Blood lead levels and longitudinal language outcomes in children from 4 to 12 years

OBJECTIVES

In this study, the authors aimed to examine the association of a range of blood lead levels on language skills assessed at 4, 6, 10 and 12 years of age using a prospective longitudinal design controlling for potential confounding variables including maternal vocabulary, caregiver's psychological distress and symptomatology, child's race and prenatal drug exposure.

METHODS

The participants (N = 278) were a subsample of a large longitudinal study that examined the association of prenatal drug exposure on children who were followed prospectively from birth and assessed for receptive and expressive language skills at 4, 6, 10 and 12 years of age. Blood lead levels were determined at 4-years of age by atomic absorption spectrometry. A mixed model approach with restricted maximum likelihood procedures was used to assess the association of lead on language outcomes.

RESULTS

Longitudinal mixed model analyses suggested a negative effect of lead exposure on both receptive and expressive language, with the adverse outcomes of lead exposure appearing to become more prominent at 10 and 12 years. Higher caregiver vocabulary was positively associated with child's language scores whereas caregiver psychological distress appeared to negatively affect language scores. Prenatal drug exposure was not related to the effects of lead on language skills.

CONCLUSIONS

These findings suggest that elevated blood lead levels occurring early in life may be associated with poorer language skills at older ages. A language rich environment may minimize the negative influence of early lead exposure on language skills, with psychological distress seemingly exacerbating the negative outcome.

Correcting Model Fit Criteria for Small Sample Latent Growth Models With Incomplete Data

To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor small sample performance of these global data-model fit criteria and three post hoc small sample corrections have been proposed and shown to perform well with complete data. However, these corrections use sample size in their computation-whose value is unclear when missing data are accommodated with full information maximum likelihood, as is common with LGMs. A simulation is provided to demonstrate the inadequacy of these small sample corrections in the near ubiquitous situation in growth modeling where data are incomplete. Then, a missing data correction for the small sample correction equations is proposed and shown through a simulation study to perform well in various conditions found in practice. An applied developmental psychology example is then provided to demonstrate how disregarding missing data in small sample correction equations can greatly affect assessment of global data-model fit.

Urbanization and international trade and investment policies as determinants of noncommunicable diseases in Sub-Saharan Africa

There are three dominant globalization pathways affecting noncommunicable diseases in Sub-Saharan Africa (SSA): urbanization, trade liberalization, and investment liberalization. Urbanization carries potential health benefits due to improved access to an increased variety of food imports, although for the growing number of urban poor, this has often meant increased reliance on cheap, highly processed food commodities. Reduced barriers to trade have eased the importation of such commodities, while investment liberalization has increased corporate consolidation over global and domestic food chains. Higher profit margins on processed foods have promoted the creation of ‘obesogenic’ environments, which through progressively integrated global food systems have been increasingly ‘exported’ to developing nations. This article explores globalization processes, the food environment, and dietary health outcomes in SSA through the use of trend analyses and structural equation modelling. The findings are considered in the context of global barriers and facilitators for healthy public policy.

Principled missing data methods for researchers

The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.