Non-Response in Wave III of the Add Health Study

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 purpose of this report is to investigate non-response in Wave III of Add Health and its influence on study results. Non-response in earlier waves of Add Health has been investigated by the Survey Research Unit at the University of North Carolina. Findings showed that total bias for 13 measures of health and risk behaviors rarely exceed 1% in either Wave I or Wave II, which is small relative to the 20% to 80% prevalence rates for most of these measures. In the following section, we present an overview of the Wave III sampling plan and results of the field work. Next, we characterize the non-response found in the original sampling variables. We then take advantage of the longitudinal design of Add Health to estimate total and relative bias on demographics and a variety of health and risk behaviors reported by both non-responders and responders during their Wave I In-home Interview. We conclude with a discussion of how the bias caused by non-response can be minimized during future waves of data collection

Predictors of Nonresponse in a Longitudinal Survey of Adolescents

Research in this study focuses on two related aspects of unit nonresponse (nonresponse by sampled members of study populations) in the rounds of the National Longitudinal Study of Adolescent Health (Add Health) (Chantala and Tabor, 1999): (i) round-specific nonresponse bias and its component contributions, and (ii) the statistical utility of alternative approaches to adjusting sample weights for nonresponse. This work is part of four research studies funded by CDC-NCHS, at the UNC Center for Health Statistics Research

Effects of Nonresponse on the Mean Squared Error of Estimates from a Longitudinal Study

Research in this study focuses on two related aspects of unit nonresponse (nonresponse by sampled members of study populations) in the rounds of the National Longitudinal Study of Adolescent Health (Add Health) (Chantala and Tabor, 1999): (i) round-specific nonresponse bias and its component contributions, and (ii) the statistical utility of alternative approaches to adjusting sample weights for nonresponse. This work is part of four research studies funded by CDC-NCHS, at the UNC Center for Health Statistics Research. Nonrespondents in surveys can be classified according to the reason for nonresponse (Lessler and Kalsbeek, 1992): 1) Not Solicited (NS): Sample members are not solicited as perhaps their address is unknown, or they are out of the country; 2) Solicited but Unable (SUA): Sample members are contacted but decline to participate based on inability. Reasons include physical or language limitations; 3) Solicited but Unwilling (SUW): Sample members are contacted but refuse to participate for reasons such as lack of time or, apathy; and 4) Other Nonrespondents (OTH): Sample nonrespondents give a reason that does not fit in any of the previous categories. Examples are lost schedules and partial respondents Response outcome information and data to obtain 13 different measures of health risk from Add Health are used to accomplish two main tasks in this study. First, we estimate the round-specific nonresponse bias and its component contributions corresponding to the four nonresponse categories described. The sign (negative or positive) of these components and the offsetting effects of some components on the overall bias is of particular interest. Second, we compare the statistical effects of alternate sample adjustments for nonresponse on the bias and variance of study estimates. It is important to note here that we are examining the effects of nonresponse in IH1 and IH2 separately, and not the cumulative effects of nonresponse through these rounds

Lifetime risk of symptomatic knee osteoarthritis

Objective—To estimate the lifetime risk of symptomatic knee osteoarthritis (OA), overall and stratified by sex, race, education, history of knee injury, and body mass index (BMI). Methods—The lifetime risk of symptomatic OA in at least 1 knee was estimated from logistic regression models with generalized estimating equations among 3,068 participants of the Johnston County Osteoarthritis Project, a longitudinal study of black and white women and men age ≥45 years living in rural North Carolina. Radiographic, sociodemographic, and symptomatic knee data measured at baseline (1990–1997) and first followup (1999–2003) were analyzed. Results—The lifetime risk of symptomatic knee OA was 44.7% (95% confidence interval [95% CI] 40.0–49.3%). Cohort members with history of a knee injury had a lifetime risk of 56.8% (95% CI 48.4–65.2%). Lifetime risk rose with increasing BMI, with a risk of 2 in 3 among those who were obese. Conclusion—Nearly half of the adults in Johnston County will develop symptomatic knee OA by age 85 years, with lifetime risk highest among obese persons. These current high risks in Johnston County may suggest similar risks in the general US population, especially given the increase in 2 major risk factors for knee OA, aging, and obesity. This underscores the immediate need for greater use of clinical and public health interventions, especially those that address weight loss and self-management, to reduce the impact of having knee OA

