Vital and health statistics. Series 2, Data evaluation and methods research最新文献

{"title":"Methodological Research Informing Weighting, Combination, and Quality Assessment for the National Center for Health Statistics Rapid Surveys System Rounds 1-4.","authors":"Katherine E Irimata, James M Dahlhamer, Matthew D Bramlett, Bill Cai, Yulei He, Van L Parsons, Rong Wei","doi":"10.15620/cdc/252442","DOIUrl":"10.15620/cdc/252442","url":null,"abstract":"<p><strong>Objectives: </strong>The National Center for Health Statistics's Rapid Surveys System (RSS) was established in 2022. It uses two probability-based commercial online panels, NORC at the University of Chicago's AmeriSpeak panel and Ipsos's KnowledgePanel, to enable fast data collection on emerging or priority health topics. RSS Rounds 1-4 (RSS 1-4) were collected between August 2023 and May 2024. This report describes the methodology implemented in the early rounds of RSS for weighting and variance estimation, methods for combining multiple panels, and quality assessment.</p><p><strong>Methods: </strong>RSS 1-4 were weighted to a set of control totals for selected demographic and health variables from the National Health Interview Survey (NHIS). Estimates from the two online panels were combined using a convex combination based on effective sample sizes to produce one set of health estimates for release each round. As part of an ongoing quality evaluation, quality metrics were assessed and reported for each round and were used to inform the RSS methodology in successive rounds.</p><p><strong>Results: </strong>RSS 1-4 had sample sizes ranging from 7,046 (Round 2) to 8,705 (Round 4), with cumulative response rates ranging from 3.8% to 9.9% across the two panel providers. Quality metrics including nonresponse rates and breakoff rates were evaluated and found to be consistent for the two panel providers. In addition, a set of benchmark variables collected on RSS and NHIS were evaluated to assess bias. While the magnitude of bias varied by health variable and across health domains, most health variables had low bias across the four rounds.</p><p><strong>Conclusion: </strong>The methodology for RSS has evolved and allowed for changes to improve the quality of the program and the resulting estimates. This report describes the early methodology implemented in the first four rounds of RSS.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 215","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National Health Interview Survey Follow-up Health Study: Feasibility Evaluation of Adding an In-home Physical Examination to a National Health Survey.","authors":"Adena M Galinsky, Grace E Medley, Duong T Nguyen, Maria A Villarroel, Antonia Warren, Benjamin Zablotsky, Eric Leadbetter, Aaron Maitland","doi":"10.15620/cdc/159283","DOIUrl":"https://doi.org/10.15620/cdc/159283","url":null,"abstract":"<p><strong>Background: </strong>Adding biological measure collection to a household survey can increase the usefulness of the survey data. Between 2020 and 2021, two major federal household health surveys, both conducted by the National Center for Health Statistics-the National Health Interview Survey (NHIS) and the National Health and Nutrition Examination Survey-planned and conducted a pilot study to examine the feasibility of adding physical measurements and biological measure collection to NHIS. NHIS's large nationally representative sample can be used to calculate national and subnational estimates of health conditions and healthcare access and use, but it does not collect physical measurements or biospecimens. The National Health and Nutrition Examination Survey, in addition to an in-person survey, also includes a comprehensive physical examination that collects both physical measurements and biospecimens but does so with a smaller sample.</p><p><strong>Objective: </strong>This report describes the study design and field operations in addition to presenting cooperation and response rates at each stage. It also offers insight into the challenges of gaining cooperation; scheduling home examination appointments; conducting home physical examinations; and shipping and analyzing biospecimens during COVID-19, as well as lessons learned and implications for the future of physical measurements and biological measure collection on NHIS and other large household surveys.</p><p><strong>Methods: </strong>The pilot study was fielded between June and October 2021. Adult NHIS respondents who completed their survey interview in English and lived in the selected sample areas were eligible to participate. Eligible respondents were introduced to the study at the end of their NHIS interview and those who agreed to be contacted could schedule their home health examination in the weeks following their interview. The examinations, conducted by phlebotomists, included a urine sample and a venous blood sample collection, and measurement of respondents' height, weight, waist circumference, blood pressure, and resting heart rate. Participants received a $75 prepaid card and the results of all measures that were collected.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 210","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115513","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 an Enhanced Modified Kalman Filter Approach for Estimating Health Outcomes in Small Subpopulations.","authors":"Lauren M Rossen, Makram Talih, Priyam Patel, Morgan Earp, Jennifer D Parker","doi":"10.15620/cdc/157497","DOIUrl":"https://doi.org/10.15620/cdc/157497","url":null,"abstract":"<p><strong>Background: </strong>Historically, the National Center for Health Statistics has published statistics on a variety of health outcomes by population subgroup, supporting efforts to assess and monitor health disparities in the U.S. population. With small sample or population sizes, however, direct estimates from the Center's data systems may lack precision, limiting the information available for small subpopulations. Small-domain estimation is a methodological approach that can be used to provide more precise model-based estimates of outcomes for small subgroups.