Objectives:The National Center for Health Statistics's Rapid Surveys System (RSS) was established in 2022. It uses two probability-based commercial online panels, NORC at the University of Chicago's AmeriSpeak panel and Ipsos's KnowledgePanel, to enable fast data collection on emerging or priority health topics. RSS Rounds 1-4 (RSS 1-4) were collected between August 2023 and May 2024. This report describes the methodology implemented in the early rounds of RSS for weighting and variance estimation, methods for combining multiple panels, and quality assessment. Methods:RSS 1-4 were weighted to a set of control totals for selected demographic and health variables from the National Health Interview Survey (NHIS). Estimates from the two online panels were combined using a convex combination based on effective sample sizes to produce one set of health estimates for release each round. As part of an ongoing quality evaluation, quality metrics were assessed and reported for each round and were used to inform the RSS methodology in successive rounds. Results:RSS 1-4 had sample sizes ranging from 7,046 (Round 2) to 8,705 (Round 4), with cumulative response rates ranging from 3.8% to 9.9% across the two panel providers. Quality metrics including nonresponse rates and breakoff rates were evaluated and found to be consistent for the two panel providers. In addition, a set of benchmark variables collected on RSS and NHIS were evaluated to assess bias. While the magnitude of bias varied by health variable and across health domains, most health variables had low bias across the four rounds. Conclusion:The methodology for RSS has evolved and allowed for changes to improve the quality of the program and the resulting estimates. This report describes the early methodology implemented in the first four rounds of RSS.

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.e. questions not applicable to some survey participants are thus skipped), implementation of MI may not be straightforward. In this research, we compare two approaches for MI when skip-pattern covariates with missing values exist. One approach imputes missing values in the skip-pattern variables only among applicable subjects (denoted as imputation among applicable cases (IAAC)). The second approach imputes skip-pattern covariates among all subjects while using different recoding methods on the skip-pattern variables (denoted as imputation with recoded non-applicable cases (IWRNC)). A simulation study is conducted to compare these methods. Both approaches are applied to the 2015 and 2016 Research and Development Survey data from the National Center for Health Statistics.

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.

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. Methods: NORC at the University of Chicago conducted RC-19 among US adults in 3 rounds (June–July 2020 [round 1, N = 6800], August 2020 [round 2, N = 5981], and May–June 2021 [round 3, N = 5458]) via online survey and telephone. We evaluated reduced access to ≥1 type of preventive care due to the pandemic in the past 2 months for each round by using logistic regression analysis stratified by chronic condition status and race and Hispanic origin, adjusting for sociodemographic and health variables. Results: Overall, 35.8% of US adults reported missing ≥1 type of preventive care in the previous 2 months in round 1, 26.0% in round 2, and 11.2% in round 3. Reduced access to preventive care was significantly higher among adults with ≥1 chronic condition (vs no chronic conditions) in rounds 1 and 2 (adjusted odds ratios [aOR)] = 1.5 and 1.4, respectively). Compared with non-Hispanic White adults, non-Hispanic Black adults reported significantly lower reduced access to preventive care in round 1 (aOR = 0.7), and non-Hispanic Other adults reported significantly higher reduced access to preventive care in round 2 (aOR = 1.5). Conclusions: Our findings may inform policies and programs for people at risk of reduced access to preventive care.

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. To assess question-response patterns, probe questions and four sets of experiments were embedded in RANDS 3, with panelists randomized into two groups for each set of experiments. Participants in each group received questions with differences in wording, question-andresponse formats, or question order. Results Of the 4,255 people sampled, 2,646 completed RANDS 3 for a completion rate of 62.2% and a weighted cumulative response rate of 18.1%. Iterative raking was performed using demographic and selected health condition variables to calibrate the RANDS 3 sample to 2019 National Health Interview Survey (NHIS) estimates. As a result, the overall demographic distribution and percentages of asthma, diabetes, hypertension, and high cholesterol for the calibrated RANDS 3 sample aligned with the percentages estimated from the 2019 NHIS. The distributions of demographic and healthrelated variables were comparable between the two randomized groups examined except for ever-diagnosed hypertension. Conclusion As part of a research series using probability-based survey panels, RANDS 3 included health-related questions with a focus on disability and opioids. Because RANDS is an ongoing research platform, a variety of persistent and emergent research questions relating to survey methodology will continue to be examined in current and future rounds of RANDS.

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.

