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

{"title":"The validity of race and Hispanic origin reporting on death certificates in the United States.","authors":"Elizabeth Arias,&nbsp;William S Schauman,&nbsp;Karl Eschbach,&nbsp;Paul D Sorlie,&nbsp;Eric Backlund","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objectives: </strong>This report presents the results of an evaluation study of the validity of race and Hispanic origin reporting on death certificates in the United States and its impact on race- and Hispanic origin-specific mortality estimates.</p><p><strong>Methods: </strong>The National Longitudinal Mortality Study (NLMS) was used to evaluate death certificate classification of race and Hispanic origin by comparing death certificate with survey race-ethnicity classifications for a sample of decedents identified in NLMS. NLMS consists of a series of annual Current Population Survey files (1973 and 1978-1998) linked to death certificates for years 1979-1998. To identify and measure the effect of race-ethnicity misclassification on death certificates on mortality estimates, pooled 1999-2001 vital statistics mortality data and population data from the 2000 census were used to estimate and compare observed and corrected (for death certificate misclassification) race-ethnicity specific death rates.</p><p><strong>Results: </strong>Race and ethnicity reporting on the death certificate continues to be excellent for the white and black populations. It remains poor for the American Indian or Alaska Native (AIAN) population but is reasonably good for the Hispanic and Asian or Pacific Islander (API) populations. Decedent characteristics such as place of residence and nativity have an important effect on the quality of reporting on the death certificate. The effects of misclassification on mortality estimates were most pronounced for the AIAN population, where correcting for misclassification reverses a large AIAN over white mortality advantage to a large disadvantage. Among the Hispanic and API populations, adjustment for death certificate misclassification did not significantly affect minority-majority mortality differentials.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 148","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2008-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27852509","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 healthy life expectancies using longitudinal survey data: methods and techniques in population health measures.","authors":"Michael T Molla,&nbsp;Jennifer H Madans","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Objective-Summary measures of population health are statistics that combine mortality and morbidity to represent overall population health in a single index. Such measures include healthy life expectancy, also called disability-free life expectancy and active life expectancy. Healthy life expectancy can be calculated using cross-sectional or longitudinal survey data. This report presents a comprehensive discussion of a method for calculating healthy life expectancy using data from longitudinal surveys. Methods-Healthy life expectancies are calculated using the multistate life table model. Expected life in various states of health is estimated using data from the Second Longitudinal Study of Aging and the Medicare Current Beneficiary Survey to illustrate the calculation of the statistics and the discussion of data and methodology related issues. Results-The study shows that estimating summary measures of population health using longitudinal survey data provides the opportunity of using incidence rather than prevalence rates. Health measures estimated based on incidence reflect the most recent health status of the population. Models that use longitudinal survey data measure transitions from good to poor health as well as poor to good health. That is, the models account for recovery from morbidity or illness. Longitudinal survey data canalsobeusedtocalculate healthy or active life expectancies by initial health states. </p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 146","pages":"1-24"},"PeriodicalIF":0.0,"publicationDate":"2008-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32561330","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":"Linkage of the National Health Interview Survey to air quality data.","authors":"Jennifer D Parker,&nbsp;Nataliya Kravets,&nbsp;Tracey J Woodruff","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objective: </strong>This report describes the linkage between the National Health Interview Survey (NHIS) and air monitoring data from the U.S. Environmental Protection Agency (EPA). There have been few linkages of these data sources, partly because of restrictions on releasing geographic detail from NHIS on public-use files in order to protect participant confidentiality.</p><p><strong>Methods: </strong>Pollution exposures for NHIS respondents were calculated by averaging the annual average exposure estimates from EPA air monitors both within 5, 10, 15, and 20 miles of the respondent's block-group location (which is available on restricted NHIS data files) and by county of residence. The 1987-2005 linked data files--referred to as NHIS-EPAAnnualAir--were used to describe the percentage of NHIS respondents linked and the median exposures by linkage method, survey year, and pollutant. Using the 2005 NHIS-EPAAnnualAir data file, the percentage linked and median exposure were described by respondent characteristics, linkage method, and pollutant.