A temporal network analysis of drug co-prescription during antidepressants and anxiolytics dispensing in the Netherlands from 2018 to 2022

TL;DR

This study uses network analysis to explore how antidepressants and anxiolytics are co-prescribed with other medications in the Netherlands from 2018 to 2022.

Contribution

The novel contribution is applying temporal network analysis to identify influential drug classes and co-prescription patterns linked to psychiatric and chronic disease treatments.

Findings

Multi-class co-prescription was over tenfold more common than same-class use.

Seven central drug classes bridge psychiatric and chronic disease treatments.

Network analysis identifies prescribing hubs missed by standard utilization methods.

Abstract

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.

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.

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.

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).

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.

•Multi-class co-prescription was over tenfold more common than same-class use•Seven central drug classes bridge psychiatric and chronic disease treatments•Network analysis identifies prescribing hubs missed by standard utilization methods•Results align with Dutch guidelines on anxiety and depression treatment•Co-prescription patterns mirror multimorbidity in psychiatric and chronic care

Multi-class co-prescription was over tenfold more common than same-class use

Seven central drug classes bridge psychiatric and chronic disease treatments

Network analysis identifies prescribing hubs missed by standard utilization methods

Results align with Dutch guidelines on anxiety and depression treatment

Co-prescription patterns mirror multimorbidity in psychiatric and chronic care

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