Booth Alison, McDaid Catriona, Scrimshire Ashley, Singh Harvinder Pal, Scantlebury Arabella, Hewitt Catherine
Health Sciences, University of York, York, England, YO10 5DD, UK.
Academic Centre for Surgery, South Tees Hospitals NHS Foundation Trust, Middlesbrough, England, TS4 3BW, UK.
NIHR Open Res. 2024 Sep 13;4:18. doi: 10.3310/nihropenres.13551.2. eCollection 2024.
There is strong evidence that those recruited into studies are not always representative of the population for whom the research is most relevant. Development of the study design and funding decisions are points in the research process where considerations about inclusion of under-served populations may usefully be made. Current practical guidance focuses on designing and modifying participant recruitment and retention approaches but an area that has not been addressed is recruitment site selection.
We present case studies of three NIHR funded trials to demonstrate how publicly available UK population datasets can be used to facilitate the identification of under-served communities for inclusion in trials. The trials have different designs, address different needs and demonstrate recruitment planning across Trauma centres, NHS Trusts and special educational settings.We describe our use of national freely available datasets, such as those provided by NHS Digital and the Office for National Statistics, to identify potential recruitment sites with consideration of health status, socio-economic status and ethnicity as well as clinical and risk factors to support inclusivity.For all three studies, we produced lists of potential recruitment sites in excess of the number anticipated as necessary to meet the recruitment targets.
We reflect on the challenges to our approach and some potential future developments. The datasets used are all free to use but each has their limitations. Agreeing search parameters, acceptable proxies and identifying the appropriate datasets, then cross referencing between datasets takes considerable time and particular expertise. The case studies are trials, but the methods are generalisable for various other study types.
Through these exemplars, we aim to build on the NIHR INCLUDE project, by providing trialists with a much needed practical approach to embedding EDI into trial design at the grant application stage.
有充分证据表明,参与研究的人员并不总是最能代表该研究最相关人群。研究设计的制定和资金决策是研究过程中的关键点,在这些点上可以有效地考虑纳入服务不足人群。当前的实用指南侧重于设计和修改参与者招募与保留方法,但尚未涉及的一个领域是招募地点的选择。
我们展示了三项由英国国家卫生研究院(NIHR)资助的试验的案例研究,以说明如何利用公开可用的英国人口数据集来促进识别服务不足的社区,以便将其纳入试验。这些试验具有不同的设计,满足不同的需求,并展示了在创伤中心、国民保健服务信托基金和特殊教育环境中的招募计划。我们描述了如何使用国家免费提供的数据集,如由英国国家医疗服务体系数字化部门(NHS Digital)和国家统计局提供的数据集,在考虑健康状况、社会经济地位、种族以及临床和风险因素以支持包容性的情况下,识别潜在的招募地点。对于所有三项研究,我们列出的潜在招募地点数量超过了预期达到招募目标所需的数量。
我们思考了我们方法面临的挑战以及一些未来可能的发展。所使用的数据集都是免费的,但每个数据集都有其局限性。商定搜索参数、可接受的代理并识别合适的数据集,然后在数据集之间进行交叉引用需要相当多的时间和特定的专业知识。这些案例研究是试验,但这些方法可推广到各种其他研究类型。
通过这些范例,我们旨在在NIHR INCLUDE项目的基础上再接再厉,在资助申请阶段为试验人员提供一种急需的实用方法,将平等、多样性和包容性(EDI)融入试验设计。