Estimating State-Specific Potential Annual Healthcare Cost Savings from Reducing

描述（由申请人提供）：美国不断上升的肥胖率不仅已成为一个公共卫生问题，而且已成为一个财政问题。在联邦和州一级医疗资源有限的时代，减少肥胖及其负面影响是政策议程上的重要议题。烟草控制方面的经验在很大程度上影响了旨在在人口一级改变消费者行为的政策的设计和评价。对不健康食品和饮料征税是全国正在讨论的众多政策举措之一。虽然范围广泛的肥胖政策——其中许多是在州一级——强调了减少肥胖的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，以列举预防肥胖政策的价值，尤其是在州一级。本提案的首要目标是在一个分析模型中建立数据和方法库，以估计减少肥胖带来的州一级医疗保健成本节约和生产力提高。我们的目标是：(1)估计各州减少肥胖所节省的10年医疗保健费用。通过连接现有的州级监测系统，我们的目标是创建一种工具，在给定的整体或部分人口BMI降低的情况下，估计肥胖导致的疾病的货币价值。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)以州级税基政策为例，研究对SSBs征收消费税对肥胖水平、医疗成本和生产率增长的潜在下游影响。拟议的研究利用了我们团队的集体研究组合以及经济学、流行病学和营养政策方面的独特专业知识。这项拟议研究的创新之处在于三个方面：(1)我们将首次批判性地比较两种常用的方法（基于发病率和基于患病率）来计算肥胖的经济负担；(2)我们将纳入一份广泛的州级数据清单，以反映人口构成、饮食消费、就业和工资以及医疗成本等方面的重要地区差异；(3)我们将设计一个用户友好的基于网络的工具，让利益相关者参与进来，促进结果和方法的传播。拟议活动中揭示的数据差距、不确定性和方法挑战将有助于现有的证据基础。最近的政治气候表明，未来几年将有更多的州级立法机构将解决肥胖问题，税收将成为影响人们行为的潜在工具。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定位点。展望未来，构建模型的灵活性允许使用新数据进行后续更新和/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.