Extending an inter-generational cohort to develop a multimorbidity research platform in rural and urbanising India

在印度，有一种以上长期健康状况(“多发性疾病”)的人更有可能有较高的医疗费用，他们通常自掏腰包，并更有可能残疾，这可能会影响他们的工作能力。因此，多发病可能会影响人们家庭的经济福祉，有可能将他们推到贫困线以下。农村和城市化社区特别容易受到多发病快速上升的影响，因为社会变化正在影响行为(例如，人们吃更多的加工食品)和环境暴露(例如，交通造成的空气污染加剧)，而与贫困有关的老问题(例如营养不良)仍然普遍存在。这些地区也缺乏医疗保健，因此人们可能难以管理自己的病情。由于这些因素，多发性疾病的增加可能会抑制农村和城市化地区未来的发展。因此，重要的是要了解是什么导致了多发性疾病，这样我们才能找到预防的方法。这项研究将建立在过去30年在Telangana海得拉巴市郊区29个村庄所做的工作的基础上。研究小组此前收集了这些村庄(安得拉邦儿童和父母研究)(约5%的村庄人口，N=6972,2010-12年年龄约为22-50岁)家庭的详细健康数据，这些家庭在1980年代参与了一项针对孕妇和幼儿的营养补充剂试验。我们的最终目标是长期跟踪这群人，看看疾病是如何在不同的生命阶段发展和聚集在一起的，检查是什么导致了这些聚集，并开发了针对这些原因的方法，以防止多发病的发展。我们将特别调查早期因素(例如营养不良)和与快速变化的环境(例如空气污染)有关的因素如何与多发病有关，因为它们对多发病的影响尚未得到评估。然而，我们需要做大量的基础工作才能准确地做到这一点。这项目前的研究将分析以前收集的数据，以了解哪些条件和潜在原因对当地社区是重要的。我们还将收集队列中年龄较大的子组(N=~2,000,2020年年龄~60岁)中存在慢性病的数据，他们更有可能经历多种疾病。这将结合以前收集的数据(2010年)进行分析，并用来计算我们需要研究多少社区成员，以准确量化潜在原因对多发性疾病风险的影响。我们还将采访患有不同严重多发病的人(及其家人)，并与更广泛的社区成员进行焦点小组讨论，以了解多发病如何影响人们的生活，以及他们希望APCAPS在未来的优先事项和研究。最后一个目标是调查开发一个自动收集社区成员医疗记录数据的系统的可行性，因为这将使我们能够以低成本长期收集数据。为此，我们将随机选择200名在过去一年中使用过医疗保健的受访人群，追踪他们在当地医疗机构的记录，并将诊断结果与我们自己的临床检查结果进行比较。我们将与社区成员和主要方面(例如当地卫生工作者、村领导)密切合作，利用上述研究结果制定多病研究平台的研究建议，确定多病的原因，评估其影响，并开发和测试预防多病的方法。我们预计，我们的发现也将适用于其他经历类似社会变化的社区。

In India, people that have more than one long-term health condition ("multimorbidity") are more likely to have high healthcare costs, which they usually pay out-of-pocket, and to be disabled which can affect their ability to work. As a result, multimorbidity can impact the economic well-being of people's families, potentially pushing them below the poverty line. Rural and urbanising communities are particularly vulnerable to rapid rises in multimorbidity because social changes are affecting behaviours (e.g. people are eating more processed foods) and environmental exposures (e.g. rising air pollution from traffic), while old, poverty-related health issues (e.g. malnutrition) remain common. Healthcare is also lacking in these areas, so people can struggle to manage their conditions. As a result of these factors, rising multimorbidity could inhibit future development in rural and urbanising areas. As such, it is important to understand what causes multimorbidity, so that we can develop ways to prevent it. This study will build on work done over the past 30 years in 29 villages on the outskirts of Hyderabad city, Telangana. The study team has previously collected detailed health data on families within these villages (the Andhra Pradesh Children and Parents Study) (~5% of the village population, N=6,972, aged ~22-50 in 2010-12) who were involved in a trial of nutritional supplements for pregnant women and young children in the 1980s. Our ultimate aim is to follow this cohort up over the long-term to see how conditions develop and cluster together at different life-stages, to examine what causes these clusters, and to develop ways to target these causes to prevent multimorbidity developing. We will particularly investigate how factors at early-life (e.g. malnutrition) and related to the rapidly changing environment (e.g. air pollution) are linked to multimorbidity, as their impact on multimorbidity has not been assessed. Nevertheless, we need to do considerable groundwork to be able to do this accurately. This current study will analyse previously collected data to understand which clusters of conditions and potential causes are important in the local community. We will also collect data on the presence of chronic conditions in the older sub-group of the cohort (N=~2,000, aged ~60 in 2020), who are more likely to experience multiple conditions. This will be analysed in combination with previously collected data (2010) and used to calculate how many community members we need to study to accurately quantify the impact of potential causes on the risk of multimorbidity. We will also interview people with varying severities of multimorbidity (and their family members) and conduct focus-group discussions with wider community members, to understand how multimorbidity affects people's lives and what they wish APCAPs to prioritise and research in the future. A final goal is to investigate how feasible it would be to develop a system that automatically collects data on community members' healthcare records, as this would allow us to collect data over the long-term and at low cost. We will do this by randomly selecting 200 of the surveyed cohort who have used healthcare in the past year, tracking down their records in local facilities, and comparing the diagnosis with results from our own clinical examinations. Working closely with community members and key parties (e.g. local health workers, village leaders), we will use the above findings to develop a research proposal for a multimorbidity research platform, to establish the causes of multimorbidity, assess its impact, and develop and test ways of preventing multimorbidity. We expect our findings will also be applicable to other communities experiencing similar social changes.