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MEMORANDUM

To: Tim Leighton, David Miller, Philip Villaneuva, EPA 

From: Jonathan Cohen, ICF International

Date: 6 March, 2008

Re: EP-W-06-091, WA 0-02, TAF CM 19: Computations of human triclosan dose based on 

NHANES urine concentrations.

SUMMARY

The National Health and Nutrition Surveys (NHANES) are a series of US national surveys of the 

health and nutrition status of the non-institutionalized civilian population conducted by the 

Centers for Disease Control and Prevention. As part of the 2003-2004 NHANES, urinary 

concentrations (μg/L) of triclosan (2,4,4’-trichloro-2’-hydroxydiphenyl ether) were measured on a 

random sample of 2,517 participants of ages 6 and over. These measurements represent 

concentrations in spot urine samples, and the corresponding human dose (μg/kg-day) was not 

measured or estimated by NHANES or CDC. Various methods have been proposed to estimate 

the dose from the measured concentration, but they can be categorized into two main groups: 

one that uses measured pesticide concentrations in urine directly and the other that 

standardizes urinary concentrations on the basis of creatinine, a by-product of metabolism 

which is felt by some to be less variable than urinary output. A total of seven dose conversion 

variations are presented in this document (and are designated Dose1 to Dose7). These are 

summarized below: 

 Mage Method: Mage et al. (2004, 2007) use the estimated daily creatinine excretion for 

a demographic group; the triclosan concentration is divided by the creatinine 

concentration, multiplied by the daily creatinine excretion in μg/day, and divided by the 

body weight. There are three variations on this, designated as Dose1, Dose2, and 

Dose3.

 PANNA Method: Schafer et al (2004) use the estimated daily urine excretion in L/day 

and the average body weight for a demographic group; the triclosan concentration is 

multiplied by the daily urine excretion in L/day, and divided by the average body weight. 

There are two variations on this method, designated as Dose4 and Dose5.

 Geigy Method: For specific situations, some researchers at the EPA Office of Research 

and Development (ORD) use the estimated daily urine excretion in L/kg-day for a 

demographic group; the triclosan concentration is multiplied by the estimated daily urine 

excretion in L/kg-day. However, at this time, no ORD-wide approach has been adopted. 

The two variations on this method, using urine excretion data from Geigy (1981), are 

designated as Dose6 and Dose7. 

In this memorandum we describe the various conversion methods and compare the results by 

demographic group for the estimated triclosan dose. An important caveat to all the reported 

results should be noted: In this review, the pharmacokinetics of triclosan have not been 
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accounted for in the dose estimate because the use of human data would require prior approval 

by the Human Studies Review Board (HSRB). The calculations presented here assume 100 % 

of triclosan is excreted in urine. In fact, a pharmacokinetic study has shown that only about 50 

% of triclosan is excreted in urine. On this basis, all the dose estimates presented in this 

memorandum ought to be doubled (approximately). This constant adjustment factor does not 

affect the comparative results.

In general, all the proposed methods, excluding the Geigy upper bound method (Dose7), give 

similar estimates for the geometric mean and median dose, but the overall means are up to 

35% higher for the PANNA and Geigy methods compared to the Mage methods. The overall 

99th percentile is up to 75% higher. The highest estimated dose, based on the upper bound 

Geigy case, was 138 μg/kg-day.

NHANES

Since the 1960s, the National Center for Health Statistics, a division of the Centers for Disease 

Control and Prevention has conducted the National Health and Nutrition Surveys (NHANES), a 

series of US national surveys of the health and nutrition status of the non-institutionalized 

civilian population. NHANES 2003 to 2004 included laboratory measurements on 9,643 

subjects. This analysis uses urinary concentrations of triclosan (2,4,4’-trichloro-2’-

hydroxydiphenyl ether) measured in urine spot samples of 100 μL collected from a random onethird sample of 2,517 subjects of ages 6 and older. The dose conversion calculations also used 

the NHANES measurements of creatinine concentrations, body weight, body height, as well as 

the age, gender, and race of each subject. The NHANES 2003-2004 data were obtained from 

the NHANES website: www.cdc.gov/nchs/nhanes.htm

The NHANES use a complex multi-stage, stratified, clustered sampling design. Certain 

demographic groups were deliberately over-sampled, including Mexican-Americans and Blacks. 

The publicly released environmental phenols file L24_eph_c.xpt with the triclosan data includes 

survey weights to adjust for the one-third random sampling as well as for the over-sampling of 

certain demographic groups, non-response, and non-coverage. The statistical analyses used 

the laboratory survey weights (WTSCYR) to re-adjust the urinary triclosan data to represent the 

national population. NHANES also includes the strata and primary sampling unit (PSU) survey 

design parameters to account for the stratification and clustering, but (for confidentiality 

reasons) the public release version provides approximate pseudo-values for these parameters 

instead of the actual values. The statistical analyses used the pseudo-strata and pseudo-PSU 

values to properly account for the uncertainty of the survey estimates and thus to compute 

confidence intervals for the estimated dose.

NHANES urinary metabolite concentration data are not collected in a way to directly determine 

the dose, and CDC has not reported dose estimates for triclosan based on NHANES 

measurement data.

