 to <6 years Weibull 1.01 11.79 Fail 12 11.8 9.8 10.0 0.1 3.5 17.5 30.0

Tulve et al., 2002 3 to <6 years Weibull 0.61 11.06 Pass 104 15.5 21.2 7.5 0.1 2.0 22.0 62.0

Hore, 2003 3 to <6 years Weibull 1.90 24.91 Pass 9 22.0 13.2 19.4 8.9 11.0 26.1 47.8

Black et al., 2005 3 to <6 years Weibull 1.29 22.47 Fail 13 20.5 20.3 14.8 4.9 9.8 22.5 83.9

Freeman et al., 2001 6 to <11 years Weibull 1.26 7.25 Pass 12 6.7 6.0 4.2 1.7 2.3 10.2 20.6

subjects were not included in Tables IV and V for certain age groups because there were fewer than 9 data

points. Fitting distributions by study and age group revealed that the mean and median were constant across

studies by age group for indoor hand-to-mouth frequency, except for the 3 to <6 years age group where

Reed et al. (1999), Greene (2002), and Tulve et al.

(2002) are lower than Hore (2003) and Black et al.

(2003). The same finding was observed with the outdoor hand-to-mouth frequency data.

Table VI presents the Weibull distribution parameters and summary statistics for indoor and outdoor hand-to-mouth frequency by age group, with

data pooled from all studies. This table illustrates that

hand-to-mouth frequency generally decreases as age

increases. Table VII presents the results of the uncertainty analysis. The uncertainty was large for age

groups 6 to <11 years old and 3 to <6 years old, as can

be seen by studying the ratio of the 95th percentile to

the 5th percentile. The results in Table VII also reveal

that the variability is much bigger than the parameter

uncertainty. In comparison with other age groups, the

3 to <6 year old group had large uncertainty both for

indoor and outdoor hand-to-mouth frequency. The

6 to <11 year age group had very large uncertainty

for outdoor hand-to-mouth frequency. The age group

of 6 to <12 months also had large uncertainty for indoor hand-to-mouth frequency. These high uncertainties are due to small sample sizes and/or differences

across studies.

Fig. 2 illustrates graphically the uncertainty distributions for the 1 to <2 year and 3 to <6 year age
Estimation of Nondietary Ingestion Exposure 417

Table V. Outdoor Hand-to-Mouth Frequency (No. of Contacts/Hour) Weibull Distributions by Age Group from Various Studies

Weibull Weibull

Shape Scale No. of

Study Age Group Distribution Parameter Parameter Chi-Square Observations Mean std p50 p5 p25 p75 p95

Tulve et al., 2002 1 to <2 years Weibull 1.14 13.74 Pass 10 13.3 9.7 11.0 0.1 8.0 17.0 35.0

Black et al., 2005 1 to <2 years Weibull 1.05 12.90 Fail 12 12.6 13.7 7.0 1.3 4.2 15.6 42.2

Tulve et al., 2002 2 to <3 years Weibull 0.49 2.99 Pass 17 5.5 9.1 4.0 0.1 0.1 7.0 38.0

Black et al., 2005 2 to <3 years Weibull 0.59 3.56 Pass 20 5.3 8.2 2.2 0.1 0.4 6.7 26.6

Leckie et al., 2000 3 to <6 years Weibull 0.53 1.47 Fail 9 2.5 3.8 1.1 0.1 0.1 3.0 11.5

Tulve et al., 2002 3 to <6 years Weibull 0.63 7.90 Pass 33 10.1 10.4 7.0 0.1 2.0 16.0 36.0

Black et al., 2005 3 to <6 years Weibull 0.51 6.09 Fail 10 10.4 14.9 5.8 0.1 0.1 8.7 40.9

Freeman et al., 2001 6 to <11 years Weibull 0.45 1.02 Fail 12 2.6 4.4 0.1 0.1 0.1 3.5 11.9

groups examined, corresponding to the variability distributions in Fig. 1. The uncertainty clouds covered all

the studies well by age group. The B value ranged from

10 to 15, which indicates that we have reasonable data

to define uncertainty. As discussed for Table VII, the

uncertainty was large for age groups 6 to <11 years

old and 3 to <6 years old.

