The influence of maternal health literacy and child's age on participation in social welfare programs.

The objective of this study is to determine the influence of maternal health literacy and child's age on participation in social welfare programs benefiting children. In a longitudinal prospective cohort study of 560 Medicaid-eligible mother-infant dyads recruited in Philadelphia, maternal health literacy was assessed using the test of functional health literacy in adults (short version). Participation in social welfare programs [Temporary Assistance to Needy Families (TANF), Supplemental Nutrition Assistance Program (SNAP), Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), child care subsidy, and public housing] was self-reported at child's birth, and at the 6, 12, 18, 24 month follow-up interviews. Generalized estimating equations quantified the strength of maternal health literacy as an estimator of program participation. The mothers were primarily African-Americans (83%), single (87%), with multiple children (62%). Nearly 24% of the mothers had inadequate or marginal health literacy. Children whose mothers had inadequate health literacy were less likely to receive child care subsidy (adjusted OR = 0.54, 95% CI 0.34-0.85) than children whose mothers had adequate health literacy. Health literacy was not a significant predictor for TANF, SNAP, WIC or housing assistance. The predicted probability for participation in all programs decreased from birth to 24 months. Most notably, predicted WIC participation declined rapidly after age one. During the first 24 months, mothers with inadequate health literacy could benefit from simplified or facilitated child care subsidy application processes. Targeted outreach and enrollment efforts conducted by social welfare programs need to take into account the changing needs of families as children age.

Modeling considerations for using expression data from multiple species.

Although genome-wide expression data sets from multiple species are now more commonly generated, there have been few studies on how to best integrate this type of correlated data into models. Starting with a single-species, linear regression model that predicts transcription factor binding sites as a case study, we investigated how best to take into account the correlated expression data when extending this model to multiple species. Using a multivariate regression model, we accounted for the phylogenetic relationships among the species in two ways: (i) a repeated-measures model, where the error term is constrained; and (ii) a Bayesian hierarchical model, where the prior distributions of the regression coefficients are constrained. We show that both multiple-species models improve predictive performance over the single-species model. When compared with each other, the repeated-measures model outperformed the Bayesian model. We suggest a possible explanation for the better performance of the model with the constrained error term.

Prediction of motifs based on a repeated-measures model for integrating cross-species sequence and expression data.

De novo identification of transcription factor binding sites (TFBS) is a challenging computational problem because TFBSs are relatively short sequences buried in long genomic regions. Earlier methods incorporated genome-wide expression data and promoter sequences into a linear-model framework, regressing expression on counts of putative TFBSs in promoters for a single species. More recently, it has been shown that examining sequence data across multiple species improves the prediction of TFBSs. In this work, we describe an extension of the single-species, linear-model framework for the analysis of paired cross-species sequence and expression data. A repeated measures model for gene-expression measurements across species is used, accounting for phylogenetic relationships among species through the error covariance structure. This multiple-species algorithm is applied to a data set of four yeast species grown under heat-shock conditions and comparisons are made to the single species algorithm. Using evaluations based on transcription factor binding strength and an independent source of expression data, we find the multiple species results show an improvement in the prediction of TFBS.

Factor structure and concurrent validity of the Drug Use Screening Inventory in a community adolescent sample.

A variety of screening instruments for adolescent alcohol and drug abuse have been designed to be used within a clinical setting. This study adapted one of these instruments, the Drug Use Screening Inventory (DUSI), to be used within behavioral genetic or other epidemiological research on nonclinical samples. A primary goal of this study was to obtain a factor structure and set of subscales that might predict experimental and regular use of alcohol and drugs in a community sample. A secondary goal was to define subscales in a way that facilitated our interpretation and understanding of pathways to substance use and abuse. We investigated the underlying factor structure of items from four of the DUSI domains using a principal components analysis of the responses from 1266 adolescents from the Colorado Adolescent Twin Study. The resulting set of six subscales shows adequate internal consistency and provides an easily interpretable research tool for investigating etiological relationships between substance use problems and other risk behaviors.

Genetic and environmental analysis of behavioral risk factors for adolescent drug use in a community twin sample.

We investigated the etiology of six problem behaviors that might facilitate an understanding of behavioral pathways to substance use and abuse in adolescents. These behavioral measures, classified as Conduct Problems, Hyperactivity, School Problems, Low Self-esteem, Neuroticism, and Social Withdrawal were the result of a previously reported (Siewert et al., 2003) modification of the Drug Use Screening Inventory (DUSI; Tarter, 1990; Tarter & Hegedus, 1991). We developed these measures as interpretable components of risk for substance use and abuse in a community based sample of 633 twin pairs, who were under the legal drinking age of 21 (mean age = 15.0 years). Using multivariate analyses, model comparisons indicated that these six behavioral measures could be thought of as two heritable, and genetically distinct, dimensions of problem behavior. Two closely competing models resulted from our analyses. The best fitting model hypothesized a general genetic factor loading on all 6 behavioral measures with a second genetic factor loading on only the three internalizing behavioral measures with loadings of 0.25-0.59 and 0.26-0.44, respectively. A second model, which fit the data almost as well, hypothesized one genetic factor loading only on the externalizing behavioral measures, and a second genetic factor loading only on the internalizing behavioral measures, with a correlation between the two latent factors of 0.75. Because our analyses show that there are two genetically distinct factors influencing these six problem behaviors, we anticipate that there may be different patterns of relationship of these factors to risk for substance use, abuse, and dependence.

Genetic influences on vulnerability to, and protective factors for, adolescent drinking.

Using behavioral genetic analyses, we investigated and present a possible relationship between adolescent alcohol use and six domains of common problem behaviors in a community-based sample of 633 twin pairs who were under the legal drinking age of 21 (mean age = 15.0 years). The underlying etiology of the six problem behavioral domains, classified as conduct problems, hyperactivity, school problems, low self-esteem, neuroticism, and social withdrawal, was previously described (Siewert et al., 2003) as two heritable and genetically distinct dimensions of problem behavior. We took the two best-fitting models from that study (one that proposed a generalized behavior problem factor along with an internalizing behavior factor, and one that proposed an externalizing behavior factor along with an internalizing behavior factor) and extended the analyses in this study to include an index of alcohol use. Our results suggest that there is a strong genetic relationship between adolescent alcohol use and a broad spectrum of both externalizing and internalizing behavioral problems. The individual who seems to be at risk for either generalized or specifically externalizing behavioral problems is also at risk for adolescent alcohol use. However, the individual who exhibits internalizing problem behaviors appears to be protected from adolescent alcohol use. We propose that adolescent alcohol consumption needs to be understood in the context of these genetically influenced externalizing and internalizing propensities.