Global Epidemiology · Published 2026-01-14 · DOI 10.1016/j.gloepi.2026.100248
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 (ce) quantified relative nodal importance. Weekly-aggregated data included number of dispensing (nc) and eigenvector centrality, which were decomposed using a singular-spectrum approach. Results:: Antidepressants (ce: 0.09, nc: 28,993) and anxiolytics (ce: 0.05, nc: 14,061) had high eigenvector centrality, demonstrating frequent co-prescription. Other ATC groups with high centrality included those for the alimentary tract and metabolism (A01-A16), blood and blood-forming organs (B01-B06), cardiovascular system (C01-C10), respiratory system (R01-R07), and analgesics (N02). 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.
Abstract from DOAJ. Public domain (CC0 1.0).
Read the article at the publisher →