TRICLOSAN

Triclosan is a synthetic, broad-spectrum antimicrobial that has been used extensively for more 

than 20 years in a variety of consumer products. Human exposure from triclosan is expected to 

occur either through ingestion or dermal absorption. For more detailed discussion on triclosan 

uses and exposure pathways, please see Calafat et al, 2007. The NHANES 2003-2004 included 
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measured urinary concentrations of total triclosan (free plus conjugated species) on 2,517 

randomly selected subjects of ages 6 years and over. Total triclosan was measured on 100 μL 

samples using on-line solid-phase extraction coupled to high performance liquid 

chromatography-isotope dilution-tandem mass spectrometry. The lower detection limit was 2.27 

μg/L. 75% of the samples (survey-weighted) were above the detection limit. For these analyses, 

we followed the CDC recommended approach that substitutes the limit of detection divided by 

the square root of 2 for all non-detects (values below the detection limit). Measured 

concentrations ranged from 2.3 to 3,790 μg/L. The geometric mean concentration (surveyweighted) was 13 μg/L.

More details about the NHANES triclosan data as well as summary statistics and regression 

analyses for triclosan (μg/L) and triclosan/creatinine (μg/g), are provided in Calafat et al, 2007. 

To confirm our calculations, we calculated the same summary statistics (geometric mean, 10th, 

25th, 50th, 75th, 90th, and 95th percentiles, with 95 percent confidence intervals) for the same 

demographic groups, both for triclosan and triclosan/creatinine. Our tabulations are shown in 

the attached Excel file “dose.conversions.030608.xls.” We used SAS and SUDAAN software for 

these calculations. The geometric means and confidence intervals either agreed exactly (most 

cases) or differed by 0.1. The differences for the percentiles were larger. For example, for all 

subjects, Calafat et al (2007) report the 50th percentile triclosan (μg/L) as 9.2 (7.9-10.9) but our 

calculations gave 9.5 (8.7-11.0). Similarly, Calafat et al (2007) report the 95th percentile triclosan 

(μg/L) as 459.0 (386.0-522.0) but our calculations gave 463.0 (393.0-527.0). These differences

are likely due to the percentile calculation algorithm. We tried various alternative approaches but 

were not able to get exact agreement for any of the methods that we tried.

To calculate the percentiles and their confidence intervals for triclosan, triclosan/creatinine, and 

the estimated dose we used the CDC method given in CDC 2005, Appendix C. First, the 

percentile is estimated using the survey weights and the SAS default method (in PROC 

UNIVARIATE). Second, SUDAAN is used to estimate the proportion of values below the 

estimated percentile, the standard error of the estimated proportion, and hence the degrees-offreedom-adjusted effective sample size (see Korn and Graubard, 1999, p. 65). Third, the 

Clopper-Pearson method is used to calculate a 95% confidence interval for the proportion in 

step 2. Fourth and finally, the percentiles corresponding to the estimated proportion and its 

lower and upper bound are estimated using the survey weights and the SAS default method.

Confidence intervals around the mean, geometric mean, 90th percentile, 95th percentile, and 99th

percentile do not fully account for the variability/uncertainty inherent in each of the parameters, 

and the combination of variability and uncertainty across all of the combined parameters, used 

in estimating the dose. For example, the procedures use an assumption of steady state 

exposure. This may not be the actual situation for many or most individuals, so the variability 

associated with different exposures at different time intervals for different durations prior to 

measurement may not be captured. The impact of parameter variability and uncertainty are of 

particular concern for dose estimates made for an individual for many chemicals. On the other 

hand, because of the large NHANES sample size, some of the variability may already be 

captured in the data set. More work in this area is needed. While it is difficult at this time to 

generate accurate confidence intervals or uncertainty bounds, these analyses only account for 

the statistical variability across the estimated dose estimates for the population ignoring any 

uncertainty and variability in the dose conversion equations themselves. By comparing the 

results across the different dose estimation methods, we address only some of the uncertainty 

in the dose conversion. 
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DOSE CONVERSION

The NHANES triclosan data are spot urine samples for which the concentration in μg/L was 

measured. To interpret these concentrations is difficult because they cannot be directly related 

to a health effect. EPA evaluates health effects in terms of a reference dose that represents the 

daily intake in mg or μg per kilogram body weight that is not expected to be associated with an 

adverse health effect. The conversion of measured spot urine concentrations to daily doses is 

difficult because of highly variable dilution caused by wide fluctuations in the fluid intake and 

excretion. Dose calculation is also difficult because there is no way to determine from the 

NHANES data how (oral, dermal, inhalation) and when (duration and time interval prior to 

measurement) the actual contact with the chemical occurred, and because of uncertainty and 

variability in the absorption, distribution, metabolism, and excretion (ADME) parameters. If 

NHANES collected total daily urine excretion for each participant, then that participant’s dose 

could be accurately estimated by multiplying the triclosan concentration by the total daily urine 

excretion and then dividing by the body weight. However, NHANES only collected spot urine 

samples so that total urine excretion was not measured. The Schafer et al (2004) dose 

conversion methods and the similar methods used by some ORD researchers estimate the daily 

urine excretion using average values for a given age/gender group. The Mage et al (2004, 

2007) dose conversion methods are based on estimates of the daily creatinine excretion 

(μg/day) from various studies relating the daily creatinine excretion to age, gender, race, weight, 

and height. The dose is estimated by multiplying the concentration per g of creatinine by the 

estimated daily creatinine excretion and then dividing by the body weight. These alternative 

methods are detailed in this section.

