Vital and health statistics. Ser. 1: Programs and collection procedures最新文献

{"title":"Plan and Operations of the National Health and Nutrition Examination Survey, August 2021-August 2023.","authors":"Ana L Terry, Michele M Chiappa, Juliana McAllister, David A Woodwell, Jessica E Graber","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 66","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing Sampling Weights for Statistical Analysis of Parent-Child Pair Data From the National Health Interview Survey.","authors":"Guangyu Zhang, Yulei He, Van Parsons, Chris Moriarity, Stephen J Blumberg, Benjamin Zablotsky, Aaron Maitland, Matthew D Bramlett, Jonaki Bose","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 207","pages":"1-31"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140868430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Laboratory Method Validations for Informing Inference Across Survey Cycles in the National Health and Nutrition Examination Survey.","authors":"Kevin Chuang, Jennifer Rammon, Hee-Choon Shin, Te-Ching Chen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 206","pages":"1-41"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140858810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of the Enhanced Opioid Identification and Co-occurring Disorders Algorithms.","authors":"Amy M Brown, Donielle G White, Nikki B Adams, Rihem Rihem PharmD, Salah Shaikh, Lello Guluma","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 205","pages":"1-31"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139576825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National Center for Health Statistics' 2019 Research and Development Survey, RANDS 3.","authors":"Li-Yen R Hu,&nbsp;Paul Scanlon,&nbsp;Kristen Miller,&nbsp;Yulei He,&nbsp;Katherine E Irimata,&nbsp;Guangyu Zhang,&nbsp;Kristen Cibelli Hibben","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Objective This report on the third round of the Research and Development Survey (RANDS 3) provides a general description of RANDS 3 and presents percentage estimates of selected demographic and health-related variables from the overall sample and by one set of experimental groups embedded in the survey. Statistical tests comparing estimates for the two randomized groups were conducted to evaluate the randomization. Methods NORC at the University of Chicago conducted RANDS 3 for the National Center of Health Statistics in 2019 using its AmeriSpeak Panel in web-only mode. To assess question-response patterns, probe questions and four sets of experiments were embedded in RANDS 3, with panelists randomized into two groups for each set of experiments. Participants in each group received questions with differences in wording, question-andresponse formats, or question order. Results Of the 4,255 people sampled, 2,646 completed RANDS 3 for a completion rate of 62.2% and a weighted cumulative response rate of 18.1%. Iterative raking was performed using demographic and selected health condition variables to calibrate the RANDS 3 sample to 2019 National Health Interview Survey (NHIS) estimates. As a result, the overall demographic distribution and percentages of asthma, diabetes, hypertension, and high cholesterol for the calibrated RANDS 3 sample aligned with the percentages estimated from the 2019 NHIS. The distributions of demographic and healthrelated variables were comparable between the two randomized groups examined except for ever-diagnosed hypertension. Conclusion As part of a research series using probability-based survey panels, RANDS 3 included health-related questions with a focus on disability and opioids. Because RANDS is an ongoing research platform, a variety of persistent and emergent research questions relating to survey methodology will continue to be examined in current and future rounds of RANDS.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 65","pages":"1-55"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41157658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National Center for Health Statistics' 2019 Research and Development Survey, RANDS 3.","authors":"Li-Yen R. Hu, P. Scanlon, Kristen Miller, Yulei He, Katherine E. Irimata, Guangyu Zhang, Kristen Cibelli Hibben","doi":"10.15620/cdc:130273","DOIUrl":"https://doi.org/10.15620/cdc:130273","url":null,"abstract":"Objective This report on the third round of the Research and Development Survey (RANDS 3) provides a general description of RANDS 3 and presents percentage estimates of selected demographic and health-related variables from the overall sample and by one set of experimental groups embedded in the survey. Statistical tests comparing estimates for the two randomized groups were conducted to evaluate the randomization. Methods NORC at the University of Chicago conducted RANDS 3 for the National Center of Health Statistics in 2019 using its AmeriSpeak Panel in web-only mode. To assess question-response patterns, probe questions and four sets of experiments were embedded in RANDS 3, with panelists randomized into two groups for each set of experiments. Participants in each group received questions with differences in wording, question-andresponse formats, or question order. Results