Modeling the Co-evolution of Substance Use behavior and peer Networks of risk/support (CoSUN)

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Drug overdose deaths in the U.S. have continued to increase over the years, with over 70,000 deaths in 2019. The economic cost of substance use and abuse related crimes, healthcare, and loss in work productivity in the U.S. exceeds $600 billion each year. In order to aid individuals and reduce the associated cost effectively, we must allocate appropriate resources to individuals in the greatest emerging need (e.g., those of high future substance use). However, there are currently no data-driven tools that allow stakeholders to (stochastically) forecast an individual's substance use. Current methodologies only rely on trend analyses, establishing correlations between risk factors (e.g., friends that use drugs) and substance use. A key challenge we are facing when modelling future substance use lies in the co-evolution of behaviors (i.e., drug use) and peer networks of risk/support, which can change over time and may depend on each other. To address this scientific obstacle, we consider an innovative approach that decouples the co-evolution process of substance use and peer risk/support networks by (Aim 1A) first modelling how individual attributes (e.g., drug use and adverse childhood history) along with their peer networks (e.g., the extent of peer and confidant drug use) impact the individual’s substance use behavior and (Aim 1B) then modeling how individuals’ peer risk/support network links form or break in response to similarities or differences in the endpoints’ attributes (e.g., their drug use behaviors). Thus, for the first time, this project seeks to develop stochastic forecasting models for future substance use (FSU) and future peer risk/support networks (FPN) at long timescales within months (Aims 1-2) and for FSU at short timescales within days (Aim 3) using data on covariates of individual attributes and peer network features. We will use successful machine-learning methods to build these models and rigorously assess model generalizability/prediction performance (Aims 1-3) by making use of data that is held-out from the model building process. The Aims will provide a foundation for a future innovative NIH R01 that develops stochastic simulations of realistic SUD-related behavioral contagion in plausible dynamic networks, to inform resource allocation and contingency planning. This project also lays the groundwork for just-in-time interventions to detect imminent increases in FSU and use these risk forecasts to trigger the delivery of social and behavioral interventions.