Research Priorities for FCTC Articles 20, 21, and 22: Surveillance/Evaluation and Information Exchange

Framework Convention on Tobacco Control (FCTC) Articles 20, 21, and 22 call for strong monitoring and reporting of tobacco use and factors influencing use and disease (Articles 20 and 21) and for collaboration among the Parties and relevant organizations to share resources, knowledge, and expertise on all relevant tobacco control strategies (Article 22)

Beneficiary Survey-Based Feedback on New Medicare Informational Materials

In response to the Balanced Budget Act (BBA) of 1997, the Center for Medicare & Medicaid Services (CMS) initiated a massive information and education campaign to promote effective health plan decisionmaking. Early results suggest that the pilot version of the Medicare & You handbook and other new Medicare informational materials were viewed favorably overall. Despite their limitations, most beneficiaries found the information useful. The longer, more comprehensive materials were not perceived to be more useful than the shorter, less complicated version. Additional research is needed to determine which subgroups of beneficiaries may need more and, possibly less, information

A Genome-Wide Association Study of Red-Blood Cell Fatty Acids and Ratios Incorporating Dietary Covariates: Framingham Heart Study Offspring Cohort

Recent analyses have suggested a strong heritable component to circulating fatty acid (FA) levels; however, only a limited number of genes have been identified which associate with FA levels. In order to expand upon a previous genome wide association study done on participants in the Framingham Heart Study Offspring Cohort and FA levels, we used data from 2,400 of these individuals for whom red blood cell FA profiles, dietary information and genotypes are available, and then conducted a genome-wide evaluation of potential genetic variants associated with 22 FAs and 15 FA ratios, after adjusting for relevant dietary covariates. Our analysis found nine previously identified loci associated with FA levels (FADS, ELOVL2, PCOLCE2, LPCAT3, AGPAT4, NTAN1/PDXDC1, PKD2L1, HBS1L/MYB and RAB3GAP1/MCM6), while identifying four novel loci. The latter include an association between variants in CALN1 (Chromosome 7) and eicosapentaenoic acid (EPA), DHRS4L2(Chromosome 14) and a FA ratio measuring delta-9-desaturase activity, as well as two loci associated with less well understood proteins. Thus, the inclusion of dietary covariates had a modest impact, helping to uncover four additional loci. While genome-wide association studies continue to uncover additional genes associated with circulating FA levels, much of the heritable risk is yet to be explained, suggesting the potential role of rare genetic variation, epistasis and gene-environment interactions on FA levels as well. Further studies are needed to continue to understand the complex genetic picture of FA metabolism and synthesis

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

Indoor Air Pollutants and Health in the United Arab Emirates

Background: Comprehensive global data on the health effects of indoor air pollutants are lacking. There are few large population-based multi–air pollutant health assessments. Further, little is known about indoor air health risks in the Middle East, especially in countries undergoing rapid economic development

Genetic Association Analysis under Complex Survey Sampling: The Hispanic Community Health Study/Study of Latinos

The cohort design allows investigators to explore the genetic basis of a variety of diseases and traits in a single study while avoiding major weaknesses of the case-control design. Most cohort studies employ multistage cluster sampling with unequal probabilities to conveniently select participants with desired characteristics, and participants from different clusters might be genetically related. Analysis that ignores the complex sampling design can yield biased estimation of the genetic association and inflation of the type I error. Herein, we develop weighted estimators that reflect unequal selection probabilities and differential nonresponse rates, and we derive variance estimators that properly account for the sampling design and the potential relatedness of participants in different sampling units. We compare, both analytically and numerically, the performance of the proposed weighted estimators with unweighted estimators that disregard the sampling design. We demonstrate the usefulness of the proposed methods through analysis of MetaboChip data in the Hispanic Community Health Study/Study of Latinos, which is the largest health study of the Hispanic/Latino population in the United States aimed at identifying risk factors for various diseases and determining the role of genes and environment in the occurrence of diseases. We provide guidelines on the use of weighted and unweighted estimators, as well as the relevant software