Of the 4,255 people sampled, 2,646 completed RANDS 3 for a completion rate of 62.2% and a weighted cumulative response rate of 18.1%. Iterative raking was performed using demographic and selected health condition variables to calibrate the RANDS 3 sample to 2019 National Health Interview Survey (NHIS) estimates. As a result, the overall demographic distribution and percentages of asthma, diabetes, hypertension, and high cholesterol for the calibrated RANDS 3 sample aligned with the percentages estimated from the 2019 NHIS. The distributions of demographic and healthrelated variables were comparable between the two randomized groups examined except for ever-diagnosed hypertension. Conclusion As part of a research series using probability-based survey panels, RANDS 3 included health-related questions with a focus on disability and opioids. Because RANDS is an ongoing research platform, a variety of persistent and emergent research questions relating to survey methodology will continue to be examined in current and future rounds of RANDS.","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":"65 1","pages":"1-55"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48666018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 2021 Physician Pain Management Questionnaire Pilot Study.","authors":"Doreen M Gidali,&nbsp;Brian W Ward","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This report outlines the methodology, development, and fielding of the 2021 Physician Pain Management Questionnaire (PPMQ) pilot study. The study was conducted by the National Center for Health Statistics and was designed to test the feasibility of a large, nationally representative survey assessing physician awareness and use of established guidelines for prescribing opioids to manage pain.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 204","pages":"1-45"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41165193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling Procedures for the Collection of Electronic Health Record Data From Federally Qualified Health Centers, 2021-2022 National Ambulatory Medical Care Survey.","authors":"Sonja N Williams,&nbsp;Joy Ukaigwe,&nbsp;Brian W Ward,&nbsp;Titilayo Okeyode,&nbsp;Iris M Shimizu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>As part of modernization efforts, in 2021 the National Ambulatory Medical Care Survey (NAMCS) began collecting electronic health records (EHRs) for ambulatory care visits in its Health Center (HC) Component. As a result, the National Center for Health Statistics (NCHS)needed to adjust the approaches used in the sampling design for the HC Component. This report provides details on these changes to the 2021-2022 NAMCS.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 203","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9726955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibration Weighting Methods for the National Center for Health Statistics Research and Development Survey.","authors":"Katherine E. Irimata, Yulei He, V. Parsons, Hee-Choon Shin, Guangyu Zhang","doi":"10.15620/cdc:123463","DOIUrl":"https://doi.org/10.15620/cdc:123463","url":null,"abstract":"Objectives The Research and Development Survey (RANDS) is a series of web-based, commercial panel surveys that have been conducted by the National Center for Health Statistics (NCHS) since 2015. RANDS was designed for methodological research purposes,including supplementing NCHS' evaluation of surveys and questionnaires to detect measurement error, and exploring methods to integrate data from commercial survey panels with high-quality data collections to improve survey estimation. The latter goal of improving survey estimation is in response to limitations of web surveys, including coverage and nonresponse bias. To address the potential bias in estimates from RANDS,NCHS has investigated various calibration weighting methods to adjust the RANDS panel weights using one of NCHS' national household surveys, the National Health Interview Survey. This report describes calibration weighting methods and the approaches used to calibrate weights in web-based panel surveys at NCHS.","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":"87 1","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47915477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the National Center for Health Statistics Data Presentation Standards for Rates From Vital Statistics and Sample Surveys.","authors":"M. Talih, Katherine E. Irimata, Guangyu Zhang, J. Parker","doi":"10.15620/cdc:123462","DOIUrl":"https://doi.org/10.15620/cdc:123462","url":null,"abstract":"For the CIs used in the Standards for rates from vital statistics and complex health surveys, this report evaluates coverage probability, relative width, and the resulting percentage of rates flagged as statistically unreliable when compared with previously used standards. Additionally, the report assesses the impact of design effects and the denominator's sampling variability, when applicable.","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":"198 1","pages":"1-30"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44763698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}