Prenatal exposure to mercury through maternal

consumption of fish has been associated with

reduced performance on tests of neurologic

function in children, including tests of cognitive development, attention and behavior, and

motor skills. A comprehensive review of the

mercury literature conducted by the National

Research Council (NRC) Committee on the

Toxicological Effects of Methylmercury concluded that, based on the evidence available,

“neurodevelopmental deficits are the most

sensitive, well-documented effects” of exposure to mercury (NRC 2000).

The NRC committee’s conclusion was

based primarily on its review of epidemiologic

studies conducted in the Faroe Islands

(Grandjean et al. 1997), New Zealand (Crump

et al. 1998; Kjellstrom et al. 1989), and the

Seychelles Islands (Davidson et al. 1998;

Myers et al. 2003). These three populations

were selected for study in large part because

fish consumption was known to be relatively

high; human methylmercury exposure is

largely attributable to intake of methylmercury that has accumulated in fish tissue.

All three studies measured prenatal exposure to mercury and neurodevelopmental end

points in the children, though there were

differences in the tests used to measure potential neurodevelopmental deficits. The Faroe

Islands and New Zealand studies found a statistically significant relationship between

higher prenatal mercury exposure and poorer

scores on tests of neurologic function, but the

Seychelles study did not. The NRC committee

determined that “each of the studies was well

designed and carefully conducted, and each

examined prenatal MeHg [methylmercury]

exposures within the range of the general U.S.

population exposures” (NRC 2000). 

The U.S. Environmental Protection

Agency (EPA) developed a reference dose

(RfD) for methylmercury that draws on the

NRC analysis of data from all three epidemiologic studies. An RfD is an estimate of a daily

exposure to the human population (including

sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects

during a lifetime. However, the U.S. EPA’s

review also indicates that “no evidence of a

threshold arose for methylmercury-related

neurotoxicity within the range of exposures in

the Faroe Islands study” (U.S. EPA 2001). In

addition, the RfD does not provide information about the dose–response relationship

between prenatal mercury exposure and

related neurologic effects, because it focuses

on a single exposure level and does not identify the risk associated with that level. A

dose–response model is needed to estimate the

potential risk of neurodevelopmental effects in

the population and the benefits of any efforts

to reduce mercury exposure. 

We applied a Bayesian hierarchical

model to integrate the findings from the

three epidemiologic studies and estimate a

dose–response relationship between maternal

mercury body burden and subsequent childhood decrements in intelligence quotient (IQ).

We selected IQ for dose–response modeling

because data related to IQ were available from

all three studies, and because methods for economic valuation of IQ decrements are well

established, as applied in the U.S. EPA’s previous benefits analyses for lead (U.S. EPA 1997). 

Methods

Selection of end points and coefficients. All

cognitive end points reported in the Faroe

Islands (testing at 7 years of age), New Zealand

(6 years of age) and Seychelles (9 years of age)

studies were considered for inclusion in the

hierarchical model. Neurodevelopmental

tests conducted in each of the three studies at

these ages are listed in the Supplemental

Material (online at http://www.ehponline.

org/docs/2007/9303/suppl.pdf), and those

selected for our statistical model are shown in

Table 1. For this analysis, we assumed a linear

Environmental Health Perspectives • VOLUME 115 | NUMBER 4 | April 2007 609

Research | Children’s Health

Address correspondence to D. Axelrad, U.S.

Environmental Protection Agency, 1200 Pennsylvania

Ave. NW (1809T), Washington, DC 20460 USA.

Telephone: (202) 566-2304. Fax: (202) 566-2336.

E-mail: axelrad.daniel@epa.gov

*Current address: University of California, San

Francisco.

Supplemental Material is available online at

http://www.ehponline.org/docs/2007/9303/suppl.pdf

We appreciate the additional analysis of the Faroe

Islands data provided by E. Budtz-Jorgensen and

P. Grandjean, and the comments on an earlier version of this work provided by J. Bailar III, T. Burke,

D. Dunson, and J. Jacobson. 

L.M.R.’s work was supported by U.S. EPA contract 4W-1280-NAEX and National Institute of

Environmental Health Sciences grant ES000002.

The views expressed in this article are those of the

authors and do not necessarily reflect those of the

U.S. EPA. 

The authors declare they have no competing

financial interests.

Received 28 April 2006; accepted 11 January 2007.

Dose–Response Relationship of Prenatal Mercury Exposure and IQ: 

An Integrative Analysis of Epidemiologic Data

Daniel A. Axelrad,1 David C. Bellinger,2,3 Louise M. Ryan,4 and Tracey J. Woodruff5*

1U.S. Environmental Protection Agency, Office of Policy, Economics, and Innovation, Washington, DC, USA; 2Department of Neurology,

Harvard Medical School and Children’s Hospital Boston, and 3Department of Environmental Health, Harvard School of Public Health,

Boston, Massachusetts, USA; 4Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; 5U.S. Environmental

Protection Agency, Office of Policy, Economics and Innovation, San Francisco, California, USA 

BACKGROUND: Prenatal exposure to mercury has been associated with adverse childhood neurologic

outcomes in epidemiologic studies. Dose–response information for this relationship is useful for

estimating benefits of reduced mercury exposure. 