Fig. 3 illustrates graphically the uncertainty cumulative density functions (cdfs) for 3 selected variability percentiles (5th, 50th, and 95th) of indoor handto-mouth frequency for 1 to <2 year olds and 3 to

<6 year olds, respectively. For those 200 uncertainty

runs, 200 5th percentiles form the bottom CDF, 200

50th percentiles form the middle CDF, and 200 95th

percentiles form the top CDF. The 5th percentiles are

not stable since the values are usually very small and

sometimes even zero. For the 1 to <2 year old age

group, the 95th and 5th percentiles for the middle

CDF are 22 and 7 contacts per hour with ratio 3; the

Table VI. Indoor and Outdoor Hand-to-Mouth Frequency (No. of Contacts/Hour) Weibull Distributions from Available Studies

Weibull Weibull

Shape Scale No. of

Age Group Distribution Parameter Parameter Chi-Square Observations Mean Std p50 p5 p25 p75 p95

INDOOR

3 to <6 months Weibull 1.28 30.19 fail 23 28.0 21.7 23.0 3.0 8.0 48.0 65.0

6 to <12 months Weibull 1.02 19.01 pass 119 18.9 17.4 14.0 1.0 6.6 26.4 52.0

1 to <2 years Weibull 0.91 18.79 fail 245 19.6 19.6 14.0 0.1 6.0 27.0 63.0

2 to <3 years Weibull 0.76 11.04 fail 161 12.7 14.2 9.0 0.1 2.9 17.0 37.0

3 to <6 years Weibull 0.75 12.59 pass 169 14.7 18.4 9.0 0.1 3.7 20.0 54.0

6 to <11 years Weibull 1.36 7.34 pass 14 6.7 5.5 5.7 1.7 2.4 10.2 20.6

OUTDOOR

6 to <12 months Weibull 1.39 15.98 pass 10 14.5 12.3 11.6 2.4 7.6 16.0 46.7

1 to <2 years Weibull 0.98 13.76 pass 32 13.9 13.6 8.0 1.1 4.2 19.2 42.2

2 to <3 years Weibull 0.56 3.41 fail 46 5.3 8.1 2.6 0.1 0.1 7.0 20.0

3 to <6 years Weibull 0.55 5.53 fail 55 8.5 10.7 5.6 0.1 0.1 11.0 36.0

6 to <11 years Weibull 0.49 1.47 fail 15 2.9 4.3 0.5 0.1 0.1 4.7 11.9

95th and 5th percentiles for the top CDF are 101 and

30 with ratio 3. For the 3 to <6 year old age group,

the 95th and 5th percentiles for the middle CDF are

16 and 3 with ratio 5; the 95th and 5th percentiles for

the middle CDF are 106 and 21 with ratio 5. These

results indicate that the uncertainty is larger for the

3 to <6 year age group than for the 1 to <2 year age

group.

4. DISCUSSION

Children’s hand-to-mouth behavior is difficult to

measure for several reasons. Some of these reasons include the following: children’s contacts with surfaces

and objects are frequent and intermittent; observational studies are labor-intensive for data collection

and data analysis; and data analysis can be subjective. Interpretation of the results is also difficult. Some

researchers express mouthing behavior in terms of
418 Xue et al.