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). Methods-Data from the 2019 NHIS (n = 31,997) and RANDS 3 (n = 2,646) were used. The eight-item Patient Health Questionnaire (PHQ-8), scores ranging from 0 to 24, and the seven-item GAD scale (GAD-7), scores ranging from 0 to 21, were used to measure the severity of depression and GAD symptoms, respectively. Binary indicators of exhibiting symptoms were based on scores of 5 to 24 for depression and 5 to 21 for GAD. The estimates were compared by the following sociodemographic characteristics: age, sex, race and Hispanic origin, education, and region. Results-Nearly all of the national and subgroup estimates of adults with depression and GAD symptoms were significantly higher based on RANDS 3 compared with the 2019 NHIS. The only exception was the depression symptoms estimate among adults aged 65 and over, where the estimates were comparable across the two data sources. Both data sources found that depression symptoms were associated with sex, age, race and Hispanic origin, and education, and GAD symptoms were associated with age, race and Hispanic origin, and education. However, NHIS identified a few associations that RANDS did not, including associations between depression symptoms and region and GAD symptoms and sex. Conclusions-Mental health estimates from RANDS, a web-based survey, may be overestimated when compared with a traditional in-person household survey. These results may inform potential strategies to improve the comparability of mental health estimates from RANDS and other surveys like NHIS, such as calibration weights or other model-based methods.

This report describes care received at office-based physician offices in the United States. Estimates are presented on selected characteristics and compared by age.

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. Rounds 1 and 2 were fielded June 9-July 6, 2020 and August 3-20, 2020 using the AmeriSpeak® Panel. Existing and new approaches were used to: 1) evaluate question interpretation and performance to improve future COVID-19 data collections and 2) to produce a set of experimental estimates for public release using weights which were calibrated to NCHS' National Health Interview Survey (NHIS) to adjust for potential bias in the panel. Through the expansion of the RANDS platform and ongoing methodological research, NCHS reported timely information about COVID-19 in the United States and demonstrated the use of recruited panels for reporting national health statistics. This report describes the use of RANDS for reporting on the pandemic and the associated methodological survey design decisions including the adaptation of question evaluation approaches and calibration of panel weights.

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. A central issue is identifying variables for inclusion in PS-adjustment. In this paper, directed acyclic graph (DAG), a common graphical tool for causal studies but largely under-utilized in survey research, is used to examine and elucidate how different types of variables in the causal pathways impact the performance of PS-adjustment. While past literature generally recommends including all variables, our research demonstrates that only certain types of variables are needed in PS-adjustment. Our research is illustrated by NCHS' Research and Development Survey, a probability-sampled web survey with potential selection bias, PS-adjusted to the National Health Interview Survey, to estimate U.S. asthma prevalence. Findings in this paper can be used by National Statistics Offices to design questionnaires with variables that improve web-samples' population representativeness and to release more timely and accurate estimates for priority topics.

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. The tradeoffs and consequences of choosing the various modeling options using data from NCHS data systems are discussed.encounters.

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. This report will demonstrate this Joinpoint software procedure through replication using the SAS Institute's statistical software (that is, SAS) and discuss the implications of the different assumptions used by Joinpoint and a typical SAS model for the test of zero slope. Methods-First, Joinpoint's procedure for testing a zero slope is compared with a typical test of zero slope using SAS, and the assumptions behind both approaches are evaluated. Second, the test from the Joinpoint software is replicated in SAS using its PROC REG procedure and additional SAS programming. Trend analyses of rates of drug overdose deaths involving fentanyl from the general population and among females are used as examples. Results-In the evaluation of the trend of drug overdose deaths for the total population, Joinpoint produces a similar result to the linear model test in SAS. For the female subgroup, however, Joinpoint and SAS produce differing results for the test of zero slope. The replication of the Joinpoint test of zero slope using SAS demonstrates that Joinpoint's procedure is based on fewer degrees of freedom, which results in a larger standard error estimate. Conclusion-The Joinpoint approach accounts for the fact that the joinpoints are estimated and thus leads to a more conservative hypothesis test, particularly when the number of points in a trend analysis is small.

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. The proposed mean-variance form describes overdispersion using two parameters and implements an adjusted canonical parameter which makes this approach feasible for all distributions in the exponential family. Test statistics and confidence intervals are used to measure the deviations from the mean-variance relation parameters. We use the modified relation as a means of fitting generalized quasi-likelihood models to correlated data. The performance of the proposed modified generalized quasi-likelihood model is demonstrated through a simulation study and the importance of accounting for overdispersion is highlighted through the evaluation of adolescent obesity data collected from a U.S. longitudinal study.

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.