OBJECTIVES: We estimated a dose–response relationship between maternal mercury body burden

and subsequent childhood decrements in intelligence quotient (IQ), using a Bayesian hierarchical

model to integrate data from three epidemiologic studies. 

METHODS: Inputs to the model consist of dose–response coefficients from studies conducted in the

Faroe Islands, New Zealand, and the Seychelles Islands. IQ coefficients were available from previous work for the latter two studies, and a coefficient for the Faroe Islands study was estimated from

three IQ subtests. Other tests of cognition/achievement were included in the hierarchical model to

obtain more accurate estimates of study-to-study and end point–to–end point variability. 

RESULTS: We find a central estimate of –0.18 IQ points (95% confidence interval, –0.378 to

–0.009) for each parts per million increase of maternal hair mercury, similar to the estimates for

both the Faroe Islands and Seychelles studies, and lower in magnitude than the estimate for the

New Zealand study. Sensitivity analyses produce similar results, with the IQ coefficient central estimate ranging from –0.13 to –0.25. 

CONCLUSIONS: IQ is a useful end point for estimating neurodevelopmental effects, but may not

fully represent cognitive deficits associated with mercury exposure, and does not represent deficits

related to attention and motor skills. Nevertheless, the integrated IQ coefficient provides a more

robust description of the dose–response relationship for prenatal mercury exposure and cognitive

functioning than results of any single study. 

KEY WORDS: Bayesian hierarchical model, benefits, dose–response model, epidemiology, IQ, mercury,

neurodevelopmental effects, noncancer risk assessment, prenatal exposure. Environ Health Perspect

115:609–615 (2007). doi:10.1289/ehp.9303 available via http://dx.doi.org/ [Online 11 January 2007]
relationship between mercury body burdens

and neurodevelopmental outcomes, in keeping with the recommendation of the NRC

committee (NRC 2000). In the New Zealand

and Seychelles studies, all information necessary for our model was obtained from the

published papers, including linear regression

coefficients (Crump et al. 1998; Myers et al.

2003). The Faroe Islands publications, however, reported results with cord blood and

maternal hair mercury transformed to the log

scale and provided no results of linear models

(Grandjean et al. 1997, 1999). A report by the

Faroe Islands investigators (Budtz-Jorgensen

et al. 2005) prepared at our request provides

the additional details needed for our analysis.

For the New Zealand study, two sets of

dose–response coefficients were reported

(Crump et al. 1998): one with the complete

cohort, and the other for which one very influential observation, with unusually high maternal hair mercury, was excluded. The NRC

committee reviewed the influence of the one

observation and determined that exclusion of

this outlier was reasonable and appropriate

(NRC 2000). Our primary analysis used the

coefficients from the regression in which the

outlier child was excluded; coefficients for the

case in which this child is included were considered in a sensitivity analysis.

For several tests and end points, results for

multiple scores were reported. To avoid overrepresenting any particular test and to avoid

adding additional complexity to our modeling,

we chose only one score for a test in such cases.

For example, the Faroe Islands study presents

regression coefficients for the effect of mercury

on four tasks of the California Verbal Learning

Test, and the Seychelles study provides results

for two of these. We selected the short delay

recall task, which was common to both studies. 

Rescaling. Our next step was to rescale all

the estimated regression coefficients and standard errors so that they correspond to test

scores with the same distribution as Full-Scale

IQ (that is, an SD of 15). This rescaling allows

all inputs and outputs of our model to be

expressed in terms of the decrement in IQ

associated with a one-unit increase in mercury.

Rescaling involves multiplication by a factor

inversely proportional to the observed standard deviation of the score for each test.

Details are provided in the Supplemental

Material (online at http://www.ehponline.

org/docs/2007/9303/suppl.pdf). 

We also rescaled to adjust for differences in

mercury biomarkers used in the studies. The

New Zealand and Seychelles studies report

results in terms of parts per million hair mercury, whereas results of the Faroe Islands study

with the linear coefficients required for this

study are reported in terms of parts per billion

cord blood mercury. To combine results across

studies, we converted the Faroe Islands results

to their equivalents in units of hair mercury

using the reported median ratio of mercury in

hair to mercury in cord blood in the Faroe

Islands study population, which was approximately 200 (Budtz-Jorgensen et al. 2004a). 

IQ tests in the three studies and IQ coefficient for Faroe Islands study. The Wechsler

Intelligence Scales for Children (WISC) is a

standard test of childhood IQ that was used

in each of the three studies. The version of

the test administered in the Seychelles Islands

(3rd ed.; WISC-III) was different from the

earlier version used in New Zealand and the

Faroe Islands (revised ed.; WISC-R). In a

sample of approximately 200 children, the

correlation between the Full-Scale IQ scores

for the two versions was 0.89; thus the

WISC-R and WISC-III appear to measure

the same constructs and generate scores with

similar dispersion (Wechsler 1991).

The WISC-R includes 10 core subtests

and three supplementary subtests. For the

Faroe Islands study, the investigators administered only three subtests of the WISC-R: Digit

Span and Similarities (core subtests) and Block

Design (a supplementary subtest). We used

data for these three subtests to estimate an

IQ–mercury coefficient for the Faroe Islands

cohort. This approach is supported by the

findings of Sattler (1988), who identified the

combinations of the 10 core subtests that provide the most valid estimates of Full-Scale IQ.