</p><p><strong>Results: </strong>Many decisions were made to define pollution exposures for NHIS respondents, including monitor selection, location assignment for NHIS respondents, and geographic linkage criteria. Geographic linkage criteria for assigning area-level exposure estimates affected the percentage and composition of respondents included in the resulting linked sample. Median exposure estimates, however, were similar among geographic linkage methods.</p><p><strong>Conclusion: </strong>NHIS-EPAAnnualAir data files for 1985 through 2005 are currently available to users in the NCHS Research Data Center.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 145","pages":"1-24"},"PeriodicalIF":0.0,"publicationDate":"2008-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27330960","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 match of the March 1996 Current Population Survey and the 1995 National Health Interview Survey.","authors":"Deborah D Ingram,&nbsp;Christopher L Moriarity,&nbsp;John F O'Hare,&nbsp;Joan Turek","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objectives: </strong>Statistical matching is a method used to combine two files when it is unlikely that individuals on one file are also on the other file. The objectives of this report are to document and evaluate statistical matches of the March 1996 Current Population Survey (CPS) and the 1995 National Health interview Survey (NHIS) and give recommendations for improving future matches. The CPS-NHIS match was motivated by the need for a data set with data on health measures and family resources for use in policy analyses.</p><p><strong>Methods: </strong>Three statistical matches between the March 1996 CPS and the 1995 NHIS are described in this report. All three matches used person-level constrained matching with partitioning and a predictive mean matching algorithm to link records on the two files. For two of the matches, the CPS served as the Host file and the NHIS served as the Donor file; for the third match, the NHIS was the Host file and the CPS was the Donor file.</p><p><strong>Results: </strong>The results suggest that the constrained predictive mean matches of the March 1996 CPS and the 1995 NHIS successfully combined some of the information on the two files, but that relationships among some Host and Donor variables on the matched file may be distorted. The evaluation of the matches suggested that the variables used to partition the Host and Donor files prior to matching and the variables involved in the predictive mean matching play an important role in determining whether relationships among variables on the matched file correctly represent relationships among those variables in the population. The evaluation also indicated that estimates for small subgroups may be especially subject to error. The results reinforce the need to proceed cautiously when exploring relationships among Host and Donor variables on a statistically matched file.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 144","pages":"1-50"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27363179","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 match of the March 1996 Current Population Survey and the 1995 National Health Interview Survey.","authors":"D. D. Ingram, C. Moriarity, John F. O'Hare, Joan L. Turek","doi":"10.1037/e414732008-001","DOIUrl":"https://doi.org/10.1037/e414732008-001","url":null,"abstract":"OBJECTIVES Statistical matching is a method used to combine two files when it is unlikely that individuals on one file are also on the other file. The objectives of this report are to document and evaluate statistical matches of the March 1996 Current Population Survey (CPS) and the 1995 National Health interview Survey (NHIS) and give recommendations for improving future matches. The CPS-NHIS match was motivated by the need for a data set with data on health measures and family resources for use in policy analyses. METHODS Three statistical matches between the March 1996 CPS and the 1995 NHIS are described in this report. All three matches used person-level constrained matching with partitioning and a predictive mean matching algorithm to link records on the two files. For two of the matches, the CPS served as the Host file and the NHIS served as the Donor file; for the third match, the NHIS was the Host file and the CPS was the Donor file. RESULTS The results suggest that the constrained predictive mean matches of the March 1996 CPS and the 1995 NHIS successfully combined some of the information on the two files, but that relationships among some Host and Donor variables on the matched file may be distorted. The evaluation of the matches suggested that the variables used to partition the Host and Donor files prior to matching and the variables involved in the predictive mean matching play an important role in determining whether relationships among variables on the matched file correctly represent relationships among those variables in the population. The evaluation also indicated that estimates for small subgroups may be especially subject to error. The results reinforce the need to proceed cautiously when exploring relationships among Host and Donor variables on a statistically matched file.