@article {7630, title = {A comparison of response rates in the English Longitudinal Study of Ageing and the Health and Retirement Study.}, journal = {Longit Life Course Stud}, volume = {2}, year = {2011}, month = {2011 May 01}, pages = {127-144}, publisher = {2}, abstract = {

Survey response rates are an important measure of the quality of a survey; this is true for both longitudinal and cross-sectional surveys. However, the concept of a response rate in the context of a panel survey is more complex than is the case for a cross-sectional survey. There are typically many different response rates that can be calculated for a panel survey, each of which may be relevant for a specific purpose. The main objective of our paper is to document and compare response rates for two long-term panel studies of ageing, the English Longitudinal Study of Ageing (ELSA) and the Health and Retirement Study (HRS) in the United States. To guide our selection and calculation of response rates for the two studies, we use a framework that was developed by Peter Lynn (2005) and present several different types of longitudinal response rates for the two surveys. We discuss similarities and differences in the study designs and protocols and how some of the differences affect comparisons of response rates across the two studies.

}, issn = {1757-9597}, doi = {10.14301/llcs.v2i2.118}, author = {Hayley Cheshire and Mary Beth Ofstedal and Shaun Scholes and Mathis Schroeder} } @article {7436, title = {Characteristics of physical measurement consent in a population-based survey of older adults.}, journal = {Med Care}, volume = {48}, year = {2010}, month = {2010 Jan}, pages = {64-71}, publisher = {48}, abstract = {

BACKGROUND: Collecting physical measurements in population-based health surveys has increased in recent years, yet little is known about the characteristics of those who consent to these measurements.

OBJECTIVE: To examine the characteristics of persons who consent to physical measurements across several domains, including one{\textquoteright}s demographic background, health status, resistance behavior toward the survey interview, and interviewer characteristics.

RESEARCH DESIGN, SUBJECTS, AND MEASURES: We conducted a secondary data analysis of the 2006 Health and Retirement Study, a nationally-representative panel survey of older adults aged 51 and older. We performed multilevel logistic regressions on a sample of 7457 respondents who were eligible for physical measurements. The primary outcome measure was consent to all physical measurements.

RESULTS: Seventy-nine percent (unweighted) of eligible respondents consented to all physical measurements. In weighted multilevel logistic regressions controlling for respondent demographics, current health status, survey resistance indicators, and interviewer characteristics, the propensity to consent was significantly greater among Hispanic respondents matched with bilingual Hispanic interviewers, patients with diabetes, and those who visited a doctor in the past 2 years. The propensity to consent was significantly lower among younger respondents, those who have several Nagi functional limitations and infrequently participate in "mildly vigorous" activities, and those interviewed by black interviewers. Survey resistance indicators, such as number of contact attempts and interviewer observations of resistant behavior in prior wave iterations of the Health and Retirement Study were also negatively associated with physical measurement consent. The propensity to consent was unrelated to prior medical diagnoses, including high blood pressure, cancer (excluding skin), lung disease, heart abnormalities, stroke, and arthritis, and matching of interviewer and respondent on race and gender.

CONCLUSIONS: Physical measurement consent is not strongly associated with one{\textquoteright}s health status, though the findings are somewhat mixed. We recommend that physical measurement results be adjusted for characteristics associated with the likelihood of consent, particularly functional limitations, to reduce potential bias. Otherwise, health researchers should exercise caution when generalizing physical measurement results to the population at large, including persons with functional limitations that may affect their participation.

}, keywords = {Age Factors, Aged, Female, Health Status, Health Surveys, Humans, Male, Middle Aged, Physical Examination, Socioeconomic factors}, issn = {1537-1948}, doi = {10.1097/mlr.0b013e3181adcbd3}, author = {Joseph W Sakshaug and Mick P. Couper and Mary Beth Ofstedal} } @article {7338, title = {Comparing models of frailty: the Health and Retirement Study.}, journal = {J Am Geriatr Soc}, volume = {57}, year = {2009}, month = {2009 May}, pages = {830-9}, publisher = {57}, abstract = {

OBJECTIVES: To operationalize and compare three models of frailty, each representing a distinct theoretical view of frailty: as deficiencies in function (Functional Domains model), as an index of health burden (Burden model), and as a biological syndrome (Biologic Syndrome model).

DESIGN: Cross-sectional analysis.

SETTING: 2004 wave of the Health and Retirement Study, a nationally representative, longitudinal health interview survey.

PARTICIPANTS: Adults aged 65 and older (N=11,113) living in the community and in nursing homes in the United States.

MEASUREMENTS: The outcome measure was the presence of frailty, as defined according to each frailty model. Covariates included chronic diseases and sociodemographic characteristics.

RESULTS: Almost one-third (30.2\%) of respondents were frail according to at least one model; 3.1\% were frail according to all three models. The Functional Domains model showed the least overlap with the other models. In contrast, 76.1\% of those classified as frail according to the Biologic Syndrome model and 72.1\% of those according to the Burden model were also frail according to at least one other model. Older adults identified as frail according to the different models differed in sociodemographic and chronic disease characteristics. For example, the Biologic Syndrome model demonstrated substantial associations with older age (adjusted odds ratio (OR)=10.6, 95\% confidence interval (CI)=6.1-18.5), female sex (OR=1.7, 95\% CI=1.2-2.5), and African-American ethnicity (OR=2.1, \% CI=1.0-4.4).

CONCLUSION: Different models of frailty, based on different theoretical constructs, capture different groups of older adults. The different models may represent different frailty pathways or trajectories to adverse outcomes such as disability and death.

}, keywords = {Activities of Daily Living, Aged, Aged, 80 and over, Chronic disease, Cross-Sectional Studies, Demography, Disability Evaluation, Frail Elderly, Geriatric Assessment, Health Surveys, Humans, Interviews as Topic, Logistic Models, Models, Theoretical, United States}, issn = {1532-5415}, doi = {10.1111/j.1532-5415.2009.02225.x}, author = {Christine T Cigolle and Mary Beth Ofstedal and Zhiyi Tian and Caroline S Blaum} }