Health Promotion and Disease Prevention Research Centers: Special Interest Project(s)

 描述（由申请人提供）：肥胖和肥胖相关慢性疾病的高发病率在巴尔的摩市的低收入儿童和成人中持续存在。这一流行病的原因是多方面的，需要多层次和多组成部分的政策和方案。然而，开发，测试和评估肥胖控制干预措施可能会花费大量的时间，精力和资源。计算模拟模型提供了一种有效的方式，可以在政策和干预措施在真实的世界中实施之前确定其影响和意外后果。我们的团队在开发计算模拟模型以指导政策制定者和其他利益相关者以及将这些与数据收集和干预研究相结合方面拥有专业知识和经验，包括基于代理的基本模型（ABM），以代表低收入巴尔的摩市食品环境中的儿童。该提案的总体目标是进行混合方法的形成性研究，以进一步开发低收入巴尔的摩食品环境的基于代理的模型，然后将用于与政策制定者，资助者和其他关键利益相关者进行接触和合作。具体目标如下：1。进行形成性研究，以收集有关食物行为和环境的额外数据。2.使用收集的数据反复修订我们现有的ABM，然后利用逐步修订的模型来测试这些场馆的政策/计划策略的影响并对其进行优化。3.开发一个过程和相关的ABM工具，可用于指导数据收集，研究开发和实施肥胖预防计划和政策。4.向营养和肥胖政策研究与评估网络（NOPREN）和其他机构提供我们的方法和可转移的ABM。拟议的工作将分为5个阶段：1）混合方法形成研究; 2）修订巴尔的摩低收入食品环境模型（BLIFE）模型/开始修改可转移性; 3）迭代数据收集，模型修订，研究人员-专家团队的互动; 4）记录过程并开发BLIFE模型的用户友好版本; 5）最终确定可转移版本;支持其他NOPREN中心。拟议的工作具有很强的创新性。使用计算建模来解决肥胖控制有许多差距，包括缺乏对评估特定政策的关注;很少使用地理空间特定信息;建立在真实的数据上的个体代理的食物来源和食物选择决策的建模工作有限;以及缺乏可转移性。BLIFE模型将是第一个 这将是一个地理空间特定的计算模型，包括城市低收入食品和体育活动环境的大部分组成部分的详细数据，并将纳入真实的成人和儿童的数据。这将是第一次使用基于代理的建模来支持当地决策者 当他们制定政策和计划，以改善城市食品环境和降低低收入人群的肥胖风险，它将导致ABM工具的发展，可以传播给研究人员和决策者在其他城市环境。我们正在提交申请成为NOPREN合作中心（SIP 14-027）。我们提议的合作中心的目的和目标与HP 2020目标直接一致，这些目标涉及减少肥胖，减少糖和脂肪的消费，增加全谷物，水果和蔬菜的消费。我们的中心将通过确定政策和计划来促进国家预防战略，以改善健康食品和饮料的获取，并支持CDC以同样的目标赢得战斗。

 DESCRIPTION (provided by applicant): High rates of obesity and obesity-related chronic disease persist among low-income children and adults in Baltimore City. The causes of this epidemic are multifactorial, and require multi-level and multi-component policies and programs. However, developing, testing, and evaluating interventions for obesity control can cost considerable time, effort, and resources. Computational simulation models offer an effective manner of determining the impact and unintended consequences of policies and interventions before implementation in the real world. Our team has expertise and experience in developing computational simulation models to guide policy makers and other stakeholders and in combining these with data collection and intervention studies, including a basic agent-based model (ABM) to represent children in the low income Baltimore City food environment. The overall objective of this proposal is to conduct mixed methods formative research to further develop the agent-based model of the low income Baltimore food environment, which will then be used to engage and work with policy makers, funders and other key stakeholders. The following specific aims will be addressed: 1. To conduct formative research to collect additional data on the food behaviors and environment. 2. To iteratively revise our existing ABM with this collected data and then utilize the progressively revised models to test the impact of and refine policies/program strategies for these venues. 3. To develop a process and the associated ABM tool that can be used to guide data collection, study development and implementation of obesity prevention programs and policies. 4. To offer our approach and a transferable ABM to the Nutrition and Obesity Policy Research and Evaluation Network (NOPREN) and others. The proposed work will have 5 phases: 1) Mixed methods formative research; 2) Revision of the Baltimore Low Income Food Environment Model (BLIFE) model/begin modifications for transferability; 3) Iterative data collection, model revision, interaction of researcher-practitionr teams; 4) Documentation of process and development of user-friendly version of the BLIFE model; and 5) Finalization of transferable version; support to other NOPREN Centers. The proposed work is highly innovative. The use of computational modeling to address obesity control has many gaps, including lack of attention to evaluating specific policies; little work usig geospatially specific information; limited efforts to model food source and food choice decisions of individual agents built on real data; and lack of transferability. BLIFE model will be the first geospatially specific computational model to include detailed data on most components of a city's low income food and physical activity environments, and will incorporate data from real adults and children. It will be the first use of agent-based modeling to support local policymakers as they formulate policies and programs to improve the urban food environment and reduce obesity risk in low income populations, and it will lead to the development of an ABM tool that can be disseminated to researchers and policy makers in other urban settings. We are submitting this application to become a NOPREN Collaborating Center (SIP 14-027). The aims and objective of our proposed Collaborating Center are directly in line with the HP 2020 goals related to obesity reduction, reductions in sugar and fat consumption, and increases in whole grain, and fruit and vegetable consumption. Our Center will contribute to the National Prevention Strategy by identifying policies and programs to improve access to healthy foods and beverages, and supports a CDC Winnable Battle with the same aim.