Inequality in the socioeconomic burden and prognosis of multiple long-term conditions

近几十年来，预期寿命的急剧增加，特别是在中年和老年人中，导致患有多种复杂长期疾病（MLTC）的人更加普遍。满足那些同时患有多种长期健康状况的人的需求是政府的主要关注点，也是NHS面临的最大挑战之一。最近在英国使用电子健康记录的大规模研究估计，23-27%的人患有两种或两种以上的疾病，这些疾病可能对治疗需求产生重大影响，并降低生活质量，占初级和二级保健利用率和成本的一半以上。然而，现有的证据告知我们的MLTC的理解主要来自临床样本或横断面研究。需要大规模和丰富的纵向人口数据，以更好地了解人们对MLTC的经历-常见和新型疾病集群及其随时间的变化-以及MLTC集群的社会和经济决定因素，它们对社会和经济成果的影响，服务使用和功能、残疾、生活质量和死亡率方面的预后。我们预计MLTC的负担和预后不平等，因为有证据表明，与生活在更贫困地区的人相比，生活在社会经济贫困地区的人平均发病年龄要小10-15岁。在MLTC预测方面的进一步不平等，例如通过服务的使用或质量的差异，则不太为人所知。本课题的研究工作主要包括以下几个方面：1.哪些MLTC聚集在一起，哪些集群不太常见？2.在MLTC的发生和发展过程中，社会和经济不平等的程度如何？3.在身体、认知和社会功能、福祉和死亡率方面，与不同MLTC集群成员相关的长期社会、经济和福祉负担是什么？4.在患有MLTC的人中，在获得医疗保健或医疗保健质量方面是否存在社会或经济不平等（例如计划内和计划外护理的平衡，专科护理的转诊）？本研究将利用英国老龄化纵向研究（艾尔莎）中丰富的、纵向的、多维度的数据，结合医院事件统计（HES）、死亡率记录。作为一项具有全国代表性的英格兰老年人纵向多学科小组研究，艾尔莎是本项目的理想数据集。2001年，艾尔莎每两年从12,000多名50岁以上的人的样本中收集关于生物、健康、经济和社会经历的高质量数据，目前有9波数据可用。最近，艾尔莎与NHS医院入院（HES）和癌症登记数据相关联。虽然以前的研究使用电子健康记录来研究多发病，但艾尔莎允许纳入有关社会和经济情况以及病情严重程度的信息。 该项目将利用先进的统计技术来分析艾尔莎的重复评估，并探讨与HES和死亡率数据的联系。将使用关于健康状况的主要访谈的数据，通过潜在类别分析和潜在轨迹分析，确定MLTC的集群及其随时间的变化。将利用艾尔莎主要访谈的数据，对患有MLTC的人的社会和经济特征进行评估，这些数据涵盖了关于参与者情况及其随时间变化的大量信息。将使用混合效应模型纵向评价与多次死亡相关的负担。正在考虑的措施是抑郁症和生活质量（福祉），经济情况（收入，财富和劳动力市场参与，获得普遍信贷），社会（孤独，接受非正式护理，社会接触和关系），健康和社会问题。

Dramatic increases in life expectancy over recent decades, particularly amongst those in mid-life and older, has contributed to a greater prevalence of people living with multiple complex long-term conditions (MLTCs). Meeting the needs of those living with multiple long-term health conditions simultaneously represents a major concern for governments and is one of the biggest challenges facing the NHS. Recent large-scale studies using electronic health records in the UK estimate 23-27% of people have two or more conditions that are likely to have significant impact on need for treatment and reduced quality of life and account for over half of primary and secondary care utilisation and costs. However, existing evidence informing our understanding of MLTCs comes primarily from clinical samples or cross-sectional studies. Large-scale and rich longitudinal population data are required to better understand people's experiences of MLTCs - common and novel disease clusters and how they change over time - as well as the social and economic determinants of MLTC clusters, their impact on social and economic outcomes, service use and prognosis in terms of functioning, disability, quality of life and mortality. We anticipate inequality in the burden and prognosis of MLTC as evidence suggests that onset begins 10-15 years younger, on average for those living in socioeconomically disadvantaged areas compared with those living in more advantaged areas. Further inequality in the prognosis of MLTC, for example through differences in use or quality of services, is less well-known. The work of this project will address the following research questions:1. What MLTCs cluster together and what clusters are less common?2. To what extent are there social and economic inequality in onset and progression of MLTCs?3. What is the long-term social, economic and wellbeing burden associated with membership of different MLTC clusters in terms of physical, cognitive and social functioning, wellbeing and mortality?4. Amongst those who live with MLTCs, are there social or economic inequalities in access to or quality of health care (such as the balance of planned and unplanned care, referrals to specialist care)? This project will use the rich, longitudinal, multidimensional data of the English Longitudinal Study of Ageing (ELSA) linking with Hospital Episode Statistics (HES), mortality records. As a nationally representative longitudinal multidisciplinary panel study of older people in England, ELSA is the ideal data set for this project. ELSA has collected high quality data about the biological, health, economic, and social experiences every two years from a sample of over 12,000 people aged 50+ in 2001, with 9 waves of data currently available. Recently ELSA was linked with NHS hospital admissions (HES) and cancer registry data. While previous studies have used electronic health records to study multimorbidity, ELSA allows for the inclusion of information on social and economic circumstances and severity of conditions. The project will make use of advanced statistical techniques to analyse the repeated assessments of ELSA and to exploit the linkage to HES and mortality data. Data from the main interviews on health conditions will be used to identify clusters of MLTCs and their changes over time using latent class analysis and latent trajectory analysis. Social and economic characteristics of those with MLTCs will be evaluated using data from the main ELSA interviews, covering a wealth of information on participant's circumstances and changes over time therein. The burden associated with multimorbidity will be evaluated longitudinally using mixed effects models. The measures under consideration are depression and quality of life (wellbeing), economic circumstances (income, wealth and labour market participation, receipt of universal credits), social (loneliness, receipt of informal care, social contacts and relationships), health and social ca