We also note that we have assumed throughout this discussion that triclosan and other analytes 

of interest are not metabolized so that all the ingested analyte is excreted in the urine. To apply 

these same methods to parent contaminants that are metabolized, it is necessary to make two 

further adjustments to estimate the dose. The first adjustment is the ratio of parent to metabolite 

molecular weight. The second adjustment is the ratio of mols of parent ingested to mols of 

metabolite excreted in the urine. Human pharmacokinetic data do exist for triclosan but would 

require prior approval by the HSRB before the Office of Pesticides Program (OPP) can use the 

data in risk assessments. As mentioned above, a pharmacokinetic study has shown that only 

about 50 % of triclosan is excreted in urine. On this basis, all the dose estimates presented in 

this memorandum ought to be doubled (approximately).

Mage et al (2004): Dose1

Mage et al (2004) derived creatinine excretion rates for adults 18 and older using creatinine 

clearance equations that were obtained by Cockcroft and Gault (1976) based on 24-hour urine 

data from 249 male and 18 female adults in Canada. They obtained the following equations:

Creatinine Excretion (μg/day) = 1.93 (140 – Age) Weight1.5 Height0.5, Males 18+ (1) 

Creatinine Excretion (μg/day) = 1.64 (140 – Age) Weight1.5 Height0.5, Females 18+ (2)

In these equations, and throughout this memorandum, Age is age in years, Weight is weight in 

kg, Height is height in cm.

The dose conversion equation is given by:
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Dose (μg/kg-day) = 

{Triclosan (μg/L) / Creatinine (μg/L)}  Creatinine Excretion (μg/day) / Weight (3)

To compare this method to the other dose conversion methods, we applied the same equations 

to all adults and children, regardless of age.

The creatinine conversion Equation 3 uses the concentration of creatinine to adjust for varying 

urine volumes, due to variation among the individuals in a demographic group and due to 

variation within the same individual, e.g., the higher levels of fluid intake and excretion in the 

hotter summer season. Creatinine is a natural metabolic byproduct of muscle tissue creatine. 

Creatinine is picked up by the blood at a rate roughly proportional to the amount of muscle 

tissue. Additional amounts of creatinine are obtained from dietary red meat intake or from 

creatine body-building supplements. Creatinine, triclosan, and other blood solutes are excreted 

from the blood into the urine by passive glomerular filtration and/or by secretion through the 

renal tubules. 

Assumptions: 

 The subjects in the creatinine studies are representative of the demographic groups.

 The spot sample triclosan/creatinine ratio is representative of the 24-hour average.

 Subjects have reached steady state, equilibrium conditions with respect to creatinine 

and triclosan urinary concentrations (in particular, this assumes an approximately 

constant daily triclosan exposure). 

 For each demographic group, the absorption, distribution, metabolism, and excretion 

(ADME) parameters are the same across all individuals and are constant within 

individuals over time.

 Subjects are omnivorous, neither eating excessive red meat nor being strictly vegetarian 

(since creatinine increases with the amount of red meat eaten).

 Subjects are moderately active (neither sedentary nor body builders). 

 Subjects are healthy (kidney disease or muscle wasting diseases such as Duchenne 

muscular dystrophy or Cushing’s syndrome reduce the creatinine excretion rate).

 Subjects do not exceed the triclosan’s tubular secretion transport maximum, so that the 

tubular secretion mechanism is not saturated and the process is (approximately) linear.

 Subjects are not on medicines such as penicillin that reduce creatinine tubular secretion.

 Subjects have not been significantly exposed to other contaminants, since if not, those 

contaminants might compete for the same carrier protein used to facilitate tubular 

secretion.

 Subjects are not taking creatinine dietary supplements.

 Subjects have moderate muscle mass.

 Subjects do not have febrile disorders.

 Subjects do not have decreased renal function (e.g., caused by GFR or tubular 

disorders).

 Female subjects are not pregnant (since pregnancy increases creatinine excretion).
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Mage et al (2007) without obesity correction: Dose2

Mage et al (2007) extended the analyses of Mage et al (2004) to include creatinine excretion 

rates for children and to make adjustments for race, obesity, and continuity. In this section we 

present the equations without the extra adjustment for obesity. The list of assumptions needed 

for these analyses is the same as the list in the previous subsection “Mage et al (2004).” Since 

the demographic groups are smaller (including separate groups for children and by race), the 

assumption that the creatinine subjects are representative of their demographic groups is more 

valid. 

The Mage et al (2007) includes creatinine excretion equations for children up to 36 months 

(from Viteri and Alverado, 1970), children ages 3 to 18 years (from Remer et al, 2002), and 

adults 18 and older (from Cockcroft and Gault, 1976). In this memorandum we do not report or 

use the equations for children up to 36 months since the urinary triclosan concentration data, 

and, indeed, most of the NHANES concentration data, are only for persons ages 6 and older.

The following creatinine excretion equations were used, together with the dose conversion 

Equation 3.

