National Center for Health Statistics' 2019 Research and Development Survey, RANDS 3.

Objective This report on the third round of the Research and Development Survey (RANDS 3) provides a general description of RANDS 3 and presents percentage estimates of selected demographic and health-related variables from the overall sample and by one set of experimental groups embedded in the survey. Statistical tests comparing estimates for the two randomized groups were conducted to evaluate the randomization. Methods NORC at the University of Chicago conducted RANDS 3 for the National Center of Health Statistics in 2019 using its AmeriSpeak Panel in web-only mode.

Comparison of Mental Health Estimates by Sociodemographic Characteristics in the Research and Development Survey 3 and the 2019 National Health Interview Survey.

Objectives-This report compares national and subgroup estimates of any (mild, moderate, or severe) level of major depressive disorder (depression) and generalized anxiety disorder (GAD) symptoms among the U.S. adult population from two data sources, the 2019 National Health Interview Survey (NHIS) and the third round of the Research and Development Survey (RANDS 3).

Evaluation of the National Center for Health Statistics Data Presentation Standards for Rates From Vital Statistics and Sample Surveys.

For the CIs used in the Standards for rates from vital statistics and complex health surveys, this report evaluates coverage probability, relative width, and the resulting percentage of rates flagged as statistically unreliable when compared with previously used standards. Additionally, the report assesses the impact of design effects and the denominator's sampling variability, when applicable.

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.

Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies.

Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI can have various distributional forms with different degrees of missingness. However, when variables with missing data contain skip patterns (i.

Reduced Access to Preventive Care Due to the COVID-19 Pandemic, by Chronic Disease Status and Race and Hispanic Origin, United States, 2020-2021.

Objectives: The COVID-19 pandemic has disproportionately affected racial and ethnic minority populations in the United States. The National Center for Health Statistics adapted the Research and Development Survey (RANDS), a commercial panel survey, to track selected health outcomes during the pandemic using the series RANDS during COVID-19 (RC-19). We examined access to preventive care among adults by chronic condition status, race, and Hispanic origin.

Guidance for Selecting Model Options in the National Cancer Institute Joinpoint Regression Software.

The purpose of this report is to provide guidance to users of NCHS data in the selection of modeling options when using the NCI Joinpoint regression software to analyze trends. This report complements another report, "National Center for Health Statistics Guidelines for Analysis of Trends." Considerations are presented for selecting the modeling options, with examples illustrating the choices.

Variable inclusion strategies through directed acyclic graphs to adjust health surveys subject to selection bias for producing national estimates.

Along with the rapid emergence of web surveys to address time-sensitive priority topics, various propensity score (PS)-based adjustment methods have been developed to improve population representativeness for nonprobability- or probability-sampled web surveys subject to selection bias. Conventional PS-based methods construct pseudo-weights for web samples using a higher-quality reference probability sample. The bias reduction, however, depends on the outcome and variables collected in both web and reference samples.

The Research and Development Survey (RANDS) during COVID-19.

The National Center for Health Statistics' (NCHS) Research and Development Survey (RANDS) is a series of commercial panel surveys collected for methodological research purposes. In response to the COVID-19 pandemic, NCHS expanded the use of RANDS to rapidly monitor aspects of the public health emergency. The RANDS during COVID-19 survey was designed to include COVID-19 related health outcome and cognitive probe questions.

Using SAS/STAT to Understand the NCI Joinpoint Regression Software: Testing for a Zero Slope Using Rates of Drug Overdose Deaths Involving Fentanyl, 2011-2016.

Background-The National Cancer Institute (NCI) Joinpoint regression software is a widely used software program for evaluating trends. In addition to producing model estimates for trend models, this software can search for changes in slope along the trend line. One component of the software, which tests whether line segment slopes are zero, is different from the usual t-test of zero slope that is used in linear models.

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.

Modification of the generalized quasi-likelihood model in the analysis of the Add Health study.

The relationship between the mean and variance is an implicit assumption of parametric modeling. While many distributions in the exponential family have a theoretical mean-variance relationship, it is often the case that the data under investigation are correlated, thus varying from the relation. We present a generalized method of moments estimation technique for modeling certain correlated data by adjusting the mean-variance relationship parameters based on a canonical parameterization.

Impact of the Presence of Select Cardiovascular Risk Factors on Cognitive Changes among Dementia Subtypes.

Background: Studies have shown select associations between cardiovascular risk factors and dementia, but mostly focused on Alzheimer's Disease (AD).

Objective: We enhance these works by evaluating the relationship between the presence of cardiovascular risk factors and the rate of cognitive decline, measured using the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating Sum of Boxes (CDR-SUM) on four common dementia subtypes (AD, dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and vascular dementia (VaD), as well as non-demented elderly individuals (normal)).

Method: We used generalized linear mixed models with random intercepts to account for correlation at the patient and center levels for each dementia subtype adjusting for time since initial visit, baseline cognitive score, age, and demographic factors.