BMI trajectories in middle to old age: methodological developments, missing data and cost-effectiveness

英国的人口老龄化和日益严重的肥胖症是公共卫生面临的两大挑战。目前，超过300万80岁及以上的老人生活在英国，预计到2050年，这一数字将超过800万，这将增加工作和养老金部以及国家卫生服务的成本。在过去的30年里，肥胖率几乎增加了两倍，每年的成本超过30亿英镑。如果预测是正确的，到2050年，我们将有一半以上的人口肥胖。与肥胖相关的严重合并症（如糖尿病、骨关节炎、心血管疾病），加上与衰老相关的多种健康问题（如阿尔茨海默病、关节炎和癌症），使老年人面临重大风险。虽然人们关注年轻人和普通成年人的肥胖研究，但关于肥胖如何发展到老年以及老年人肥胖的后果的研究相对较少。预测具有不同特征的患者的BMI轨迹有助于为公共卫生政策提供信息。如果可以对病人未来的BMI进行预测，那么这些长期的估计就可以用于经济模型中，以估计潜在的干预措施、行动或政策的成本效益。出于政策目的，了解这些影响如何随着时间的推移而发展是至关重要的，而提供这些长期预测依赖于纵向分析。许多成本效益分析都使用了BMI轨迹，包括糖尿病模型。先前的研究已经调查了平均BMI轨迹，并表明随着患者从中年到老年，它们会有所不同。然而，众所周知，肥胖患病率和BMI在不同的社会群体之间是不同的，而且个人的环境对他们的体重有很大的影响。该项目将确定可能经历不同类型BMI轨迹的不同人群。医学文献表明，某些类型的人进入老年后，身体质量指数更有可能下降，这被称为“肥胖悖论”，因为身体质量指数下降，但体脂率上升。在这群人中，BMI可能会产生误导，肌肉质量（如握力）的测量可能是识别BMI轨迹相似但肥胖风险不同的个体的重要指标。这些不同的轨迹也将与长期的健康结果联系起来，包括一系列疾病和死亡。在研究老年人时，由于疾病、记忆丧失和与养老院的个人接触，数据缺失可能是一个特别的问题。这可能意味着，在50岁以上的个体样本中，更多的老年人代表性不足。为了解释某些个体的潜在代表性不足，分析应该考虑缺失的数据，本研究将调查缺失数据可能对估计的BMI轨迹的影响。这项研究将使用英国老龄化纵向研究（ELSA）的数据，上面描述的分析将为卫生保健专业人员提供更详细的证据，帮助他们根据预期遵循的BMI轨迹确定那些最有可能患病和死亡的人。研究结果还将用于成本效益分析，这可以帮助决策者确定最具成本效益的干预措施，以帮助改善后代进入老年后的健康状况。我将使用糖尿病预防模型作为案例研究，以说明潜在的糖尿病预防干预措施如何影响具有不同特征的患者，并证明如果数据缺失未被解释，可能对政策产生的影响。

The UK's aging population and increasing obesity prevalence are two of the most significant public health challenges. Currently, more than 3 million people aged 80 years and over live in the UK, and this figure is projected to exceed 8 million by 2050 at increasing costs to the Department of Work and Pensions and the National Health Service. Obesity rates have almost trebled in the last 30 years at a cost of over £3 billion a year and, if forecasting is correct, over half our population will be obese by 2050. Significant obesity related co-morbidities (e.g. diabetes, osteoarthritis, cardiovascular disease), coupled with multiple health problems associated with aging (e.g. Alzheimer's disease, arthritis and cancer), places older people at significant risk. While there is a focus on obesity research in young people and the general adult population, there is relatively little research into how obesity develops into old age and the consequences of obesity in older adults. It is useful to predict BMI trajectories in patients with different characteristics in order to inform public health policies. If predictions can be made for a patient's future BMI, then these long-term estimates can be used in economic models in order to estimate how cost-effective potential interventions, actions or policies are expected to be. For policy purposes, it is critical to understand how these effects develop over time, and longitudinal analyses are relied upon to provide these long-term predictions. A number of cost-effectiveness analyses have used BMI trajectories, including models for diabetes. Previous research has investigated mean BMI trajectories and has shown that they differ as patients go from middle age to older age. However, it is well known that obesity prevalence and BMI differ between different social groups and that an individual's environment can have a large influence on their weight. This project will identify different groups of individuals who are likely to experience different types of BMI trajectories. Medical literature suggests that there are certain types of individual who are more likely to see a drop in BMI as they enter old age, known as the 'obesity paradox' because BMI drops but body fat percentage increases. In this group of people, BMI might be misleading and measures of muscle mass (such as grip strength) could be an important in identifying individuals with similar BMI trajectories but different risks of obesity. These different trajectories will also be linked with long term health outcomes, including a range of diseases and death. Missing data can be a particular problem when researching the elderly due to illness, memory loss and access to individuals in care homes. This could mean that in a sample of individuals over the age of 50 years, more elderly individuals are under-represented. In order to account for the potential underrepresentation of certain individuals, analysis should account for missing data and this study will investigate the influence that missing data might have on estimated BMI trajectories.The study will use data from the English Longitudinal Study of Aging (ELSA) and the analysis described above will provide more detailed evidence for health care professionals helping to identify those most at risk of morbidities and mortality by the BMI trajectories that they are expected to follow. The results will also be used to inform cost-effectiveness analyses, which can help policy makers to determine the most cost-effective interventions to help improve the health of future generations as they enter old age. I will use a diabetes prevention model as a case study, to illustrate how potential diabetes prevention interventions might influence patients with different characteristics and to demonstrate the impact that missing data could have on policy if it remained unaccounted for.