Creatinine Excretion (μg/day), Males 3-14, Height 90-168 cm, 

= 1.085 Height {6.265 + 0.0564 (Height – 168)} M  1000 (4) 

Creatinine Excretion (μg/day), Males 3-14, Height 168-186 cm, 

= 1.085 Height {6.265 + 0.2550 (Height – 168)} M  1000 (5)

Creatinine Excretion (μg/day), Males 15-17, Height 90-168 cm, 

= 1.085 Height {6.265 + 0.0564 (Height – 168)} {1 + 0.045 (Age – 14) B} M  1000 (6)

Creatinine Excretion (μg/day), Males 15-17, Height 168-186 cm,

= 1.085 Height {6.265 + 0.2550 (Height – 168)} {1 + 0.045 (Age – 14) B} M  1000 (7)

Creatinine Excretion (μg/day), Males 18+,

= 0.926  1.93 (140 – Age) Weight1.5 Height0.5 (1 + 0.18 B) (8) 

Creatinine Excretion (μg/day), Females 3-14, 

= 1.008 Height {2.045 exp(0.01552 (Height – 90)} F  1000 (9)

Creatinine Excretion (μg/day), Females 15-17, 

= 1.008 Height {2.045 exp(0.01552 (Height – 90)} {1 + 0.045 (Age – 14) B} F  1000 (10)

Creatinine Excretion (μg/day), Females 18+,

= 0.993  1.64 (140 – Age) Weight1.5 Height0.5 (1 + 0.18 B) (11)

In these equations, B is the indicator for Black, Non-Hispanic, based on the NHANES variable 

RIDRETH2 that re-assigns the race to the main race category if a subject reports multiple races 

and also reports a primary race. Note that the NHANES public release data does not separate 

race and ethnicity. The available categories are: White, Non-Hispanic; Black, Non-Hispanic;

Mexican- American; and Other. M and F are adjustments for children’s body surface area when 

their body weight does not equal the median weight for their age:
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B = 1, if Black, Non-Hispanic

B = 0, if not Black, Non-Hispanic (12)

M = {Weight / [14 + 1.433 (Age – 3) + 0.22 (Age – 3)2– 0.00533 (Age – 3)3] }0.5 (13)

F = {Weight / [14 + 0.4 (Age – 3) + 0.52 (Age – 3)2– 0.024 (Age – 3)3] }0.5 (14)

A few comments are needed to explain how the Mage et al (2007) equations were interpreted. 

In Mage et al (2007), Equations 5 and 6 are assumed to apply to males 15-18. The adjustment 

factors 1.085 and 0.926 in Equations 4 to 8 were chosen by Mage et al (2007) to make the 

equations continuous at age 18 for a male of the median height and weight. Thus Equation 8 is 

the continuity- and race-adjusted version of Equation 1. However, these adjustments do not 

make the equations consistent at age 18 for a male of a different height or weight. Therefore we 

assume here that Equations 5 and 6 only apply to males 15-17 and not to males 18 or over 

(where Equation 8 applies). Similarly, for females, the factors 1.008 and 0.993 were chosen by 

Mage et al (2007) to make the Equations 9 to 11 continuous at age 18 for a female of the 

median height and weight, and we decided here to make Equations 9 and 10 apply to females 

15-17 only. Another issue is that Mage et al (2007) do not state how the category Black, NonHispanic is to be defined for NHANES data, although it is apparent from the text that the 

intended adjustment is for all Black persons but it has to be applied to the slightly smaller 

category Black, Non-Hispanic since Black is not reported as a separate category in the public 

release version. We used the RIDRETH2 definition since it appears from the tabulated results 

that is the definition used in the triclosan paper by Calafat et al, 2007. The impacts of these 

choices are expected to be small since the number of NHANES subjects affected is small.

Mage et al (2007) with obesity correction: Dose3

Mage et al (2007) noted that the creatinine excretion Equations 4 to 14 do not apply for the 

obese. Subjects with excessive weight for their height caused by ascites (abdominal fluid 

retention) or an increase in adipose tissue (non-lean body mass) do not have excess creatinine 

since serous fluids and adipose tissue do not produce creatinine. Thus the original equations 

will tend to overestimate creatinine excretion in the obese. For this version of the dose 

conversion equations we applied the following equations from Mage et al (2007) for obese 

subjects:

Creatinine Excretion (μg/day), Obese Males,

= (137 – Age) (0.0805 + 3.42 / BMI) (15)

Creatinine Excretion (μg/day), Obese Females,

= (146 – Age) (0.081 + 2.75 / BMI) (16)

BMI = Body Mass Index = Weight (kg) / [Height (m)]2(17)

Although a more accurate definition of obesity could be obtained based on the waist 

circumference, and skinfold thicknesses (to separate muscularity from obesity), we used the 

simple definition of obesity as BMI > 30. Thus for the obesity-adjusted Mage et al (2007) 

equations we apply Equations 4 to 14 if BMI <= 30 and apply Equations 15 and 16 if BMI > 30.
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Mage et al (2007) also offer an alternative obesity adjustment method based on estimating lean 

body mass; we did not use this alternative method.