Table VII. Uncertainty Analyses

Selected Variability Runs Selected Variability Percentiles

Ratio of 95th vs. 5th Percentile Ratio of 95th vs. 5th Percentile

Age Group 50th Percentile 95th Percentile 50th Percentile 95th Percentile

Indoor Hand-to-Mouth Frequency (No. of contacts/hr)

3 to <6 months 16 26 2 2

6 to <12 months 13 6 5 4

1 to <2 years 8 77 3 3

2 to <3 years 23 138 3 5

3 to <6 years 73 211 5 5

6 to <11 years 6 6 2 2

Outdoor Hand-to-Mouth Frequency (No. of contacts/hr)

6 to <12 months 38 12 3 3

1 to <2 years 113 20 2 2

2 to <3 years 13 73 8 7

3 to <6 years 932 378 7 9

6 to <11 years 53077 1287 17 17

frequency of occurrence (e.g., contacts per hour or

contacts per minute). Others express mouthing behavior as an exposure period (i.e, minutes). This discrepancy makes it more difficult to compare results

Fig. 2. Uncertainty bootstrap distributions for indoor hand-tomouth frequency (contacts per hour).

among studies. To conduct the meta-analysis presented in this article, investigators of individual studies were contacted to provide, and in some cases reanalyze, their original study data by age, gender, and

Fig. 3. CDFs for 3 selected variability percentiles.
Estimation of Nondietary Ingestion Exposure 419

location. The uncertainty analyses in this article only

focus on parameter uncertainty, and do not account

for other uncertainties from differences in study approaches and their associated reliability and validity

issues.

The meta-analysis revealed that there was not a

statistically significant difference in hand-to-mouth

frequency between genders or across studies. However, there was a statistically significant difference

in hand-to-mouth behavior with regard to location

(indoor versus outdoor) and age groups. Hand-tomouth frequency indoors was consistently higher

for all age groups than outdoor hand-to-mouth frequency. The highest difference was observed for children 2 years old with an indoor hand-to-mouth frequency of 2.4 times higher than outdoors. As age

increases, both indoor and outdoor hand-to-mouth

frequencies decrease. These findings of differences

between mouthing behaviors indoors versus outdoors

and across ages are consistent with those discussed in

some of the individual studies (Freeman et al., 2001;

AuYeung et al., 2004; Tulve et al., 2002). More reliable estimates of daily mouthing frequency should

take into account these differences due to location

and age, as well as longitudinal estimates of mouthing

behavior.

Because children can be practically observed or

videotaped for only several hours in a day, available

hand-to-mouth frequency data are typically crosssectional and short term. However, exposure assessors are often interested in estimating chronic

exposures. Exposure assessors typically extrapolate

mouthing behaviors from several hours of observations to daily estimates using data or assumptions on

the amount of time children are awake during the

day and not otherwise eating. Thus, future research

on children’s mouthing behavior would also ideally

examine longitudinal estimates within a day and over

time.

The analyses in this article represent a first effort

to fit hand-to-mouth frequency distributions by indoor/outdoor location, and by age using the new U.S.

EPA Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). Thus,

the results presented here can be used to enhance

future exposure and risk assessments. Despite differences in the available studies for hand-to-mouth frequency, the results were generally consistent across

studies. This is the first comprehensive analysis to report Weibull distributions as the best-fitting distribution for hand-to-mouth frequency.

This article also highlights the need for more research on mouthing behavior for children at various

stages of development. For example, no data were

identified for infants between birth to <1 month old

or for 1 to < 3 months old. More data are also needed

for the 3 to <6 year age group because the uncertainty

in the available data was large. Because collection of

micro-activity data is intrusive, expensive, and laborintensive, any new data collection efforts should use

a standardized protocol for micro-activity data gathering and analysis and be carefully designed so that

they can be combined more readily across studies.

This article has focused on frequency of handto-mouth contact. Future research could also include

collection and analyses of available data for handand object-to-mouth duration; frequency of hand-toobject, hand-to-surface, other-body-part-to-surface,

and object-to-mouth contact; and surface area of objects mouthed, to assist with estimation of dermal and

nondietary ingestion exposures.

ACKNOWLEDGMENTS

We gratefully acknowledge the following individuals for providing assistance on this article: James

Leckie of Stanford University; Celestine Kiss of the

Consumer Product Safety Commission; and Paul Lioy

of the Environmental and Occupational Health Sciences Institute.

DISCLAIMER

The U.S. Environmental Protection Agency

through its Office of Research and Development

funded and managed the research described here. It

has been subjected to Agency’s administrative review

and approved for publication as an EPA document.

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