Estimating cost-effectiveness of diet and physical activity interventions for the primary prevention of non-communicable disease at the Local Authorit

评估地方当局非传染性疾病一级预防的饮食和身体活动干预措施的成本效益

• 批准号: 2106399
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2106399
• 关键词: Estimating; cost; effectiveness; diet; physical

Preventable disease is responsible for approximately 40% of the UK's disease burden, largely in the form of non-communicable diseases such as heart disease and stroke. Causes of these diseases, such as obesity, can be prevented, providing an opportunity to avert disability and deaths. It is useful to know how much disease is likely to be prevented for each pound of spending. To work out this value for money, complicated sums called models are used. At present, Local Authorities have no models that account for the characteristics of the local area, such as people's ages, weights or exercise levels. This acts as a translational gap, preventing the consistent application of the best interventions. This DPhil will develop a model for chronic disease prevention that can compare value for money across Local Authorities for approaches to improving diet and physical activity. This will not only help local decision-makers with funding decisions but also allow the local impacts of national public health policy to be estimated. Modelling works by estimating the effect of each step that leads from an intervention to a disease outcome. At present, models produce an estimate for England as a whole, without being able to estimate how this might vary for different areas. For example, if a local council spends money on gym memberships for diabetic patients, modelling could estimate the increase in activity those individuals do in the real world, the weight they lose and improvement in diabetes control; all based on published clinical research and standard health economic methods. More affluent areas tend to have better responses to health promotion, as well as very different patterns of diseases and risks (eg, having lower obesity rates), so an approach that is effective and good value for money in one area may not be in another. Local area estimates will also allow impacts on health inequalities to be estimated.The modelling method is called proportionate multistate lifetable modelling, which simulates a population moving through time, aging and developing disease. From there, NHS costs can be estimated. By doing this twice in parallel, the difference between a baseline and an intervention can be estimated. To make the modelling process flexible for local outputs, two additional steps will be added. Firstly, the input risk factor profiles need to reflect local areas, and secondly the results will be standardised to local area age and sex composition. Local risk factor profiles are estimated as locally-collected data are poor. This process uses regression modelling to estimate an area's risk factor profile based on demographics and national risk factor data. Standardisation will be performed based on census data. Two scenarios will be modelled. The first will estimate local implications of the calorie reduction plan from Public Health England's report Childhood Obesity: A Plan for Action, Chapter 2 (2018), which has major spillover health implications for adults. The second will estimate the local impacts of recent salt reduction in the UK. This second scenario will compare progress under the scrapped industry Responsibility Deal (2011) against the trend under the previous Food Standards Agency's Salt Reduction Strategy from its initiation in 2000 to 2011. Validation and sensitivity analyses will follow. Validation will be performed against international examples of chronic disease scenario models. Deterministic sensitivity analyses will be based on the uncertainty intervals produced in the risk factor profile estimates and probabilistic sensitivity analyses will be based uncertainty around the Relative Risks through Monte Carlo Analysis. This proposal involves MRC Strategic Skill Priority areas in quantitative skills (applied statistics, computer modelling) and interdisciplinary skills (epidemiology, clinical application, health economics, policy analysis).

英国约40%的疾病负担是由易患疾病造成的，主要是心脏病和中风等非传染性疾病。这些疾病的原因，如肥胖，是可以预防的，从而提供了避免残疾和死亡的机会。了解每一磅的支出可能预防多少疾病是有用的。为了计算出这种资金价值，使用了称为模型的复杂计算。目前，地方当局没有考虑到当地特点的模型，如人们的年龄、体重或运动水平。这是一个翻译差距，阻碍了最佳干预措施的一致应用。该博士将开发一种慢性病预防模型，可以比较地方当局改善饮食和体力活动的方法的资金价值。这不仅有助于地方决策者作出供资决定，而且还可以估计国家公共卫生政策对地方的影响。建模的工作原理是估计从干预到疾病结果的每一步的效果。目前，模型对整个英格兰进行了估计，但无法估计不同地区的情况。例如，如果一个地方理事会花钱为糖尿病患者购买健身房会员资格，建模可以估计这些人在真实的世界中活动的增加，他们减轻的体重和糖尿病控制的改善;所有这些都基于已发表的临床研究和标准健康经济方法。更富裕的地区往往对健康促进有更好的反应，以及非常不同的疾病和风险模式（例如，肥胖率较低），因此，在一个地区有效且物有所值的方法可能不适用于另一个地区。局部地区的估计也将使人们能够估计对健康不平等的影响，建模方法被称为比例多状态生命表建模，它模拟人口随时间的变化、老化和疾病的发展。由此可以估算出NHS的成本。通过两次平行操作，可以估计基线和干预措施之间的差异。为了使建模过程灵活地适应本地输出，将添加两个额外步骤。首先，输入的风险因素概况需要反映当地的情况，其次，结果将根据当地的年龄和性别构成进行标准化。由于当地收集的数据不足，对当地风险因素概况进行了估计。这一过程使用回归模型，根据人口统计和国家风险因素数据估计一个地区的风险因素概况。将根据普查数据进行标准化。将模拟两种情况。第一个将从英国公共卫生报告《儿童肥胖：行动计划》第2章（2018年）中估计卡路里减少计划的当地影响，该报告对成年人的健康有重大影响。第二部分将评估英国近期减盐对当地的影响。第二种方案将比较废弃的行业责任协议（2011年）下的进展情况与2000年至2011年启动的食品标准局减盐战略下的趋势。随后将进行验证和敏感性分析。将根据国际上的慢性病情景模型实例进行验证。确定性敏感性分析将基于风险因素概况估计中产生的不确定性区间，概率敏感性分析将基于通过蒙特卡罗分析得出的相对风险的不确定性。该提案涉及MRC战略技能优先领域的定量技能（应用统计，计算机建模）和跨学科技能（流行病学，临床应用，卫生经济学，政策分析）。