Methods for retrospectively improving race/ethnicity data quality: a scoping review.

IF 5.2 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Matthew K Chin, Lan N Đoàn, Rienna G Russo, Timothy Roberts, Sonia Persaud, Emily Huang, Lauren Fu, Kiran Y Kui, Simona C Kwon, Stella S Yi
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Abstract

Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases in July 2022. A total of 2 441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis. Six main method types for improving race/ethnicity data were identified: expert review (n = 9; 8%), name lists (n = 27, 23%), name algorithms (n = 55, 46%), machine learning (n = 14, 12%), data linkage (n = 9, 8%), and other (n = 6, 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56, 47%) and White (n = 51, 43%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.

提高种族/族裔数据质量的方法:范围综述。
提高种族和人种(以下简称种族/人种)数据的质量对于确保未得到充分服务的人群在用于识别健康差异并为医疗保健政策提供信息的数据集中得到代表是势在必行的。我们对改进二级数据集中种族/人种分类的方法进行了一次范围性综述。根据《系统综述和元分析首选报告项目》指南,我们于 2022 年 7 月在 MEDLINE、Embase 和 Web of Science Core Collection 数据库中进行了检索。共筛选了 2 441 篇摘要,审阅了 453 篇全文,纳入了 120 篇文章。通过叙事分析提取并描述了研究特征。确定了改进种族/人种数据的六种主要方法类型:专家评审(n = 9;8%)、名称列表(n = 27,23%)、名称算法(n = 55,46%)、机器学习(n = 14,12%)、数据关联(n = 9,8%)和其他(n = 6,5%)。分类的主要种族/民族群体是亚裔(n = 56,47%)和白人(n = 51,43%)。有 86 篇文章(72%)进行了某种形式的验证评估。我们讨论了不同方法类型的优势和局限性,以及已确定方法的潜在危害。需要创新方法来更好地识别种族/民族亚群,并进一步开展验证研究。准确收集和报告按种族/人种分列的数据对于解决相关人口数据的系统性缺失至关重要,这些数据可能会错误地指导决策,并阻碍医疗保健实践和干预措施的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Epidemiologic Reviews
Epidemiologic Reviews 医学-公共卫生、环境卫生与职业卫生
CiteScore
8.10
自引率
0.00%
发文量
10
期刊介绍: Epidemiologic Reviews is a leading review journal in public health. Published once a year, issues collect review articles on a particular subject. Recent issues have focused on The Obesity Epidemic, Epidemiologic Research on Health Disparities, and Epidemiologic Approaches to Global Health.
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