Project 4 will focus on understanding the role that social determinants play in the link between obesity and cancer at the population level across the lifespan by developing a multi-cohort simulation model of otiosity and non-Hodgkin's lymphoma (NHL).
Aim 1 : Develop a multi-cohort system dynamics computer simulation model of obesity and NHL population level incidence, treatment toxicity and survival trends. This will extend the obesity modeling from childhood to adult populations, develop a population level model of NHL, and based on emerging individual level analysis from the VHA cancer registry database, integrate these two models over the life course.
Aim 2 : Analyze the resulting model to identify how social determinants influence obesity and NHL population level incidence and outcome trends. This analysis will identify dominant social determinants, delayed effects, and temporal relationships across the life course.
Aim 3 : Design guidelines along with their implementation strategies to identify the most effective way to reduce the impact of social determinants of NHL population level outcomes. This will generate different guidelines (e.g., policy, prevention, screening, treatment, and survivorship care) along with potential implementation strategies to determine the best combination of guideline-implementation strategy for reducing the burden of NHL. This project is transdisciplinary. It will complement existing and separately funded work to develop an innovative system dynamics model of childhood obesity by extending the model into adulthood across the lifespan and develop a NHL model that will be combined with the extended obesity model. Once established, additional cancers can be added to the model from this or other TRECs. This project is significant. It will not only provide rigorous conceptual models of how social determinants for obesity and NHL might interact over time, but also help identify key areas for future transdisciplinary research that have high potential for population level impact. Involvement of the expert panel in the modeling process will facilitate the development of transdisciplinary knowledge. The model will also be one of the first to link efforts from the NIH funded Cancer Intervention and Surveillance Modeling Network (CISNET) and the NIH/RWJF funded Comparative Modeling (CompMod) Network for obesity prevention.
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