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 often exert influence on others in their network. In our setting, the study design is observational with a non-randomized network-based HIV prevention intervention. The information is available on each participant and their connections that confer possible shared HIV risk behaviors through injection and sexual risk behaviors. We consider two inverse probability weighted (IPW) estimators to quantify the population-level effects of non-randomized 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 HIV risk behavior in this observed network, where the 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 both IPW estimators demonstrated a protective effect. The results suggest that HIV risk behavior can be mitigated by exposure to a community alert when an increased risk of HIV is detected in the network.
翻译:与许多这类人口一样,注射毒品(PWID)被植入社会网络,并经常影响其网络中的其他人。在我们所处的环境中,研究设计是观察性的,以非随机网络为基础的艾滋病毒预防干预措施为主;每个参与者都有信息,他们之间的关联使得有可能通过注射和性风险行为而共享艾滋病毒风险行为。我们认为,有两个反概率加权估计(IPW),以量化非随机干预对随后的卫生结果产生的人口影响。我们表明,这两个IPW估计值是一致的,是正常的,而且往往对其网络中的其他人产生影响。 在我们看来,从2013年到2015年,通过观察社区对艾滋病毒风险的网络和网络的近距离,我们评估了艾滋病毒风险,从2013年到2015年,我们评估了艾滋病毒风险网络中的最新风险。我们评估了在雅典、希腊的网络中观察到的艾滋病毒风险,从2013年到2015年,我们评估了艾滋病毒风险网络的网络中,我们评估了社区对艾滋病毒风险的近距离。