Objectives:This report describes the development and operations of the 2016 National Culturally and Linguistically Appropriate Services Survey for Office-based Physicians (National CLAS Physician Survey). The survey was developed to understand awareness, adoption, and implementation of the National CLAS Standards in health and health care among office-based physicians. Methods:Survey development included a literature review of survey and assessment instruments that evaluated cultural and linguistic appropriateness in health care. Survey questions were pretested during a cognitive interview study of 20 office-based physicians in the District of Columbia metropolitan area. The cognitive interviews were analyzed using a grounded theory approach. The final survey was administered via web, mail, and computer-assisted telephone interview to 2,400 sampled physicians between August 2016 and December 2016. A nonresponse bias assessment was conducted. Results:The literature review identified five survey and assessment instruments. Collectively, survey content included: cultural competency training, cultural awareness, and adoption of the National CLAS Standards. Cognitive interviews showed respondent difficulty in question interpretation and survey completion of some items. Survey revisions addressed these issues. The final overall weighted survey response rate was 33.8%. Final weights produced a lower standardized bias than base weights. Conclusions:The National CLAS Physician Survey is the first nationally representative survey to describe the use and implementation of culturally and linguistically appropriate services by office-based physicians. Data can serve as a baseline for future studies and as a benchmark for meeting the key objectives of the National CLAS Standards.
The National Health Interview Survey (NHIS), conducted by the National Center for Health Statistics since 1957, is the principal source of information on the health of the U.S. civilian noninstitutionalized population. NHIS selects one adult (Sample Adult) and, when applicable, one child (Sample Child) randomly within a family (through 2018) or a household (2019 and forward). Sampling weights for the separate analysis of data from Sample Adults and Sample Children are provided annually by the National Center for Health Statistics. A growing interest in analysis of parent-child pair data using NHIS has been observed, which necessitated the development of appropriate analytic weights. Objective This report explains how dyad weights were created such that data users can analyze NHIS data from both Sample Children and their mothers or fathers, respectively. Methods Using data from the 2019 NHIS, adult-child pair-level sampling weights were developed by combining each pair's conditional selection probability with their household-level sampling weight. The calculated pair weights were then adjusted for pair-level nonresponse, and large sampling weights were trimmed at the 99th percentile of the derived sampling weights. Examples of analyzing parent-child pair data by means of domain estimation methods (that is, statistical analysis for subpopulations or subgroups) are included in this report. Conclusions The National Center for Health Statistics has created dyad or pair weights that can be used for studies using parent-child pairs in NHIS. This method could potentially be adapted to other surveys with similar sampling design and statistical needs.
Background and objectives Laboratory tests conducted on survey respondents' biological specimens are a major component of the National Health and Nutrition Examination Survey. The National Center for Health Statistics' Division of Health and Nutrition Examination Surveys performs internal analytic method validation studies whenever laboratories undergo instrumental or methodological changes, or when contract laboratories change. These studies assess agreement between methods to evaluate how methodological changes could affect data inference or compromise consistency of measurements across survey cycles. When systematic differences between methods are observed, adjustment equations are released with the data documentation for analysts planning to combine survey cycles or conduct a trend analysis. Adjustment equations help ensure that observed differences from methodological changes are not misinterpreted as population changes. This report assesses the reliability of statistical methods used by the Division of Health and Nutrition Examination Surveys when conducting method validation studies to address concerns that adjustment equations are being overproduced (recommended too frequently). Methods Public-use 2017-2018 National Health and Nutrition Examination Survey laboratory data were used to simulate "new" measurements for 120 analytic method validation studies. Blinded studies were analyzed to determine the final adjustment recommendation for each study using difference plots, descriptive statistics, t-tests, and Deming regressions. Final recommendations were compared with simulated difference types to assess how often spurious results were observed. Concordance estimates (concordance, misclassification, sensitivity, specificity, and positive and negative predictive values) informed assessments. Results Adjustment equations were appropriately recommended for 75.0% of the studies, over-recommended for 5.8%, under-recommended for 15.8%, and recommended with an inappropriate technique for 3.3%. Across simulated difference types, sensitivity ranged from 65.9% to 84.4% and specificity from 74.7% to 97.5%. Conclusions Findings from this report suggest that the current methodology used by the Division of Health and Nutrition Examination Surveys performs moderately well. Based on these data and analyses, underadjustment was more prevalent than overadjustment, suggesting that the current methodology is conservative.
Objectives This report documents the results of a validation study conducted to assess the reliability of two algorithms applied to the 2016 National Hospital Care Survey. One algorithm identifies opioid-involved and opioid overdose hospital encounters, and the other identifies encounters with patients that have substance use disorders and selected mental health issues. These algorithms use both medical codes and natural language processing to identify encounters. Methods To validate the algorithms, medical record abstraction was performed on a stratified sample of 900 hospital encounters from the 2016 National Hospital Care Survey. The abstractors recorded their determinations of opioid involvement, opioid overdose, substance use disorder, and mental health issues on a standard form. Abstractors' determinations were compared with algorithm output to assess the overall performance using F-score and Matthews correlation coefficient. The latter provided a secondary measure of performance. The 2016 National Hospital Care Survey data are unweighted and not nationally representative. Results Overall algorithm performance varied by topic and by metric. The opioid-involvement algorithm achieved the highest performance, performing well with an F-score of 0.95, followed by the substance use disorder algorithm (F-score of 0.79), the mental health issues algorithm (F-score of 0.68), and the opioid overdose algorithm (F-score of 0.48). Assessment by Matthews correlation coefficient indicated an overall poorer level of performance, ranging from a high of 0.57 for the mental health issues algorithm to a low of 0.33 for the opioid-involvement algorithm. The causes of false positives and false negatives likewise varied, including both overly broad code and keyword inclusions as well as incompleteness of data submitted to the National Hospital Care Survey. Conclusion The validation study illustrates which aspects of the developed algorithms performed well and which aspects should be altered or discarded in future iterations. It further emphasizes the importance of data completeness, therefore laying the groundwork for improvements to future survey analyses.
The continuous National Health and Nutrition Examination Survey began data collection in 1999 and proceeded without interruption until operations were suspended in March 2020 in response to the COVID-19 pandemic. Once the Division of Health and Nutrition Examination Surveys was able to determine and resume safe field operations, the next survey cycle was conducted between August 2021 and August 2023. This report describes the survey content, procedures, and methodologies implemented in the August 2021-August 2023 National Health and Nutrition Examination Survey cycle.
