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. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Modeling Techniques for the High-Resolution Interpretation of Cryo-Electron Microscopy Reconstructions

Essential biological processes are governed by organized, dynamic interactions between multiple biomolecular systems. Complexes are thus formed to enable the biological function and get dissembled as the process is completed. Examples of such processes include the translation of the messenger RNA into protein by the ribosome, the folding of proteins by chaperonins or the entry of viruses in host cells. Understanding these fundamental processes by characterizing the molecular mechanisms that enable then, would allow the (better) design of therapies and drugs. Such molecular mechanisms may be revealed trough the structural elucidation of the biomolecular assemblies at the core of these processes. Various experimental techniques may be applied to investigate the molecular architecture of biomolecular assemblies. High-resolution techniques, such as X-ray crystallography, may solve the atomic structure of the system, but are typically constrained to biomolecules of reduced flexibility and dimensions. In particular, X-ray crystallography requires the sample to form a three dimensional (3D) crystal lattice which is technically di‑cult, if not impossible, to obtain, especially for large, dynamic systems. Often these techniques solve the structure of the different constituent components within the assembly, but encounter difficulties when investigating the entire system. On the other hand, imaging techniques, such as cryo-electron microscopy (cryo-EM), are able to depict large systems in near-native environment, without requiring the formation of crystals. The structures solved by cryo-EM cover a wide range of resolutions, from very low level of detail where only the overall shape of the system is visible, to high-resolution that approach, but not yet reach, atomic level of detail. In this dissertation, several modeling methods are introduced to either integrate cryo-EM datasets with structural data from X-ray crystallography, or to directly interpret the cryo-EM reconstruction. Such computational techniques were developed with the goal of creating an atomic model for the cryo-EM data. The low-resolution reconstructions lack the level of detail to permit a direct atomic interpretation, i.e. one cannot reliably locate the atoms or amino-acid residues within the structure obtained by cryo-EM. Thereby one needs to consider additional information, for example, structural data from other sources such as X-ray crystallography, in order to enable such a high-resolution interpretation. Modeling techniques are thus developed to integrate the structural data from the different biophysical sources, examples including the work described in the manuscript I and II of this dissertation. At intermediate and high-resolution, cryo-EM reconstructions depict consistent 3D folds such as tubular features which in general correspond to alpha-helices. Such features can be annotated and later on used to build the atomic model of the system, see manuscript III as alternative. Three manuscripts are presented as part of the PhD dissertation, each introducing a computational technique that facilitates the interpretation of cryo-EM reconstructions. The first manuscript is an application paper that describes a heuristics to generate the atomic model for the protein envelope of the Rift Valley fever virus. The second manuscript introduces the evolutionary tabu search strategies to enable the integration of multiple component atomic structures with the cryo-EM map of their assembly. Finally, the third manuscript develops further the latter technique and apply it to annotate consistent 3D patterns in intermediate-resolution cryo-EM reconstructions. The first manuscript, titled An assembly model for Rift Valley fever virus, was submitted for publication in the Journal of Molecular Biology. The cryo-EM structure of the Rift Valley fever virus was previously solved at 27Å-resolution by Dr. Freiberg and collaborators. Such reconstruction shows the overall shape of the virus envelope, yet the reduced level of detail prevents the direct atomic interpretation. High-resolution structures are not yet available for the entire virus nor for the two different component glycoproteins that form its envelope. However, homology models may be generated for these glycoproteins based on similar structures that are available at atomic resolutions. The manuscript presents the steps required to identify an atomic model of the entire virus envelope, based on the low-resolution cryo-EM map of the envelope and the homology models of the two glycoproteins. Starting with the results of the exhaustive search to place the two glycoproteins, the model is built iterative by running multiple multi-body refinements to hierarchically generate models for the different regions of the envelope. The generated atomic model is supported by prior knowledge regarding virus biology and contains valuable information about the molecular architecture of the system. It provides the basis for further investigations seeking to reveal different processes in which the virus is involved such as assembly or fusion. The second manuscript was recently published in the of Journal of Structural Biology (doi:10.1016/j.jsb.2009.12.028) under the title Evolutionary tabu search strategies for the simultaneous registration of multiple atomic structures in cryo-EM reconstructions. This manuscript introduces the evolutionary tabu search strategies applied to enable a multi-body registration. This technique is a hybrid approach that combines a genetic algorithm with a tabu search strategy to promote the proper exploration of the high-dimensional search space. Similar to the Rift Valley fever virus, it is common that the structure of a large multi-component assembly is available at low-resolution from cryo-EM, while high-resolution structures are solved for the different components but lack for the entire system. Evolutionary tabu search strategies enable the building of an atomic model for the entire system by considering simultaneously the different components. Such registration indirectly introduces spatial constrains as all components need to be placed within the assembly, enabling the proper docked in the low-resolution map of the entire assembly. Along with the method description, the manuscript covers the validation, presenting the benefit of the technique in both synthetic and experimental test cases. Such approach successfully docked multiple components up to resolutions of 40Å. The third manuscript is entitled Evolutionary Bidirectional Expansion for the Annotation of Alpha Helices in Electron Cryo-Microscopy Reconstructions and was submitted for publication in the Journal of Structural Biology. The modeling approach described in this manuscript applies the evolutionary tabu search strategies in combination with the bidirectional expansion to annotate secondary structure elements in intermediate resolution cryo-EM reconstructions. In particular, secondary structure elements such as alpha helices show consistent patterns in cryo-EM data, and are visible as rod-like patterns of high density. The evolutionary tabu search strategy is applied to identify the placement of the different alpha helices, while the bidirectional expansion characterizes their length and curvature. The manuscript presents the validation of the approach at resolutions ranging between 6 and 14Å, a level of detail where alpha helices are visible. Up to resolution of 12 Å, the method measures sensitivities between 70-100% as estimated in experimental test cases, i.e. 70-100% of the alpha-helices were correctly predicted in an automatic manner in the experimental data. The three manuscripts presented in this PhD dissertation cover different computation methods for the integration and interpretation of cryo-EM reconstructions. The methods were developed in the molecular modeling software Sculptor (http://sculptor.biomachina.org) and are available for the scientific community interested in the multi-resolution modeling of cryo-EM data. The work spans a wide range of resolution covering multi-body refinement and registration at low-resolution along with annotation of consistent patterns at high-resolution. Such methods are essential for the modeling of cryo-EM data, and may be applied in other fields where similar spatial problems are encountered, such as medical imaging.