Shafer et al (2004): Dose4

Shafer et al (2004), for the Pesticide Action Network of North America (PANNA), used a simpler 

approach based on scaling up the spot urine volumes to the estimated daily urine volume 

excreted (L/day). They used the following equation:

Dose (μg/kg-day) = 

Triclosan (μg/L)  Urine Volume (L/day) / Average Weight (18)

They used average urine excretion and weight values quoted from Snyder et al (1975), although 

those average values are not precisely reported in the tables and graphs in Snyder et al (1975).

Table 1. Urine volumes and body weights used in PANNA (2004), based on Snyder et al (1975) 

Gender Age Body Weight (kg) Urine volume (L/day)

All 6-11 30 0.8

All 12-19 50 1.0

Female 20-59 55 1.1

Male 20-59 70 1.5

For these analyses we modified the Schafer et al (2004) equations so that the averages for 

ages 20-59 were assumed to apply to all adults 20 and over.

Assumptions: 

 The average urine volumes (L/day) and body weights (kg) are representative of the 

demographic groups. More precisely, since only the ratios are used, the assumption is 

that the ratio of average urine volume to average body weight is representative of the 

urine volume/weight ratios for the demographic groups.

 Subjects have reached steady state, equilibrium conditions with respect to triclosan 

urinary concentrations.

 For each demographic group, the absorption, distribution, metabolism, and excretion 

(ADME) parameters are the same across all individuals and are constant within 

individuals over time.

 The spot sample triclosan concentration is representative of the 24-hour average.

Although the number of assumptions made is only four, these assumptions are very 

approximate. The first assumption is that body weight and daily urine volume are approximately 

constant for all subjects in each demographic group, but these values vary appreciably. Mage et 

al (2007) pointed out that Snyder et al (1975) say that “within an age group, the daily volume of 

urine probably increases with body size.” They also point out that the variability in daily urine 

volume is much greater than the variability in daily urine excretion. For example, for males 9-13, 

they quote that the 5th to 95th percentile daily urine volume is from 12 to 43.2 mL/kg-day

(Mattson and Lindstrom, 1995) but the daily creatinine excretion is from 11.3 to 27.7 mg/kg-day

(Remer et al, 2002). Furthermore, urine volumes are not consistent from day to day and tend to 

be higher in hot weather; the Mage et al (2004, 2007) method uses the creatinine concentration 

to normalize this effect.
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The Schafer et al (2004) approach can be expected to be more accurate if a finer demographic 

classification could be made available; this assumes that more detailed daily urine volume data 

can be made available.

Schafer et al (2004) with actual body weights: Dose5

The Schafer et al (2004) approach can be slightly improved if the actual body weight is used 

instead of the demographic group average body weight:

Dose (μg/kg-day) = 

Triclosan (μg/L)  Urine Volume (L/day) / Weight (18)

We applied this revised approach using the same demographic groups as in Table 1. The 

assumptions are similar:

 The average urine volumes in L/day are representative of the demographic groups.

 Subjects have reached steady state, equilibrium conditions with respect to triclosan 

urinary concentrations.

 For each demographic group, the absorption, distribution, metabolism, and excretion 

(ADME) parameters are the same across all individuals and are constant within 

individuals over time.

 The spot sample triclosan concentration is representative of the 24-hour average.

Geigy Method – Mean: Dose6 

ORD has not established a standardized or recommended procedure for converting urinary 

concentrations to dose, but they routinely do not correct for creatinine excretion (as, e.g., Mage 

does) Although no universally recommended ORD procedure exists, in some situations, some 

researchers from ORD have and do use a very similar approach to Schafer et al (2004). 

However, instead of using the demographic group average urine volume in L/day, they have

used the demographic group average urine volume in L/kg-day. This gives the equation:

Dose (μg/kg-day) = Triclosan (μg/L)  Average Urine Volume (L/kg-day) (19)

Morgan et al (2008) applied this method to estimate the children’s dose of 2,4-D. Since a default 

appropriate set of average urine excretion volumes has not been formalized by ORD, and the 

choice of such standard urine volumes may depend upon the study and the available urine 

volume data, we have attempted to simulate this method by using age group and body-weight 

urine excretion volume data from Geigy Scientific Tables (Geigy, 1981).

Geigy (1981) provides two tables with urine volume data. Their Table 2 gives the mean (m) and 

standard deviation (s) of the urine volume (mL/kg-day) for the following groups of ages 6 and 

above. Since the groups overlap, we re-defined the groups to avoid overlaps: 

 “5-7 years,” redefined as ages 5 and 6. m = 25, s = 7.

 “7-11 years,” redefined as ages 7, 8, 9, and 10. m = 25, s = 7.

 “11-14 years,” redefined as ages 11, 12, and 13. m = 19, s = 3.
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 ‘Young males,” not used.

To obtain data for adults we used their Table 1, which includes the mean and standard deviation 

of the urine volume (mL/day) for the following groups:

 “33 men,” redefined as males ages 14 or over. m = 1360, s = 443.

 “39 women, no ovulation inhibitors,” redefined as females ages 14 or over. m = 1130, s = 

423.

 ‘30 women, ovulation inhibitors.” m = 980, s = 449. Not used.

Note that the two sets of data use different units (mL/kg-day and mL/day). To calculate the 

mean and standard deviation for persons 14 and over, we divided the urine volume (mL/day) by 

the body weight. We also divided these rates by 1000 to convert from mL to L. Also note that for 

simplicity we chose not to separate the females according to their use of ovulation inhibitors, 

although such information can be extracted from NHANES.

