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本文引用的文献

1
Science Visioning in Minority Health and Health Disparities.少数族裔健康与健康差异领域的科学展望
Am J Public Health. 2019 Jan;109(S1):S5. doi: 10.2105/AJPH.2019.304962.
2
Science Visioning to Advance the Next Generation of Health Disparities Research.推进下一代健康差异研究的科学展望
Am J Public Health. 2019 Jan;109(S1):S11-S13. doi: 10.2105/AJPH.2018.304944.
3
Methodological Approaches to Understanding Causes of Health Disparities.理解健康差异成因的方法学途径。
Am J Public Health. 2019 Jan;109(S1):S28-S33. doi: 10.2105/AJPH.2018.304843.
4
Characteristics And Spending Patterns Of Persistently High-Cost Medicare Patients.持续性高额医保患者的特征和支出模式。
Health Aff (Millwood). 2019 Jan;38(1):107-114. doi: 10.1377/hlthaff.2018.05160.
5
A Retrospective Study of Administrative Data to Identify High-Need Medicare Beneficiaries at Risk of Dying and Being Hospitalized.基于行政数据的回顾性研究:识别高医疗需求的 Medicare 受益人群,这些人有死亡和住院的风险。
J Gen Intern Med. 2019 Mar;34(3):405-411. doi: 10.1007/s11606-018-4781-3. Epub 2019 Jan 2.
6
Indirect Estimation of Race/Ethnicity for Survey Respondents Who Do Not Report Race/Ethnicity.对于不报告种族/族裔的调查受访者,间接估计其种族/族裔。
Med Care. 2019 May;57(5):e28-e33. doi: 10.1097/MLR.0000000000001011.
7
Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity.通过种族/民族推断来实现 HEDIS 绩效的种族/民族测量。
Health Serv Res. 2019 Feb;54(1):13-23. doi: 10.1111/1475-6773.13099. Epub 2018 Dec 3.
8
A Comparison of Methods for Classifying and Modeling Respondents Who Endorse Multiple Racial/Ethnic Categories: A Health Care Experience Application.多类种族/民族认同受访者的分类和建模方法比较:医疗保健经验应用
Med Care. 2019 Jun;57(6):e34-e41. doi: 10.1097/MLR.0000000000001012.
9
Examining Race and Ethnicity Information in Medicare Administrative Data.在医疗保险行政数据中审查种族和民族信息。
Med Care. 2017 Dec;55(12):e170-e176. doi: 10.1097/MLR.0000000000000608.
10
Association Between Race, Neighborhood, and Medicaid Enrollment and Outcomes in Medicare Home Health Care.医疗保险家庭保健护理中种族、社区和医疗补助计划参保与结果的关联。
J Am Geriatr Soc. 2018 Feb;66(2):239-246. doi: 10.1111/jgs.15082. Epub 2017 Oct 4.

医疗保险行政数据中种族和民族代码的有效性与常规家庭保健就诊期间收集的黄金标准自我报告种族进行比较。

Validity of Race and Ethnicity Codes in Medicare Administrative Data Compared With Gold-standard Self-reported Race Collected During Routine Home Health Care Visits.

机构信息

Division of Nursing Science, School of Nursing.

Institute for Health, Health Care Policy, and Aging Research.

出版信息

Med Care. 2020 Jan;58(1):e1-e8. doi: 10.1097/MLR.0000000000001216.

DOI:10.1097/MLR.0000000000001216
PMID:31688554
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6904433/
Abstract

BACKGROUND

Misclassification of Medicare beneficiaries' race/ethnicity in administrative data sources is frequently overlooked and a limitation in health disparities research.

OBJECTIVE

To compare the validity of 2 race/ethnicity variables found in Medicare administrative data [enrollment database (EDB) and Research Triangle Institute (RTI) race] against a gold-standard source also available in the Medicare data warehouse: the self-reported race/ethnicity variable on the home health Outcome and Assessment Information Set (OASIS).

SUBJECTS

Medicare beneficiaries over the age of 18 who received home health care in 2015 (N=4,243,090).

MEASURES

Percent agreement, sensitivity, specificity, positive predictive value, and Cohen κ coefficient.

RESULTS

The EDB and RTI race variable have high validity for black race and low validity for American Indian/Alaskan Native race. Although the RTI race variable has better validity than the EDB race variable for other races, κ values suggest room for future improvements in classification of whites (0.90), Hispanics (0.87), Asian/Pacific Islanders (0.77), and American Indian/Alaskan Natives (0.44).

DISCUSSION

The status quo of using "good-enough for government" race/ethnicity variables contained in Medicare administrative data for minority health disparities research can be improved through the use of self-reported race/ethnicity data, available in the Medicare data warehouse. Health services and policy researchers should critically examine the source of race/ethnicity variables used in minority health and health disparities research. Future work to improve the accuracy of Medicare beneficiaries' race/ethnicity data should incorporate and augment the self-reported race/ethnicity data contained in assessment and survey data, available within the Medicare data warehouse.

摘要

背景

医疗保险行政数据来源中对参保者种族/民族的分类错误经常被忽视,这也是健康差异研究的一个局限性。

目的

比较医疗保险行政数据中发现的 2 种种族/民族变量(参保数据库[EDB]和研究三角研究所[RTI]种族])与医疗保险数据仓库中可用的黄金标准源(家庭健康结局和评估信息集[OASIS]上的自我报告种族/民族变量)的有效性。

受试者

2015 年接受家庭保健服务的 18 岁以上医疗保险参保者(N=4,243,090)。

测量方法

百分比一致性、敏感度、特异度、阳性预测值和 Cohen κ 系数。

结果

EDB 和 RTI 种族变量对黑人种族的有效性较高,对美洲印第安人/阿拉斯加原住民种族的有效性较低。虽然 RTI 种族变量对其他种族的有效性优于 EDB 种族变量,但 κ 值表明,在白人(0.90)、西班牙裔(0.87)、亚洲/太平洋岛民(0.77)和美洲印第安人/阿拉斯加原住民(0.44)的分类方面仍有改进的空间。

讨论

医疗保险行政数据中包含的“足以用于政府”的种族/民族变量的现状可以通过使用医疗保险数据仓库中可用的自我报告种族/民族数据得到改善。卫生服务和政策研究人员应仔细审查少数民族健康和健康差异研究中使用的种族/民族变量的来源。未来提高医疗保险参保者种族/民族数据准确性的工作应纳入并扩充医疗保险数据仓库中评估和调查数据中包含的自我报告种族/民族数据。