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Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort
Tino Prell1, Gabriele Helga Franke2, Melanie Jagla-Franke2,3
1Department of Geriatrics, Halle University Hospital, Halle, Germany.
Patient Preference and Adherence
|May 10, 2022
Summary
Understanding medication nonadherence is key to improving patient outcomes. Network analysis reveals distinct nonadherence categories, including intentional modification based on health or beliefs, with adverse effects being highly influential.
Area of Science:
- Pharmacology
- Health Psychology
- Network Science
Background:
- Nonadherence to medication significantly impacts patient health outcomes.
- Understanding the complex structure of nonadherence behaviors is crucial for developing effective interventions.
- Network analysis offers a novel approach to explore relationships between various adherence-related variables.
Purpose of the Study:
- To investigate the underlying structure of medication nonadherence using network analysis.
- To explore the relationships between different types of nonadherence behaviors as measured by the Stendal Adherence to Medication Score (SAMS).
Main Methods:
- Pooled data from 1,746 patients across four studies who completed the SAMS.
- Employed network analysis (EBICglasso) to map relationships between nonadherence items.
- Utilized confirmatory factor analysis to validate network structures.
Main Results:
- Network analysis identified distinct nonadherence categories: lack of knowledge, forgetting, and intentional modification.
- Intentional modification was sub-categorized into adjustments based on health changes versus medication beliefs.
- Adverse effects and polypharmacy (taking multiple medications) emerged as the most influential factors in the network.
Conclusions:
- Differentiating intentional medication modification based on health status versus personal beliefs is vital for targeted interventions.
- Network analysis demonstrates potential as a valuable tool for future adherence research.
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