Evaluating causal effects in the presence of interference is challenging in network-based studies of hard-to-reach populations. Like many such populations, people who inject drugs (PWID) are embedded in social networks and one person’s treatment can affect the outcomes of others in the network. In our setting, the study design is observational with a nonrandomized network-based HIV prevention intervention. Information is available on each participant and their connections that confer possible HIV risk through injection and sexual behaviors. We considered two inverse probability weighted (IPW) estimators to quantify the population-level spillover effects of nonrandomized interventions on subsequent health outcomes. We demonstrated that these two IPW estimators are consistent, asymptotically normal, and derived a closed-form estimator for the asymptotic variance, while allowing for overlapping interference sets (groups of individuals in which the interference is assumed possible). A simulation study was conducted to evaluate the finite-sample performance of the estimators. We analyzed data from the Transmission Reduction Intervention Project which ascertained a network of PWID and their contacts in Athens, Greece, from 2013 to 2015. We evaluated the effects of community alerts on subsequent HIV risk behavior in this observed network, where the connections or links between participants were defined by using substances or having unprotected sex together. In the study, community alerts were distributed to inform people of recent HIV infections among individuals in close proximity in the observed network. The estimates of the risk differences for spillover, using either IPW estimator demonstrated a protective effect. The results suggest that HIV risk behavior could be mitigated by exposure to a community alert when an increased risk of HIV is detected in the network.
These findings are presented on behalf of the Transmission Reduction Intervention Project (TRIP). We would like to thank all of the TRIP investigators, data management teams, and participants who contributed to this project. We thank Ke Zhang for her editorial comments. The project described was supported by the Avenir Award Program for Research on Substance Abuse and HIV/AIDS (DP2) from National Institute on Drug Abuse of the National Institutes of Health Award Number DP2DA046856. Dr. Samuel Friedman was partially supported by the National Institute on Drug Abuse of the National Institutes of Health Award Number DP1DA034989, which funded Preventing HIV Transmission by Recently-Infected Drug Users (TRIP), and the National Institute on Drug Abuse of the National Institutes of Health Award Number P30DA011041 which supported the Center for Drug Use and HIV Research at New York University. Dr. M. Elizabeth Halloran was partially supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health Award Number R01AI085073 titled Causal Inference in Infectious Disease Prevention Studies. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
"Estimating causal effects of HIV prevention interventions with interference in network-based studies among people who inject drugs." Ann. Appl. Stat. 17 (3) 2165 - 2191, September 2023. https://doi.org/10.1214/22-AOAS1713