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

Nutrition and the circadian system

The human circadian system anticipates and adapts to daily environmental changes to optimise behaviour according to time of day and temporally partitions incompatible physiological processes. At the helm of this system is a master clock in the suprachiasmatic nuclei (SCN) of the anterior hypothalamus. The SCN are primarily synchronised to the 24-h day by the light/dark cycle; however, feeding/fasting cycles are the primary time cues for clocks in peripheral tissues. Aligning feeding/fasting cycles with clock-regulated metabolic changes optimises metabolism, and studies of other animals suggest that feeding at inappropriate times disrupts circadian system organisation, and thereby contributes to adverse metabolic consequences and chronic disease development. ‘High-fat diets’ (HFD) produce particularly deleterious effects on circadian system organisation in rodents by blunting feeding/fasting cycles. Time-of-day-restricted feeding, where food availability is restricted to a period of several hours, offsets many adverse consequences of HFD in these animals; however, further evidence is required to assess whether the same is true in humans. Several nutritional compounds have robust effects on the circadian system. Caffeine, for example, can speed synchronisation to new time zones after jetlag. An appreciation of the circadian system has many implications for nutritional science and may ultimately help reduce the burden of chronic diseases

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)

Caries prevalence and tooth loss in Hungarian adult population: results of a national survey

<p>Abstract</p> <p>Background</p> <p>Oral health is basicly important for the well-being of people. Thus, it is strongly suggested to organize epidemiological surveys in order to gain representative data on oral condition of the given population. The purpose of the cross-sectional study was to determine the results on tooth loss and caries prevalence of Hungarian adults in different age groups.</p> <p>Methods</p> <p>Altogether 4606 persons (2923 women, 1683 men) participated in the study who were classified into different age groups: 19 [less than or equal to], 20–24, 35–44, 45–64, 65–74, [greater than or equal to]75 year olds. Probands were selected randomly from the population attending the compulsory lung screening examinations. The participants were examined by calibrated dentists, according to the WHO (1997) criteria. In order to produce representative data, the chosen localities for these examinations covered the capital, the largest towns, the villages, and case weights were used for the statistical evaluation.</p> <p>Results</p> <p>The mean values of DMF-T were found between 11.79±5.68 and 21.90±7.61 These values were significantly higher in women compared to men (p < 0.05). In all age groups the values of M were the highest. Except for the women in the groups of 35–44 and 45–64 year olds, these values showed an increasing tendency both in women and men by age (from 5.50±6.49, and 4.70±4.08 to 21.52±9.07 and 18.41±8.89 respectively). The values of D components reached the highest values in 45–64 year olds (4.54±2.12 and 4.22±2.81, by gender, respectively), then in the older age groups there was a high reduction in these values (in 65–74 year olds: 2.72±1.88 and 1.36±2.48; in 75 or more than 75 year olds: 1.05±1.41 and 1.03±1.76 by gender, respectively). The ratio of D and F values was the highest in the age group of 65–74 year olds (2.12), the lowest ratio could be calculated in 20–34 year olds (0.65).</p> <p>Data showed some decrease in caries experience in 35–44 years of age between 2000 and 2004. The prevalence of persons with 21 or more teeth had been increased from 65.6% to 73.1%. This positive tendency has not been occured in prevalence of edentulousness in this age group: the prevalence of edentulous persons changed from 1.4 to 1.9%. In 65–74 year olds the level of edentulousness became lower, from 25.9 to 14.8% and the prevalence of persons with 21 or more teeth is higher (22.6%) than it was in 2000 (13.0%).</p> <p>Conclusion</p> <p>Present data from Hungary show some slight decrease in caries experience between 35–44 years of age, although this positive tendency has not been occured in prevalence of edentulousness in this age group. A positive tendency could be experienced in the group of 65–74 year olds in edentulousness and in number of teeth, but further efforts are needed to reach a better situation.</p

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

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