Body mass index trajectories and predictors among 3rd to 12th graders using growth curve mixture modeling the Child and Adolescent Trial for Cardiovascular Health (CATCH) study

This dissertation examined body mass index (BMI) growth trajectories and the effects of gender, ethnicity, dietary intake, and physical activity (PA) on BMI growth trajectories among 3rd to 12th graders (9-18 years of age). Growth curve model analysis was performed using data from The Child and Adolescent Trial for Cardiovascular Health (CATCH) study. The study population included 2909 students who were followed up from grades 3-12. The main outcome was BMI at grades 3, 4, 5, 8, and 12. ^ The results revealed that BMI growth differed across two distinct developmental periods of childhood and adolescence. Rate of BMI growth was faster in middle childhood (9-11 years old or 3rd - 5th grades) than in adolescence (11-18 years old or 5th - 12th grades). Students with higher BMI at 3rd grade (baseline) had faster rates of BMI growth. Three groups of students with distinct BMI growth trajectories were identified: high, average, and low. ^ Black and Hispanic children were more likely to be in the groups with higher baseline BMI and faster rates of BMI growth over time. The effects of gender or ethnicity on BMI growth differed across the three groups. The effects of ethnicity on BMI growth were weakened as the children aged. The effects of gender on BMI growth were attenuated in the groups with a large proportion of black and Hispanic children, i.e., “high” or “average” BMI trajectory group. After controlling for gender, ethnicity, and age at baseline, in the “high BMI trajectory”, rate of yearly BMI growth in middle childhood increased 0.102 for every 500 Kcals increase (p=0.049). No significant effects of percentage of energy from total fat and saturated fat on BMI growth were found. Baseline BMI increased 0.041 for every 30 minutes increased in moderate-to-vigorous PA (MVPA) in the “low BMI trajectory”, while Baseline BMI decreased 0.345 for every 30 minutes increased in vigorous PA (VPA) in the “high BMI trajectory”. ^ Childhood overweight and obesity interventions should start at the earliest possible ages, prior to 3rd grade and continue through grade school. Interventions should focus on all children, but specifically black and Hispanic children, who are more likely to be highest at-risk. Promoting VPA earlier in childhood is important for preventing overweight and obesity among children and adolescents. Interventions should target total energy intake, rather than only percentage of energy from total fat or saturated fat. ^

Prevalence of symptoms of neurotoxicity and potential occupational pesticide and solvent exposures among high school students in Starr County, Texas: A pilot study

Numerous harmful occupational exposures affect working teens in the United States. Teens working in agriculture and other heavy-labor industries may be at risk for occupational exposures to pesticides and solvents. The neurotoxicity of pesticides and solvents at high doses is well-known; however, the long term effects of these substances at low doses on occupationally exposed adolescents have not been well-studied. To address this research gap, a secondary analysis of cross-sectional data was completed in order to estimate the prevalence of self-reported symptoms of neurotoxicity among a cohort of high school students from Starr County, Texas, a rural area along the Texas-Mexico border. Multivariable linear regression was used to estimate the association between work status (i.e., no work, farm work, and non-farm work) and symptoms of neurotoxicity, while controlling for age, gender, Spanish speaking preference, inhalant use, tobacco use, and alcohol use. The sample included 1,208 students. Of these, the majority (85.84%) did not report having worked during the prior nine months compared to 4.80% who did only farm work, 6.21% who did only non-farm work, and 3.15% who did both types of work. On average, students reported 3.26 symptoms with a range from 0-16. The most commonly endorsed items across work status were those related to memory impairment. Adolescents employed in non-farm work jobs reported more neurotoxicity symptoms than those who reported that they did not work (Mean 4.31; SD 3.97). In the adjusted multivariable regression model, adolescents reporting non-farm work status reported an average of 0.77 more neurotoxicity symptoms on the Q16 than those who did not work (P = 0.031). The confounding variables included in the final model were all found to be factors significantly associated with report of neurotoxicity symptoms. Future research should examine the relationship between these variables and self-report of symptoms of neurotoxicity.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^