Exploring the Association Between Household Food Insecurity, Parental Self-Efficacy, and Fruit and Vegetable Parenting Practices Among Parents of 5- to 8-Year-Old Overweight Children

Background: Food insecurity may negatively impact children’s nutritional status by affecting parenting quality. Because parents have a strong influence on their children’s eating and food choices, examining the effects of food insecurity on parenting may provide important insights into obesity prevention efforts. Objectives: This study explored whether food insecurity was associated with parental self-efficacy and parenting practices related to fruit and vegetable consumption. Methods: Secondary analysis was performed using baseline data from 31 mothers of 5-8 year old overweight or obese children who had participated in a pilot obesity treatment program. Household food security status, fruit and vegetable parental self-efficacy (modeling/socialization, planning/encouraging and availability/accessibility) and fruit and vegetable parenting practices (structure, responsiveness, non-directive control, and external control) were assessed using validated measures. Students' t-test investigated differences in subscales by food security status. Results: There were no significant differences in fruit and vegetable parenting practices and parental self-efficacy between food secure and insecure groups. There was a trend towards a decrease in parental self-efficacy for making fruit and vegetables available in the home among food insecure parents (p=.06). Conclusions: In this small sample no significant associations were found between food insecurity and fruit and vegetable parenting practices and parental self-efficacy. However, the trend observed in this exploratory analysis supports further hypothesis-driven research with a larger sample size able to detect more subtle differences.

