Methylmercury and the Developing Brain
Environ Health Perspect 115:395-399 (2007). doi:10.1289/ehp.10302 available via http://dx.doi.org [Online 24 June 2007]
Referencing: A Comparison of the Monetized Impact of IQ Decrements from Mercury Emissions
We reported that prenatal exposure to methylmercury causes cognitive impairment in an estimated 316,588 children born in the United States each year, costing this nation $8.7 billion annually in lost productivity (Trasande et al. 2005). Each year, this exposure also causes an estimated 1,566 cases of mental retardation (Trasande et al. 2006). The principal (70%) source of the mercury that enters the bodies of American children is combustion of coal in electricity-generating plants.
In their reanalysis of our data, Griffiths et al. (2007) made a series of incorrect judgments and poorly considered assumptions, each of which diminishes the import of our findings. We note the following errors in their analysis:
First, Griffiths et al. (2007) incorrectly used a linear model to relate cognitive function to prenatal methylmercury exposure, despite the National Research Council's (NRC) clear finding that a logarithmic model provides a better statistical fit. The NRC, in their examination of the Faroe Islands cohort study, the study on which they place greatest reliance, stated that "[b]ecause these calculations necessitate extrapolating to estimate the mean response at zero exposure level," logarithmic models "lead to lower estimates of the Benchmark Dose (BMD) than linear or K-power models" (NRC 2000, p. 294).
Recent analyses of early childhood lead exposure further corroborate the validity of logarithmic models in representing subclinical dose–response relationships of neurodevelopmental injury (Canfield et al. 2003).
Second, Griffiths et al. (2007) unwisely based their analysis on potentially biased data from the Seychelles cohort study. By contrast, our model (Trasande et al. 2005), like that of the NRC, is based primarily on Faroes data. We chose not to use Seychelles data because of concern that the tests of neurobehavioral function used there were not well validated for a non-American population and therefore may not have been sensitive to detect cognitive impairment (Landrigan and Goldman 2003).
Another major potential source of bias in the Seychelles study, not acknowledged by Griffiths et al. (2007), is that it fails to consider the potentially beneficial nutrients found in the fish-based diet of the Seychelles. These nutrients, omega-3 fatty acids in particular, may partially offset the toxicity of methylmercury. Indeed, if maternal fish intake is taken into account in the Seychelles cohort, as recently was done, the estimate of methylmercury toxicity increases (Budtz-Jorgensen et al. 2007).
Griffiths et al. (2007) cited previous meta-analyses of the Faroes, Seychelles, and New Zealand studies by Ryan (2005) in applying IQ decrements of 0.13–0.18 points/ppm hair mercury, but these are likely underestimates, and further invalidate the analysis of Griffiths et al.
Third, in attributing mercury deposition to sources of emission, Griffiths et al. (2007) relied inexplicably and without justification on a mathematical model that posits that only 16% of deposits are attributable to American sources. They ignored empiric data from the U.S. Environmental Protection Agency (EPA)-sponsored Steubenville study, which found that 80–90% of mercury emissions deposit within 30–50 miles of the source (U.S. EPA 2007); and from the Electric Power Research Institute, which estimated that 30% of mercury deposits are attributable to American sources (Seigneur et al. 2004).
Fourth, Griffiths et al. (2007) incorrectly assumed that reductions in mercury emissions from power plants do not result in any reduced levels of fish contamination until after 15 years. This is not correct. Reductions in power-plant emissions in 2008 will, in fact, begin immediately to minimize methylmercury body burden among children born to women in 2008, and the degree of reduction will increase further in subsequent years, perhaps through 2038, thus reducing the number of children damaged, the severity of the prenatal brain damage in these children, and the resulting economic burden.
Finally, Griffiths et al. (2007) incorrectly based their estimate of the economic value of a child's social productivity on the 1992 Current Population Survey rather than on the currently available 2005 data set. This miscalculation substantially underestimates the economic impact of methylmercury on the developing brain. Viscusi and Aldy (2004) estimated that this value is currently on the order of $4–9 million/child, a value far greater than that used by Griffiths et al., and greater even than our estimate.
The authors declare they have no competing financial interests.