Of the 45 possible combinations of two core

subtests (i.e., 10 subtests taken two at a time),

the combination of Similarities and Block

Design ranked third in the magnitude of the

validity coefficient (0.885). It is reasonable to

expect that adding the information about FullScale IQ conveyed by the Digit Span score

would produce an even higher validity coefficient. This indicates that combining the scores

of the Faroese children on Similarities, Block

Design, and Digit Span will provide valid estimates of their Full-Scale IQ scores.

Regression coefficients and standardized

coefficients (coefficient as percent of corresponding response standard deviation) for the

three subtests are shown in Table 2. At our

Axelrad et al.

610 VOLUME 115 | NUMBER 4 | April 2007 • Environmental Health Perspectives

Table 1. Cognitive tests included in the primary mercury–IQ dose–response analysis model.

Test Domain assessed

Faroe Islands study (917 children tested at 7 years of age)

WISC-R (Full-Scale IQa) General intelligence

CVLT (short term) Verbal learning and memory

Bender-Gestalt Test (errors on copying) Visual–motor integration

BNT (no cues) Confrontational naming

New Zealand study (237 children tested at 6 years of age)

WISC-R (Full-Scale IQ) General intelligence

WISC-R (Performance IQ) General intelligence

MCC (Perceptual) General development

TOLD (spoken language quotient) General verbal skills

Seychelles study (643 children tested at 9 years of age)

WISC-III (Full-Scale IQ) General intelligence

CVLT (short term) Verbal learning and memory

VMI Visual–motor integration

BNT (total score) Confrontational naming

WRAML (design memory) Visual memory

Abbreviations: BNT, Boston Naming Test; CVLT, California Verbal Learning Test; MCC, McCarthy Scales of Children’s

Abilities; TOLD, Test of Language Development; VMI, Developmental Test of Visual–Motor Integration; WISC-R, Wechsler

Intelligence Scales for Children, Revised; WISC-III, Wechsler Intelligence Scales for Children, 3rd ed.; WRAML, Wide

Range Assessment of Memory and Learning.

aEffect of prenatal mercury exposure on Full-Scale IQ, as derived from a structural equation model of the three WISC-R

subtests conducted in the Faroe Islands study (Digit Span, Similarities, Block Design).

Table 2. Estimated regression coefficients for the three WISC-R subtests conducted in the Faroe Islands

study, and SEM-derived estimates of Full-Scale IQ. 

Response Standardized

End point β (SE)a SDb coefficientc

WISC-R subtests

Digit Span –0.025 (0.018) 1.45 –1.72

Similarities –0.039 (0.050) 3.86 –1.01

Block Design –0.175 (0.098) 8.92 –1.94

SEM estimates of WISC-R Full-Scale IQ

Estimate A –0.024 (0.011) 1.45 –1.65

Estimate B –0.024 (0.011) 0.586 –4.10

Abbreviations: SEM, structural equation model. 

aEffect per 10-ppb increase in cord blood mercury, as reported by Budtz-Jorgensen et al. (2005). bThe response SD for the

three WISC-R subtests is the SD for the Faroe Islands cohort for each subtest. For the SEM estimates, two choices are

available: Estimate A assumes that the response SD for Digit Span applies to the SEM estimate, because the SEM latent

variable representing IQ was estimated under the assumption that it has the same scale as the Digit Span end point.

Estimate B uses the SD of the SEM latent variable itself, obtained as part of the SEM estimation procedure. Estimate A is

used in the primary analysis, and Estimate B is applied in a sensitivity analysis. cThe standardized coefficient is the estimated coefficient (β) as a percentage of the response SD.
request, the Faroe Islands investigators fit data

for these three subtests in a structural equation model (SEM) to estimate a standardized

coefficient for a hypothetical Full-Scale IQ

(Budtz-Jorgensen et al. 2005). Structural

equation modeling allows the combination of

multiple exposures and responses via the use

of latent variables (Budtz-Jorgensen et al.

2002). In the SEM analysis of IQ, the three

WISC-R subtests are viewed as representative

of an underlying latent IQ variable. 

When fitting an SEM, it is necessary to

specify the scaling of any latent variables

involved in the model. The Faroe Islands

investigators assumed that the IQ latent variable was on the same scale as Digit Span. The

analysis estimated a coefficient of –0.024 and

a standard error of 0.011 for the effect of each

10 ppb of cord blood mercury on latent FullScale IQ, with a p-value of 0.031 (BudtzJorgensen et al. 2005). 

As with the general case discussed above,

the coefficient of the SEM latent variable also

requires rescaling so that it is comparable to

Full-Scale IQ. In this particular case, there are

two possible approaches to rescaling (Table 2):

One uses the standard deviation for Digit

Span (because the latent variable is assumed to

be on the same scale as Digit Span), whereas

the second uses the estimated standard deviation of the latent variable itself, obtained as

part of the SEM fitting procedure. Our primary approach uses the IQ estimate derived

with the Digit Span standard deviation.

However, the estimate derived with the standard deviation of the SEM latent variable may

also be valid, so we used this estimate in a sensitivity analysis. The standard deviation for the

SEM latent variable is considerably smaller

than the Digit Span standard deviation, resulting in a larger estimated impact of mercury

exposure on IQ for the Faroe Islands cohort. 

