Overview and Initial Results of the National Center for Health Statistics' Research and Development Survey

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.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.

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. 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 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. 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.