Leonardo Trasande
Philip J. Landrigan
Mount Sinai School of Medicine
New York, New York
Clyde B. Schechter
Albert Einstein College of Medicine, Bronx, New York
Richard F. Bopp
Rensselaer Polytechnic Institute
Troy, New York
References
Budtz-Jorgensen E, Grandjean P, Weihe P. 2007. Separation of risks and benefits of seafood intake. Environ Health Perspect 115:323–327.
Canfield RL, Henderson CR Jr, Cory-Slechta DA, Cox C, Jusko TA, Lanphear BP. 2003. Intellectual impairment in children with blood lead concentrations below 10 microg per deciliter. N Engl J Med 348(16):1517–1526.
Griffiths C, McGartland A, Miller M. 2007. A comparison of the monetized impact of IQ decrements from mercury emissions. Environ Health Perspect 115:841–847.
Landrigan PJ, Goldman L. 2003. Prenatal methylmercury exposure in the Seychelles [Letter]. Lancet 362(9384):666.
NRC (National Research Council). 2000. Toxicological Effects of Methylmercury. Washington, DC:National Academy Press.
Ryan LM. 2005. Effects of Prenatal Methylmercury on Childhood IQ: A Synthesis of Three Studies. EPA-HQ-OAR-2002-0056-6048 and EPA-HQ-OAR-2002-0056-6049. Available: http://www.regulations.gov [accessed 20 January 2006].
Seigneur C, Vijayaraghavan K, Lohman K, Karamchandani P, Scott C. 2004. Global source attributions for mercury deposition in the United States. Environ Sci Technol 38(2):555–569.
Trasande L, Schechter C, Haynes KA, Landrigan PJ. 2006. Mental retardation and prenatal methylmercury toxicity. Am J Ind Med 49:153–158.
Trasande L, Schechter C, Landrigan PJ. 2005. Public health and economic consequences of environmental methylmercury toxicity to the developing brain. Environ Health Perspect 113:590–596.
U.S. EPA. 2006. Evaluation Report. Monitoring Needed to Assess Impact of EPA's Clean Air Mercury Rule on Potential Hotspots. Report No. 2006-P-00025. Available http://www.epa.gov/oig/reports/2006/20060515-2006-P-00025.pdf [accessed 29 January 2007.]
Viscusi WK, Aldy JE. 2004. The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World. NBER Working Paper 9487. Available http://www.nber.org/papers/w9487 [accessed 2 July 2004].
Methylmercury and the Brain: Griffiths et al. Respond
Environ Health Perspect 115:395-399 (2007). doi:10.1289/ehp.10302R available via http://dx.doi.org [Online 24 June 2007]
In our review of the article by Trasande et al. (2005), we used their published linear model to evaluate the monetized impact of IQ decrements associated with prenatal mercury exposure to methylmercury (MeHg) under different assumptions (Griffiths et al. 2007). First, we used a corrected dose–response slope to address the error that the authors made in the conversion of the relationship between cord blood and neurodevelopment effects. We then introduced the assumptions that the U.S. Environmental Protection Agency (EPA) used in its Clean Air Mercury Rule (CAMR). Introducing the U.S. EPA assumptions decreased the undiscounted monetized impact of global anthropogenic mercury emissions in the corrected Trasande et al. model by 81% and decreased the estimated impact of U.S. sources by almost 97%. When discounting is included, the U.S. EPA assumptions decreased the monetized estimate of global impacts by 88% and the impact of U.S. power plants by 98%.
The choice of a linear model (i.e., a K-power model, with K = 1) was based on the recommendation of the National Research Council (NRC 2000):
After extensive discussion, the committee concluded that the most reliable and defensible results for the purpose of risk assessment are those based on the K-power model.
Trasande et al. choose to emphasize the results of their logarithmic model, which produces their highest estimates of monetized impacts. We do not dispute that there may be cases in which a logarithmic model might be appropriate, but in the case of methylmercury, the NRC (2000) was unequivocal:
For MeHg, the committee believes that a good argument can be made for the use of a K-power model with K constrained to be greater than or equal to 1. That rules out square-root (K = 0.5) and log models (the limiting case as K approaches 0).
