Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States of America.
Department of Sociology, Indiana University, Bloomington, IN, United States of America.
PLoS One. 2022 Aug 30;17(8):e0273569. doi: 10.1371/journal.pone.0273569. eCollection 2022.
Visiting multiple prescribers is a common method for obtaining prescription opioids for nonmedical use and has played an important role in fueling the United States opioid epidemic, leading to increased drug use disorder and overdose. Recent studies show that centrality of the bipartite network formed by prescription ties between patients and prescribers of opioids is a promising indicator for drug seeking. However, node prominence in bipartite networks is typically estimated with methods that do not fully account for the two-mode topology of the underlying network. Although several algorithms have been proposed recently to address this challenge, it is unclear how these algorithms perform on real-world networks. Here, we compare their performance in the context of identifying opioid drug seeking behaviors by applying them to massive bipartite networks of patients and providers extracted from insurance claims data. We find that two variants of bipartite centrality are significantly better predictors of subsequent opioid overdose than traditional centrality estimates. Moreover, we show that incorporating non-network attributes such as the potency of the opioid prescriptions into the measures can further improve their performance. These findings can be reproduced on different datasets. Our results demonstrate the potential of bipartiteness-aware indices for identifying patterns of high-risk behavior.
就诊多位医生是获取处方类阿片药物进行非医疗用途的常见方法，这种行为在美国阿片类药物泛滥中发挥了重要作用，导致药物使用障碍和过量用药的情况增加。最近的研究表明，由患者和开处方医生之间的处方联系形成的二分网络的中心性是一种有前途的觅药指标。然而，二分网络中节点的显著程度通常是使用未充分考虑底层网络的双模拓扑结构的方法来估计的。尽管最近已经提出了几种算法来解决这个问题，但尚不清楚这些算法在真实网络中的表现如何。在这里，我们通过将它们应用于从保险索赔数据中提取的大规模患者和提供者的二分网络，来比较它们在识别阿片类药物觅药行为方面的性能。我们发现，两种变体的二分中心性是随后阿片类药物过量的显著更好的预测指标，而不是传统的中心性估计。此外，我们表明，将非网络属性（如阿片类药物处方的效力）纳入这些指标中可以进一步提高它们的性能。这些发现可以在不同的数据集上重现。我们的研究结果表明，二分网络感知指标具有识别高风险行为模式的潜力。