Structural equation modeling of the medical outcome study: Short Form 36 (SF-36)

The factorial validity of the SF-36 was evaluated using confirmatory factor analysis (CFA) methods, structural equation modeling (SEM), and multigroup structural equation modeling (MSEM). First, the measurement and structural model of the hypothesized SF-36 was explicated. Second, the model was tested for the validity of a second-order factorial structure, upon evidence of model misfit, determined the best-fitting model, and tested the validity of the best-fitting model on a second random sample from the same population. Third, the best-fitting model was tested for invariance of the factorial structure across race, age, and educational subgroups using MSEM.^ The findings support the second-order factorial structure of the SF-36 as proposed by Ware and Sherbourne (1992). However, the results suggest that: (a) Mental Health and Physical Health covary; (b) general mental health cross-loads onto Physical Health; (c) general health perception loads onto Mental Health instead of Physical Health; (d) many of the error terms are correlated; and (e) the physical function scale is not reliable across these two samples. This hierarchical factor pattern was replicated across both samples of health care workers, suggesting that the post hoc model fitting was not data specific. Subgroup analysis suggests that the physical function scale is not reliable across the "age" or "education" subgroups and that the general mental health scale path from Mental Health is not reliable across the "white/nonwhite" or "education" subgroups.^ The importance of this study is in the use of SEM and MSEM in evaluating sample data from the use of the SF-36. These methods are uniquely suited to the analysis of latent variable structures and are widely used in other fields. The use of latent variable models for self reported outcome measures has become widespread, and should now be applied to medical outcomes research. Invariance testing is superior to mean scores or summary scores when evaluating differences between groups. From a practical, as well as, psychometric perspective, it seems imperative that construct validity research related to the SF-36 establish whether this same hierarchical structure and invariance holds for other populations.^ This project is presented as three articles to be submitted for publication. ^

A LAPLACE TRANSFORM PAIR MODEL TO DETERMINE BREMSSTRAHLUNG SPECTRA FROM ATTENUATION DATA (RADIATION, X-RAY, SPECTRAL MODELING)

Every x-ray attenuation curve inherently contains all the information necessary to extract the complete energy spectrum of a beam. To date, attempts to obtain accurate spectral information from attenuation data have been inadequate.^ This investigation presents a mathematical pair model, grounded in physical reality by the Laplace Transformation, to describe the attenuation of a photon beam and the corresponding bremsstrahlung spectral distribution. In addition the Laplace model has been mathematically extended to include characteristic radiation in a physically meaningful way. A method to determine the fraction of characteristic radiation in any diagnostic x-ray beam was introduced for use with the extended model.^ This work has examined the reconstructive capability of the Laplace pair model for a photon beam range of from 50 kVp to 25 MV, using both theoretical and experimental methods.^ In the diagnostic region, excellent agreement between a wide variety of experimental spectra and those reconstructed with the Laplace model was obtained when the atomic composition of the attenuators was accurately known. The model successfully reproduced a 2 MV spectrum but demonstrated difficulty in accurately reconstructing orthovoltage and 6 MV spectra. The 25 MV spectrum was successfully reconstructed although poor agreement with the spectrum obtained by Levy was found.^ The analysis of errors, performed with diagnostic energy data, demonstrated the relative insensitivity of the model to typical experimental errors and confirmed that the model can be successfully used to theoretically derive accurate spectral information from experimental attenuation data. ^

FAMILY MEDICINE RESIDENT SURVEY ON HEALTH PROMOTION (PERSONAL EXPECTATIONS, BEHAVIORS, PERCEIVED SELF-EFFICACY, COGNITIVE ASSESSMENT, OUTCOME EXPECTANCY)

