Yang Yong
School of Public Health, University of Memphis, Memphis, Tennessee.
Ann N Y Acad Sci. 2017 Nov;1408(1):7-14. doi: 10.1111/nyas.13558.
Most health studies focus on one health outcome and examine the influence of one or multiple risk factors. However, in reality, various pathways, interactions, and associations exist not only between risk factors and health outcomes but also among the risk factors and among health outcomes. The advance of system science methods, Big Data, and accumulated knowledge allows us to examine how multiple risk factors influence multiple health outcomes at multiple levels (termed a 3M study). Using the study of neighborhood environment and health as an example, I elaborate on the significance of 3M studies. 3M studies may lead to a significantly deeper understanding of the dynamic interactions among risk factors and outcomes and could help us design better interventions that may be of particular relevance for upstream interventions. Agent-based modeling (ABM) is a promising method in the 3M study, although its potentials are far from being fully explored. Future challenges include the gap of epidemiologic knowledge and evidence, lack of empirical data sources, and the technical challenges of ABM.
大多数健康研究聚焦于单一健康结果，并考察一个或多个风险因素的影响。然而，在现实中，各种途径、相互作用和关联不仅存在于风险因素与健康结果之间，也存在于风险因素之间以及健康结果之间。系统科学方法、大数据和积累的知识的进步使我们能够考察多个风险因素如何在多个层面上影响多个健康结果（称为3M研究）。以邻里环境与健康的研究为例，我阐述了3M研究的重要性。3M研究可能会使我们对风险因素与结果之间的动态相互作用有更深入得多的理解，并有助于我们设计出可能与上游干预特别相关的更好的干预措施。基于主体的建模（ABM）是3M研究中一种很有前景的方法，尽管其潜力远未得到充分发掘。未来的挑战包括流行病学知识和证据的差距、缺乏实证数据来源以及ABM的技术挑战。
