Suppr超能文献

美国癌症监测数据中美洲印第安人和阿拉斯加原住民身份的种族错误分类评估及口腔健康考量:一项系统综述

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.

作者信息

Llaneza Amanda J, Holt Alex, Seward Julie, Piatt Jamie, Campbell Janis E

机构信息

Southern Plains Tribal Health Board, Oklahoma City, Oklahoma, USA.

Department of Biostatistics & Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.

出版信息

Health Equity. 2024 Jun 26;8(1):376-390. doi: 10.1089/heq.2023.0252. eCollection 2024.

Abstract

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.

摘要

引言

在研究和临床实践的各种数据库中,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生存率相关;然而,错误分类给理解口腔健康不佳的影响增加了另一层复杂性。本综述的目的是系统评估和分析关注癌症监测数据中AI/AN种族身份错误分类的出版物。

方法

本系统综述采用PRISMA声明和CONSIDER声明。筛选涉及癌症监测数据中AI/AN身份种族错误分类的研究是否符合纳入标准。从种族错误分类的讨论、减少此类错误的方法以及涉及原住民的研究报告等方面对数据进行分析。

结果

共纳入66篇文章,发表年份从1972年至2022年。66篇文章中有55篇(83%)讨论了种族错误分类。这些文章中处理种族错误分类最常用的方法是与印第安卫生服务机构或部落诊所记录建立联系(45篇文章,占82%)。CONSIDER清单领域的平均数量为3个,纳入的领域范围从零到8个。最常被提及的领域是优先排序(60),其次是治理(47)、方法学(31)、传播(27)、关系(22)、参与(9)、能力(9)以及分析与结果(8)。

结论

为确保AI/AN社区得到公平代表,并防止对少数群体,特别是AI/AN人群的进一步压迫,需要通过准确的数据收集和报告流程来实现。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验