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Two models of vulnerability to socioenvironmental risk were examined in 337 African American children (M = 7.8 years) recruited to overrepresent prenatal alcohol or cocaine exposure: The cumulative risk model predicted synergistic effects from exposure to multiple risk factors, and the fetal patterning of disease model predicted that prenatal insult will increase vulnerability to environmental risk. Four or more risks emerged as a threshold for poorer cognitive and behavioral outcome among the non-substance-exposed children, whereas substance-exposed children showed greater vulnerability to lower levels of environmental risk. Cumulative risk was associated with increased delinquent and internalizing behaviors only for the substance-exposed group. Results support the cumulative risk model for non-substance-exposed children and increased vulnerability to environmental risk among the substance-exposed group.

In this paper, we respond to the criticisms and concerns raised by D.V. Cicchetti, A.S. Kaufman, & S.S. Sparrow (this issue) in their review of the PCB literature, with particular attention to our own research in Michigan. We agree that multiple comparisons and functional significance are issues that would benefit from more discussion. However, because the effects associated with exposure to environmental contaminants are generally subtle, the risk of Type II error would be unacceptably high if researchers were to adopt the authors' recommendation to use a Bonferroni correction. We describe the hierarchical approach we have used to deal with the issue of multiple comparisons, which emphasizes the need to base interpretation on consistent patterns in the data and on replicated findings. The issue of confounding is one that has received considerable attention in the PCB studies and, given that one can never measure every possible confounder, the range of control variables that have been evaluated is impressive. We disagree with the authors' assertion that only standardized test scores are sufficiently reliable for use in these studies; behavioral teratogens often involve subtle effects, which can be identified most effectively by innovative, narrow-band tests that have not yet been normed. Moreover, longitudinal statistical analysis is not necessarily the method of choice for the issues being addressed in this literature. One important new development that Cicchetti et al. fail to note is the emergence of evidence from both the Michigan and Dutch cohorts indicating that breast-fed children are markedly less vulnerable. It is not yet clear to what degree this protective effect is attributable to nutrients in breast milk or to more optimal intellectual stimulation by nursing mothers, or both. However, the discovery of effect modifiers that can explain individual differences in vulnerability marks an important advance in our growing understanding of the teratogenic effects of exposure to environmental contaminants on child development. (Contains 4 tables and 1 figure.)

Evidence from animal models and epidemiological studies has linked prenatal alcohol exposure (PAE) to a broad range of long-term cognitive and behavioural deficits. However, there is a paucity of evidence regarding the nature and levels of PAE associated with increased risk of clinically significant cognitive deficits. To derive robust and efficient estimates of the effects of PAE on cognitive function, we have developed a hierarchical meta-analysis approach to synthesize information regarding the effects of PAE on cognition, integrating data on multiple outcomes from six U.S. longitudinal cohort studies. A key assumption of standard methods of meta-analysis, effect sizes are independent, is violated when multiple intercorrelated outcomes are synthesized across studies. Our approach involves estimating the dose–response coefficients for each outcome and then pooling these correlated dose–response coefficients to obtain an estimated “global” effect of exposure on cognition. In the first stage, we use individual participant data to derive estimates of the effects of PAE by fitting regression models that adjust for potential confounding variables using propensity scores. The correlation matrix characterizing the dependence between the outcome-specific dose–response coefficients estimated within each cohort is then run, while accommodating incomplete information on some outcome. We also compare inferences based on the proposed approach to inferences based on a full multivariate analysis. [ABSTRACT FROM AUTHOR]

The article focuses on the correlation between breast feeding and intelligence in children. A study which determines the value of nutrition and the socioeconomic environment in which a child develops is discussed. The author suggests that the research indicates that the mother's intellect is a larger factor than simply breast feeding by itself. Additional known benefits of breast feeding are also discussed.