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

{"title":"Sample Design and Estimation Structures for the National Health Interview Survey, 2016-2025.","authors":"C. Moriarity, V. Parsons, Kimball Jonas, B. Schar, Jonaki Bose, M. Bramlett","doi":"10.15620/cdc:115394","DOIUrl":"https://doi.org/10.15620/cdc:115394","url":null,"abstract":"This report presents operating characteristics of the NHIS 2016-2025 sample design. The general sampling structure is presented, along with a discussion of weighting and variance estimation techniques primarily for 2016-2018. This report is organized into four major sections. The first section presents a general overview of NHIS and its sample design. The second section describes the redesign process, updates for 2016-2025, and includes general frame and sample design considerations. The third section provides a more detailed description of the sample design and how the sample was selected. The last two sections present a description of the estimators used in NHIS for analyzing and summarizing survey results. Documentation for subsequent changes to the sampling and weighting procedures is available on the NCHS website as separate reports and through each year's survey description document. This report is intended for general users of NHIS data.","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":"191 1","pages":"1-30"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46498453","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":"Sample Design and Estimation Structures for the National Health Interview Survey, 2016-2025.","authors":"Chris Moriarity,&nbsp;Van L Parsons,&nbsp;Kimball Jonas,&nbsp;Bryan G Schar,&nbsp;Jonaki Bose,&nbsp;Matthew D Bramlett","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This report presents operating characteristics of the NHIS 2016-2025 sample design. The general sampling structure is presented, along with a discussion of weighting and variance estimation techniques primarily for 2016-2018. This report is organized into four major sections. The first section presents a general overview of NHIS and its sample design. The second section describes the redesign process, updates for 2016-2025, and includes general frame and sample design considerations. The third section provides a more detailed description of the sample design and how the sample was selected. The last two sections present a description of the estimators used in NHIS for analyzing and summarizing survey results. Documentation for subsequent changes to the sampling and weighting procedures is available on the NCHS website as separate reports and through each year's survey description document. This report is intended for general users of NHIS data.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 191","pages":"1-30"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40478111","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":"Machine Learning for Medical Coding in Healthcare Surveys.","authors":"Christine A Lucas, Emily C Hadley, Robert F. Chew, Jason Nance, Peter Baumgartner, Rita Thissen, D. Plotner, Christine Carr, Aerian Tatum","doi":"10.15620/cdc:109828","DOIUrl":"https://doi.org/10.15620/cdc:109828","url":null,"abstract":"Objectives Medical coding, or the translation of healthcare information into numeric codes, is expensive and time intensive. This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statistical healthcare surveys.","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":"189 1","pages":"1-29"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49123545","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":"Machine Learning for Medical Coding in Healthcare Surveys.","authors":"Christine A Lucas,&nbsp;Emily Hadley,&nbsp;Robert Chew,&nbsp;Jason Nance,&nbsp;Peter Baumgartner,&nbsp;Rita Thissen,&nbsp;David M Plotner,&nbsp;Christine Carr,&nbsp;Aerian Tatum","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Objectives Medical coding, or the translation of healthcare information into numeric codes, is expensive and time intensive. This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statistical healthcare surveys.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 189","pages":"1-29"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39531053","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":"Enhancing Identification of Opioid-involved Health Outcomes Using National Hospital Care Survey Data.","authors":"Donielle G White,&nbsp;Nikki B Adams,&nbsp;Amy M Brown,&nbsp;Adaeze O'Jiaku-Okorie,&nbsp;Rihem Badwe,&nbsp;Salah Shaikh,&nbsp;Adewumi Adegboye","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Objectives This report documents the development of the 2016 National Hospital Care Survey (NHCS) Enhanced Opioid Identification Algorithm, an algorithm that can be used to identify opioid-involved and opioid overdose hospital encounters. Additionally, the algorithm can be used to identify opioids and opioid antagonists that can be used to reverse opioid overdose (naloxone) and to treat opioid use disorder (naltrexone).