Bringing Innovative Research Methods to Clustering Analysis of Multimorbidity (BIRM-CAM)

多发病是指人们患有一种以上的长期疾病。随着人们寿命的延长，这种情况越来越普遍。这一点很重要，因为个别疾病会对其他疾病产生连锁反应，管理多种疾病比处理单一疾病更复杂，而且多病患者大量使用药物和医疗服务。要了解多病，我们需要知道哪些疾病倾向于同时发生，哪些疾病组合对健康影响最大。为了适应卫生服务，我们需要知道哪些类型的人患有多发病：他们的年龄、性别、族裔、社会经济地位以及他们是否倾向于生活在同一个家庭。为了了解如何预防它，我们需要确定与多病相关的生活方式因素(体育活动、饮食、吸烟、酒精)以及可能是早期征兆的测量(实验室测试结果、体重、血压)。电子健康记录是关于多病的很好的信息来源，因为它们包括同一患者多年来的信息。它们包括关于疾病、药物、入院情况、测量(实验室测试、体重、血压)和生活方式(吸烟、酒精)的信息。以前的研究已经使用各种统计方法来研究多发性疾病。它发现一些疾病，如糖尿病和心脏病往往会同时发生。但不同的统计方法往往会发现不同的疾病组。我们需要一种单一、一致的方法来进行这种类型的分析，以确保我们研究的是同一组疾病。以往的研究一般没有充分利用所有可用的信息。例如，患者被认为要么患有糖尿病，要么没有糖尿病，但研究没有利用实验室测量(如血糖)来确定一些人可能患上糖尿病。之前的研究根据疾病一起发生的频率对疾病进行了分组，没有赋予与死亡风险或住院风险有关的疾病组合任何特殊意义。显然，这种疾病的组合更重要。有更先进的分析方法可以解决这些和其他缺点。我们的研究的第一部分将发展数据分析方法。我们将回顾将疾病分组的不同统计方法的研究。我们将举办一个研讨会，邀请该领域的顶尖英国研究人员参加，试图就这类分析的最佳方法达成一致。受此启发，我们将分析两个大型电子健康记录数据库，每个数据库包括数百万名患者。在每个数据库中，我们将识别共同发生的疾病组，并在另一个数据库中检查我们的发现。这被认为是分析中的良好实践。在这一步结束时，我们将开发软件来分析和发现电子健康记录中的疾病组，并将其免费提供给其他研究人员使用。我们研究的下一部分将使用来自两个大型调查的其他信息。这两项调查都包括了在健康记录中并不总是可用的细节，例如职业、饮食、生活方式和脆弱程度。其中一个包括500,000人，另一个拥有14年来同一人的信息。我们将描述不同疾病组合对患者的影响：他们的虚弱程度，因为这与社会护理需求有关；进一步疾病的发展；药物、卫生服务的使用和死亡。我们将与患者顾问合作，帮助指导通过医疗服务对患者行程的分析。我们将调查多发性疾病的可能原因，包括人们的社会环境、环境、生活方式(吸烟、酒精、饮食和锻炼)，以及可能有助于确定病因的实验室测试结果。这一步骤将指向环境和生活方式领域，这些领域应该作为可能的原因进行进一步调查。

Multimorbidity is when people suffer from more than one long-term illness. It is increasingly common as people live longer. It is important because individual illnesses have knock-on effects on others, it is more complex managing multiple than single illnesses, and multimorbid patients are heavy users of medications and health services.To understand multimorbidity we need to know which illnesses tend to occur together and which illness combinations most affect health. To adapt health services we need to know which types of people develop multimorbidity: their age, sex, ethnicity, socio-economic status and whether they tend to live in the same households. To learn how to prevent it we need to identify lifestyle factors (physical activity, diet, smoking, alcohol) linked to multimorbidity and the measurements (laboratory test results, weight, blood pressure) that might be early signs.Electronic health records are a good source of information on multimorbidity because they include information on the same patient over many years. They include information on illnesses, medications, hospital admissions; measurements (laboratory tests, weight, blood pressure) and lifestyle (smoking, alcohol). Previous research has studied multimorbidity using a variety of statistical methods. It finds some illnesses, such as diabetes and heart disease tend to occur together. But different statistical methods often find different groups of illnesses. We need a single, consistent approach to this type of analysis to ensure we are researching the same groups of illnesses. Previous research generally has not made best use of all the available information. For example, patients are considered either to have or not have diabetes but research did not make use of laboratory measurements (such as blood glucose) identifying some people as likely to develop diabetes. Previous research grouped illnesses according to how commonly they occur together, without giving any special significance to combinations of illnesses linked to risk of death or hospital admission. Clearly such combinations of illness are of more importance. There are more advanced analysis methods which can address these and other shortcomings.The first part of our research will develop methods of data analysis. We will review research on different statistical methods for grouping illnesses together. We will hold a workshop involving leading UK researchers in the field to try to agree on the best approach to this type of analysis. Informed by this we will analyse two large databases of electronic health records, each including several million patients. In each database we will identify the groups of illnesses that co-occur and check our findings in the other database. This is considered good practice in analysis. At the end of this step we will produce software to analyse and find groups of illnesses in electronic health records and make this freely available for other researchers to use.The next part of our research will use additional information from two large surveys. Both surveys include details not always available in health records e.g. occupation, diet, lifestyle and measures of frailty. One includes 500,000 people the other has information on the same people over a period of 14 years. We will describe the consequences for patients of different combinations of illnesses: their levels of frailty because it is linked to need for social care; development of further illnesses; medications, use of health services and death. We will work with patient advisors to help guide analysis of patients journeys through health services. We will investigate possible causes of multimorbidity including people's social circumstances, the environment, lifestyle (smoking, alcohol, diet and exercise) and laboratory test results that might help indicate causes. This step will point to the areas of environment and lifestyle which should be investigated further as possible causes.