Consolidating the UK Longitudinal Linkage Collaboration for the longitudinal research community.

巩固英国纵向研究界的纵向联系合作。

• 批准号: ES/X000567/1
• 负责人: Andy Boyd
• 金额: $ 120.9万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FX000567%2F1
• 关键词: Consolidating; UK; Longitudinal; Linkage; Collaboration

The UK Longitudinal Linkage Collaboration (UK LLC) is a research resource providing a 'Trusted Research Environment' (TRE) that enables safe, secure and privacy-protecting analyses of longitudinal research data. UK LLC contains data on many aspects of the lives of more than 200,000 volunteer participants in 20+ Longitudinal Population Studies (LPS). These are studies measuring volunteers' social and economic circumstances, health, development and wellbeing, and changes in these, over time. By bringing this data together with their NHS records, the UK LLC is facilitating research that meets the needs of all our communities across the UK, improving lives by better understanding determinants of health, and informing improvements to public health policy and services.Purpose: Through the UK LLC we hope to find out more about how we can improve people's lives than was possible before this way of working was set up. We are now up and running: hosting study data provided by research volunteers linked with their NHS data and data about the environment we live in. Approved researchers within the LHW have started their analyses.We seek funding to extend the scope of this innovative resource, making it a long-term solution that supports studies and helps researchers answer important research questions and help improve the lives of people across the UK. We want to enhance the resource by adding earnings, employment, benefit and education records. Availability of a wide variety of linked data available through a single application will enable researchers to conduct better investigations of interactions between health, society and the environment, address government research priorities and more efficiently respond to the UK's emerging economic, social and health needs.Objectives: It is essential that we further develop the UK LLC and build on our core function as a high-quality 'research-ready' scientific resource. We will deliver our proposal through our skilled data management, operational management and governance teams with expert executive and governance structures in place. We will:- Expand the UK LLC beyond COVID-19 research to benefit all; working with studies and the public to ensure this is done safely and transparently- Maximise available linked data by linking employment, benefits and education records- Develop supporting guides and documentation for approved researchers- Create a library of the tools (eg, software code) and methods developed by UK LLC staff and researchers using the TRE, to allow future users to check and re-use them- Widen visibility and access to data using the 'five safes' application approach with public input and in line with FAIR data principles (making data Findable, Accessible, Interoperable and Reusable).- Support studies and the research community to work effectively together.We also propose expansion of our public involvement model by piloting a novel 'Citizen Panel', a concept developed by Understanding Patient Data. This pilot will establish a dialogue between the data community and wider public that ensures good governance and enhances mutual learning.These enhancements will be in addition to our core function, funded through the NCS programme.Benefits: Research is facilitated by diverse data because our society, and health and wellbeing issues, are also diverse. Bringing this data together will help us understand more about health, social and environmental inequalities and regional differences and opportunities across the four nations.By increasing the number of public contributors we interact with we will inform wider efforts to build confidence in this way of working with data that will prove beneficial to the public and wider research community.

英国纵向联系合作组织（UK LLC）是一个研究资源，提供“可信研究环境”（TRE），可以对纵向研究数据进行安全、可靠和隐私保护的分析。UK LLC包含20多项纵向人口研究（LPS）中超过20万名志愿者参与者生活的许多方面的数据。这些研究衡量志愿人员的社会和经济状况、健康、发展和福祉，以及这些方面随着时间的推移而发生的变化。通过将这些数据与他们的NHS记录结合在一起，英国有限责任公司正在促进满足英国所有社区需求的研究，通过更好地了解健康的决定因素来改善生活，并为公共卫生政策和服务的改进提供信息。目的：通过英国有限责任公司，我们希望找到更多关于我们如何改善人们生活的信息。我们现在正在运行：托管研究志愿者提供的研究数据，这些数据与他们的NHS数据和我们生活的环境数据相关联。LHW内部的批准研究人员已经开始他们的分析。我们寻求资金来扩大这一创新资源的范围，使其成为一个长期的解决方案，支持研究，帮助研究人员回答重要的研究问题，并帮助改善英国各地人民的生活。我们希望通过增加收入、就业、福利和教育记录来增强资源。通过一个应用程序提供各种各样的关联数据，将使研究人员能够更好地调查健康、社会和环境之间的相互作用，解决政府的研究优先事项，并更有效地应对英国新兴的经济、社会和健康需求。目标：我们必须进一步发展英国有限责任公司，并将我们的核心职能作为一个高质量的“研究准备”的科学资源。我们将通过我们熟练的数据管理、运营管理和治理团队以及专家执行和治理结构来交付我们的提案。我们将：-将英国有限责任公司扩展到COVID-19研究之外，使所有人受益;与研究和公众合作，确保安全和透明地完成这项工作-通过链接就业，福利和教育记录最大限度地利用可用的链接数据-为批准的研究人员制定支持指南和文件-创建工具库（例如，软件代码）和方法开发的英国有限责任公司的工作人员和研究人员使用的TRE，允许未来的用户检查和重新使用它们-使用“五个保险箱”的应用方法，利用公众的投入，并根据公平的数据原则，扩大数据的可见性和访问范围（使数据可查找、可访问、可互操作和可重复使用）。-支持研究和研究社区有效地合作。我们还建议通过试行一个新的“公民小组”来扩展我们的公众参与模式，这是一个由理解患者数据开发的概念。这个试点项目将在数据社区和更广泛的公众之间建立对话，确保良好的治理，并加强相互学习。这些增强将是我们通过NCS计划资助的核心功能的补充。好处：多样化的数据促进了研究，因为我们的社会，健康和福祉问题也是多样化的。将这些数据汇集在一起将有助于我们更多地了解这四个国家的健康、社会和环境不平等以及地区差异和机会。通过增加与我们互动的公共贡献者的数量，我们将为更广泛的努力提供信息，以建立对这种数据工作方式的信心，这将证明对公众和更广泛的研究界有益。

项目成果

Associations between self-reported healthcare disruption due to covid-19 and avoidable hospital admission: evidence from seven linked longitudinal studies for England.

• DOI: 10.1136/bmj-2023-075133
• 发表时间: 2023-07-19
• 期刊: BMJ-BRITISH MEDICAL JOURNAL
• 影响因子: 105.7
• 作者: Green, Mark A.;McKee, Martin;Hamilton, Olivia K. L.;Shaw, Richard J.;Macleod, John;Boyd, Andy;Katikireddi, Srinivasa Vittal
• 通讯作者: Katikireddi, Srinivasa Vittal

Associations between SARS-CoV-2 infection and subsequent economic inactivity and employment status: pooled analyses of five linked longitudinal surveys.

SARS-CoV-2 感染与随后的经济不活动和就业状况之间的关联：五项相关纵向调查的汇总分析。

• DOI: 10.1101/2023.07.31.23293422
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Shaw,RichardJ;Rhead,Rebecca;Silverwood,RichardJ;Wels,Jacques;Zhu,Jingmin;Hamilton,OliviaKl;Gessa,GiorgioDi;Bowyer,RuthCe;Moltrecht,Bettina;Green,MichaelJ;Demou,Evangelia;Pattaro,Serena;Zaninotto,Paola;Boyd,Andy;Greaves,F
• 通讯作者: Greaves,F

The long COVID evidence gap: comparing self-reporting and clinical coding of long COVID using longitudinal study data linked to healthcare records

长期新冠病毒证据缺口：使用与医疗记录相关的纵向研究数据来比较长期新冠病毒的自我报告和临床编码

• DOI: 10.1101/2023.02.10.23285717
• 发表时间: 2023
• 作者: Knuppel A