Public Health Research & Systems/ Network Science: Childhood Obesity

描述(由申请者提供)：申请的资金将用于支持一系列科学会议，以促进新墨西哥大学预防研究中心(PRC)的公共卫生研究科学家和桑迪亚国家实验室(SNL)的系统建模科学家最近建立的独特的、跨学科和机构间的合作。我们提议的工作重点是儿童健康，重点是儿童肥胖症。我们正在制定一项研究议程，将基于社区的公共卫生调查与基于系统的概念和计算建模相结合。我们认为，这两种方法的综合将创建一个更复杂的框架，为加强社区一级的预防和促进更健康的社区提供见解。肥胖症是一种全国性流行病，儿童超重和肥胖率在过去的几十年里以惊人的速度增长。1-7然而，迄今为止，肥胖症在多个层面上给预防科学带来了难题。挑战的一部分是问题的复杂性，既有影响人口中个人肥胖症发展的因素，也有影响预防工作成功的因素。8-14这种复杂性很难概念化，因为它包括同时存在于多个影响层面(个人、家庭、社区、国家、国家)的各种因素，以及这些因素及其影响范围汇合形成的相互联系和关系。越来越多的人认识到，这种因素和背景的多样性需要新的方法和视角，这鼓励了在肥胖预防研究中采用面向系统的概念框架的支持。15-22计算建模方面的最新进展使适应日益复杂的情况成为可能，这与跨学科面向系统的研究的发展相吻合。目前，人们对应用网络科学了解肥胖等大规模公共卫生问题所涉及的复杂和交叉的社会生态因素非常感兴趣。19-21，23考虑到这一点，拟议的会议将使我们的工作组能够利用我们独特的多学科伙伴关系，并努力将系统和网络科学整合到中华人民共和国以社区为基础的公共卫生方法中。我们的工作将最终开发一个合作的PRC/SNL R21或R01研究资助申请。这些会议将为我们提供举办定期团队研讨会和定期密集的战略概念化研讨会的机会，这些研讨会将磨练我们对计算建模和肥胖预防框架的理解，建设我们工作组进行连接学科的研究的能力，并使我们能够概念化如何将基于社区的方法与基于系统的计算建模所提供的进步相结合。我们将邀请相关领域的知名专家通过研讨会、演讲和工作讨论与我们分享他们的知识和经验。我们的目标将是确定最好的建模方法，并与社区合作创建一个原型概念模型，该模型将成为R21或R01应用程序以及未来更深入工作的基础。我们还将组建一个对这种方法感兴趣的研究人员联盟。我们目前考虑了一个潜在的合作者网络，其中可能包括来自全国各地学术机构的其他预防研究中心的研究人员。

DESCRIPTION (provided by applicant): The requested funding will support a series of scientific meetings to foster a unique, inter- disciplinary and inter-institutional collaboration that has recently been established between public health research scientists at the University of New Mexico Prevention Research Center (PRC) and systems modeling scientists at Sandia National Laboratories (SNL). The focus of our proposed work is on child health with an emphasis on childhood obesity. We are developing a research agenda to integrate community-based public health investigation with systems-based conceptual and computational modeling. We believe that the synthesis of these two approaches will create a more sophisticated framework that will provide insights to strengthen community-level prevention and promote healthier communities. Obesity is a national epidemic with the prevalence of overweight and obesity among children increasing alarmingly over the last several decades.1-7 To date however, obesity has presented a conundrum to prevention science on a number of levels. Part of the challenge has been the complex nature of the problem, both in terms of the factors that influence the development of obesity among individuals in a population, but also in terms of those that influence the success of prevention efforts.8-14 This complexity is difficult to conceptualize, as it includes a variety of factors that exist simultaneously at multiple levels of influence (individual, family, community, state, national), together with the interconnections and relationships formed by the confluence of these factors and their spheres of influence. Increasing acknowledgement that this multiplicity of factors and contexts requires new approaches and perspectives has encouraged support for employing systems-oriented conceptual frameworks in obesity prevention research.15-22 Recent advances in computational modeling make it possible to accommodate increasingly-greater complexity which dovetails with the development of systems-oriented research across disciplines. Considerable interest now exists for applying network science to understanding complex and intersecting socio-ecological factors involved in large-scale public health issues like obesity.19-21, 23 With this in mind, the proposed meetings will allow our workgroup to capitalize on our unique multi-disciplinary partnership and work to integrate systems and network science into the community-based public health approach of the PRC. Our work will culminate in the development of a collaborative PRC/SNL R21 or R01 research grant application. These meetings will provide us with the opportunity to hold regular Team Workshops and periodic intensive Strategic Conceptualization Workshops that will hone our understanding of computational modeling and obesity-prevention frameworks, build the capacity of our workgroup to conduct research that bridges disciplines, and allow us to conceptualize how to integrate community-based approaches with the advances offered by systems-based computational modeling. We will invite prominent experts from relevant fields to share their knowledge and experience with us through workshops, presentations and working discussions. Our goal will be to identify the best modeling approaches and to work with a community to create a prototype Conceptual Model that will be the basis for the R21 or R01 application and for more in-depth future work. We will also form a consortium of researchers interested in this approach. We currently have in mind a network of potential collaborators that could include researchers from other Prevention Research Centers at academic institutions across the country.