</p><p><strong>Methods: </strong>This report describes the performance of an updated methodology to estimate health outcomes in small subpopulations using the enhanced modified Kalman filter (eMKF). The eMKF procedure borrows strength across subgroups and over time to produce more precise estimates of health outcomes in small subpopulations. This report evaluates the performance of the eMKF procedure under different scenarios based on simulated data, to quantify differences in accuracy and precision of model-based estimates compared with direct estimates.</p><p><strong>Results: </strong>Using simulated data, model-based estimates using the eMKF Bayesian model averaging approach presented marked improvements in root mean squared error compared with direct estimates, with larger improvements seen for smaller sample sizes. Improvements were seen across a wide array of analytic scenarios, including outcomes with higher or lower prevalence, trends that varied from linear to cubic, trends that were shared or varied by group, as well as trends that involved unequally spaced time points. In all cases, relative root mean squared errors were smaller for eMKF estimates than direct estimates. Gains in equivalent sample size of up to 420% were observed.</p><p><strong>Conclusions: </strong>Model-based estimates of health outcomes among small subpopulations, where direct estimates may be statistically unreliable, can help inform policies and programs to address disparities in the United States.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 208","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115437","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,&nbsp;Yulei He,&nbsp;Van L Parsons,&nbsp;Hee-Choon Shin,&nbsp;Guangyu Zhang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 199","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":"9318830","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 Linkage Eligibility Bias in the National Health Interview Survey.","authors":"Jonathan Aram,&nbsp;Cindy Zhang,&nbsp;Cordell Golden,&nbsp;Carla E Zelaya,&nbsp;Christine S Cox,&nbsp;Yeats Ye,&nbsp;Lisa B Mirel","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Background Linking health survey data to administrative records expands the analytic utility of survey participant responses, but also creates the potential for new sources of bias when not all participants are eligible for linkage. Residual differences-bias-can occur between estimates made using the full survey sample and the subset eligible for linkage. Objective To assess linkage eligibility bias and provide examples of how bias may be reduced by changes in questionnaire design and adjustment of survey weights for linkage eligibility. Methods Linkage eligibility bias was estimated for various sociodemographic groups and health-related variables for the 2000-2013 National Health Interview Surveys. Conclusions Analysts using the linked data should consider the potential for linkage eligibility bias when planning their analyses and use approaches to reduce bias, such as survey weight adjustments, when appropriate.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 186","pages":"1-28"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25431004","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 Linkage Eligibility Bias in the National Health Interview Survey.","authors":"Jonathan Aram, Cindy Zhang, C. Golden, C. Zelaya, C. Cox, Yeats Ye, L. Mirel","doi":"10.15620/CDC:100469","DOIUrl":"https://doi.org/10.15620/CDC:100469","url":null,"abstract":"Background Linking health survey data to administrative records expands the analytic utility of survey participant responses, but also creates the potential for new sources of bias when not all participants are eligible for linkage. Residual differences-bias-can occur between estimates made using the full survey sample and the subset eligible for linkage. Objective To assess linkage eligibility bias and provide examples of how bias may be reduced by changes in questionnaire design and adjustment of survey weights for linkage eligibility. Methods Linkage eligibility bias was estimated for various sociodemographic groups and health-related variables for the 2000-2013 National Health Interview Surveys. Conclusions Analysts using the linked data should consider the potential for linkage eligibility bias when planning their analyses and use approaches to reduce bias, such as survey weight adjustments, when appropriate.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":"186 1","pages":"1-28"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48941356","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":"An Investigation of Nonresponse Bias and Survey Location Variability in the 2017-2018 National Health and Nutrition Examination Survey.","authors":"Tala H I Fakhouri,&nbsp;Crescent B Martin,&nbsp;Te-Ching Chen,&nbsp;Lara J Akinbami,&nbsp;Cynthia L Ogden,&nbsp;Ryne Paulose-Ram,&nbsp;Minsun K Riddles,&nbsp;Wendy Van de Kerckhove,&nbsp;Shelley B Roth,&nbsp;Jason Clark,&nbsp;Leyla K Mohadjer,&nbsp;Robert E Fay","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Over the past two decades, a steady decline in response rates on national face-to-face surveys has been documented, with steeper declines observed in recent years. The impact of nonresponse on survey estimates is inconsistent and depends on the correlation between response propensity and the survey estimates. To better understand the impact of declining response rates on the 2017-2018 National Health and Nutrition Examination Survey (NHANES), potential nonresponse bias (NRB) was investigated. NRB was assessed using three approaches: (a) studying variation within the respondent set; (b) benchmarking and comparisons to external data; and (c) comparing alternative weighting adjustments. Because NHANES only samples 30 counties in every 2-year cycle, the sample of counties in any given cycle may be an outlier on some characteristics. Such sampling variability may compound the effects of NRB. For this reason, the representativeness