Insight into Selecting Adolescents for Drinking Intervention Programs: a Simulation Based on Stochastic Actor-Oriented Models

Simulating social network-based interventions for adolescent cigarette smoking

Social network-based adolescent substance use interventions have demonstrated potential for reducing adolescent cigarette smoking. This approach is premised upon leveraging youths' social networks for the diffusion of peer influence. Determining which adolescents to select in network interventions for reducing smoking is a major consideration. We utilize a simulation approach that first estimates Stochastic Actor-Oriented models (SAOM) of adolescent smoking using data from two of the largest schools from the longitudinal saturation sample of the National Study of Adolescent to Adult Health (Add Health) (n = 3,154). We then conduct Agent-Based Simulation models which mimic the consequences of intervention strategies selecting adolescents in network positions and structures that are salient for smoking and the diffusion of peer influence within school-based networks, and we select adolescents smoking at different levels. Our findings indicate that selecting adolescents occupying central network positions yielded the greatest reductions in the number of smokers in a school, one year post intervention. Moreover, our findings indicate that in the school with the higher smoking prevalence, there was a beneficial network multiplier effect one year later, which resulted in more non-smokers than those smokers initially intervened upon. When examining the effects of varying the magnitude of peer influence, we find that targeting central positions in networks led to even greater decreases in smoking in schools with higher levels of peer influence. Our findings highlight interdependence and sensitivity of peer influence to network position and have implications for informing school-based network interventions for adolescent smoking.

Induction of social contagion for diverse outcomes in structured experiments in isolated villages

Certain people occupy topological positions within social networks that enhance their effectiveness at inducing spillovers. We mapped face-to-face networks among 24,702 people in 176 isolated villages in Honduras and randomly assigned villages to targeting methods, varying the fraction of households receiving a 22-month health education package and the method by which households were chosen (randomly versus using the friendship-nomination algorithm). We assessed 117 diverse knowledge, attitude, and practice outcomes. Friendship-nomination targeting reduced the number of households needed to attain specified levels of village-wide uptake. Knowledge spread more readily than behavior, and spillovers extended to two degrees of separation. Outcomes that were intrinsically easier to adopt also manifested greater spillovers. Network targeting using friendship nomination effectively promotes population-wide improvements in welfare through social contagion.