Assessing Laboratory Method Validations for Informing Inference Across Survey Cycles in the National Health and Nutrition Examination Survey.

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.

Multiple imputation to account for linkage ineligibility in the NHANES-CMS Medicaid linked data: General use versus subject specific imputation models.

Data from the National Health and Nutrition Examination Survey (NHANES) have been linked to the Center for Medicare and Medicaid Services' Medicaid Enrollment and Claims Files. As not all survey participants provide sufficient information to be eligible for record linkage, linked data often includes fewer records than the original survey data. This project presents an application of multiple imputation (MI) for handling missing Medicaid/CHIP status due to linkage refusals in linked NHANES-Medicaid data using the linked 1999-2004 NHANES data. By examining multiple outcomes and subgroups among children, the analyses compare the results from a multi-purpose dataset produced from a single MI model to that of individualized MI models. Outcomes examined here include obesity, untreated dental caries, attention deficit hyperactivity disorder (ADHD), and exposure to second hand smoke.

National Health and Nutrition Examination Survey: Estimation Procedures, 2011-2014.

This report describes the methods used to create NHANES 2011-2014 sample weights and variance units for the public-use data files, including sample weights for selected subsamples, such as the fasting subsample. The impacts of sample design changes on estimation for NHANES 2011-2014 and the addition of the NHANES National Youth Fitness Survey (NNYFS) 2012 are described. Approaches that data users can employ to modify sample weights when combining survey cycles or when combining subsamples are also included.

Accounting for study participants who are ineligible for linkage: a multiple imputation approach to analyzing the linked National Health and Nutrition Examination Survey and Centers for Medicare and Medicaid Services' Medicaid data.

Data from the National Health and Nutrition Examination Survey have been linked to the Center for Medicare and Medicaid Services' Medicaid Enrollment and Claims Files for the survey years 1999-2004. The linked data are produced by the National Center for Health Statistics' (NCHS) Data Linkage Program and are available in the NCHS Research Data Center. This project compares the usefulness of multiple imputation to account for data linkage ineligibility and other survey nonresponse with currently recommended weight adjustment procedures. Estimated differences in environmental smoke exposure across Medicaid/Children's Health Insurance Program (CHIP) enrollment status among children ages 3-15 years are examined as a motivating example. Comparisons are drawn across the three different estimates: one that uses MI to impute the administrative Medicaid/CHIP status of those who are ineligible for linkage, a second that uses the linked data restricted to linkage eligible participants with a basic weight adjustment, and a third that uses self-reported Medicaid/CHIP status from the survey data. The results indicate that estimates from the multiple imputation analysis were comparable to those found when using weight adjustment procedures and had the added benefit of incorporating all survey participants (linkage eligible and linkage ineligible) into the analysis. We conclude that both multiple imputation and weight adjustment procedures can effectively account for survey participants who are ineligible for linkage.