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. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

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.

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.

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.

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 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.

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.