Estimating State-Specific Potential Annual Healthcare Cost Savings from Reducing

描述（由申请人提供）：美国各地不断上升的肥胖率不仅已成为一个公共卫生问题，而且也是一个财政问题。在一个联邦和州一级医疗资源都有限的时代，减少肥胖及其负面后果在政策议程上占据突出地位。烟草控制方面的经验在很大程度上影响了旨在改变人口一级消费者行为的政策的设计和评价。征税以阻止不健康的食品和饮料是全国正在讨论的许多政策举措之一。虽然广泛的肥胖政策-其中许多是在州一级-强调了减少肥胖下游健康和经济负担的潜力，但缺乏估计其规模的分析工具。全国各地的公共卫生研究人员和立法者都在积极寻求具体的、以证据为基础的工具，以列举肥胖预防政策的价值，特别是在州一级。该提案的总体目标是在一个分析模型中建立数据和方法库，以估计州一级的医疗保健成本节约和减少肥胖的生产率提高。我们的目标是：（1）估计10年的医疗保健成本节省从肥胖减少的国家。通过连接现有的州一级监测系统，我们的目标是创建一个工具，估计在BMI降低的情况下，预防肥胖相关疾病的货币价值。我们还将在探索性分析中评估所获得的生产力的货币价值（以减少缺勤和出勤的形式）。(2)调查潜在的下游影响肥胖水平，医疗保健成本，从国家一级消费税的SSB，一个国家一级的税收政策的案例研究生产力的提高。拟议的研究利用了我们团队的集体研究组合和经济学，流行病学和营养政策方面的独特专业知识。本文的创新之处在于：（1）首次对两种常用的方法进行了批判性的比较（基于发病率和患病率）来计算肥胖的经济负担;（2）我们将纳入一个广泛的州一级数据列表，以反映人口组成、饮食消费、就业和工资以及医疗保健费用的重要地区差异;及（3）我们会设计一个方便使用的网上工具，让持份者参与，并方便传播研究结果和方法。拟议活动中揭示的数据差距、不确定性和方法学挑战将有助于现有的证据基础。最近的政治气候表明，更多的州一级立法机构将在未来几年解决肥胖问题，税收是影响行为的潜在工具。拟议的研究将为研究人员评估潜在的公共卫生效益提供基于证据的工具和锚点。展望未来，所构建的模型的灵活性允许随后使用新数据进行更新和/或使用本地（例如城市级）数据进行定制。它也可能为设计潜在的自然实验铺平道路。

DESCRIPTION (provided by applicant): Rising obesity rates across the United States have become not only a public health concern, but also a fiscal one. In an era with constrained healthcare resources both at the federal and state levels, reducing obesity and its negative consequences are prominent on the policy agenda. Experiences in tobacco control have greatly shaped the design and evaluation of policies aimed to change consumer behavior at the population level. Tax to discourage unhealthy food and beverages is one of many policy initiatives being discussed nationwide. While a wide range of obesity policies-many of them at the state level-highlight the potential to reduce the downstream health and economic burdens of obesity, analytical tools to estimate their magnitude are lacking. Public health researchers and legislators across the country intensely seek concrete, evidence-based tools to enumerate the value of obesity prevention policies especially at the state level. The overarching goal of this proposal is to establish the data and methods repertoire within an analytical model for estimating state-level healthcare cost savings and productivity gains from reducing obesity. We aim to: (1) Estimate 10-year healthcare cost savings from obesity reduction by state. By linking existing state-level surveillance systems, we aim to create a tool that estimates the monetary values of obesity-attributable diseases prevented under given reductions in BMI for the whole or a segment of the population. We will also assess the monetary values of the productivity gained (in the forms of reduced absenteeism and presenteeism) in an exploratory analysis. (2) Investigate the potential downstream impact on obesity levels, healthcare costs, and productivity gain from a state-level excise tax on SSBs, a case study of a state-level tax-based policy. The proposed research capitalizes our team's collective research portfolio and the unique assembly of expertise in economics, epidemiology, and nutrition policy. The innovation of this proposed research is three- fold: (1) for the first time we will critically compare two commonly-used approaches (incidence-based and prevalence-based) to tally the economic burden of obesity; (2) we will incorporate an extensive list of state- level data to reflect importnt regional variations on population composition, dietary consumption, employment and wage, and healthcare costs; and (3) we will design a user-friendly, web-based tool to engage stakeholders and facilitate the dissemination of results and methods. Data gaps, uncertainties, and methodological challenges revealed in the proposed activities will contribute to the existing evidence base. The recent political climate indicates that more state-level legislatures will tackl obesity in the years to come, with taxation as a potential vehicle to influence behavior. The proposed research will provide evidence-based tools and anchor points for researchers to assess potential public health benefits. Going forward, the flexibility of the model constructed allows subsequent updates with new data and/or customization with local (e.g. city- level) data. It may also pave the way for designing potential natural experiments.