</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 188","pages":"1-31"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39531054","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":"Comparing Blood Pressure Values Obtained by Two Different Protocols: National Health and Nutrition Examination Survey, 2017-2018.","authors":"Y. Ostchega, Tatiana Nwankwo, M. Chiappa, M. Wolz, Jessica E. Graber, Duong T Nguyen","doi":"10.15620/CDC:104185","DOIUrl":"https://doi.org/10.15620/CDC:104185","url":null,"abstract":"Objectives Blood pressure (BP) is traditionally measured using a mercury sphygmomanometer. Given environmental concerns about mercury, clinical and survey settingsare moving to automated devices with an oscillometric protocol to obtain BP. This report compares BP measurement using the mercury and oscillometric protocols.","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":"87 1","pages":"1-26"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41935680","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":"Comparing Blood Pressure Values Obtained by Two Different Protocols: National Health and Nutrition Examination Survey, 2017-2018.","authors":"Yechiam Ostchega,&nbsp;Tatiana Nwankwo,&nbsp;Michele Chiappa,&nbsp;Michael Wolz,&nbsp;Jessica Graber,&nbsp;Duong T Nguyen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Objectives Blood pressure (BP) is traditionally measured using a mercury sphygmomanometer. Given environmental concerns about mercury, clinical and survey settingsare moving to automated devices with an oscillometric protocol to obtain BP. This report compares BP measurement using the mercury and oscillometric protocols.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 87","pages":"1-26"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39012711","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 National Center for Health Statistics' 2015 and 2016 Research and Development Surveys.","authors":"Yulei He,&nbsp;Bill Cai,&nbsp;Hee-Choon Shin,&nbsp;Vladislav Beresovsky,&nbsp;Van Parsons,&nbsp;Katherine Irimata,&nbsp;Paul Scanlon,&nbsp;Jennifer Parker","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Objective: This report provides a general description of the background and operation of the first two rounds of the Research and Development Survey (RANDS), a series of cross-sectional surveys from probability-sampled commercial survey panels. The Division of Research and Methodology of the National Center for Health Statistics (NCHS) conducted the first two rounds of RANDS in 2015 and 2016. RANDS 1 and 2 are being used primarily for question design evaluation and for investigating statistical methodologies for estimation.\u0000Methods: NCHS contracted with Gallup, Inc. to conduct RANDS 1 in Fall 2015 and RANDS 2 in Spring 2016. RANDS 1 and 2 were conducted using a web survey mode and included survey questions from the National Health Interview Survey (NHIS) that were specifically chosen to provide comparison and evaluation of the survey methodology properties of web surveys and traditional household surveys. In this report, some demographic and health estimates are provided from both sources to describe the RANDS data.\u0000Results: In RANDS 1, 2,304 out of the original 9,809 invited panel members completed the survey, for a completion rate of 23.5%. In RANDS 2, 2,480 of the initial 8,231 invited respondents completed the survey, for a completion rate of 30.1%. RANDS 1 and 2 participants were similar to the quarterly NHIS participants with respect to sex, census region, and whether they had worked for pay in the previous week. Other characteristics varied, including age, race and ethnicity, and income. Most health estimates differed between RANDS and NHIS. Public-use versions of the RANDS data can be found at: https://www.cdc.gov/nchs/rands.\u0000Conclusion: RANDS is an ongoing platform for research to understand the properties of probability-sampled recruited panels of primarily web users, investigating and developing statistical methods for using such data in conjunction with large nationally representative health surveys, and for extending question-design evaluations.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 59","pages":"1-60"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38576593","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 U.S. National Vital Statistics System: Transitioning Into the 21st Century, 1990-2017.","authors":"Stephanie J Ventura","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This report describes the history of the National Vital Statistics System, with a focus on the period 1990-2017. The vital statistics system is the country's most enduring program of data collection on the health of the population. It is based on information reported on the certificates of births and deaths and reports of fetal deaths, collected in each of the states and independent registration areas. Over the last two decades, the vital statistics system has experienced far-reaching changes, and has shifted in important ways to emphasize data quality, timeliness, and analysis. The changes underlying these areas are described.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 62","pages":"1-84"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36519727","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":"Statistical Methodology of the National Immunization Survey, 2005-2014.","authors":"Kirk K Wolter,&nbsp;Philip J Smith,&nbsp;Meena Khare,&nbsp;Bess Welch,&nbsp;Kennon R Copeland,&nbsp;Vicki J Pineau,&nbsp;Nicholas Davis","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The National Immunization Survey (NIS) family of surveys includes NIS-Child, which monitors vaccination coverage for the U.S. population of children aged 19-35 months; NIS- Teen, which monitors vaccination coverage for the U.S. population of adolescents aged 13-17; and NIS-Flu, which monitors influenza vaccination coverage for the U.S. population of children aged 6 months through 17 years. This report describes the methods used in this family of surveys during the 2005-2014 period.</p>","PeriodicalId":38828,"journal":{"name":"Vital and health statistics. Ser. 1: Programs and collection procedures","volume":" 61","pages":"1-107"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35850758","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}