Add Health Wave IV Documentation: Prescription Medication Use

Data on respondent use of prescription and select over-the-counter (aspirin-containing and non-steroidal anti-inflammatory) medications were collected during the Wave IV in-home interview. The process of collecting the prescription medication data, therapeutically classifying the prescription medications, and structuring a file of the therapeutic classifications for dissemination to users is described below. The questions concerning use of aspirin-containing and non-steroidal anti-inflammatory medications are detailed in the Section 6 codebook of the in-home interview

Factors Associated With Ostomy Adjustment In People Living With An Intestinal Or Urinary Ostomy

poster abstractMore than 120,000 new ostomies, or surgically created openings through the abdomen for bowel or urinary elimination, are created annually in North America. Up to 80% of patients with a new ostomy experience ostomy-related complications that can interfere with adjusting to living with an ostomy and diminish quality of life. Short hospital stays and fragmented follow-up care make it difficult for people with new ostomies to adjust and find the support and resources they need. Little is known about factors that influence positive adjustment to a new ostomy. The purpose of this study was to explore demographic factors that may be associated with adjustment to living with an ostomy. Potentially eligible participants who had ostomy surgery in the past 24 months were identified from lists generated by wound, ostomy, and continence nurses in 5 hospitals affiliated with a major health system in Indiana. Introductory study letters were mailed to potentially eligible participants. Trained research assistants telephoned participants who did not call the office to decline in order to assess eligibility, explain the study, and answer questions. Quantitative and qualitative data were collected via telephone interviews from 203 participants and entered directly into the RedCap database. The Ostomy Adjustment Inventory-23 was used to assess adjustment to living with an ostomy. Data were analyzed using correlations, t-tests, and analyses of variance using the Statistical Package for the Social Sciences. Results indicated that participants with higher incomes had significantly better adjustment scores than those with lower incomes (p<.000). No other demographic variables were associated with ostomy adjustment. People with lower incomes may be at risk for poor adjustment after ostomy surgery. Additional support and education may be needed to enhance ostomy adjustment for people at risk. Future research is needed to develop and test the effectiveness of interventions to support ostomy adjustment

Study of interfacial stiffness ratio of a rough surface in contact using a spring model

This study proposes the use of a simple spring model that relates the interfacial stiffness with the complex reflection coefficient of ultrasound in a rough contact. The spring model cannot be directly related to the real area of contact as this depends on the amount, shape and distribution of contacting asperities. However, it is clear that the model provides a non-destructive tool to easily evaluate both longitudinal and shear interfacial stiffnesses and their ratio. Experimental findings indicate that the interfacial stiffness ratio K-tau/K-sigma determined during loading/unloading cycles is sensitive to the roughness level and load hysteresis. The results deviate from the theoretical available micromechanical models, indicating that actual contacting phenomenon is more complex and other variables needed are not accounted for by the models. (C) 2009 Elsevier B.V. All rights reserved

Construction of Wave IV dbGaP GWAS Sample Weight

This document provides a brief overview of the steps in constructing GWAS sample weights. It also reports results of some statistical analysis using the constructed weights. The total number of GWAS sample (N=9,975) who were actually assayed and number of those (N=12,234) who consented to be archived and assayed are different. When the proportions of subjects who have consented and who have actually been assayed are different for certain Add Health post-stratification domains, this might cause problems if we simply use grand sample weights for GWAS sample (Bethlehem 2002). We thus developed weights for this special sample

The National Longitudinal Study of Adolescent to Adult Health (Add Health) Sibling Pairs Genome-Wide Data

Here we provide a detailed description of the genome-wide information available on the National Longitudinal Study of Adolescent to Adult Health (Add Health) sibling pair subsample (Harris et al., 2012). A total of 2020 samples were genotyped (including duplicates) arising from 1946 Add Health individuals from the sibling pairs subsample. After various steps for quality control (QC) and quality assurance (QA), we have high quality genome-wide data available on 1,888 individuals. In this report, we first highlight theQC and QA steps that were taken to prune the data of poorly performing samples and genetic markers. We further estimate the pairwise biological relationships using genome-wide data and compare those estimates to the assumed relationships in Add Health. Additionally, using genome-wide data from knownregional reference populations from Europe, West Africa, North and South America, Japan and China, weestimate the relative genetic ancestry of the respondents. Finally, rather than conducting a traditional cross-sectional genome-wide association study (GWAS) of body mass index (BMI), we opted to utilize the extensivepublicly available genome-wide information to conduct a weighted genome-wide association study (GWAS) of longitudinal BMI while accounting for both family and ethnic variation

The intrafamilial transmission of rheumatoid arthritis--I : Design of the study

A sampling of 49 family clusters consisting of a key person with arthritis, his spouse, a sibling and the sibling's spouse, 2 cousins and an unrelated individual have been interviewed 3 times with regard to their arthritis and a variety of social and psychological factors. The sample has been drawn in part from a national random sample and in part from an arthritis clinic. The two subsamples have been found sufficiently homogeneous for combination and some of the strengths and limitations of the design have been discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32904/1/0000284.pd

Protecting Adolescents from Harm: Findings from the National Longitudinal Study on Adolescent Health.

