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Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics

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 form...

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Autores principales: Yang, Kai-Cheng, Aronson, Brian, Odabas, Meltem, Ahn, Yong-Yeol, Perry, Brea L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9426918/
https://www.ncbi.nlm.nih.gov/pubmed/36040880
http://dx.doi.org/10.1371/journal.pone.0273569
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author Yang, Kai-Cheng
Aronson, Brian
Odabas, Meltem
Ahn, Yong-Yeol
Perry, Brea L.
author_facet Yang, Kai-Cheng
Aronson, Brian
Odabas, Meltem
Ahn, Yong-Yeol
Perry, Brea L.
author_sort Yang, Kai-Cheng
collection PubMed
description 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.
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spelling pubmed-94269182022-08-31 Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics Yang, Kai-Cheng Aronson, Brian Odabas, Meltem Ahn, Yong-Yeol Perry, Brea L. PLoS One Research Article 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. Public Library of Science 2022-08-30 /pmc/articles/PMC9426918/ /pubmed/36040880 http://dx.doi.org/10.1371/journal.pone.0273569 Text en © 2022 Yang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yang, Kai-Cheng
Aronson, Brian
Odabas, Meltem
Ahn, Yong-Yeol
Perry, Brea L.
Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics
title Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics
title_full Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics
title_fullStr Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics
title_full_unstemmed Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics
title_short Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics
title_sort comparing measures of centrality in bipartite patient-prescriber networks: a study of drug seeking for opioid analgesics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9426918/
https://www.ncbi.nlm.nih.gov/pubmed/36040880
http://dx.doi.org/10.1371/journal.pone.0273569
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