为医疗保险管理数据提供更准确的种族和民族代码。

Health Care Financing Review Pub Date : 2008-01-01
Celia Eicheldinger, Arthur Bonito
{"title":"为医疗保险管理数据提供更准确的种族和民族代码。","authors":"Celia Eicheldinger, Arthur Bonito","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Analyses of health care disparities in Medicare using administrative race and ethnicity data have typically been limited to Black and White beneficiaries. This is in part due to the small size of the other categories, inaccuracies in the race and ethnicity codes, and caveats that more extensive analyses would produce biased results. While previous Medicare efforts certainly improved the accuracy of race and ethnicity coding, we have developed an imputation algorithm that dramatically improves the accuracy of coding for Hispanic and Asian or Pacific Islander beneficiaries. When compared with self-reported race and ethnicity, sensitivity increased from 29.5 to 76.6 percent for Hispanic and from 54.7 to 79.2 percent for Asian and Pacific Islander beneficiaries, with no loss of specificity, and Kappa coefficients reaching 0.80. As a result, 2,245,792 beneficiaries were recoded to Hispanic and 336,363 to Asian or Pacific Islander.</p>","PeriodicalId":55071,"journal":{"name":"Health Care Financing Review","volume":"29 3","pages":"27-42"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4195038/pdf/","citationCount":"0","resultStr":"{\"title\":\"More accurate racial and ethnic codes for Medicare administrative data.\",\"authors\":\"Celia Eicheldinger, Arthur Bonito\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Analyses of health care disparities in Medicare using administrative race and ethnicity data have typically been limited to Black and White beneficiaries. This is in part due to the small size of the other categories, inaccuracies in the race and ethnicity codes, and caveats that more extensive analyses would produce biased results. While previous Medicare efforts certainly improved the accuracy of race and ethnicity coding, we have developed an imputation algorithm that dramatically improves the accuracy of coding for Hispanic and Asian or Pacific Islander beneficiaries. When compared with self-reported race and ethnicity, sensitivity increased from 29.5 to 76.6 percent for Hispanic and from 54.7 to 79.2 percent for Asian and Pacific Islander beneficiaries, with no loss of specificity, and Kappa coefficients reaching 0.80. As a result, 2,245,792 beneficiaries were recoded to Hispanic and 336,363 to Asian or Pacific Islander.</p>\",\"PeriodicalId\":55071,\"journal\":{\"name\":\"Health Care Financing Review\",\"volume\":\"29 3\",\"pages\":\"27-42\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4195038/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Care Financing Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Care Financing Review","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

利用行政种族和民族数据对医疗保险中的医疗差距进行的分析通常仅限于黑人和白人受益人。这部分是由于其他类别的人数较少,种族和族裔编码不准确,以及更广泛的分析会产生偏差结果的警告。尽管之前的医疗保险工作确实提高了种族和民族编码的准确性,但我们开发了一种估算算法,大大提高了西班牙裔和亚裔或太平洋岛民受益人编码的准确性。与自我报告的种族和民族相比,西班牙裔受益人的灵敏度从 29.5% 提高到 76.6%,亚太裔受益人的灵敏度从 54.7% 提高到 79.2%,特异性没有损失,卡帕系数达到 0.80。因此,2,245,792 名受益人被重新编码为西班牙裔,336,363 名受益人被重新编码为亚裔或太平洋岛民。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
More accurate racial and ethnic codes for Medicare administrative data.

Analyses of health care disparities in Medicare using administrative race and ethnicity data have typically been limited to Black and White beneficiaries. This is in part due to the small size of the other categories, inaccuracies in the race and ethnicity codes, and caveats that more extensive analyses would produce biased results. While previous Medicare efforts certainly improved the accuracy of race and ethnicity coding, we have developed an imputation algorithm that dramatically improves the accuracy of coding for Hispanic and Asian or Pacific Islander beneficiaries. When compared with self-reported race and ethnicity, sensitivity increased from 29.5 to 76.6 percent for Hispanic and from 54.7 to 79.2 percent for Asian and Pacific Islander beneficiaries, with no loss of specificity, and Kappa coefficients reaching 0.80. As a result, 2,245,792 beneficiaries were recoded to Hispanic and 336,363 to Asian or Pacific Islander.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Health Care Financing Review
Health Care Financing Review 医学-卫生保健
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信