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

多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，管理多种疾病比管理单一疾病要复杂得多，而且患有多种疾病的患者大量使用药物和卫生服务。要了解多重疾病，我们需要知道哪些疾病往往同时发生，哪些疾病组合最影响健康。为了适应卫生服务，我们需要知道哪些类型的人会患上多重疾病：他们的年龄、性别、种族、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，我们需要确定与多重疾病相关的生活方式因素（体育活动、饮食、吸烟、饮酒）和可能是早期迹象的测量（实验室测试结果、体重、血压）。电子健康记录是关于多重疾病的一个很好的信息来源，因为它们包括同一患者多年来的信息。它们包括疾病、药物、住院信息；测量（实验室测试、体重、血压）和生活方式（吸烟、饮酒）。以前的研究使用了各种统计方法来研究多发病。它发现一些疾病，如糖尿病和心脏病往往同时发生。但不同的统计方法通常会发现不同的疾病群体。我们需要一种单一的、一致的方法来进行这类分析，以确保我们研究的是同一组疾病。以前的研究一般没有充分利用所有可用的信息。例如，患者要么被认为患有糖尿病，要么被认为没有糖尿病，但研究没有利用实验室测量（如血糖）来确定一些人可能患糖尿病。以前的研究根据疾病同时发生的频率对疾病进行分组，没有对与死亡或住院风险相关的疾病组合给予任何特别的重视。显然，这种疾病的组合更为重要。有更先进的分析方法可以解决这些和其他缺点。研究的第一部分将发展数据分析的方法。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，邀请该领域的英国主要研究人员，试图就这类分析的最佳方法达成一致。据此，我们将分析两个大型电子健康记录数据库，每个数据库都包括数百万患者。在每个数据库中，我们将确定共同发生的疾病组，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。在这一步的最后，我们将制作软件来分析和查找电子健康记录中的疾病组，并使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额外信息。这两项调查都包括健康记录中并不总是有的细节，例如职业、饮食、生活方式和虚弱程度。一个包含50万人，另一个包含同一个人14年的信息。我们将描述不同疾病组合对患者的影响：他们的虚弱程度，因为它与社会护理需求有关；进一步疾病的发展；药物、保健服务的使用和死亡。我们将与患者顾问合作，帮助指导分析患者通过卫生服务的旅程。我们将调查多重发病的可能原因，包括人们的社会环境、环境、生活方式（吸烟、饮酒、饮食和运动）以及可能有助于指出病因的实验室测试结果。这一步骤将指出环境和生活方式的领域，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.