Statistical modeling. To estimate the association between mercury and IQ using information from the three studies, we used a

hierarchical random-effects model that includes

study-to-study as well as end point–to–end

point variability. Such models are commonly

used in settings where the goal is to combine

related information from several different

sources. For example, Dominici et al. (2000)

used such a model to combine dose–response

data related to particulate matter from different

U.S. cities. The approach used here extends the

Dominici work by including random effects

that reflect two levels of variability. Our model

is similar to the one described by Coull et al.

(2000) in their response to Dominici et al. [see

also Coull et al. (2003)].

Our analysis can be described as follows:

Let b1, b2,....bL represent the set of L estimated standardized regression coefficients

that we wish to analyze in a combined model.

Similarly, we index the associated standard

errors as s1, s2,....sL. Along with each bi we

assign a covariate studyi

, which takes the value

1, 2 or 3 and indicates whether the coefficient

came from New Zealand, Seychelles, or the

Faroe Islands study, respectively. We also

assign another covariate endpointi that indicates which particular developmental end

point the coefficient bi was based on. We then

fit the model

bi = β0 + δendpointi + ηstudyi + ei , [1]

where β0 is the overall mean, ei is a random

error term assumed to be normally distributed

with mean 0 and known variance s 2

i, ηstudyi is

a study-specific random effect, assumed to be

normally distributed with mean 0 and variance σ2

study , and δendpointi is an end point–specific normal random effect with mean 0 and

variance σ2

endpoint. 

Although it is technically feasible to fit

our model using maximum-likelihood estimation, the limited data meant that there was little information available to reliably estimate

the variance components. Instead we implement the model with a Bayesian approach.

Maximum-likelihood estimation is based on

so-called frequentist inference, which refers to

the properties of estimators and random variables under hypothetical replications of the

experiment that generated the data. For

example, a sample mean will hover around

the true but unknown population mean

under repeated sampling from the population. Frequentist inference treats model parameters as fixed, albeit unknown, quantities to

be estimated. In contrast, a Bayesian approach

treats not only the data but also all unknown

model parameters as random variables. Thus,

Bayesian inference requires specification not

only of the probability distribution of the

data, but also the probability distributions

(priors) of model parameters. 

In recent years, advances in computational

tools for Bayesian modeling have led to vastly

increased usage of these methods. The most

widespread computational approach, Markov

Chain Monte Carlo, has been implemented in

the user-friendly package WinBUGS (Lunn

et al. 2000). WinBUGS has become popular

even among frequentist statisticians because,

when sample sizes are large and the assumed

distributions on unknown model parameters

are very broad (i.e., noninformative or “flat”

priors), Bayesian inference will provide results

very close to those obtained through a frequentist approach (Carlin and Louis 2000).

WinBUGS is particularly useful for fitting

complex hierarchical models that would be

difficult to handle using a maximum-likelihood approach, which was important to our

decision to fit our models with a Bayesian

approach. Additionally, as discussed below,

the approach allowed us to overcome some

computational problems through the use of

slightly informative priors. Our analysis used

WinBUGS version 1.4 (http://www.mrc-bsu.

cam.ac.uk/bugs/).

The Bayesian approach requires the specification of prior distributions for all model

parameters, including β0, σ2

study, and σ2endpoint .

Data limitations in our setting precluded the

specification of fully noninformative priors on

the variance components. To address this

concern, we reparameterized to

σ2

study = Rσ2endpoint , [2]

so that R is a ratio of study-to-study variability relative to end point–to–end point variability. We then fitted the model for various

fixed, reasonable values of R. 

Although it is common for Bayesian modelers to use an inverse gamma to specify a

prior distribution on a variance component,

we found this formulation to be unstable in

that our results were highly sensitive to the

gamma parameters. This finding is consistent

with a number of reports in the literature (e.g.,

Gelfand et al. 1995). Gelman (2006) argues

that more stable results can be obtained by

specifying priors directly on the variance components or their square root. We used this

approach, with appropriate prior distributions

determined by examination of a profile likelihood surface obtained by treating the parameters R and σstudy as fixed and known. This

analysis found that although there was little

information in the data to estimate R, the

most likely values for σstudy ranged between 0

and 0.2. We therefore specified a uniform

prior on σstudywith this range.

All fitted models were checked for convergence and refit with different starting values

to ensure that reliable estimates had been

obtained. These procedures yielded computationally stable results and allowed us to explicitly evaluate the sensitivity of our results to

the values of the variance components.

Sample code is provided in the Supplemental

Material (online at http://www.ehponline.

org/docs/2007/9303/suppl.pdf). 

In the frequentist approach to statistical

analysis, confidence intervals (CIs) are typically based on a normality assumption and, in

the case of a 95% confidence interval, correspond to the estimated parameter ± 1.96 times

the standard error. A confidence interval is

based on the probability distribution of the

estimated parameter, and should not be interpreted as a probability statement about the

parameter of interest, which is assumed to be

fixed (nonrandom) but unknown. In contrast,

because a Bayesian approach treats model

parameters as random variables, the distribution of the unknown parameter of interest can

be computed. This distribution is known as

the posterior, and the highest posterior density

Dose response for mercury and IQ

Environmental Health Perspectives • VOLUME 115 | NUMBER 4 | April 2007 611
(HPD) interval refers to the most probable

range of the parameter of interest, given the

observed data. In settings where sample sizes

are large and flat priors have been used, confidence and HPD intervals will generally be

indistinguishable. Although our Bayesian

analysis yields HPD intervals, we refer to these

as confidence intervals to aid in the interpretation of our results.

