Studies of the effects of environmental exposures on human health typically require estimation of both exposure and outcome. Standard methods for the assessment of the association between exposure and outcome include multiple linear regression analysis, which assumes that the outcome variable is observed with error, while the levels of exposure and other explanatory variables are measured with complete accuracy, so that there is no deviation of the measured from the actual value. The term measurement error in this discussion refers to the difference between the actual or true level and the value that is actually observed. In the investigations of the effects of prenatal methylmercury (MeHg) exposure from fish consumption on child development, the only way to obtain a true exposure level (producing the toxic effect) is to ascertain the concentration in fetal brain, which is not possible. As is often the case in studies of environmental exposures, the measured exposure level is a biomarker, such as the average maternal hair level during gestation. Measurement of hair mercury is widely used as a biological indicator for exposure to MeHg and is the only indicator that has been calibrated against the target tissue, the developing brain. Variability between the measured and the true values in explanatory variables in a multiple regression analysis can produce bias, leading to either over or underestimation of regression parameters (slopes). Fortunately, statistical methods known as measurement error models (MEM) are available to account for measurement errors in explanatory variables in multiple regression analysis, and these methods can provide an (either "unbiased" or "bias-corrected") estimate of the unknown outcome/exposure relationship. In this paper, we illustrate MEM analysis by reanalyzing data from the 5.5-year test battery in the Seychelles Child Development Study, a longitudinal study of prenatal exposure to MeHg from maternal consumption of a diet high in fish. The use of the MEM approach was made possible by the existence of independent, calibration data on the magnitude of the variability of the measurement error deviations for the biomarker of prenatal exposure used in this study, the maternal hair level. Our reanalysis indicated that adjustment for measurement errors in explanatory variables had no appreciable effect on the original results.
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 April 2022.
All research outputs
#6,086,972
of 27,777,630 outputs
Outputs from Environmental Research
#2,715
of 8,717 outputs
Outputs of similar age
#12,356
of 63,070 outputs
Outputs of similar age from Environmental Research
#9
of 20 outputs
Altmetric has tracked 27,777,630 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 8,717 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.1. This one has gotten more attention than average, scoring higher than 63% of its peers.
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We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
