Understanding the quality of ethnicity data recorded in health-related administrative data sources compared with Census 2021 in England.

IF 15.8 1区医学 Q1 Medicine

PLoS Medicine Pub Date : 2025-02-26 eCollection Date: 2025-02-01 DOI:10.1371/journal.pmed.1004507

Cameron Razieh, Bethan Powell, Rosemary Drummond, Isobel L Ward, Jasper Morgan, Myer Glickman, Chris White, Francesco Zaccardi, Jonathan Hope, Veena Raleigh, Ashley Akbari, Nazrul Islam, Thomas Yates, Lisa Murphy, Bilal A Mateen, Kamlesh Khunti, Vahe Nafilyan

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引用次数: 0

Abstract

Background: Electronic health records (EHRs) are increasingly used to investigate health inequalities across ethnic groups. While there are some studies showing that the recording of ethnicity in EHR is imperfect, there is no robust evidence on the accuracy between the ethnicity information recorded in various real-world sources and census data.

Methods and findings: We linked primary and secondary care NHS England data sources with Census 2021 data and compared individual-level agreement of ethnicity recording in General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR), Hospital Episode Statistics (HES), Ethnic Category Information Asset (ECIA), and Talking Therapies for anxiety and depression (TT) with ethnicity reported in the census. Census ethnicity is self-reported and, therefore, regarded as the most reliable population-level source of ethnicity recording. We further assessed the impact of multiple approaches to assigning a person an ethnic category. The number of people that could be linked to census from ECIA, GDPPR, HES, and TT were 47.4m, 43.5m, 47.8m, and 6.3m, respectively. Across all 4 data sources, the White British category had the highest level of agreement with census (≥96%), followed by the Bangladeshi category (≥93%). Levels of agreement for Pakistani, Indian, and Chinese categories were ≥87%, ≥83%, and ≥80% across all sources. Agreement was lower for Mixed (≤75%) and Other (≤71%) categories across all data sources. The categories with the lowest agreement were Gypsy or Irish Traveller (≤6%), Other Black (≤19%), and Any Other Ethnic Group (≤25%) categories.

Conclusions: Certain ethnic categories across all data sources have high discordance with census ethnic categories. These differences may lead to biased estimates of differences in health outcomes between ethnic groups, a critical data point used when making health policy and planning decisions.

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了解与英国2021年人口普查相比，与健康相关的行政数据源中记录的种族数据的质量。

背景：电子健康记录（EHRs）越来越多地用于调查不同种族群体之间的健康不平等。虽然有一些研究表明电子病历中的种族记录是不完善的，但没有强有力的证据表明在各种现实世界来源中记录的种族信息与人口普查数据之间的准确性。方法和发现：我们将初级和二级保健NHS英格兰数据来源与2021年人口普查数据联系起来，并比较了人口普查中报告的种族在全科提取服务（GPES）大流行计划和研究数据（GDPPR）、医院事件统计（HES）、种族类别信息资产（ECIA）和焦虑和抑郁谈话疗法（TT）中种族记录的个人水平一致性。人口普查族裔是自我报告的，因此被认为是最可靠的人口层面族裔记录来源。我们进一步评估了将一个人划分为种族类别的多种方法的影响。ECIA、GDPPR、HES和TT可与人口普查相关联的人数分别为4740万、4350万、4780万和630万。在所有4个数据来源中，英国白人类别与人口普查的一致性最高（≥96%），其次是孟加拉国类别（≥93%）。在所有来源中，巴基斯坦、印度和中国分类的一致性水平分别为≥87%、≥83%和≥80%。在所有数据源中，混合（≤75%）和其他（≤71%）类别的一致性较低。一致性最低的类别是吉普赛人或爱尔兰旅行者（≤6%），其他黑人（≤19%）和任何其他种族（≤25%）类别。结论：所有数据源中的某些族裔类别与人口普查族裔类别存在高度不一致。这些差异可能导致对族裔群体之间健康结果差异的估计存在偏差，这是制定卫生政策和规划决策时使用的一个关键数据点。

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

PLoS Medicine MEDICINE, GENERAL & INTERNAL-

CiteScore

17.60

自引率

0.60%

发文量

227

审稿时长

4-8 weeks

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