Further discussion of our modeling process

may be found in a separate paper (Ryan LM,

in press). 

Sensitivity analyses. We conducted several

sensitivity analyses to examine the impacts of

alternate input data. The first sensitivity analysis

considers a model that includes only the IQ

dose–response coefficients estimated for the

three studies. Maximum-likelihood estimation

was straightforward in this case, because no end

point–to–end point variation was involved. 

Other sensitivity analyses used the Bayesian

approach to incorporate alternate input values.

We considered the use of coefficients from the

New Zealand study in which a single highly

exposed child is included. We also repeated the

analysis using the alternate estimate of the

rescaled IQ dose–response coefficient for the

Faroe Islands study, where the rescaled coefficient uses the standard deviation of the latent

variable from the SEM. 

Results

Primary analysis. Table 3 shows the cognitive

end points from each of the three studies used

in this analysis, the regression coefficients

reported in the three studies, and coefficients

rescaled so that they are all expressed in comparable terms (i.e., rescaled using the standard

deviation of IQ, and with exposure expressed

in terms of hair mercury). 

Using values of R (ratio of study-to-study

variability relative to end point–to–end point

variability) between 0.25 and 4 produced central estimate dose–response coefficients ranging from –0.15 (R = 0.25) to –0.19 (R = 4.0)

IQ points per parts per million of maternal

hair mercury, which were statistically significant in all cases (Table 4). As R increases, the

study-to-study variance component also

increases (Table 4). Although there is not

enough information available to reliably estimate both R and σstudy, visual inspection of

the data displayed in Figure 1 suggests that

there is likely to be more study-to-study than

end point–to–end point variation. Because

the results appear to stabilize at a value of R =

3.0 and because this value seems reasonable,

we use this value for all subsequent analysis

and as the basis of our main study findings. 

The integrated analysis produced a central

estimate of –0.18 (95% CI, –0.378 to –0.009)

IQ points for each part per million maternal

hair mercury, similar to the results found for

both the Faroe Islands and Seychelles studies,

and lower than the estimate found in the New

Zealand study (Figure 2). 

Sensitivity analyses. Our first sensitivity

analysis, using simple maximum-likelihood

analysis, includes only the IQ dose–response

coefficients from the three studies, and does

not include the other cognitive outcomes. We

find an overall mean dose–response coefficient

Axelrad et al.

612 VOLUME 115 | NUMBER 4 | April 2007 • Environmental Health Perspectives

Table 4. Estimates of an IQ–mercury dose–response coefficient from a hierarchical model, integrating

data from three epidemiologic studies, for different values of R = σ2

study /σ2endpoint.

R σstudy (SE) βIQ (SE)a 95% CIb

4.0 0.118 (0.051) –0.188 (0.096) –0.398 to –0.010

3.5 0.116 (0.050) –0.182 (0.091) –0.390 to –0.007

3.0 0.112 (0.051) –0.180 (0.092) –0.378 to –0.009

2.5 0.110 (0.051) –0.183 (0.090) –0.384 to –0.017

2.0 0.107 (0.050) –0.178 (0.088) –0.371 to –0.012

1.5 0.095 (0.053) –0.168 (0.086) –0.360 to –0.003

1.0 0.086 (0.051) –0.165 (0.080) –0.338 to –0.015

0.5 0.068 (0.046) –0.160 (0.071) –0.321 to –0.026

0.25 0.049 (0.038) –0.151 (0.061) –0.283 to –0.033

aEstimated IQ decrement per part per million maternal hair mercury. The square root of the study-to-study variance component, σstudy, is assumed to have a uniform distribution with a range of 0–0.2. bSee “Statistical modeling” for discussion

of how confidence intervals relate to highest posterior density intervals.

Table 3. Original and rescaled regression coefficients and associated standard errors for cognitive end

points from the Faroe Islands, New Zealand, and Seychelles studies of prenatal mercury exposure.

Scaling Original scale Rescaleda

Study End point factora β SE β SE

Primary analysis inputs

Faroe Islands Full-Scale IQb 5.17 –0.024 0.011 –0.124 0.057

(Budtz-Jorgensen et al. 2005) Benderc –1.42 0.073 0.059 –0.104 0.083

BNT 1.37 –0.190 0.063 –0.260 0.086

CVLT 2.91 –0.058 0.032 –0.169 0.093

New Zealand, outlier excludedd Full-Scale IQ 0.94 –0.53 0.285 –0.50 0.268

(Crump et al. 1998) Performance IQ 0.94 –0.54 0.330 –0.51 0.310

TOLD 0.94 –0.60 0.300 –0.56 0.282

MCC 1.5 –0.53 0.210 –0.80 0.315

Seychelles Full-Scale IQ 1.29 –0.13 0.10 –0.17 0.130

(Myers et al. 2003) CVLT 14.42 0.013 0.010 0.19 0.144

BNT 3.13 –0.012 0.046 –0.038 0.144

WRAML 5.17 –0.021 0.029 –0.109 0.150

VMI 1.28 –0.010 0.12 –0.013 0.150

Sensitivity analysis inputs

Faroe Islands, alternate IQ Full-Scale IQ 12.8 –0.024 0.011 –0.307 0.141

(Budtz-Jorgensen et al. 2005)

