Ethnic inequalities in health and use of healthcare in the UK: how computerised health records can contribute substantively to the knowledge base
Ethnic inequalities in health and use of healthcare in the UK: how computerised health records can contribute substantively to the knowledge base
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英国健康和医疗保健使用方面的种族不平等：计算机化健康记录如何为知识库做出实质性贡献
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
R. Mathur
中科院分区：
文献类型：
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作者：
R. Mathur
英国以前的研究已经确定，少数民族群体作为一个整体，经历了更多的健康状况不佳，发病年龄更小或在较低的风险水平比“白色英国”人口。自1968年《种族关系法》颁布以来，所有政府机构正式收集族裔群体统计数据已被授权作为查明和处理族裔不平等现象的先决条件。在英国国民健康服务体系的常规健康记录中收集种族数据是这一举措的一部分。虽然自1991年以来在初级保健和自1995年以来在二级保健中已经有了记录族裔的设施，但直到最近，不系统的记录导致初始数据质量差，限制了这些数据对临床护理、委托和研究的有用性。2006年作为质量和成果框架的一部分，种族记录的激励措施提高了这些数据的质量，尽管在本博士学位开始时，尚未探索它们是否适合用于英国范围内以人口为基础的研究。
本论文所报告的研究探讨电子健康档案在研究族群健康差异上的效用，并包含三个子研究。首先，评估了初级和二级医疗数据库中采集的种族数据的完整性、可用性和普遍性。结果显示，2012年，在临床实践研究数据链（CPRD）中，78.3%的患者、79.4%的住院患者以及英格兰医院事件统计（HES）中50%的A&E患者和门诊患者记录了有效种族。超过80%记录的多种族患者的编码相同或属于相同的五个高级别种族组分类。CPRD的种族分类与英格兰、威尔士、苏格兰和北方爱尔兰的联合人口普查结果相当，这表明CPRD中的种族人口研究可以推广到英国人口，特别是使用2006年以后的数据时，完整性和一致性最高。
其次，与英国生物库研究合作，开发了一个实用和全面的糖尿病定义，用于电子健康数据库。一旦应用于CPRD，该算法确定了34，530名1型糖尿病患者和355，717名2型糖尿病患者。与白色人群相比，南亚人群2型糖尿病的发病率几乎翻了一番（70.7 vs 42.0起事件/10，000人年）。调整性别和年龄组后，南亚组2型糖尿病的风险是白色组的3倍多（风险比3.27 95%CI 3.19，3.35）。
最后，对在CPRD登记的860，000例患者进行了前瞻性队列研究，以量化冠心病（CHD）事件风险的种族差异以及2型糖尿病的存在对这种关系的影响程度。糖尿病的存在使CHD的风险增加了40%，尽管在考虑年龄、性别和贫困后这一风险降低至22%（风险比1.22 CI 95 1.20，1.25）。与糖尿病相关的额外风险在少数民族群体中明显更高，南亚和非洲/加勒比黑人群体分别调整增加了60%和75%，而白色群体为28%。调整后的冠心病发病率在南亚群体中一直较高，在非洲黑人/加勒比群体中较低，男性之间的差异比女性更明显。冠心病风险的种族差异在非2型糖尿病患者中始终比2型糖尿病患者更明显。
这些研究产生了新的结果，提供了有关英国电子健康记录中种族数据的可用性和普遍性的有价值的信息。他们复制了糖尿病患病率和发病率的非数据库研究的结果，并扩展了我们对心脏病结局种族差异模式的了解。他们代表了英国首次使用常规电子健康记录来回答这些与种族有关的问题。总之，在这篇论文中报告的研究结果提供了一个独特的见解，在常规记录的种族数据可以最大限度地为目的的流行病学研究到英国各地的健康不平等的方式。
Previous studies in the UK have established that minority ethnic groups as a whole experience more ill-health and onset of morbidity at younger ages or at lower levels of risk than the ‘White British’ population. Since the Race Relations Act of 1968, the official collection of ethnic group statistics by all government bodies has been mandated as a pre-requisite for identifying and tackling ethnic inequalities. The capture of ethnicity data in routine health records across the UK National Health Service forms part of this initiative. Although the facility to record ethnicity has been available in primary care since 1991 and in secondary care since 1995, until recently, unsystematic recording resulted in poor quality of the initial data, limiting the usefulness of these data for clinical care, commissioning and research. The incentivisation of ethnicity recording in 2006 as part of the Quality and Outcomes Framework has resulted in an improvement of the quality of these data, though their suitability for use in UK-wide population-based research, at the commencement of this PhD, had not yet been explored.
