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ADMISSION UK Multimorbidity Research Collaborative on Multiple Long-Term Conditions in Hospital: from burden and inequalities to underlying mechanisms

ADMISSION 英国多种疾病研究合作医院多种长期状况：从负担和不平等到潜在机制

基本信息

批准号：
MR/V033654/1
负责人：
Avan Aihie Sayer
金额：
$ 491.14万
依托单位：
Newcastle University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FV033654%2F1
关键词：
ADMISSION UK Multimorbidity Research Collaborative

项目摘要

Why is multimorbidity important?Multimorbidity, that is living with multiple long-term health conditions, is very common in people admitted to hospital. These patients tend to stay in hospital for longer, are more likely to die, and may take much longer to recover when they are discharged. However, the way we deliver care for people with multiple long-term conditions is not ideal; in a system that was designed for single conditions, care can be unsatisfactory and inefficient for the patient - and is expensive for the healthcare provider, such as the NHS. The need for improvement is recognised, but there is currently little research on multimorbidity in hospital patients to help us to know how services need to be changed. Our research is designed to address this gap in understanding and is focused on people with multiple long-term conditions who are admitted to hospital.What is ADMISSION?We have formed a new Research Collaborative (called ADMISSION) that includes data scientists, statisticians, laboratory researchers, social scientists and clinical teams from UK universities (Newcastle, Birmingham, Manchester Met, UCL, Dundee) to carry out research that will transform our understanding of multiple long-term conditions in hospital patients. By bringing together this expertise we will be able to use the power of 'big data' (from routine NHS and other datasets) to better understand the patterns and causes of multiple long-term conditions, and the effects of living with them. The Collaborative will use information collected by hospitals in the North East of England, Birmingham and Dundee, and from intensive care wards across the UK, to identify patients with long-term conditions. We will pay particular attention to patients who have a combination of physical health conditions (for example heart disease, lung disease, arthritis, falls and poor mobility) and mental health conditions (for example dementia and depression).What will ADMISSION do?Our planned research is divided into five linked work packages, with each helping the work of the others. Our first work package will build a library of information gathered from Newcastle Hospitals and will compare this with similar libraries of information from Birmingham hospitals and intensive care units across the UK. Our second work package will analyse this information to find patterns of health conditions as these tend to cluster together, so that we can look at the effects of background factors such as age, sex, and ethnicity on them (in our third work package). Our fourth work package will use information from ambulance services, accident and emergency, acute hospital admissions and general practice records to understand how we deliver health care to people with clusters of multiple long-term conditions, how they journey through the healthcare system and how we might be able to improve their experience of health and social care. Our final work package looks at the mechanisms that could explain what causes the clusters of long-term conditions; we will analyse genetic and other information from half a million people who signed up to the UK Biobank study to find out how genes vary between different clusters of conditions, and then test these ideas using blood samples collected from 3000 hospital patients in the SHARE Scotland registry. What will the end result of ADMISSION be?This work will lead to a step change in our understanding of how long-term conditions cluster together in hospital patients, why they cluster, and how these different clusters affect health and the delivery of health care. With this understanding we will be able to design new approaches to treat and prevent multiple long-term conditions, and to improve the health, function and quality of life of people who have them. It will also inform the redesign of health and social care systems so that they are better able to care for patients with multiple long-term conditions in the future.

为什么多病很重要？多病，即患有多种长期健康状况的人，在住院的人中非常常见。这些患者往往住院时间更长，死亡的可能性更大，出院后可能需要更长的时间才能康复。然而，我们为患有多种长期疾病的人提供护理的方式并不理想；在为单一疾病设计的系统中，患者的护理可能不令人满意和效率低下-而且对NHS等医疗保健提供者来说代价高昂。人们认识到需要改进，但目前几乎没有关于医院患者多发病的研究，以帮助我们了解服务需要如何改变。我们的研究旨在解决这一认识上的差距，重点关注住院的多种长期疾病患者。什么是入院？我们成立了一个新的研究协作组(称为入院)，成员包括来自英国大学(纽卡斯尔大学、伯明翰大学、曼彻斯特大都会大学、伦敦大学学院、邓迪大学)的数据科学家、统计学家、实验室研究人员、社会科学家和临床团队，开展的研究将改变我们对医院患者多种长期疾病的理解。通过将这些专业知识结合在一起，我们将能够利用‘大数据’的力量(来自NHS和其他常规数据集)，更好地了解多种长期疾病的模式和原因，以及与它们生活在一起的影响。Collaborative将使用英格兰东北部、伯明翰和邓迪的医院以及英国各地重症监护病房收集的信息来识别患有长期疾病的患者。我们将特别关注那些身体健康状况(例如心脏病、肺部疾病、关节炎、跌倒和行动不便)和精神健康状况(例如痴呆症和抑郁症)的患者。入院将起到什么作用？我们计划的研究分为五个相互关联的工作包，每个工作包帮助其他人的工作。我们的第一个工作包将建立一个从纽卡斯尔医院收集的信息图书馆，并将其与英国各地伯明翰医院和重症监护病房的类似信息图书馆进行比较。我们的第二个工作包将分析这些信息，以找出健康状况的模式，因为这些往往聚集在一起，以便我们可以查看年龄、性别和种族等背景因素对它们的影响(在我们的第三个工作包中)。我们的第四个工作包将使用来自救护车服务、急救、急性入院和全科医疗记录的信息，以了解我们如何为患有多种长期疾病的人提供医疗保健，他们如何在医疗保健系统中旅行，以及我们如何能够改善他们的健康和社会护理体验。我们的最终工作包着眼于解释长期疾病集群的机制；我们将分析50万注册英国生物库研究的人的遗传和其他信息，以找出不同疾病集群之间的基因差异，然后使用从苏格兰共享登记处收集的3000名医院患者的血液样本来测试这些想法。入院的最终结果是什么？这项工作将导致我们对医院患者长期疾病如何聚集在一起，为什么聚集，以及这些不同的聚集如何影响健康和医疗保健提供的理解发生重大变化。有了这样的理解，我们将能够设计新的方法来治疗和预防多种长期疾病，并改善患有这些疾病的人的健康、功能和生活质量。它还将为重新设计医疗和社会护理系统提供信息，以便它们能够更好地照顾未来患有多种长期疾病的患者。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Experiences of hospital care for people with multiple long-term conditions: a scoping review of qualitative research.

DOI：
10.1186/s12916-023-03220-y
发表时间：
2024-01-17
期刊：
BMC MEDICINE
影响因子：
9.3
作者：
Bellass, Sue;Scharf, Thomas;Errington, Linda;Davies, Kelly Bowden;Robinson, Sian;Runacres, Adam;Ventre, Jodi;Witham, Miles D.;Sayer, Avan A.;Cooper, Rachel
通讯作者：
Cooper, Rachel

Simple approaches to characterising multiple long-term conditions (multimorbidity) and rates of emergency hospital admission: Findings from 495,465 UK Biobank participants.