New Zealand, outlier includedd Full-Scale IQ 0.94 –0.18 0.155 –0.17 0.15

(Crump et al. 1998) Performance IQ 0.94 –0.12 0.165 –0.11 0.16

TOLD 0.94 –0.19 0.145 –0.18 0.14

MCC 1.5 –0.18 0.110 –0.27 0.17

Abbreviations: BNT, Boston Naming Test; CVLT, California Verbal Learning Test; MCC, McCarthy Scales of Children’s

Abilities; TOLD, Test of Language Development; VMI, Developmental Test of Visual–Motor Integration; WRAML, Wide

Range Assessment of Memory and Learning.

aSee Supplemental Material (http://www.ehponline.org/docs/2007/9303/suppl.pdf) for derivation of scaling factors.

Rescaled coefficients are interpretable in the same scale as Full-Scale IQ. For the Faroe Islands study, rescaling also

converts the values from units of cord blood mercury to units of hair mercury, to be comparable with the New Zealand

and Seychelles exposure metrics. bFull-Scale IQ for the Faroe Islands is estimated with an SEM combining three WISC-R

subtests (Digit Span, Similarities, Block Design). The primary estimate is scaled using the response SD of Digit Span

(Table 2, Estimate A); the alternate estimate is scaled using the standard deviation of the SEM latent variable itself,

obtained as part of the SEM estimation procedure (Table 2, Estimate B). c The scaling factor for Bender is negative

because higher scores on this test represent poorer performance. dThe primary analysis inputs for the New Zealand

study are derived with one highly exposed child excluded; the sensitivity analysis inputs are derived with that child

included. SEs for the New Zealand study are obtained by subtracting the reported regression coefficient from the

reported upper confidence limit and dividing by two.

Figure 1. Coefficients and 95% confidence intervals

for the dose–response relationship between neurodevelopmental test scores and maternal hair

mercury from three epidemiologic studies. Solid

lines indicate coefficients for Full-Scale IQ, and

dashed lines indicate coefficients for other neurodevelopmental tests included in the primary

analysis (see Table 3). Coefficients for end points

other than IQ are rescaled to be expressed in

equivalent terms.

1.0

0.5

0

–0.5

–1.0

–1.5

Study

New Zealand Seychelles Faroe Islands

IQ points per ppm hair mercury
of –0.145 (95% CI, –0.259 to –0.047)

(Table 5). Note that the study-to-study variance component had an estimated value of 0

in this analysis. 

Using the New Zealand coefficients with

the outlier included in the hierarchical analysis reduces the central estimate of the

dose–response coefficient, compared with the

primary analysis, to –0.125 (Table 5). The

study-to-study variance component was

reduced, and the precision associated with the

coefficient increased, with 95% CI of –0.236

to –0.007).

The Bayesian model was also rerun with

the alternate estimate of the IQ dose–response

coefficient for the Faroe Islands, where the IQ

coefficient from the SEM analysis was rescaled

using the estimated standard deviation of the

latent variable (0.586) rather than the standard deviation of the Digit Span subtest

(1.45). This produced a rescaled Faroe Islands

dose–response coefficient of –0.307, compared with the rescaled coefficient of –0.124

used as input to the primary analysis. The

resulting integrated dose–response coefficient

for IQ increases in magnitude to –0.25 (95%

CI, –0.491 to –0.052) (Table 5). 

Discussion

Our analysis integrated data from three

epidemiologic studies to estimate a change in

childhood IQ of –0.18 IQ points (95% CI,

–0.378 to –0.009) for every part per million

mercury in maternal hair. This central estimate

is relatively close to the values for the Faroe

Islands and Seychelles studies, suggesting less

influence on the integrated value from the

larger coefficient estimated in the New Zealand

study. The smaller influence of the New

Zealand coefficient is attributed to the smaller

size of the cohort and the greater uncertainty in

the central estimate of the dose–response coefficient, as depicted in Figure 2. 

Our analysis provides the ability to estimate

benefits from reductions in mercury exposure,

similar to previous analyses estimating benefits

of reducing childhood lead exposure. We

assume a linear, nonthreshold relationship

between prenatal mercury exposure and IQ

deficits in the children. The choice of a linear

nonthreshold dose–response model was based

on several considerations: the shape of the

dose–response in the range of the observed

data; the magnitude of the extrapolation below

the observed data; relevant biologic considerations; and the available information for the

Seychelles and New Zealand studies, which

consisted of linear dose–response coefficients.

The NRC panel concluded that linear models

are most appropriate for dose–response modeling of mercury’s neurodevelopmental effects in

the absence of persuasive evidence supporting

an alternative functional form (NRC 2000). In

addition, the U.S. EPA has concluded that “no

evidence of a threshold arose for methylmercury-related neurotoxicity within the range of

exposures in the Faroe Islands study” (U.S.

EPA 2001). 

