Van L. Parsons

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OBJECTIVES This report presents an overview, a detailed description of the sample design features, and estimation structures for the 2006-2015 National Health Interview Survey NHIS). It fulfills the same role for the current 2006-2015 NHIS design as NCHS Series 2, No. 130, "Design and Estimation for the National Health Interview Survey, 1995-2004" provided(More)
OBJECTIVE To provide estimates by sex and age and by sex and race/ethnicity of the proportion of older Americans who have difficulty with functional limitations and daily activities. SETTING The Third National Health and Nutrition Examination Survey (NHANES III) 1988-1994. DESIGN A cross-sectional nationally representative survey. PARTICIPANTS All(More)
Life expectancy is an important measure for health research and policymaking. Linking individual survey records to mortality data can overcome limitations in vital statistics data used to examine differential mortality by permitting the construction of death rates based on information collected from respondents at the time of interview and facilitating(More)
OBJECTIVES We compared national and state-based estimates for the prevalence of mammography screening from the National Health Interview Survey (NHIS), the Behavioral Risk Factor Surveillance System (BRFSS), and a model-based approach that combines information from the two surveys. METHODS At the state and national levels, we compared the three estimates(More)
The National Health Interview Survey, conducted by the National Center for Health Statistics, is designed to provide reliable design-based estimates for a wide range of health-related variables for national and four major geographical regions of the USA. However, state-level or substate-level estimates are likely to be unreliable because they are based on(More)
In large-scale government surveys, sample designs based upon complex probability sampling methods are typically used. The resulting data are frequently analyzed using so-called design-based or randomization inference and not with strong model-based assumptions about sampling distributions. Such design-based techniques are considered an objective approach to(More)
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