METHODS: For this bibliographical review, we reviewed a selection of the 1% most highly cited population health papers reporting UK ethnicity data in MEDLINE and Web of Science databases between Jan 1, 1946, and July 31, 2022, and extracted how ethnicity was recorded and analysed. We included cross-sectional, longitudinal cohort studies, and randomised trials using only UK populations, which were peer-reviewed, were written in English, and reported ethnicity and any health-related outcomes. We held three focus groups with ten participants aged 18-25 years, from Nigeria, Turkistan, Syria, Yemen, and Iran to help us shape and interpret our findings, and asked \"How should ethnicity be asked inclusively, and better recorded?\" and \"Does ethnicity change over time or context? If so, why?\". We consolidated feedback from our focus groups into a co-created poster with recommendations for researchers studying ethnicity and health. Written informed consent was obtained for focus group participation.
RESULTS: Of 44 papers included in the review, 19 (43%) used self-reported ethnicity, but the number of ethnic categories provided varied. Of 27 papers that aggregated ethnicity, 13 (48%) provided justification. Only eight (18%) explicitly theorised how ethnicity related to health. The focus groups agreed that (1) ethnicity should not be prescribed by others (individuals could be asked to describe their ethnicity in free-text, which researchers could synthesise to extract relevant dimensions of ethnicity for their research) and (2) Ethnicity changes over time and context according to personal experience, social pressure, and nationality change. The lived experience of ethnicity of migrants and non-migrants is not fully interchangeable, even if they share the same ethnic category.
CONCLUSIONS: Researchers should communicate clearly how ethnicity is operationalised in their studies, with appropriate justification for clustering and analysis that is meaningfully theorised. Our study was limited by its non-systematic nature. Implementing the recommendation to capture ethnicity via free text remains challenging in administrative data systems.
BACKGROUND: UCL Engagement Beacon Bursary.
方法:对于本书目审查,我们回顾了1946年1月1日至2022年7月31日期间MEDLINE和WebofScience数据库中报告英国种族数据的1%被引用率最高的人群健康论文,并提取了如何记录和分析种族.我们包括横截面,纵向队列研究,以及仅使用英国人群的随机试验,经过同行评审,是用英语写的,并报告了种族和任何与健康相关的结果。我们举行了三个焦点小组,有十名18-25岁的参与者,来自尼日利亚,突厥斯坦,叙利亚,也门,和伊朗来帮助我们塑造和解释我们的发现,并问道:“如何包容种族,和更好的记录?\"和\"种族会随着时间或背景而变化吗?如果是这样,为什么?\"我们将焦点小组的反馈整合到共同创建的海报中,并为研究种族和健康的研究人员提供建议。获得焦点小组参与的书面知情同意书。
结果:在评论中包含的44篇论文中,19(43%)使用自我报告的种族,但是提供的种族类别的数量各不相同。在汇总种族的27篇论文中,13(48%)提供了理由。只有8人(18%)明确提出了种族与健康的关系。焦点小组一致认为,(1)种族不应由其他人规定(可以要求个人以自由文本描述他们的种族,研究人员可以综合起来提取种族的相关维度进行研究)和(2)根据个人经验,种族随着时间和背景的变化,社会压力,国籍变更。移民和非移民的种族生活经验不能完全互换,即使他们共享相同的种族类别。
结论:研究人员应该清楚地传达在他们的研究中如何操作种族,具有有意义的理论化的聚类和分析的适当理由。我们的研究受到其非系统性的限制。在行政数据系统中,实施通过自由文本捕获种族的建议仍然具有挑战性。
背景:UCL订婚信标助学金。