An important consideration in extrapolating below the observed data is the extent of

the extrapolation. The lowest exposure in the

Faroe Islands study is 0.9 ppb mercury in

cord blood, equivalent to 0.53 ppb mercury

in maternal blood [assuming a ratio of mercury in cord blood to maternal blood equal to

1.7 (Stern and Smith 2003)]. More than 50%

of U.S. women had blood mercury concentrations > 0.53 ppb in 1999–2002 (Centers for

Disease Control and Prevention 2004).

Although there is limited information on the

shape of the dose–response relationship at

lower exposure levels, it is reasonable to

assume that the linear dose–response relationship recommended by the NRC for the

observed range of the data in the epidemiologic studies applies as well in extrapolating to

the range of the U.S. data. Scientific findings

suggest that the slope of the dose–response

curve may in fact be steeper at lower doses

(i.e., supralinear). A log-linear model was

found to provide the best fit between cord

blood mercury and cognitive effects in the

Faroe Islands study (Budtz-Jorgensen et al.

2000). Also, analyses of the relationship

between childhood lead exposure and IQ

have found a steeper response at exposures

< 10 μg/dL (Lanphear et al. 2005), and other

findings in the literature suggest the plausibility of supralinear dose–response relationships

(Castorina and Woodruff 2003; Thompson

and Myers 2006). If such a relationship

applies in the case of mercury and IQ, a linear

term will underestimate the effect. 

In addition, recent commentaries have

proposed using linear dose–response models

for noncancer end points (Clewell and

Crump 2005; Crawford and Wilson 1996).

The authors note that assuming linearity is a

reasonable approach, based on similar considerations underpinning linear nonthreshold

dose–response models for carcinogens,

including the presence of background biologic processes, background exposures to

other chemicals, and variability in human

response (Clewell and Crump 2005;

Crawford and Wilson 1996; Crump et al.

1976). Considering all of the available information, the assumption of linearity in our

analysis is a reasonable approach.

Full-Scale IQ is a composite index that

averages a child’s performance across many

functional domains, providing an overall picture of cognitive health. IQ as measured at

school age has been shown to be predictive of

later outcomes such as academic and occupational success (Neisser et al. 1996). However,

if mercury affects only specific cognitive functions, using Full-Scale IQ as the end point for

a benefits analysis will underestimate the neurodevelopmental impacts on other targeted

functions. 

Moreover, there may be substantial

deficits in cognitive well-being even in individuals with normal or above average IQ. For

example, two of the most sensitive end points

in the Faroe Islands study were the Boston

Naming Test (BNT), which assesses word

retrieval, and the California Verbal Learning

Test (CVLT), which assesses the acquisition

and retention of information presented verbally. A child who has deficits in either of

these skills could, depending on their severity,

be at a considerable disadvantage in the classroom and at substantial educational risk.

Neither of these abilities is directly assessed by

Dose response for mercury and IQ

Environmental Health Perspectives • VOLUME 115 | NUMBER 4 | April 2007 613

Table 5. Sensitivity analysis for estimation of integrated IQ dose–response coefficients. 

Analysis σstudy (SE) βIQ (SE) 95% CIa

Primary analysis 0.112 (0.051) –0.180 (0.092) –0.378 to –0.009

Use only IQ coefficientsb 0.0 (NAc) –0.145 (0.051) –0.259 to –0.047

Include New Zealand outlierd 0.056 (0.042) –0.125 (0.056) –0.236 to –0.007

Alternate Faroe Islands IQe 0.132 (0.044) –0.254 (0.112) –0.491 to –0.052

NA, not applicable. The value of R is set to 3.0. aSee “Statistical modeling” for discussion of how confidence intervals relate to highest posterior density intervals.

bMaximum-likelihood estimation of integrated IQ dose–response coefficient considering only the IQ dose–response coefficients from the three epidemiologic studies. cSE could not be estimated because the estimated value of σstudy was on

the boundary of the parameter space. dEstimation of integrated IQ dose–response coefficient with a Bayesian hierarchical model that incorporates all end points listed in Table 1, and using alternate values for New Zealand study shown in

Table 3. eEstimation of integrated IQ dose–response coefficient with a Bayesian hierarchical model that incorporates all

end points listed in Table 1, and using the alternate value for Full-Scale IQ for the Faroe Islands study shown in Table 3. 

Figure 2. Coefficients and 95% confidence intervals

for the dose–response relationship between IQ and

maternal hair mercury from the three epidemiologic studies and for the results of the integrated

analysis.

Study

New

Zealand

Seychelles Faroe

Islands

IQ points per ppm hair mercury

0

–0.5

–1.0

Integrated
Axelrad et al.

614 VOLUME 115 | NUMBER 4 | April 2007 • Environmental Health Perspectives

the WISC IQ test, however, so they do not

explicitly contribute to a child’s IQ score.

Therefore, benefits calculations relying solely

on IQ decrements are likely to underestimate

the benefits to cognitive functioning of

reduced mercury exposures. In addition,

impacts on other neurologic domains (such as

motor skills and attention/behavior) are not

represented by IQ scores and thus are also

excluded from the analysis.

An earlier version of this work, included

in the technical documentation for the U.S.