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":"144 1","pages":"1-50"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57762933","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 Survey of Family Growth, Cycle 6: sample design, weighting, imputation, and variance estimation.","authors":"J. Lepkowski, W. Mosher, K. Davis, R. Groves, John van Hoewyk, Jennifer Willem","doi":"10.1037/e414742008-001","DOIUrl":"https://doi.org/10.1037/e414742008-001","url":null,"abstract":"OBJECTIVES\u0000Cycle 6 of the National Survey of Family Growth (NSFG) was conducted by the National Center for Health Statistics in 2002 and early 2003. This report describes how the sample was designed, shows response rates for various subgroups of men and women, describes how the sample weights were computed to make national estimates possible, shows how missing data were imputed for a limited set of key variables, and describes the proper ways to estimate sampling errors from the NSFG. The report includes both nontechnical summaries for readers who need only general information and more technical detail for readers who need an in-depth understanding of these topics.\u0000\u0000\u0000METHODS\u0000The NSFG Cycle 6 was based on an independent, national probability sample of men and women 15-44 years of age. It was the first NSFG ever to include a national sample of men 15-44 as well as a sample of women. Fieldwork was carried out by the University of Michigan's Institute for Social Research (ISR) under a contract with NCHS. In-person, face-to-face interviews were conducted by professional female interviewers using laptop computers. In all, 12,571 women and men-7,643 females and 4,928 males-were interviewed, the largest NSFG ever done.\u0000\u0000\u0000RESULTS\u0000Analysis of NSFG Cycle 6 data requires the use of sampling weights and estimation of sampling errors that accounts for the complex sample design and estimation features of the survey. Examples of how to use several available software packages that incorporate complex design features in estimation, such as SAS, SUDAAN, and STATA, are presented.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":"142 1","pages":"1-82"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57763004","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 Survey of Family Growth, Cycle 6: sample design, weighting, imputation, and variance estimation.","authors":"James M Lepkowski,&nbsp;William D Mosher,&nbsp;Karen E Davis,&nbsp;Robert M Groves,&nbsp;John van Hoewyk,&nbsp;Jennifer Willem","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objectives: </strong>Cycle 6 of the National Survey of Family Growth (NSFG) was conducted by the National Center for Health Statistics in 2002 and early 2003. This report describes how the sample was designed, shows response rates for various subgroups of men and women, describes how the sample weights were computed to make national estimates possible, shows how missing data were imputed for a limited set of key variables, and describes the proper ways to estimate sampling errors from the NSFG. The report includes both nontechnical summaries for readers who need only general information and more technical detail for readers who need an in-depth understanding of these topics.</p><p><strong>Methods: </strong>The NSFG Cycle 6 was based on an independent, national probability sample of men and women 15-44 years of age. It was the first NSFG ever to include a national sample of men 15-44 as well as a sample of women. Fieldwork was carried out by the University of Michigan's Institute for Social Research (ISR) under a contract with NCHS. In-person, face-to-face interviews were conducted by professional female interviewers using laptop computers. In all, 12,571 women and men-7,643 females and 4,928 males-were interviewed, the largest NSFG ever done.</p><p><strong>Results: </strong>Analysis of NSFG Cycle 6 data requires the use of sampling weights and estimation of sampling errors that accounts for the complex sample design and estimation features of the survey. Examples of how to use several available software packages that incorporate complex design features in estimation, such as SAS, SUDAAN, and STATA, are presented.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 142","pages":"1-82"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26273076","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 the potential of national strategies for electronic health records for population health monitoring and research.","authors":"Daniel J Friedman","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objectives: </strong>This report assesses the potential of national strategies for electronic health records for population health monitoring and research.</p><p><strong>Methods: </strong>This study: (1) Reviewed national strategies for electronic health records in Australia, Canada, England, and New Zealand, through written materials available before January 2006. (2) Identified the potential of national strategies for electronic health records for population health monitoring and research through interviews with 96 experts in the U.S., Australia, Canada, England, and New Zealand. (3) Delineated fundamental issues that must be confronted to maximize the contribution of national strategies for electronic health records to population health monitoring and research.