Biomedical subjects

Frank R Lawrence

Publications and source records attributed to Frank R Lawrence.

3 recordsLinked to original sources

PubMed2006

Comparative strategies for using cluster analysis to assess dietary patterns.

OBJECTIVES: To characterize dietary patterns using two different cluster analysis strategies. DESIGN: In this cross-sectional study, diet information was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. Demographic, health, and anthropometric data were collected via home visit. SUBJECTS: One hundred seventy-nine community-dwelling adults, aged 66 to 87 years, in rural Pennsylvania. STATISTICAL ANALYSIS: Cluster analysis was performed. RESULTS: The methods differed in the food subgroups that clustered together. Both methods produced clusters that had significant differences in overall diet quality as assessed by Healthy Eating Index (HEI) scores. The clusters with higher HEI scores contained significantly higher amounts of most micronutrients. Both methods consistently clustered subgroups with high energy contribution (eg, fats and oils and dairy desserts) with a lower HEI score. Clusters resulting from the percent energy method were less likely to differentiate fruit and vegetable subgroups. The higher diet quality dietary pattern derived from the number of servings method resulted in more favorable weight status. CONCLUSIONS: Cluster analysis of food subgroups using two different methods on the same data yielded similarities and dissimilarities in dietary patterns. Dietary patterns characterized by the number of servings method of analysis provided stronger association with weight status and was more sensitive to fruit and vegetable intake with regard to a more healthful dietary pattern within this sample. Public health recommendations should evaluate the methodology used to derive dietary patterns.

Aged↗

PubMed2005

Combining in-school and community-based media efforts: reducing marijuana and alcohol uptake among younger adolescents.

This study tests the impact of an in-school mediated communication campaign based on social marketing principles, in combination with a participatory, community-based media effort, on marijuana, alcohol and tobacco uptake among middle-school students. Eight media treatment and eight control communities throughout the US were randomly assigned to condition. Within both media treatment and media control communities, one school received a research-based prevention curriculum and one school did not, resulting in a crossed, split-plot design. Four waves of longitudinal data were collected over 2 years in each school and were analyzed using generalized linear mixed models to account for clustering effects. Youth in intervention communities (N = 4,216) showed fewer users at final post-test for marijuana [odds ratio (OR) = 0.50, P = 0.019], alcohol (OR = 0.40, P = 0.009) and cigarettes (OR = 0.49, P = 0.039), one-tailed. Growth trajectory results were significant for marijuana (P = 0.040), marginal for alcohol (P = 0.051) and non-significant for cigarettes (P = 0.114). Results suggest that an appropriately designed in-school and community-based media effort can reduce youth substance uptake. Effectiveness does not depend on the presence of an in-school prevention curriculum.

Adolescent↗

PubMed2003

Factorial invariance in preventive intervention: modeling the development of intelligence in low birth weight, preterm infants.

The paper addresses the issue of measurement invariance in the analysis of preventive intervention data. Procedures for testing the invariance of covariance matrices, measurement models, and structural growth models are illustrated using data from an early intervention program for low birth weight, preterm infants. Intelligence Quotient (IQ) measured at 5 time points was modeled using piecewise growth curve analysis. Change over time in children's cognitive development was modeled separately for the pre- and postintervention periods. The first period was during the intervention, which lasted from birth through age 3 years. The second was during the postintervention period, which included follow-up assessments at ages 5 and 8 years. The analytical approach is illustrated with findings indicating that the development of IQ was different for higher low birth weight (HLBW: 2001-2500 g) and lower low birth weight (LLBW: <2001 g) infants. Examination of the effects of covariates on the IQ trajectories associated with the two birth weight groups indicated differential effects on IQ depending on the period of development and birth weight. Neonatal health and maternal age were predictors of IQ change in the LLBW group whereas maternal education was related to change in the HLBW group. During the posttreatment period, only Hispanic ethnicity and treatment group status were related to change. Hispanic participants exhibited an accelerated trajectory between program end and age 8 years. Intervention recipients exhibited a flatter trajectory during the same time period whereas participants in the control group exhibited an accelerated development, essentially catching up with their counterparts in the treatment group. Results clearly illustrate the utility of invariance testing in modeling developmental trajectories in response to preventive intervention.

Child↗