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.

Propensity-score Adjusted Estimates for Selected Health Outcomes From the Research and Development Survey.

To evaluate the quality of web surveys, the National Center for Health Statistics' Division of Research and Methodology has been conducting a series of studies with survey data from commercially recruited panels,referred to as the Research and Development Survey (RANDS). This report describes the propensity-score adjusted estimates from the second round of RANDS (RANDS 2) using the 2016 National Health Interview Survey (NHIS).

The National Center for Health Statistics' 2015 and 2016 Research and Development Surveys.

Objective: This report provides a general description of the background and operation of the first two rounds of the Research and Development Survey (RANDS), a series of cross-sectional surveys from probability-sampled commercial survey panels. The Division of Research and Methodology of the National Center for Health Statistics (NCHS) conducted the first two rounds of RANDS in 2015 and 2016. RANDS 1 and 2 are being used primarily for question design evaluation and for investigating statistical methodologies for estimation. Methods: NCHS contracted with Gallup, Inc. to conduct RANDS 1 in Fall 2015 and RANDS 2 in Spring 2016. RANDS 1 and 2 were conducted using a web survey mode and included survey questions from the National Health Interview Survey (NHIS) that were specifically chosen to provide comparison and evaluation of the survey methodology properties of web surveys and traditional household surveys. In this report, some demographic and health estimates are provided from both sources to describe the RANDS data. Results: In RANDS 1, 2,304 out of the original 9,809 invited panel members completed the survey, for a completion rate of 23.5%. In RANDS 2, 2,480 of the initial 8,231 invited respondents completed the survey, for a completion rate of 30.1%. RANDS 1 and 2 participants were similar to the quarterly NHIS participants with respect to sex, census region, and whether they had worked for pay in the previous week. Other characteristics varied, including age, race and ethnicity, and income. Most health estimates differed between RANDS and NHIS. Public-use versions of the RANDS data can be found at: https://www.cdc.gov/nchs/rands. Conclusion: RANDS is an ongoing platform for research to understand the properties of probability-sampled recruited panels of primarily web users, investigating and developing statistical methods for using such data in conjunction with large nationally representative health surveys, and for extending question-design evaluations.

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

The National Center for Health Statistics is assessing the usefulness of recruited web panels in multiple research areas. One research area examines the use of close-ended probe questions and split-panel experiments for evaluating question-response patterns. Another research area is the development of statistical methodology to leverage the strength of national survey data to evaluate, and possibly improve, health estimates from recruited panels. Recruited web panels, with their lower cost and faster production cycle, in combination with established population health surveys, may be useful for some purposes for statistical agencies. Our initial results indicate that web survey data from a recruited panel can be used for question evaluation studies without affecting other survey content. However, the success of these data to provide estimates that align with those from large national surveys will depend on many factors, including further understanding of design features of the recruited panel (e.g. coverage and mode effects), the statistical methods and covariates used to obtain the original and adjusted weights, and the health outcomes of interest.

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.

Negative Binomials Regression Model in Analysis of Wait Time at Hospital Emergency Department.

Wait time is the differences between the time a patient arrives in the emergency department (ED) and the time an ED provider examines that patient. This study focuses on the development of a negative binomial model to examine factors associated with ED wait time using the National Hospital Ambulatory Medical Care Survey (NHAMCS). Conducted by National Center for Health Statistics (NCHS), NHAMCS has been gathering, analyzing, and disseminating information annually about visits made for medical care to hospital outpatient department and EDs since 1992. To analyze ED wait times, a negative binomial model was fit to the ED visit data using publically released micro data from the 2009 NHAMCS. In this model, the wait time is the dependent variable while hospital, patient, and visit characteristics are the independent variables. Wait time was collapsed into discrete values representing 15 minutes intervals. The findings are presented.

Electronic health record systems and intent to apply for meaningful use incentives among office-based physician practices: United States, 2001-2011.

An increasing trend in EMR/EHR system use among office-based physicians was noted from 2001 through preliminary 2011 estimates. In 2011, the NAMCS mail survey showed about 57% of office-based physicians used any EMR/EHR system, a 12% increase from the 2010 estimate. Between 2010 and 2011, the percentage of physicians who reported having systems meeting the criteria for a basic system increased 36%. Adoption of EMR/EHR systems varied greatly by state. In 2011, the percentage of physicians using any EMR/EHR system ranged from 40% in Louisiana to 84% in North Dakota. Compared with the national average, 3 states had a significantly lower percentage of office-based physicians using any EMR/EHR system, and 11 states had a significantly higher percentage. The percentage of physicians having a system that met the criteria for a basic system ranged from 16% in New Jersey to 61% in Minnesota. Compared with the national average, six states had a significantly lower percentage of office-based physicians with a basic system, and eight states had a significantly higher percentage. In 2011, 52% of physicians reported intending to apply for the Medicare or Medicaid EHR incentive payments, a 26% increase from 2010. In 2010, interest among physicians in applying for meaningful use incentive payments was similar to the national average (41%) across most states. In only four states (Alaska, New York, North Dakota, and West Virginia) and the District of Columbia was the percentage lower than the national average. To qualify for Stage 1 meaningful use incentive payments, eligible physicians need to meet all 15 Stage 1 Core Set objectives and 5 of 10 Menu Set objectives, using certified EHR systems (see "Definitions"). In this report, estimates of physicians' readiness to meet Stage 1 Core Set meaningful use measures were limited to data collected on the computerized functions needed to meet eight Stage 1 objectives. A previous study found that 15% of physicians eligible to apply for meaningful use incentives had EHR systems with basic functions capability (3). In the present study, 43% of physicians planning to apply for incentives had EHR systems with functions that would allow them to meet eight Stage 1 Core Set meaningful use objectives. In 2010, the percentage of physicians planning to apply for incentives with EHR systems able to support eight Stage 1 Core Set objectives exceeded the national average in eight states (Iowa, Maine, Massachusetts, Minnesota, Oregon, Utah, Washington, and Wisconsin) and was below the national average only in Texas. The 2010 estimates represent an overestimate of physician readiness because not all physicians with systems supporting all eight objectives examined in this report have systems also capable of supporting the remaining seven Core Set objectives and 5 of 10 Menu Set objectives required for payment. As federal programs to provide incentives for meaningful use and local technical support are implemented, monitoring EHR system adoption will continue to be important in evaluating the effectiveness of these policies and targeting efforts in certain areas.