Emdin_2026_Int.J.Popul.Data.Sci_11_2980

INTRODUCTION: Prescribing for people living with dementia can be challenging. Emerging research methods present an opportunity to learn about complex patterns of medication use and leverage this understanding to optimize care. OBJECTIVE: We describe network analysis, an unsupervised machine learning method, to understand population-level prescribing in older adults living with dementia. METHODS: We included community-dwelling adults aged 67 and older, newly ascertained as having dementia between April 1, 2014, and March 31, 2016 in Ontario, Canada. Using medication dispensation data, we created network graphs at ascertainment and five years later. Each node represented a medication subclass; subclasses concurrently dispensed to the same individual were considered linked by an edge. Atributes of networks were used to characterize prescribing across individuals: nodes, edges (including network density), and node centrality metrics. RESULTS: We identified 99,064 individuals with incident dementia, of which 15,222 were alive and not living in a nursing home after five years. Network graphs visually demonstrated trends at the subclass level, such as a high prevalence of cardiovascular medications, and showed changes between times, such as an increase in dispensation of central nervous system active medications, particularly cholinesterase inhibitors (15.5% at index compared to 26.4% at five years). Co-dispensing (edge width) remained mostly consistent over time. Metrics derived from the networks highlighted differences, such as increased density (proportion of co-dispensed medication subclasses of all possible pairs) at five years compared to at ascertainment. Node centrality established frequently prescribed medication subclasses (statins, proton pump inhibitors, and beta blockers) as important within networks in this population. CONCLUSIONS: This study offered an introductory review of the fundamental aspects of network analysis and demonstrated the complexity of prescribing patterns in people with incident dementia at the population-level. This showed that networks analysis can be used in future studies to compare population-level prescribing patterns across patient subgroups, prescribers, settings of care and regions to identify important differences. KEY POINTS: Network graphs and associated metrics offer a novel way of summarizing complex prescribing data across populations and may allow the discovery of important medication differences over time, between patient subgroups, and across prescribers.For older adults living with dementia in the community, medication network graphs were highly connected and showed a high prevalence of cardiovascular in addition to central nervous system medications.Corresponding network graph metrics supported these findings quantitatively and showed increased density at five years compared to at ascertainment.