Network analysis as a tool to illustrate the population-level complex prescribing to community-dwelling people living with dementia

IntroductionPrescribing 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. ObjectiveWe describe network analysis, an unsupervised machine learning method, to understand population-level prescribing in older adults living with dementia. MethodsWe 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. Attributes of networks were used to characterize prescribing across individuals: nodes, edges (including network density), and node centrality metrics. ResultsWe 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. ConclusionsThis 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.