For the Geigy – Mean approach we used the values of m to get the mean urine volumes for 

each age/gender group.

Assumptions: 

 The average urine volumes in L/kg-day are representative of the demographic groups. 

 Subjects have reached steady state, equilibrium conditions with respect to triclosan 

urinary concentrations.

 For each demographic group, the absorption, distribution, metabolism, and excretion 

(ADME) parameters are the same across all individuals and are constant within 

individuals over time.

 The spot sample triclosan concentration is representative of the 24-hour average.

Just as for Schafer et al (2004), the first assumption is very approximate.

Geigy Method – Upper Bound: Dose7

Finally, we used the above-described (ORD-like) approach to get an upper bound estimate of 

the dose by using information on the variability associated with urine volumes (specifically, the 

standard deviation listed in the Geigy (1981) publication). Assuming the urine volumes are 

normally distributed, the 95th percentile urine volume is given by

95th percentile urine volume = Mean + Z95  Standard Deviation (20) 

Z95 is the 95th percentile of a standard normal distribution, approximately 1.68.

The values of mean and standard deviation are estimated by the values of m and s in the 

subsection “Geigy Method – Mean.” To calculate the mean and standard deviation for persons 

14 and over, we divided the m and s for urine volume (mL/day) by the body weight. The upper 

bound dose estimate is then given by:

Dose (μg/kg-day) = 

Triclosan (μg/L)  95th Percentile Urine Volume (L/kg-day) (21)
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This estimate is not the 95 percentile estimated dose, but rather an upper bound estimate of the 

mean dose which was derived by using an 95 percentile urine volume. If all the dose conversion 

assumptions applied, if the triclosan concentration was exactly measured, and if the only 

uncertainty and variability was the urine volume, then this estimate would also be the 95th

percentile estimate dose for that subject. 

RESULTS

We applied the seven dose conversion methods listed in the previous section to the NHANES 

2003-2004 triclosan data.

The attached Excel file “triclosan.data.030608.xls” contains the raw data and the estimated 

doses for each subject. The “Variables” worksheet lists and describes the variables in the 

database and the “TRICLOSAN” worksheet contains the data. The same data is provided in the 

SAS file “triclosan.sas7bdat.” The SAS code used is in the attached SAS program 

“convert.030608.sas.” 

Using the same demographic groupings as in Calafat et al (2007), we calculated various 

summary statistics for the triclosan concentration, triclosan/creatinine, and the seven estimated 

dose values. We computed the mean, geometric mean, and selected percentiles. We also 

computed 95% confidence intervals for these summary statistics. The detailed results are given 

in the attached Excel file “dose.conversions.030608.xls.” The means, geometric means, 

medians, and 99th percentiles for each dose conversion method are compared in the attached 

Excel file “dose.conversions.short version.030608.xls” and in Table 2.

We can examine Table 2 in detail to compare the dose conversion methods overall and for 

different demographic groups. Since the Dose7 method is intended to be an upper bound 

estimate, we will discuss the other six dose conversion methods. For all subjects, the means 

vary from 1.4 (Mage et al methods Dose2 and Dose3) to 1.9 (Schafer et al method Dose4) 

μg/kg-day, so that the maximum difference is 35%. However, the differences in the means when 

population subgroups are considered range from 22% for females to 67% for children ages 6-11 

and 72% for black, non-hispanics. For the age group 6-11, the minimum is 0.6 for Dose1 (Mage 

et al (2004)) and the maximum is 1.1 for Dose5 (Shafer et al (2004) with actual body weight). 

This difference is mainly because the Mage et al (2004) approach was based on data from 

adults and was intended to be applied to adults 18 and older. The Mage et al (2007) paper 

extends the analysis to children and these revised equations give a mean dose of 0.9 for 

children ages 6-11. For the black, non-hispanic group, the minimum estimated dose is 1.07

ug/kg-day derived from Mage et al (2004) while the maximum estimated dose is 1.83 ug/kgday from the Schafer et al (2004) Dose 4 approach. Similar findings apply to the geometric 

means and medians. For all subjects the geometric means range from 0.22 to 0.27 (25%

difference) and the medians range from 0.15 to 0.20 (29% difference). The biggest differences 

are for the black, non-hispanic subpopulation where the geometric means differ by 57% and the 

medians differ by 70%. In general the lowest estimated doses are for the three Mage et al.

methods (Dose1-Dose3), and the highest estimated dose is for the Schafer et al (2004) Dose4 

method, with the ORD-type method (Dose6) providing intermediate estimates. 

The 99th percentile dose estimates show even larger percentage differences. For all subjects, 

the 99th percentiles range from 16 (Mage et al, 2007) to 27 (Schafer et al, 2004), a 75%
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difference. The biggest demographic group differences are 243% (7 to 25) for the 6-11 age 

group and 200% (12 to 36) for black, non-hispanics.

The same comparisons are displayed graphically in Figures 1 to 13. Figures 1 to 3 display all 

the mean, geometric mean, and 99th percentile dose estimates, respectively, for each 

demographic group. The dose is plotted on a linear scale. Figures 4 to 13 display the mean, 

geometric mean, 90th, 95th, and 99th percentile dose estimates for three methods on a 

logarithmic scale. Each of Figures 4 to 13 shows all the summary statistics for a different 

demographic group. The three methods shown are Mage et al (2007) with obesity correction 

(Dose3), Schafer et al (2004) with actual bodyweights (Dose 5), and the Geigy Method – Mean 

(Dose6).

