PDF(Pubmed)
Abstract：
The ethnicity data gap pertains to 3 major challenges to address ethnic health inequality: 1) Under-representation of ethnic minorities in research; 2) Poor data quality on ethnicity; 3) Ethnicity data not being meaningfully analysed. These challenges are especially relevant for research involving under-served migrant populations in the UK. We aimed to review how ethnicity is captured, reported, analysed and theorised within policy-relevant research on ethnic health inequities.
We reviewed a selection of the 1% most highly cited population health papers that reported UK data on ethnicity, and extracted how ethnicity was recorded and analysed in relation to health outcomes. We focused on how ethnicity was obtained (i.e. self reported or not), how ethnic groups were categorised, whether justification was provided for any categorisation, and how ethnicity was theorised to be related to health. We held three 1-h-long guided focus groups with 10 young people from Nigeria, Turkistan, Syria, Yemen and Iran. This engagement helped us shape and interpret our findings, and reflect on. 1) How should ethnicity be asked inclusively, and better recorded? 2) Does self-defined ethnicity change over time or context? If so, why?
Of the 44 included papers, most (19; 43%) used self-reported ethnicity, categorised in a variety of ways. Of the 27 papers that aggregated ethnicity, 13 (48%) provided justification. Only 8 of 33 papers 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; 2) Ethnicity changes over time and context according to personal experience, social pressure, and nationality change; 3) Migrants and non-migrants\' lived experience of ethnicity is not fully inter-changeable, even if they share the same ethnic category.
Ethnicity is a multi-dimensional construct, but this is not currently reflected in UK health research studies, where ethnicity is often aggregated and analysed without justification. Researchers should communicate clearly how ethnicity is operationalised for their study, with appropriate justification for clustering and analysis that is meaningfully theorised. We can only start to tackle ethnic health inequity by treating ethnicity as rigorously as any other variables in our research.
We reviewed a selection of the 1% most highly cited population health papers that reported UK data on ethnicity, and extracted how ethnicity was recorded and analysed in relation to health outcomes. We focused on how ethnicity was obtained (i.e. self reported or not), how ethnic groups were categorised, whether justification was provided for any categorisation, and how ethnicity was theorised to be related to health. We held three 1-h-long guided focus groups with 10 young people from Nigeria, Turkistan, Syria, Yemen and Iran. This engagement helped us shape and interpret our findings, and reflect on. 1) How should ethnicity be asked inclusively, and better recorded? 2) Does self-defined ethnicity change over time or context? If so, why?
Of the 44 included papers, most (19; 43%) used self-reported ethnicity, categorised in a variety of ways. Of the 27 papers that aggregated ethnicity, 13 (48%) provided justification. Only 8 of 33 papers 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; 2) Ethnicity changes over time and context according to personal experience, social pressure, and nationality change; 3) Migrants and non-migrants\' lived experience of ethnicity is not fully inter-changeable, even if they share the same ethnic category.
Ethnicity is a multi-dimensional construct, but this is not currently reflected in UK health research studies, where ethnicity is often aggregated and analysed without justification. Researchers should communicate clearly how ethnicity is operationalised for their study, with appropriate justification for clustering and analysis that is meaningfully theorised. We can only start to tackle ethnic health inequity by treating ethnicity as rigorously as any other variables in our research.
摘要：
背景：种族数据差距涉及解决种族健康不平等的3个主要挑战：1）研究中少数民族代表性不足；2）种族数据质量差；3）种族数据没有得到有意义的分析。这些挑战与涉及英国服务不足的移民人口的研究尤其相关。我们的目的是回顾种族是如何被捕获的，报告,在有关种族健康不平等的政策相关研究中进行了分析和理论化。
方法：我们回顾了引用率最高的1%的人口健康论文，这些论文报道了英国的种族数据，并提取了如何记录和分析种族与健康结果的关系。我们专注于种族是如何获得的(即自我报告与否)，种族如何分类，是否为任何分类提供了理由，以及理论上种族与健康的关系。我们与来自尼日利亚的10名年轻人举行了三个长达1小时的指导焦点小组，突厥斯坦,叙利亚,也门和伊朗。这种参与帮助我们塑造和解释了我们的发现，并反思。1)应该如何包容地询问种族，和更好的记录？2）自我定义的种族是否会随着时间或背景而变化？如果是，为什么？
结果：在44篇论文中，大多数(19；43%)使用自我报告的种族，以各种方式分类。在汇总种族的27篇论文中，13（48%）提供了理由。33篇论文中只有8篇明确论证了种族与健康的关系。焦点小组一致认为，1）种族不应由他人规定；可以要求个人以自由文本描述他们的种族，研究人员可以综合这些自由文本，以提取种族的相关维度进行研究；2）种族根据个人经验随时间和背景而变化，社会压力,和国籍的变化；3)移民和非移民的种族生活经验不是完全可以互换的，即使他们共享相同的种族类别。
结论：种族是一种多维结构，但这目前还没有反映在英国的健康研究中，种族经常被汇总和分析，而没有理由。研究人员应该清楚地传达种族如何在他们的研究中运作，具有有意义的理论化的聚类和分析的适当理由。我们只能通过像研究中的任何其他变量一样严格地对待种族来解决种族健康不平等问题。
方法：我们回顾了引用率最高的1%的人口健康论文，这些论文报道了英国的种族数据，并提取了如何记录和分析种族与健康结果的关系。我们专注于种族是如何获得的(即自我报告与否)，种族如何分类，是否为任何分类提供了理由，以及理论上种族与健康的关系。我们与来自尼日利亚的10名年轻人举行了三个长达1小时的指导焦点小组，突厥斯坦,叙利亚,也门和伊朗。这种参与帮助我们塑造和解释了我们的发现，并反思。1)应该如何包容地询问种族，和更好的记录？2）自我定义的种族是否会随着时间或背景而变化？如果是，为什么？
结果：在44篇论文中，大多数(19；43%)使用自我报告的种族，以各种方式分类。在汇总种族的27篇论文中，13（48%）提供了理由。33篇论文中只有8篇明确论证了种族与健康的关系。焦点小组一致认为，1）种族不应由他人规定；可以要求个人以自由文本描述他们的种族，研究人员可以综合这些自由文本，以提取种族的相关维度进行研究；2）种族根据个人经验随时间和背景而变化，社会压力,和国籍的变化；3)移民和非移民的种族生活经验不是完全可以互换的，即使他们共享相同的种族类别。
结论：种族是一种多维结构，但这目前还没有反映在英国的健康研究中，种族经常被汇总和分析，而没有理由。研究人员应该清楚地传达种族如何在他们的研究中运作，具有有意义的理论化的聚类和分析的适当理由。我们只能通过像研究中的任何其他变量一样严格地对待种族来解决种族健康不平等问题。
