Calibration Weighting Methods for the National Center for Health Statistics Research and Development Survey.

Objectives The Research and Development Survey (RANDS) is a series of web-based, commercial panel surveys that have been conducted by the National Center for Health Statistics (NCHS) since 2015. RANDS was designed for methodological research purposes,including supplementing NCHS' evaluation of surveys and questionnaires to detect measurement error, and exploring methods to integrate data from commercial survey panels with high-quality data collections to improve survey estimation. The latter goal of improving survey estimation is in response to limitations of web surveys, including coverage and nonresponse bias. To address the potential bias in estimates from RANDS,NCHS has investigated various calibration weighting methods to adjust the RANDS panel weights using one of NCHS' national household surveys, the National Health Interview Survey. This report describes calibration weighting methods and the approaches used to calibrate weights in web-based panel surveys at NCHS.

Sample Design and Estimation Structures for the National Health Interview Survey, 2016-2025.

This report presents operating characteristics of the NHIS 2016-2025 sample design. The general sampling structure is presented, along with a discussion of weighting and variance estimation techniques primarily for 2016-2018. This report is organized into four major sections. The first section presents a general overview of NHIS and its sample design. The second section describes the redesign process, updates for 2016-2025, and includes general frame and sample design considerations. The third section provides a more detailed description of the sample design and how the sample was selected. The last two sections present a description of the estimators used in NHIS for analyzing and summarizing survey results. Documentation for subsequent changes to the sampling and weighting procedures is available on the NCHS website as separate reports and through each year's survey description document. This report is intended for general users of NHIS data.

A method for calculating BMI z-scores and percentiles above the 95th percentile of the CDC growth charts.

BACKGROUND: The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3rd and 97th and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95th percentile. Extrapolating beyond the 97th percentile is not recommended and leads to compressed z-score values. AIM: This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95th percentile using an extended reference population. SUBJECTS AND METHODS: Data from youth at or above the 95th percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. RESULTS: The modelled distributions above the 95th percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. CONCLUSION: This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

Comparison of Quarterly and Yearly Calibration Data for Propensity Score Adjusted Web Survey Estimates.

While web surveys have become increasingly popular as a method of data collection, there is concern that estimates obtained from web surveys may not reflect the target population of interest. Web survey estimates can be calibrated to existing national surveys using a propensity score adjustment, although requirements for the size and collection timeline of the reference data set have not been investigated. We evaluate health outcomes estimates from the National Center for Health Statistics' Research and Development web survey. In our study, the 2016 National Health Interview Survey as well as its quarterly subsets are considered as reference datasets for the web data. It is demonstrated that the calibrated health estimates overall vary little when using the quarterly or yearly data, suggesting that there is flexibility in selecting the reference dataset. This finding has many practical implications for constructing reference data, including the reduced cost and burden of a smaller sample size and a more flexible timeline.

Design and estimation for the national health interview survey, 2006-2015.

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 for the previous design, which was extended through 2005. METHODS: The 2006-2015 NHIS sample design uses cost-effective complex sampling techniques including stratification, clustering, and differential sampling rates to achieve several objectives, among them improved reliability of racial, ethnic, and geographical domains. This report describes these methods. RESULTS: This report presents operating characteristics of NHIS 2006-2015. The general sampling structure is presented, along with a discussion of weighting and variance estimation techniques. This report is intended for general users of NHIS data systems.

Estimating standard errors for life expectancies based on complex survey data with mortality follow-up: A case study using the National Health Interview Survey Linked Mortality Files.

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 estimation of life expectancies for subgroups of interest. However, use of complex survey data linked to mortality data can complicate the estimation of standard errors. This paper presents a case study of approaches to variance estimation for life expectancies based on life tables, using the National Health Interview Survey Linked Mortality Files. The approaches considered include application of Chiang's traditional method, which is straightforward but does not account for the complex design features of the data; balanced repeated replication (BRR), which is more complicated but accounts more fully for the design features; and compromise, 'hybrid' approaches, which can be less difficult to implement than BRR but still account partially for the design features. Two tentative conclusions are drawn. First, it is important to account for the effects of the complex sample design, at least within life-table age intervals. Second, accounting for the effects within age intervals but not across age intervals, as is done by the hybrid methods, can yield reasonably accurate estimates of standard errors, especially for subgroups of interest with more homogeneous characteristics among their members.

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.