Van L. Parsons

The Prevalence of Functional Limitations and Disability in Older Persons in the US: Data from the National Health and Nutrition Examination Survey III

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.

Reliability and Prevalence of Physical Performance Examination Assessing Mobility and Balance in Older Persons in the US: Data from the Third National Health and Nutrition Examination Survey

OBJECTIVE: This report provides reliability and prevalence estimates by sex, age, and race/ethnicity of an observed physical performance examination (PPE) assessing mobility and balance.

Combining Information From Two Surveys to Estimate County-Level Prevalence Rates of Cancer Risk Factors and Screening

Cancer surveillance research requires estimates of the prevalence of cancer risk factors and screening for small areas such as counties. Two popular data sources are the Behavioral Risk Factor Surveillance System (BRFSS), a telephone survey conducted by state agencies, and the National Health Interview Survey (NHIS), an area probability sample survey conducted through face-to-face interviews. Both data sources have advantages and disadvantages. The BRFSS is a larger survey and almost every county is included in the survey, but it has lower response rates as is typical with telephone surveys and it does not include subjects who live in households with no telephones. On the other hand, the NHIS is a smaller survey, with the majority of counties not included; but it includes both telephone and nontelephone households, and has higher response rates. A preliminary analysis shows that the distributions of cancer screening and risk factors are different for telephone and nontelephone households. Thus, information from the two surveys may be combined to address both nonresponse and noncoverage errors. A hierarchical Bayesian approach that combines information from both surveys is used to construct county-level estimates. The proposed model incorporates potential noncoverage and nonresponse biases in the BRFSS as well as complex sample design features of both surveys. A Markov chain Monte Carlo method is used to simulate draws from the joint posterior distribution of unknown quantities in the model that uses design-based direct estimates and county-level covariates. Yearly prevalence estimates at the county level for 49 states, as well as for the entire state of Alaska and the District of Columbia, are developed for six outcomes using BRFSS and NHIS data from the years 1997–2000. The outcomes include smoking and use of common cancer screening procedures. The NHIS/BRFSS combined county-level estimates are substantially different from those based on the BRFSS alone.

State-based estimates of mammography screening rates based on information from two health surveys

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 of prevalence for two time periods (1997–1999 and 2000–2003) and the estimated difference between the periods. We included state-level covariates in the model-based approach through principal components. Results. The national mammography screening prevalence estimate based on the BRFSS was substantially larger than the NHIS estimate for both time periods. This difference may have been due to nonresponse and noncoverage biases, response mode (telephone vs. in-person) differences, or other factors. However, the estimated change between the two periods was similar for the two surveys. Consistent with the model assumptions, the model-based estimates were more similar to the NHIS estimates than to the BRFSS prevalence estimates. The state-level covariates (through the principal components) were shown to be related to the mammography prevalence with the expected positive relationship for socioeconomic status and urbanicity. In addition, several principal components were significantly related to the difference between NHIS and BRFSS telephone prevalence estimates. Conclusions. Model-based estimates, based on information from the two surveys, are useful tools in representing combined information about mammography prevalence estimates from the two surveys. The model-based approach adjusts for the possible nonresponse and noncoverage biases of the telephone survey while using the large BRFSS state sample size to increase precision.

Methods and results for small area estimation using smoking data from the 2008 National Health Interview Survey

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 small sample sizes. In this paper, we compare the efficiency of different area-level models in estimating smoking prevalence for the 50 US states and the District of Columbia. Our study is based on survey data from the 2008 National Health Interview Survey in conjunction with a number of potentially related auxiliary variables obtained from the American Community Survey, an ongoing large complex survey conducted by the US Census. A major portion of this study is devoted to the investigation of several methods for estimating survey sampling variances needed to implement an area-level hierarchical model. Based on our findings, a hierarchical Bayesian method that uses a survey-adjusted random sampling variance model to capture the complex survey sampling variability appears to be somewhat superior to the other considered area-level models in accounting for small sample behavior of estimated survey sampling variances. Several diagnostic procedures are presented to compare the proposed methods.

Breast Cancer Prognosis Using Survival Forests

Combinations of survival regression trees called survival forests (SF) have been proposed by Breiman to estimate survival functions and assess variable importance. We examine some operating characteristics of the method on simulated data, and we apply it to breast cancer data for prognosis.

Power Comparisons of Some Nonparametric Tests for Lattice Ordered Alternatives in Two-Factor Experiments

In biological or medical situations the expectations of response variables may be ordered by a rectangular grid partial ordering. For example, serum glucose as a function of body mass index and age would typically be assumed to be nondecreasing in each predictive variable. An order-restricted least squares approach to hypothesis testing may be implemented, but the practical implementation of estimation techniques and sampling theory tend to be complicated. However, advances in computer processing have now made computer intensive methods for such inference more practical.

Fitting Growth Curve Allowing for Periodicities

Curve fitting appeals to many students of growth as a method of, getting parsimonious summary of longitudinal growth data yt in terms of a trend mt, the model being

Hierarchical and empirical Bayes small domain estimation of the proportion of persons without health insurance for minority subpopulations

{y_t} = {m_t}\; + \;{e_t}