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

{"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}

{"title":"Issues in Developing Multidimensional Indices of State-level Health Inequalities: National Health Interview Survey, 2013-2015.","authors":"Makram Talih,&nbsp;Maria A Villarroel","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>To describe methodological issues that arise in the construction and design-based estimation of multidimensional indices that aggregate state-specific inequalities in core health measures, using data from the National Health Interview Survey (NHIS).</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 180","pages":"1-40"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36519725","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 Guidelines for Analysis of Trends.","authors":"Deborah D Ingram,&nbsp;Donald J Malec,&nbsp;Diane M Makuc,&nbsp;Deanna Kruszon-Moran,&nbsp;Renee M Gindi,&nbsp;Michael Albert,&nbsp;Vladislav Beresovsky,&nbsp;Brady E Hamilton,&nbsp;Julia Holmes,&nbsp;Jeannine Schiller,&nbsp;Manisha Sengupta","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Many reports present analyses of trends over time based on multiple years of data from National Center for Health Statistics (NCHS) surveys and the National Vital Statistics System (NVSS). Trend analyses of NCHS data involve analytic choices that can lead to different conclusions about the trends. This report discusses issues that should be considered when conducting a time trend analysis using NCHS data and presents guidelines for making trend analysis choices. Trend analysis issues discussed include: choosing the observed time points to include in the analysis, considerations for survey data and vital records data (record level and aggregated), a general approach for conducting trend analyses, assorted other analytic issues, and joinpoint regression. This report provides 12 guidelines for trend analyses, examples of analyses using NCHS survey and vital records data, statistical details for some analysis issues, and SAS and SUDAAN code for specification of joinpoint regression models. Several an lytic choices must be made during the course of a trend analysis, and the choices made can affect the results. This report highlights the strengths and limitations of different choices and presents guidelines for making some of these choices. While this report focuses on time trend analyses, the issues discussed and guidelines presented are applicable to trend analyses involving other ordinal and interval variables.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 179","pages":"1-71"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36110279","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":"Estimating Usual Dietary In take From National Health and Nut rition Examination Survey Data Using the National Cancer Institute Method.","authors":"Kristen A Herrick,&nbsp;Lauren M Rossen,&nbsp;Ruth Parsons,&nbsp;Kevin W Dodd","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Dietary recommendations are intended to be met based on dietary intake over long periods, as associations between diet and health result from habitual intake, not a single eating occasion or day of intake. Measuring usual intake directly is impractical for large population-based surveys due to the respondent burden associated with reporting habitual intake over longer periods. Therefore, analytical techniques were developed to estimate usual intake using as few as 2 days of 24-hour dietary recall data. With National Health and Nutrition Examination Survey (NHANES) data, this report demonstrates how to estimate usual intake using the National Cancer Institute (NCI). This report demonstrates how to estimate the usual intake of nutrients consumed daily or episodically using NHANES data. Means, percentiles, and the percentages above or below specified Dietary Reference Intake (DRI) values for given day, within-person mean (WPM), and estimates of usual intake are presented. Consistent with previous analyses, mean intakes were similar across methods. However, the distributions estimated by nonusual intake methods were wider compared with the NCI Method, which can lead to misclassification of the percentage of the population above or below certain DRIs. Use of NHANES data to examine the proportion of the population at risk of insufficiency or excess of certain nutrients, with methods like given day and WPM that do not address within-person variation, may lead to biased estimates.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 178","pages":"1-63"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36110711","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}