As shown in Table 2 and Figures 1-3, the Geigy Method – Upper Bound dose estimate is the 

highest of the seven estimated doses. This is, of course, expected, since this method was 

intended as an upper bound method. The maximum 99th percentile estimated dose was 62.4 

μg/kg-day for the Mexican-American group. The maximum estimated dose for all subjects was 

138 μg/kg-day.
Table 2. Selected summary statistics for estimated triclosan dose (ug/kg-day), by calculation method*, age, gender, and race.

Mean Geometric Mean Median 99th Percentile

Group 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

All 1.5 1.4 1.4 1.9 1.6 1.6 2.4 0.2 0.2 0.2 0.3 0.2 0.2 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.3 16.8 15.5 15.5 27.2 23.6 23.6 38.1

6-11 0.6 0.9 0.9 1.0 1.1 0.9 1.3 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.1 0.1 0.1 0.2 0.1 0.1 0.2 7.2 10.9 10.9 10.3 24.6 9.7 14.2

12-19 1.5 1.4 1.4 2.3 1.7 2.2 3.4 0.2 0.2 0.2 0.3 0.2 0.3 0.4 0.2 0.1 0.1 0.2 0.2 0.2 0.3 20.1 16.6 16.6 42.0 25.5 28.8 46.5

20-59 1.7 1.6 1.5 2.0 1.7 1.6 2.6 0.3 0.3 0.3 0.3 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.3 20.6 19.1 19.1 28.1 29.1 29.9 48.3

>= 60 1.1 1.1 1.0 1.5 1.2 1.2 1.8 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 14.4 14.1 14.4 21.4 17.1 14.8 22.7

Male 1.9 1.8 1.7 2.4 2.0 2.0 3.0 0.3 0.3 0.3 0.4 0.3 0.3 0.4 0.2 0.2 0.2 0.3 0.2 0.2 0.3 20.6 19.0 19.0 36.0 35.1 35.2 54.1

Female 1.1 1.1 1.1 1.3 1.1 1.2 1.9 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.1 0.1 0.1 0.2 0.1 0.1 0.2 14.2 14.9 14.7 23.4 17.8 17.6 28.5

Mexican-American 2.0 2.0 1.9 2.6 2.3 2.2 3.5 0.3 0.3 0.2 0.3 0.3 0.3 0.4 0.2 0.2 0.1 0.2 0.2 0.2 0.3 26.0 24.0 20.6 44.4 42.4 40.6 62.4

White, Non-Hispanic 1.5 1.4 1.4 1.8 1.5 1.5 2.3 0.3 0.2 0.2 0.3 0.2 0.2 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 15.0 15.0 15.0 23.4 16.3 19.0 29.1

Black, Non-Hispanic 1.1 1.2 1.1 1.8 1.6 1.5 2.3 0.2 0.2 0.2 0.3 0.2 0.2 0.4 0.2 0.2 0.1 0.2 0.2 0.2 0.3 12.1 14.0 13.7 36.3 26.1 28.3 45.6

* Calculation Method:

1. Mage et al (2004).

2. Mage et al (2007), without obesity correction.

3. Mage et al (2007), with obesity correction.

4. Schafer et al (2004).

5. Schafer et al (2004) with actual bodyweight.

6. Geigy Method - Mean.

7. Geigy Method – Upper Bound.













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CONCLUSION

NHANES urinary metabolite concentration data are not collected in a way to directly determine 

the dose, and CDC has not reported dose estimates for triclosan based on NHANES 

measurement data. The conversion of measured spot urine concentrations to daily doses is 

difficult because of highly variable dilution caused by wide fluctuations in the fluid intake and 

excretion. Dose calculation is also difficult because there is no way to determine from the 

NHANES data how (oral, dermal, inhalation) and when (duration and time interval prior to 

measurement) the actual contact with the chemical occurred, and because of uncertainty and 

variability in the absorption, distribution, metabolism, and excretion (ADME) parameters. We

have presented several alternative methods of estimating human dose (μg/kg-day) from a spot 

urine concentration (μg/L) of an analyte and applied these methods to NHANES 2003-2004 

triclosan concentrations. The analyses presented in this document assumes that total triclosan 

is fully excreted in the urine. Therefore, the use of these analyses for risk assessment purposes 

will require correction for human pharmacokinetics.

There are essentially two main approaches for this conversion. The creatinine adjustment 

approach of Mage et al (2004, 2007) uses an estimate of the daily creatinine excretion (L/day) 

for each demographic group and applies this to the analyte/creatinine concentration ratio. The 

urine volume adjustment approach using the methods of Schafer et al (2004) or the ORD-type

methods (as used by some ORD researchers in some situations) applies the estimated daily 

urine volume in L/day or L/kg-day, respectively, to the analyte concentration. Within these two 

main approaches, different versions use different sets of equations or tables for the estimated 

daily creatinine or urine excretion by demographic group. In general the two main approaches 

give similar results for the mean, median, and geometric mean. The differences between the 

two main approaches are more pronounced at the 90th and higher percentiles. There are 

smaller differences between the different versions of these approaches. The ORD-type method

is preferable to the Schafer et al (2004) method since urine volumes can be expected to 

increase with increased body size. For triclosan, all subjects, the means are up to 35% higher 

for the urine volume adjustment methods compared to the Mage methods. The 99th percentile is 

up to 75% higher.