Some children who are at highrisk for health-compromising behaviors successfully negotiate adolescence, avoiding the behaviors that predispose them to negative health outcomes; while others, relatively advantaged socially and economically, sustain significant morbidity as a consequence of their behaviors. These issues of vulnerability and resilience have stimulated an interest in the identification of protective factors in the lives of young people—factors that,if present, diminish the likelihood of negative health and social outcomes.20"26 Of the constellation of forces that influence adolescent health-risk behavior, the most fundamental are the social contexts in which adolescents are embed¬ded20;the family and school contexts are among the most critical. Yet, how adolescents' connections to these contexts shape their health-risk behaviors is poorly understood

Non-Response in Wave IV of the National Longitudinal Study of Adolescent Health

Non-response is a potential threat to the accuracy of estimates obtained from sample surveys and can be particularly difficult to avoid in longitudinal studies. The objective of this report is to investigate non-response and consequent bias in estimates for Wave IV of the National Longitudinal Study of Adolescent Health (Add Health). The Survey Research Unit at the University of North Carolina at Chapel Hill previously analyzed the non-response rates for the first three waves of Add Health. As shown in Chantala, Kalsbeek and Andraca, 2005, the total bias in Waves I, II, and III for 13 measures of health and risk behaviors rarely exceed 1%, which is small relative to the 20% to 80% prevalence rates for most of these measures. Results are similar for Wave IV. In this paper, first, we outline the Wave IV sampling design and results of the field work. Second, we characterize the non-response rates overall and stratified by a number of demographic variables. Next, we use data on the health risk measures reported by Wave IV responders and non-responders during their Wave I In-home interview to estimate total and relative bias due to non-response in Wave IV. We conclude with a discussion of Wave IV bias due to non-response

Add Health Wave IV Documentation: Cardiovascular and Anthropometric Measures

During the Wave II and III in-home interviews, Add Health collected measures of respondent height and weight. To better understand the social, behavioral and biological linkages in health trajectories as the Add Health cohort ages through adulthood, the Wave IV study design included an expanded set of anthropometric measures as well as several new measures of cardiovascular health. The Wave IV cardiovascular and anthropometric measures were collected in the following order: • Blood pressure cuff size • Systolic blood pressure (SBP, mmHg) • Diastolic blood pressure (DBP, mmHg) • Pulse rate (PR, beats/min) • Height (cm) • Weight (kg) • Waist circumference (cm); In addition, the Add Health Wave IV data set includes the following constructed measures, derived from those listed above: • Blood pressure classification1 • Pulse pressure (mmHg) • Mean arterial pressure (mmHg) • Body mass index (BMI, kg/m2) • BMI classification; This document summarizes the rationale, measurement, equipment, protocol and data cleaning procedures for each of the cardiovascular and anthropometric measures collected at Wave IV. It also documents how constructed variables were derived from the cardiovascular and anthropometric measures collected in the field. Documentation of other Wave IV biological measures, including metabolic, inflammatory, immune and genetic measures, will be provided in separate reports

Genotype, Childhood Maltreatment, and Their Interaction in the Etiology of Adult Antisocial Behaviors

BACKGROUND: Maltreatment by an adult or caregiver during childhood is a prevalent and important predictor of antisocial behaviors in adulthood. A functional promoter polymorphism in the monoamine oxidase A (MAOA) gene has been implicated as a moderating factor in the relationship between childhood maltreatment and antisocial behaviors. Although there have been numerous attempts at replicating this observation, results remain inconclusive. METHODS: We examined this gene-environment interaction hypothesis in a sample of 3356 white and 960 black men (aged 24-34) participating in the National Longitudinal Study of Adolescent Health. RESULTS: Primary analysis indicated that childhood maltreatment was a significant risk factor for later behaviors that violate rules and the rights of others (p .05). Power analyses indicated that these results were not due to insufficient statistical power. CONCLUSIONS: We could not confirm the hypothesis that MAOA genotype moderates the relationship between childhood maltreatment and adult antisocial behaviors