This study was designed to test the theoretical predictors of personal efficacy expectations among family medicine resident physicians for helping their patients change thirteen high risk health behaviors. A survey questionnaire was sent to 781 family medicine residents in the six state south central region. The response rate was 60 percent. The hypothesized relationship between lower levels of difficulty and higher personal efficacy expectations was supported by the data. Effort was a significant predictor of perceived self efficacy for health behaviors considered less difficult to change. Situational support did not prove to be a significant predictor for many of the health behaviors. Rate and pattern of success were consistent and significant predictors of perceived self efficacy for helping patients change all thirteen of the health behaviors. Modeling of effective methods by faculty was a significant predictor of efficacy expectations for several but not all of the behaviors. Personal modeling was a significant predictor of perceived efficacy for helping patients change behaviors related to alcohol misuse and exercise. The respondents personally modeled positive health behaviors more consistently than their older colleagues or the general population.^ The results of this study lend substantially to the usefulness of the cognitive-behavioral theory of perceived self efficacy and provide a mechanism for assessing the predictors of personal efficacy expectations of family medicine resident physicians. The findings are expected to have direct implications for faculty to institute systematic programs of interventions designed to increase residents' perceptions of efficacy in facilitating more positive health behaviors among their patients. ^

Parent modeling and child food consumption

Obesity, among both children and adults, is a growing public health epidemic. One area of interest relates to how and why obesity is developing at such a rapid pace among children. Despite a broad consensus about how controlling feeding practices relate to child food consumption and obesity prevalence, much less is known about how non-controlling feeding practices, including modeling, relate to child food consumption. This study investigates how different forms of parent modeling (no modeling, simple modeling, and enthusiastic modeling) and parent adiposity relate to child food consumption, food preferences, and behaviors towards foods. Participants in this experimental study were 65 children (25 boys and 40 girls) aged 3-9 and their parents. Each parent was trained on how to perform their assigned modeling behavior towards a food identified as neutral (not liked, nor disliked) by their child during a pre-session food-rating task. Parents performed their assigned modeling behavior when cued during a ten-minute observation period with their child. Child food consumption (pieces eaten, grams eaten, and calories consumed) was measured and food behaviors (positive comments toward food and food requests) were recorded by event-based coding. After the session, parents self-reported on their height and weight, and children completed a post-session food-rating task. Results indicate that parent modeling (both simple and enthusiastic forms) did not significantly relate to child food consumption, food preferences, or food requests. However, enthusiastic modeling significantly increased the number of positive food comments made by children. Children's food consumption in response to parent modeling did not differ based on parent obesity status. The practical implications of this study are discussed, along with its strengths and limitations, and directions for future research.^

Self -management behavior in patients with cystic fibrosis

Self-management is being promoted in cystic fibrosis (CF). However, it has not been well studied. Principal aims of this research were (1) to evaluate psychometric properties of a CF disease status measure, the NIH Clinical Score; (2) to develop and validate a measure of self-management behavior, the SMQ-CF scale, and (3) to examine the relation between self-management and disease status in CF patients over two years.^ In study 1, NIH Clinical Scores for 200 patients were used. The scale was examined for internal consistency, interrater reliability, and content validity using factor analysis. The Cronbach's alpha (.81) and interrater reliability (.90) for the total scale were high. General scale items were less reliable. Factor analysis indicated that most of the variance in disease status is accounted for by Factor 1 which consists of pulmonary disease items.^ The SMQ-CF measures the performance of CF self-management. Pilot testing was done with 98 CF primary caregivers. Internal consistency reliability, social desirability bias, and content validity using factor analysis were examined. Internal consistency was good (alpha =.95). Social desirability correlation was low (r =.095). Twelve factors identified were consistent with conceptual groupings of behaviors. Around two hundred caregivers from two CF centers were surveyed and multivariate analysis of variance was used to assess construct validity. Results confirmed expected relations between self-management, patient age, and disease status. Patient age accounted for 50% and disease status 18% of the variance in the SMQ-CF scale.^ It was hypothesized that self-management would positively affect future disease status. Data from 199 CF patients (control and education intervention groups) were examined. Models of hypothesized relations were tested using LISREL structural equation modeling. Results indicated that the relations between baseline self-management and Time 1 disease status were not significant. Significant relations were observed in self-management behaviors from time 1 to time 2 and patterns of significant relations differed between the two groups.^ This research has contributed to refinements in the ability to measure self-management behavior and disease status outcomes in cystic fibrosis. In addition, it provides the first steps in exploratory behavioral analysis with regard to self-management in this disease. ^