For the U.S. EPA dose–response slope, we used the results of an integrated statistical analysis by Ryan (2005), which has been recently updated (Axelrad et al. 2007). The analysis of Axelrad et al. includes results from the Seychelles study and also those of the Faroe Islands study (which was used by Trasande et al. 2005), as well as the New Zealand study. All three of these studies were used by the NRC (2000) and are described as being "well designed and carefully conducted, and each examined prenatal MeHg exposures within the range of the general U.S. population exposures." We will concede that controlling for maternal fish intake when assessing the impact of mercury on neurodevelopment is an important consideration that can be addressed in the future.
The assumption that, on average, 16% of the total mercury deposition in the United States is from American and Canadian sources comes straight from the U.S. EPA model used for the CAMR. As discussed in our article (Griffiths et al. 2007), the U.S. EPA used a spatially explicit air quality model to simulate the location of mercury deposition, but we used the average value to compare it to Trasande et al.'s (2005) assumption that 60% of the mercury content in all domestically caught fish is due to American sources. It is true that in the study of Steubenville, Ohio, published after the CAMR was promulgated, Keeler (2006) found a much higher percentage of local and regional deposition (70% of the mercury wet deposition, not 80–90%), but this is an estimate of deposition at a single point and cannot be extrapolated to the entire country. Furthermore, the same U.S. EPA model that produced the 16% average value predicts comparatively high values for the Steubenville region of Ohio (U.S. EPA 2006).
With regard to the charge that we assumed there will be no reductions in fish contamination until after 15 years, Transande et al. are wrong. In our article (Griffiths et al. 2007) we are clear in our position that benefits build over time during the transition path from the current conditions to the new equilibrium. The choice of 15 years is an average period over which to discount the benefits, reflecting the 5–30 years for freshwater systems and the 30–200 years for ocean systems to reach equilibrium. Furthermore, we reported the undiscounted monetized results, which could be compared to Trasande et. al's (2005) implicit assumption of the instantaneous elimination of all anthropogenic mercury from the environment.
Finally, Trasande et al.'s reference to Viscusi and Aldy (2004) is truly baffling. That article is a review and evaluation of dozens of studies on the value of a statistical life (VSL). A VSL is derived from the tradeoffs witnessed in the market and elsewhere between income and small changes in risk of death. The value for a small change in mortality risk is aggregated to statistical lives in order to be comparable to risk assessment estimates. Because mortality risk and IQ decrements are vastly different items, there is no expected relationship between these two values.
The views expressed here 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.
Charles Griffiths
Al McGartland
Maggie Miller
National Center for Environmental Economics
U.S. Environmental Protection Agency
Washington, DC
References
Axelrad DA, Bellinger DC, Ryan LM, Woodruff TJ. 2007. Dose–response relationship of prenatal mercury exposure and IQ: an integrative analysis of epidemiologic data. Environ Health Perspect 115:609–615.
Griffiths C, McGartland A, Miller M. 2007. A comparison of the monetized impact of IQ decrements from mercury emissions. Environ Health Perspect 115:841–847.
Keeler GJ, Landis MS, Norris GA, Christianson EM, Dvonch JT. 2006. Sources of mercury wet deposition in eastern Ohio, USA. Environ Sci Technol 40: 5874–5881.
NRC (National Research Council). 2000. Toxicological Effects of Methylmercury. Washington, DC:National Academy Press.
Ryan LM. 2005. Effects of Prenatal Methylmercury on Childhood IQ: A Synthesis of Three Studies. EPA-HQ-OAR-2002-0056-6048 and EPA-HQ-OAR-2002-0056-6049. Available: http://www.regulations.gov [accessed 20 January 2006].
Trasande L, Landrigan PJ, Schechter C. 2005. Public health and economic consequences of methyl mercury toxicity to the developing brain. Environ Health Perspect 113: 590–596.
U.S. EPA. 2006. Revision of December 2000 Clean Air Act Section 112(n) Finding Regarding Electric Utility Steam Generating Units; and Standards of Performance for New and Existing Electric Utility Steam Generating Units: Reconsideration. Fed Reg 71:33388–33402.
Viscusi WK, Aldy JE. 2004. The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World. NBER Working Paper 9487. Available: http://www.nber.org/papers/w9487 [accessed 9 July 2004].