Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI 48824, United States.
Department of Anthropology, College of Social Science, Michigan State University, East Lansing, MI 48824, United States.
Epidemiol Rev. 2023 Dec 20;45(1):63-81. doi: 10.1093/epirev/mxad001.
Indigenous people are often misracialized as other racial or ethnic identities in population health research. This misclassification leads to underestimation of Indigenous-specific mortality and health metrics, and subsequently, inadequate resource allocation. In recognition of this problem, investigators around the world have devised analytic methods to address racial misclassification of Indigenous people. We carried out a scoping review based on searches in PubMed, Web of Science, and the Native Health Database for empirical studies published after 2000 that include Indigenous-specific estimates of health or mortality and that take analytic steps to rectify racial misclassification of Indigenous people. We then considered the weaknesses and strengths of implemented analytic approaches, with a focus on methods used in the US context. To do this, we extracted information from 97 articles and compared the analytic approaches used. The most common approach to address Indigenous misclassification is to use data linkage; other methods include geographic restriction to areas where misclassification is less common, exclusion of some subgroups, imputation, aggregation, and electronic health record abstraction. We identified 4 primary limitations of these approaches: (1) combining data sources that use inconsistent processes and/or sources of race and ethnicity information; (2) conflating race, ethnicity, and nationality; (3) applying insufficient algorithms to bridge, impute, or link race and ethnicity information; and (4) assuming the hyperlocality of Indigenous people. Although there is no perfect solution to the issue of Indigenous misclassification in population-based studies, a review of this literature provided information on promising practices to consider.
在人口健康研究中，原住民经常被错误地归类为其他种族或族裔身份。这种错误分类导致对原住民特有死亡率和健康指标的低估，进而导致资源分配不足。为了认识到这个问题，世界各地的研究人员已经设计了分析方法来解决原住民的种族错误分类问题。我们根据 2000 年后发表的包含原住民特有健康或死亡率估计值且采取分析步骤纠正原住民种族错误分类的实证研究，在 PubMed、Web of Science 和 Native Health Database 中进行了范围综述。然后，我们考虑了实施分析方法的优缺点，重点是美国背景下使用的方法。为此，我们从 97 篇文章中提取信息并比较了所使用的分析方法。解决原住民错误分类的最常见方法是使用数据链接；其他方法包括限制在错误分类较少的地区、排除某些亚组、推断、汇总和电子健康记录提取。我们确定了这些方法的 4 个主要局限性：（1）合并使用不一致的过程和/或种族和族裔信息来源的数据来源；（2）混淆种族、族裔和国籍；（3）应用不足的算法来桥接、推断或链接种族和族裔信息；（4）假设原住民的超局部性。尽管在基于人口的研究中没有解决原住民错误分类问题的完美解决方案，但对这一文献的回顾提供了有关值得考虑的有希望的做法的信息。
