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，特别是在中年及以上人群中，导致患有多种复杂长期疾病(MLTC)的人更普遍。同时满足那些患有多种长期健康状况的人的需求是各国政府的主要关切，也是NHS面临的最大挑战之一。英国最近使用电子健康记录进行的大规模研究估计，23%-27%的人有两种或两种以上的疾病，这些疾病可能会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，现有的证据告诉我们对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如何随时间变化--以及MLTC集群的社会和经济决定因素，以及它们对社会和经济成果、服务使用和功能、残疾、生活质量和死亡率方面的预后的影响。我们预计MLTC的负担和预后不平等，因为有证据表明，与生活在较有利地区的人相比，生活在社会经济贫困地区的人平均年轻10-15岁开始发病。MLTC预后的进一步不平等，例如由于使用或服务质量的差异，则不太为人所知。本项目的工作将解决以下研究问题：1.哪些MLTC集中在一起，哪些集群不太常见？2.MLTC的发生和发展在多大程度上存在社会和经济不平等？3.不同MLTC集群的成员在身体、认知和社会功能、幸福感和死亡率方面的长期社会、经济和福祉负担是什么？4.在那些与MLTC生活在一起的人中，在获得或获得卫生保健质量方面是否存在社会或经济不平等(如计划和非计划护理的平衡，转诊到专家护理)？该项目将使用英国老龄化纵向研究(ELSA)的丰富的、纵向的、多维的数据，并与医院事件统计(HES)、死亡记录相联系。作为一项具有全国代表性的对英格兰老年人的纵向多学科小组研究，ELSA是该项目的理想数据集。ELSA每两年收集一次关于生物、健康、经济和社会经历的高质量数据，这些数据来自2001年超过12,000名50岁以上的人，目前有9波数据可用。最近，ELSA与NHS入院(HES)和癌症登记数据联系在一起。虽然以前的研究使用电子健康记录来研究多发性疾病，但ELSA允许包括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ELSA的重复评估，并利用与HES和死亡率数据的联系。关于健康状况的主要访谈数据将被用来利用潜在类别分析和潜在轨迹分析来确定大规模毁灭性武器集群及其随时间的变化。将使用ELSA主要访谈的数据来评估那些患有MLTC的人的社会和经济特征，包括关于参与者的情况和随着时间的变化的大量信息。与多发病相关的负担将使用混合效应模型进行纵向评估。正在审议的措施是抑郁和生活质量(福祉)、经济状况(收入、财富和劳动力市场参与度、接受普遍信贷)、社会(孤独、接受非正式护理、社会联系和关系)、健康和社会状况。

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