EPA’s Clean Air Mercury Rule of March

2005, reported a central estimate of –0.13 for

the mercury–IQ coefficient (Bellinger 2005;

Ryan 2005; U.S. EPA 2005). A revised version, reflecting corrections to the original

model code, was included in subsequent rulemaking documentation, and reports a central

estimate of –0.16 (U.S. EPA 2006). The central estimate of –0.18 reported here reflects

revisions that adapt the recommendations of

Gelman (2006) for specification of prior

assumptions in Bayesian analysis, as discussed

above. 

Although exploratory likelihood-based

analysis indicated a likely range for the prior

distribution on σstudy of 0–0.2, sensitivity of

the model to the uniform prior distribution

on σstudy was assessed by changing the specification to a range of 0–0.3 or 0–0.4. Posterior

estimates of σstudy increased slightly. The estimated dose–response coefficient remained

stable at approximately –0.18, and there was

some variation in width of the confidence

intervals, with upper confidence limits marginally exceeding zero with the broader priors. 

This analysis relies on use of summary statistics for each of the three studies. Original

data were not available for this analysis.

Although a lack of original data is a potential

limitation, its impact here is lessened for several reasons. All three epidemiologic studies

had careful prospective designs and measured

a variety of important potential confounders.

The dose–response coefficients were derived

from well-documented regression models that

adjusted for age, maternal education, and

other important factors. Dominici et al.

(2000) took a similar approach for hierarchical

modeling of estimated dose–response coefficients extracted from separate studies.

We converted the Faroe Islands cord blood

mercury coefficients to hair mercury units

using the study’s median hair:cord blood ratio

of 200. However, this ratio is not constant over

the range of exposures (Budtz-Jorgensen et al.

2004a). To evaluate the impact of the varying

ratio, we conducted a simulation using parameters reported by Budtz-Jorgensen et al.

(2004a). The simulation produced multiple

paired estimates, each consisting of a direct

hair mercury–IQ coefficient and an indirect

hair mercury–IQ coefficient derived by

estimating a cord blood mercury–IQ coefficient

then dividing by the hair:cord blood ratio of

200. On average, the direct estimate of the

hair mercury–IQ coefficient was around 10%

smaller than the indirect estimate. Using a

constant ratio therefore had a small impact on

the estimated hair mercury–IQ coefficient for

the Faroe Islands study; the impact on the

integrated coefficient derived from all three

studies would be even smaller.

We focused the selection of outcomes for

this analysis on tests of cognitive functioning.

We did evaluate an alternative formulation of

the model that included tests of attention,

behavior, and motor skills (Ryan 2005). Not

surprisingly, the results of this model displayed

greater uncertainty than the primary analysis,

indicating that the overall signal is dampened if

we include end points unrelated to cognition. 

An advantage of our hierarchical modeling

approach is that it can produce separate

dose–response coefficients for each of the outcomes included in the model, as well as a coefficient integrating all outcomes. An important

reason for focusing on IQ was that methods

already exist for valuing this end point in economic benefit–cost analysis. However, it would

be useful and appropriate for economic analyses to consider a broader range of outcomes.

For example, in the primary analysis model,

the overall mean coefficient for the achievement/cognition domain is –0.19 (95% CI,

–0.394 to –0.021), and the coefficient for the

BNT is –0.21 (95% CI, –0.443 to –0.037).

These additional dose–response estimates

should be considered for use in expanded economic analyses of neurodevelopmental effects;

this would require economic research on individuals’ willingness-to-pay for reducing risks of

neurodevelopmental effects. 

A recent article by Trasande et al. (2005)

used results from Budtz-Jorgensen et al.

(2005) to estimate a Faroe Islands IQ decrement of 4–8% of a standard deviation for

each 10 ppb cord blood mercury. The comparable Faroe Islands estimate in our analysis

is either 1.65 or 4.10% of a standard deviation (Table 2). The Trasande estimate is

based on the results for the BNT, the CVLT

(both tests of cognitive function), and the

Continuous Performance Test (test of attention/behavior, and which provided the upper

end of the range). Trasande et al. (2005) did

not consider the results of the WISC subtests

(for which the decrements generally equal

around 2% of a standard deviation), which

are most directly relevant to estimation of the

mercury–IQ relationship. Our estimate is

based on the SEM result that was derived

from the three WISC subtests. 

Finally, the integrated dose–response

analysis assumes that the exposures assigned to

each study subject are accurate representations

of true exposure. In reality, there is likely to be

some discrepancy between measured and

actual exposures—for example, due to variation in hair length. Alternatively, the true

exposure of interest may have occurred during

the first trimester of pregnancy, whereas mercury in maternal hair samples only a few centimeters in length collected at birth and in

cord blood samples reflect exposures later in

pregnancy. Presence of exposure measurement

error could introduce a bias in the results,

most likely toward the null (Budtz-Jorgensen

et al. 2004b). 

Using a statistical technique that accounts

for variability within and between studies, we

have produced an integrated estimate of the

dose–response relationship between prenatal

mercury exposure and IQ. IQ does not represent all neurodevelopmental deficits associated with mercury, so estimates of effects

using this relationship will understate the

overall impacts of prenatal mercury exposure.

Nevertheless, the estimated mercury–IQ relationship provides a broad-based measure of

effects on cognitive development and can be

readily applied to estimate benefits of reducing mercury exposures in the population.

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