美国癌症监测数据中美国印第安人和阿拉斯加原住民身份的种族分类错误评估及口腔健康考虑因素:系统回顾。

IF 2.6 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Health Equity Pub Date : 2024-06-26 eCollection Date: 2024-01-01 DOI:10.1089/heq.2023.0252
Amanda J Llaneza, Alex Holt, Julie Seward, Jamie Piatt, Janis E Campbell
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引用次数: 0

摘要

导言:在研究和临床实践中,各种数据库都存在对美国印第安人和阿拉斯加原住民(AI/AN)的错误分类。口腔健康与癌症发病率和存活率相关;然而,错误分类为了解不良口腔健康的影响增加了另一层复杂性。本文献综述的目的是系统评估和分析有关癌症监测数据中阿拉斯加原住民/印第安人种族身份分类错误的出版物:本系统性文献综述采用了 PRISMA 声明和 CONSIDER 声明。对涉及在癌症监测数据中对阿拉斯加原住民/印第安人种族身份进行错误分类的研究进行了资格筛选。数据分析包括对种族误分类的讨论、减少误分类的方法以及涉及土著居民的研究报告:共收录了 66 篇文章,发表年份从 1972 年到 2022 年不等。在这 66 篇文章中,共有 55 篇(83%)讨论了种族分类错误。在这些文章中,解决种族分类错误的最常见方法是与印第安人健康服务机构或部落诊所记录建立联系(45 篇文章,占 82%)。CONSIDER 核对表域的平均数量为 3 个,范围从 0 到 8 个不等。最常见的领域是优先化(60 个),其次是治理(47 个)、方法(31 个)、传播(27 个)、关系(22 个)、参与(9 个)、能力(9 个)以及分析和结果(8 个):要确保阿拉斯加原住民/印第安人社区的公平代表性,阻止对少数群体,特别是对阿拉斯加原住民/印第安人的进一步压迫,就要通过准确的数据收集和报告程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of Racial Misclassification Among American Indian and Alaska Native Identity in Cancer Surveillance Data in the United States and Considerations for Oral Health: A Systematic Review.

Introduction: Misclassification of American Indian and Alaska Native (AI/AN) peoples exists across various databases in research and clinical practice. Oral health is associated with cancer incidence and survival; however, misclassification adds another layer of complexity to understanding the impact of poor oral health. The objective of this literature review was to systematically evaluate and analyze publications focused on racial misclassification of AI/AN racial identities among cancer surveillance data.

Methods: The PRISMA Statement and the CONSIDER Statement were used for this systematic literature review. Studies involving the racial misclassification of AI/AN identity among cancer surveillance data were screened for eligibility. Data were analyzed in terms of the discussion of racial misclassification, methods to reduce this error, and the reporting of research involving Indigenous peoples.

Results: A total of 66 articles were included with publication years ranging from 1972 to 2022. A total of 55 (83%) of the 66 articles discussed racial misclassification. The most common method of addressing racial misclassification among these articles was linkage with the Indian Health Service or tribal clinic records (45 articles or 82%). The average number of CONSIDER checklist domains was three, with a range of zero to eight domains included. The domain most often identified was Prioritization (60), followed by Governance (47), Methodologies (31), Dissemination (27), Relationships (22), Participation (9), Capacity (9), and Analysis and Findings (8).

Conclusion: To ensure equitable representation of AI/AN communities, and thwart further oppression of minorities, specifically AI/AN peoples, is through accurate data collection and reporting processes.

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来源期刊
Health Equity
Health Equity Social Sciences-Health (social science)
CiteScore
3.80
自引率
3.70%
发文量
97
审稿时长
24 weeks
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