</p><p><strong>Results: </strong>National strategies for electronic health records reflect the political, healthcare, and market systems of individual countries. National strategies also reflect technical decisions and political judgments. National strategies are evolving, and passing through stages of conceptualization, design, pilot testing, and implementation. Only England has moved to implementation. Population health monitoring and research are secondary to the primary uses of clinical care and management in all national strategies for electronic health records. Only England has conceptualized, designed, and is implementing the use of electronic health records for population health monitoring and research. Canada's strategy includes communicable disease surveillance, but not broader population health monitoring for developing health statistics. This study identifies definitional, numerator, denominator, and overarching issues that must be evaluated in assessing the potential of national strategies for electronic health records for population health monitoring and research. It delineates success factors that increase the potential for those national strategies to contribute to population health monitoring and research. Finally, this study assesses barriers that must be overcome if national strategies for electronic health records can contribute to population health monitoring and research, and especially to health statistics.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 143","pages":"1-83"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26762942","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":"Methodological issues in measuring health disparities.","authors":"Kenneth Keppel, Elsie Pamuk, John Lynch, Olivia Carter-Pokras, Kim Insun, Vickie Mays, Jeffrey Pearcy, Victor Schoenbach, Joel S Weissman","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objectives: </strong>This report discusses six issues that affect the measurement of disparities in health between groups in a population: Selecting a reference point from which to measure disparity. Measuring disparity in absolute or in relative terms. Measuring in terms of favorable or adverse events. Measuring in pair-wise or in summary fashion. Choosing whether to weight groups according to group size. Deciding whether to consider any inherent ordering of the groups. These issues represent choices that are made when disparities are measured.</p><p><strong>Methods: </strong>Examples are used to highlight how these choices affect specific measures of disparity.</p><p><strong>Results: </strong>These choices can affect the size and direction of disparities measured at a point in time and conclusions about the size and direction of changes in disparity over time. Eleven guidelines for measuring disparities are presented.</p><p><strong>Conclusions: </strong>Choices concerning the measurement of disparity should be made deliberately, recognizing that each choice will affect the results. When results are presented, the choices on which the measurements are based should be described clearly and justified appropriately.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 141","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2005-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3681823/pdf/nihms312672.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25201071","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":"Effects of form length and item format on response patterns and estimates of physician office and hospital outpatient department visits. National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, 2001.","authors":"Esther Hing,&nbsp;Susan M Schappert,&nbsp;Catharine W Burt,&nbsp;Iris M Shimizu","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objectives: </strong>This report describes effects due to form length and/or item formats on respondent cooperation and survey estimates.</p><p><strong>Methods: </strong>Two formats were used for the Patient Record form for the 2001 NAMCS and OPD component of the NHAMCS: a short form with 70 subitems and a long form with 140 subitems. The short form also contained many write-in items and fit on a one-sided page. The long form contained more check boxes and other unique items and required a two-sided page. The NAMCS sample of physicians and NHAMCS sample of hospitals were randomly divided into two half samples and randomly assigned to either the short or long form. Unit and item nonresponse rates, as well as survey estimates from the two forms, were compared using SUDAAN software, which takes into account the complex sample design of the surveys.</p><p><strong>Results: </strong>Physician unit response was lower for the long form overall and in certain geographic regions. Overall OPD unit response was not affected by form length, although there were some differences in favor of the long form for some types of hospitals. Despite having twice the number of check boxes on the long form as the short form, there was no difference in the percentage of visits with any diagnostic or screening services ordered or provided. However, visit estimates were usually higher for services collected with long form check-boxes than with (recoded) short form write-in entries. Finally, the study confirmed the feasibility of collecting certain items found only on the long form.</p><p><strong>Conclusion: </strong>Overall, physician cooperation was more sensitive to form length than was OPD cooperation. The quality of the data was not affected by form length. Visit estimates were influenced by both content and item format.</p>","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 139","pages":"1-32"},"PeriodicalIF":0.0,"publicationDate":"2005-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25161050","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}