SIMULATING COLLATERAL WEIGHT LOSS WITHIN SOCIAL NETWORKS: CAPITALIZING ON SPILLOVER EFFECTS TO IMPROVE HEALTH

Purpose: Researchers have observed that obesity spreads within social networks, but the impact of this contagion on population health is difficult to measure. We aimed to quantify the collateral health effects of a theoretical obesity intervention within a family and measure the ultimate impact such spillover has on intervention effectiveness.

Method: We developed an agent-based model to simulate the dynamics of the spread of obesity within a social network. This dynamic microsimulation model can simulate the contagion of obesity between specific social network contacts, as well as an individual’s natural history of weight loss and weight gain over time. We calibrated the model to published data on trends in the prevalence of obesity in the United States population from 2000 to 2010, and simulated weight loss that would be observed via a theoretical intervention.  We modeled multiple intervention scenarios that targeted one or more family members.

Result: We found that the contagion of obesity could result in significant collateral weight loss in family members not directly targeted in an intervention. Consideration of collateral weight loss in intervention effectiveness estimates was estimated to reduce the prevalence of overweight and obesity by 8% to 26% in the model population. Moreover, we found that the contagion of obesity within social networks can hinder weight loss in children unless interventions are targeted toward all obese children in a family. No single intervention target scenario was significantly better than another in reducing population weight.  The model projections were sensitive to the age of the family member, the levels of social influence parents had over children, the effectiveness of the intervention, and baseline weight.

Conclusion: Ignoring obesity spillover effects can measurably impact assessments of the effectiveness of obesity interventions. As a result, comparative effectiveness analyses may underestimate the benefits of obesity interventions. This model can be leveraged as a low-cost, low-burden tool to optimize obesity prevention strategies and can inform the design of randomized controlled obesity prevention trials. Future research should incorporate heterogeneity in social influence and intervention effectiveness by sex, age, and family type.