ArticleGlobal epidemiology2026
A temporal network analysis of drug co-prescription during antidepressants and anxiolytics dispensing in the Netherlands from 2018 to 2022.
Article in Global epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background: Drug prescription networks (DPNs) model the temporal dynamics of medication co-prescription within a population. Understanding these networks can provide insights into polypharmacy and prescribing behaviors. Objective: This study assesses the structural characteristics of temporal DPNs derived from daily co-prescriptions of antidepressants, anxiolytics, and other therapeutic drug classes. By analyzing these networks using eigenvector centrality, we identify influential medications and prescribing patterns. Methods: We utilized the IADB.nl database, including prescriptions from 128 Dutch pharmacies (2018-2022). A cohort of patients prescribed antidepressants/anxiolytics was extracted. Medications were classified using the Anatomical Therapeutic Chemical (ATC) system into 24 therapeutic classes. Time-varying DPNs were constructed as undirected graphs using symmetric daily dose-adjusted co-prescriptions. Eigenvector centrality ( Results: Antidepressants ( Discussion: DPNs revealed key co-prescription patterns. High-centrality medications highlight potential targets for drug monitoring, such as identifying co-prescription trends that may warrant evaluation for safety, appropriateness, or policy oversight. This approach aids in identifying influential medications and refining prescribing oversight.
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