Complex Systems Science Approaches to Addressing Oral Health Disparities

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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.