The studies reported in this thesis investigated the utility of electronic health records for research into ethnic differences in health and comprised three sub-studies. Firstly, the completeness, usability and generalisability of ethnicity data captured in primary and secondary care databases were assessed. Results showed that in 2012, valid ethnicity was recorded for 78.3% of patients in the Clinical Practice Research Datalink (CPRD), 79.4% of inpatients, and 50% of A&E patients and outpatients in the Hospital Episode Statistics for England (HES). Over 80% of patients with multiple ethnicities recorded had codes which either were identical or fell into the same five high-level ethnic group categorisation. The ethnic breakdown of the CPRD was found to be comparable to that of the combined censuses for England, Wales, Scotland and Northern Ireland, suggesting that studies of ethnic populations within the CPRD can be generalised to the UK population, particularly when using data from 2006 onwards, where completeness and consistency are highest.
Secondly, in collaboration with the UK Biobank study, a pragmatic and comprehensive definition of diabetes mellitus for use in electronic health databases was developed. Once applied to the CPRD, the algorithms identified 34,530 individuals with type 1 diabetes and 355,717 individuals with type 2 diabetes. The incidence of type 2 diabetes was almost doubled in South Asian compared with White groups (70.7 vs 42.0 events per 10,000 person years). After adjustment for gender and age group, the risk of type 2 diabetes was over three times higher in the South Asian group compared with White the group (Hazard Ratio 3.27 95%CI 3.19, 3.35).
Finally, a prospective cohort study of 860,000 patients registered with the CPRD was undertaken to quantify ethnic differences in the risk of incident coronary heart disease (CHD) and the extent to which this relationship is modified by the presence of type 2 diabetes. The presence of diabetes increased the risk of CHD by 40%, although this reduced to 22% after accounting for age, gender and deprivation (Hazard Ratio 1.22 CI95 1.20, 1.25). The excess risk associated with diabetes was markedly higher for ethnic minority groups, with an adjusted increase of 60% and 75% in South Asian and Black African/Caribbean groups respectively, compared with 28% in the White groups. Adjusted rates of CHD were consistently higher in South Asian groups and lower in Black African/Caribbean groups, with differences more pronounced amongst men than women. Ethnic differences in CHD risk were consistently more pronounced amongst patients without type 2 diabetes than in those with type 2 diabetes.
The studies have generated novel results which provide valuable information about the usability and generalisability of ethnicity data available in UK electronic health records. They have replicated findings from non-database studies of the prevalence and incidence of diabetes and extended our knowledge of the patterning of ethnic differences in heart disease outcomes. They represent the first ever use of UK routine electronic health records to answer these questions in relation to ethnicity. Together, the findings reported in this thesis provide a unique insight into the ways in which routinely recorded ethnicity data can be maximised for the purposes of epidemiological research into health inequalities across the UK.
脂蛋白（a）是年轻女性冠心病的决定因素。
影响因子：
37.8
作者：
Orth-Gomér,K;Mittleman,MA;Schenck-Gustafsson,K;Wamala,SP;Eriksson,M;Belkic,K;Kirkeeide,R;Svane,B;Rydén,L
通讯作者：
Rydén,L
卫生服务研究中的种族概念：1966 年至 1990 年。
影响因子：
3.4
作者：
Williams,DR
通讯作者：
Williams,DR