Complex Systems Science Approaches to Addressing Oral Health Disparities

口腔健康差距，特别是儿童之间的差距，在美国仍然是一个紧迫的问题。当口交时 儿童的健康状况总体上有所改善，弱势群体的儿童继续遭受 疾病负担高于平均水平。口腔健康差异反映了一套复杂的途径和 具有双向关联(反馈环路)的机制，这些机制遍历 疾病原因。这项提议的总体目标是促进候选人的长期职业目标 成为一名独立的社会流行病学家，能够调查疾病的原因和预防 通过基于复杂系统的视角。目前的提案旨在为候选人提供 有机会发展必要的技能和经验，通过扩展实现研究的独立性 他们在流行病学方面的正规培训和在口腔健康差异研究方面的应用经验。发展 在指导阶段提出的活动与培训候选人在复杂系统中的能力有关 科学(CS)方法，即社会网络分析(SNA)和基于代理的建模(ABM)。培训 将通过课程作业、多学科指导和应用研究经验进行。初级阶段 车辆的开发是研究的计划。拟议的研究旨在使用css方法来更好地 了解弱势群体口腔健康的潜在因素和机制，并 改进旨在改善口腔健康的战略，从而减少观察到的差距。这些 方法有助于了解人口、社会互动和环境影响 同时，还可以探索独立和独立的 因变量。拟议工作的第一阶段旨在开发一个详细的口语概念模型 健康差距，抓住了多层次的机制，并将被用来指导 基于代理的模型。ABM指的是使用随机计算机模拟来理解 健康和疾病在人群水平上的分布来自明确编程的健康行为， 在模拟时间内模拟个人的社会互动和运动。口腔健康的ABM和 发展和利用公屋居民之间的社交网络，以确定可行的干预措施。 目标。这项工作的第二阶段建议对成年照顾者进行纵向国民账户体系 居住在公共住房的儿童。SNA是一种经验工具，已被用来更好地理解 个人在其中互动的社会背景。计划中的分析将用于提供纵向数据， 可用于验证从基于代理的模型获得的干预目标的估计。 此外，潜在的基于网络的干预的可行性和可接受性将在 样本。上述目标的完成将代表复杂系统的首批应用之一 科学的方法来区分口腔健康的差距和候选人的独立研究生涯。

Oral health disparities, particularly among children, continue to be a pressing problem in the US. While the oral health of children has improved overall, those from disadvantaged population groups continue to suffer from a greater than average disease burden. Oral health disparities reflect a complex set of pathways and mechanisms with bidirectional associations (feedback loops) that traverse the macro- and micro-levels of disease causation. The overall goal of this proposal is to facilitate the candidate’s long term career goal of becoming an independent social epidemiologist capable of investigating disease causation and prevention through a complex systems-based perspective. The current proposal aims to provide the candidate with the opportunity to develop the necessary skills and experience to achieve research independence by extending their formal training in epidemiology and applied experiences in oral health disparities research. Development activities proposed during the mentored phase are related to training the candidate in Complex Systems Science (CSS) approaches, namely social network analysis (SNA) and agent-based modeling (ABM). Training will occur through coursework, multidisciplinary mentoring and applied research experiences. The primary vehicle for development is the research plan. The proposed research aims to use CSS approaches to better understand the factors and mechanisms underlying oral health among a disadvantaged population, and to improve the strategies aimed at improving oral health, thereby reducing observed disparities. These approaches facilitate an understanding of the population, social interaction and environmental influence simultaneously and can explore interactions, feedback loops and reciprocity between independent and dependent variables. The first phase of the proposed work aims to develop a detailed conceptual model of oral health disparities that captures the multi-level mechanisms and will be used to inform the development of an agent-based model. ABM refers to the use of stochastic computer simulations to understand how the distribution of health and disease on a population level emerge from explicitly programmed health behaviors, social interactions and movements of simulated individuals over simulated time. An ABM of oral health and social networks among residents of public housing will be developed and utilized to identify viable intervention targets. The second phase of the work proposes to conduct a longitudinal SNA of adult caregivers of young children residing in public housing. SNA is an empirical tool that has been utilized to better understand the social context within which individuals interact. The planned analysis will serve to provide longitudinal data that can be used to validate the estimates of intervention targets obtained from the agent-based model. Additionally, the feasibility and acceptability of a potential network-based intervention will be evaluated within the sample. Completion of the described aims will represent one of the first applications of complex systems science methods to oral health disparities and demarcate the independent research career of the candidate.