The Mage et al approach requires numerous assumptions for its validity: 

 The subjects in the creatinine studies are representative of the demographic groups.

 The spot sample analyte/creatinine ratio is representative of the 24-hour average.

 Subjects have reached steady state, equilibrium conditions with respect to creatinine 

and analyte urinary concentrations. 

 For each demographic group, the absorption, distribution, metabolism, and excretion 

(ADME) parameters are the same across all individuals and are constant within 

individuals over time.

 Subjects are omnivorous, neither eating excessive red meat nor being strictly 

vegetarian.

 Subjects are moderately active. 
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 Subjects are healthy.

 Subjects do not exceed the analyte’s tubular secretion transport maximum.

 Subjects are not on medicines such as penicillin that reduce creatinine tubular secretion.

 Subjects have not been significantly exposed to other contaminants.

 Subjects are not taking creatinine dietary supplements.

 Subjects have moderate muscle mass.

 Subjects do not have febrile disorders.

 Subjects do not have decreased renal function (e.g., caused by GFR or tubular 

disorders).

 Female subjects are not pregnant.

Of these assumptions the steady state assumption, the related assumption that the spot sample 

analyte/creatinine concentration ratio is representative of the concentration ratios over the 24-

hour period, and the assumption about not reaching the tubular secretion transport maximum 

are difficult, if not impossible, to verify since there are no NHANES data available to check these 

assumptions. In particular, the recent paper by Scher et al (2007) throws some doubt on the 

representativeness of spot urine samples. Using NHANES data, some of the other assumptions 

can be evaluated, and, if creatinine excretion data are available, further adjustments can be 

made for those subjects. We note that a recent presentation by Dr. Allen discusses an 

additional 20% adjustment for pregnancy. The Mage et al approach is also relatively complex.

The urine volume adjustment approach is simpler. The main assumptions are that the daily 

urine excretion is constant across each demographic group, and that absorption, distribution, 

metabolism, and excretion (ADME) parameters are the same across all individuals in a 

demographic group and are constant within individuals over time. The method also makes a

steady state assumption and the related assumption that the spot sample analyte concentration 

is representative of the concentration over the 24-hour period. As applied by Schafer et al 

(2004) or by our analyses of the ORD-type method using the Geigy (1981) tables, the 

demographic groups appear to be too broad and would benefit from a large set of urine 

excretion data by demographic group so that the urine volume adjustment approach could be 

refined and more accurately applied. Ideally, this data would contain information on both intraand inter-person variability in 24 hour urinary volumes so the issue of whether the urinary 

volume or creatinine excretion is more stable could be addressed. Mage et al (2007) point out 

some disadvantages of the urine volume adjustment approach. They note that the creatinine 

excretion rates are less variable than urine excretion rates for the same demographic groups,

and also that the urine volume adjustment approach does not adjust for the within-person 

variability of urine excretion such as due to increased fluid intake in hot weather. Since Mage et 

al. normalize the analyte concentration by the creatinine concentration, the Mage et al approach 

better adjusts for the within-person variability. This assumes that (i) the Mage creatiine 

excretion equation more accurately predicts creatine excretion; and (ii) that intra-individual (e.g, 

day-to-day) percent variability in creatine excretion is smaller than the corresponding figure for 

urine volume. This could be addressed by looking at the intra-class correlation coefficient, if 

data are available.

If the triclosan results hold more generally, the urine volume adjustment approach results in 

estimates of dose that are higher than the Mage et al creatinine adjustment approach. 

Therefore, the urine volume adjustment approach is more conservative (i.e., more protective of 
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human health). A more conservative approach may be better given the uncertainties in the 

dose conversion from spot urine samples. However, it is not clear whether the same finding 

would apply to other analytes.

Different readers will reach different conclusions about which method to recommend. My own 

feeling at this time is that the ORD-type method based on urine volume in L/kg-day is preferred 

for routine analyses, since it is simpler to apply and explain, requires fewer assumptions for its 

validity, and may well be more conservative. The crucial issue is to decide which is a more 

accurate estimator for an given individual : a L/kg-day estimate based on the age group or of 

the individual of interest or a gram creatinine excretion rate as estimated for that individual using 

the Mage et al (2007) equation. Also of value is to decide how consistent is within individual 

(day-to-day) variability of L/kg urine volume vs. day-to-day variability in creatinine excretion. 

However, for routine application of the ORD-type method it is necessary to find a compendium 

of urine excretion data for several demographic groups (by age, gender, race), since the Snyder 

et al (1975) and Geigy (1981) urine excretion data are more generalized. For less routine 

analyses, the Mage et al (2007) method can be used, but the results should be compared to the 

ORD-type method. 

ACKNOWLEDGEMENT

This note acknowledges the comments and editorial suggestions from the EPA OPP and ORD 

reviewers of the first draft. In several cases I have used their suggested additional text in this 

revised draft. 

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