of the 2017-2018 NHANES counties was examined by comparing: (a) the characteristics of the 2017-2018 sampled counties with those from prior cycles; (b) each sampled county with the average of all the counties in the sampling stratum from which that county was selected; and (c) the 2017-2018 counties with 5,000 other samples that could have been drawn under the same sample design using a simulation study. The NRB analyses showed that the 2017-2018 NHANES sample had a lower proportion of college graduates and higher-income individuals compared with prior cycles. Additionally, the 2017-2018 NHANES counties had lower proportions of college graduates and lower mean incomes compared with counties from prior cycles and counties not selected in 2017-2018, which exacerbated the effects of NRB. Weighting adjustments used in prior cycles were not sufficient to address the bias in the 2017-2018 NHANES. Instead, enhanced weighting adjustments for education and income reduced the bias resulting from nonresponse and location sampling variability.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 185","pages":"1-36"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25334328","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 Health and Nutrition Examination Survey, 2015-2018: Sample Design and Estimation Procedures.","authors":"Te-Ching Chen,&nbsp;Jason Clark,&nbsp;Minsun K Riddles,&nbsp;Leyla K Mohadjer,&nbsp;Tala H I Fakhouri","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Background The purpose of the National Health and Nutrition Examination Survey (NHANES) is to produce national estimates representative of the total noninstitutionalized civilian U.S. population. The sample for NHANES is selected using a complex, four-stage sample design. NHANES sample weights are used by analysts to produce estimates of the health-related statistics that would have been obtained if the entire sampling frame (i.e., the noninstitutionalized civilian U.S. population) had been surveyed. Sampling errors should be calculated for all survey estimates to aid in determining their statistical reliability. For complex sample surveys, exact mathematical formulas for variance estimates that fully incorporate the sample design are usually not available. Variance approximation procedures are required to provide reasonable, approximately unbiased, and design-consistent estimates of variance. Objective This report describes the NHANES 2015-2018 sample design and the methods used to create 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 2015-2018 are described. Approaches that data users can use to modify sample weights when combining survey cycles or when combining subsamples are also included.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 184","pages":"1-35"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25431048","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":"Waist Circumference Measurement Methodology Study: National Health and Nutrition Examination Survey, 2016.","authors":"Yechiam Ostchega,&nbsp;Rie Seu,&nbsp;Neda Sarafrazi,&nbsp;Guangyu Zhang,&nbsp;Jeffery P Hughes,&nbsp;Ivey Miller","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Objective This report compares five methods of waist circumference (WC) measurements: 1) the National Heart, Lung, and Blood Institute (NHLBI-WC); 2) the World Health Organization (WHO-WC); 3) the Multi-Ethnic Study of Atherosclerosis (MESA-WC) using Gulick II Plus tape; 4) the Multi-Ethnic Study of Atherosclerosis (MESA-WC) using Lufkin tape; and 5) assisted self-measurement over clothes (MESA-assisted). Method During 2016, measurements were obtained from 2,297 participants aged 20 and over, who participated in the National Health and Nutrition Examination Survey (NHANES). The mean differences and sensitivity and specificity for abdominal obesity (AO) were calculated between the NHLBI-WC (reference) and the other four WC measurements. Results The mean difference between NHLBI-WC and WHO-WC was 0.81 cm for men and 3.21 cm for women ( p ≤ 0.0125 for both); between NHLBI-WC and MESA-WC (Gulick) was -0.68 cm for men ( p ≤ 0.0125) and -0.89 cm for women; between NHLBI-WC and MESA-WC (Lufkin) was 0.02 cm for men and 0.08 cm for women; and between NHLBI-WC and MESA-assisted was -0.71 cm for men and 1.34 cm for women ( p ≤ 0.0125 for both). Sensitivity and specificity for AO, with NHLBI-WC as a reference, for men were greater than 90% for all methods; for women, sensitivity and specificity for AO for MESA-WC (Lufkin) were greater than 90%; for women, WHO-WC, MESAWC (Gulick), and MESA-assisted methods were greater than 85%.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 182","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36918963","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":"U.S. Small-area Life Expectancy Estimates Project: Methodology and Results Summary.","authors":"Elizabeth Arias,&nbsp;Loraine A Escobedo,&nbsp;Jocelyn Kennedy,&nbsp;Chunxia Fu,&nbsp;Jodi Cisewki","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Statistically reliable, abridged, period life tables were produced for 88.7% of U.S. census tracts (65,662). A battery of tests revealed that the census-tract life table functions followed expected patterns; their distribution about state and U.S. values showed no aberrations; and their weighted mean values compared well with state- and national-level estimates. The weighted mean life expectancy at birth for the 65,662 census tracts was 78.7 years compared with the official U.S. estimate of 78.8 years in midyear 2013. The results of this study concur with previous research showing that a minimum population size of 5,000 is acceptable, with the caveat that missing age-specific death counts cannot be ignored. The methodology developed for this study addressed the issues of small populations and zero deaths as robustly as possible, although it is not without error.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 181","pages":"1-40"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36619584","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}