Academic Journal
Using Medication Dispensation Data to Identify Clusters with Similar Prescribing Patterns in Older Adults Living with Dementia.
| Τίτλος: | Using Medication Dispensation Data to Identify Clusters with Similar Prescribing Patterns in Older Adults Living with Dementia. |
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| Συγγραφείς: | Emdin A; Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada., Stukel TA; ICES, Toronto, ON, Canada.; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.; Sunnybrook Research Institute, Toronto, ON, Canada., Bethell J; ICES, Toronto, ON, Canada.; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.; KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada., Wang X; ICES, Toronto, ON, Canada., Iaboni A; KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada., Bronskill SE; Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada. susan.bronskill@ices.on.ca.; ICES, Toronto, ON, Canada. susan.bronskill@ices.on.ca.; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. susan.bronskill@ices.on.ca.; Sunnybrook Research Institute, Toronto, ON, Canada. susan.bronskill@ices.on.ca. |
| Πηγή: | Drugs & aging [Drugs Aging] 2025 Oct; Vol. 42 (10), pp. 963-974. Date of Electronic Publication: 2025 Sep 10. |
| Τύπος έκδοσης: | Journal Article; Research Support, Non-U.S. Gov't |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Adis, Springer International Country of Publication: New Zealand NLM ID: 9102074 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1179-1969 (Electronic) Linking ISSN: 1170229X NLM ISO Abbreviation: Drugs Aging Subsets: MEDLINE |
| Imprint Name(s): | Publication: Auckland : Adis, Springer International Original Publication: Mairangi Bay, Auckland, N.Z. : Adis International, c1991- |
| Ιατρικοί όροι (MeSH): | Dementia*/drug therapy , Dementia*/epidemiology , Practice Patterns, Physicians'*/statistics & numerical data , Drug Prescriptions*/statistics & numerical data, Ontario/epidemiology ; Humans ; Aged ; Male ; Female ; Aged, 80 and over ; Cluster Analysis ; Databases, Factual |
| Περίληψη: | Background and Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population. The objective of our study was to use clustering methods to determine whether common prescribing clusters exist in older adults newly identified as living with dementia in Ontario, Canada and to examine the association between individual clinical and demographic characteristics and those clusters. Methods: Data were derived from population-based health administrative databases, including medication dispensation data. The hierarchical clustering algorithm started with each individual and merged individuals with the most similar prescribing patterns into a group, continuing this process stepwise until only one cluster remained. The optimal number of clusters was selected through clinical review and fit statistics. We examined the association between individual characteristics and prescribing clusters using bivariate multinomial models. Results: In 99,046 individuals living with new dementia, we identified six prevalent clusters of individuals with common medication subclass patterns: higher dispensation of angiotensin-converting enzyme-specific cardiovascular (22.6% of the population), central nervous system-active (21.3%), hypothyroidism (22.9%), respiratory (3.9%), and angiotensin receptor blocker-specific cardiovascular (6.1%), as well as a group with lower dispensation of medications in general (23.1%). Specific demographic, clinical, and health-service-use characteristics were associated with assigned clusters. Conclusions: Within individuals living with dementia, prescribing clusters reflected meaningful differences in clinical and demographic characteristics. The results suggest that applying clustering methods to pharmacological data may be useful in estimating complex comorbidity patterns to better describe a heterogeneous population of people living with dementia. Future studies could examine whether these clusters better predict health service use, disease progression, or medication-related adverse events compared with other measures. (© 2025. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) |
| Competing Interests: | Declarations. Funding: This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). This project also received support through funding from Dalla Lana School of Public Health Data Science Seed Fund and the Canadian Consortium on Neurodegeneration in Aging (CCNA) Synapse Funding, including the Training and Capacity Building Program, the Engagement of People with Lived Experience of Dementia Program, and the Knowledge Translation and Exchange Program. The Training and Capacity Building Program is part of the CCNA, which is supported by a grant from the Canadian Institutes of Health Research, with funding from the Institute of Indigenous Peoples’ Health, the Alzheimer Society of Canada, and the Canadian Nurses Foundation. The Engagement of People with Lived Experience of Dementia Program and the Knowledge Translation and Exchange Program are part of the CCNA, which is supported by a grant from the Canadian Institutes of Health Research, with funding from the Alzheimer Society of Canada. A.E. is funded through the Alzheimer Society of Canada Research Program Doctoral Award. This document used data adapted from the Statistics Canada Postal CodeOM Conversion File, which is based on data licensed from Canada Post Corporation, and/or data adapted from the Ontario Ministry of Health Postal Code Conversion File, which contains data copied under license from ©Canada Post Corporation and Statistics Canada. Parts of this material are based on data and/or information compiled and provided by CIHI and the Ontario Ministry of Health. The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred. We thank IQVIA Solutions Canada Inc. for use of their Drug Information File. Funding sources are listed on the title page as per journal requirements. The funders had no role in the design, methods, subject recruitment, data collections, analysis, and preparation of the paper. Conflicts of Interest: The authors do not have any financial or personal conflicts of interest to disclose. Availability of Data and Materials: The dataset from this study is held securely in coded form at ICES. While legal data sharing agreements between ICES and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS (email: das@ices.on.ca). The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification. Ethics Approval: The use of the data in this project is authorized under Section 45 of Ontario’s Personal Health Information Protection Act (PHIPA) and does not require review by a research ethics board; however, we acquired research ethics board approval from University of Toronto. Consent to Participate: Not applicable. Consent for Publication: Not applicable. Code Availability: Not available but packages used in the analysis are referenced. Authors’ Contributions: A.E., S.B., J.B., T.S., and A.I. formulated the idea for the paper. All authors were involved in the study concept and design. A.E. and S.B. were involved in acquisition of data. X.W. assisted with data management. A.E. completed all statistical analysis and initial manuscript draft. All authors were involved in the interpretation of data and reviewed and revised the manuscript. |
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| Grant Information: | Canada CIHR |
| Contributed Indexing: | Local Abstract: [plain-language-summary] Older adults living with dementia are often prescribed many medications to manage both dementia and other health conditions that exist at the same time. We were interested in exploring whether unsupervised machine learning methods would identify groups of people who were taking similar medications and applied a computing algorithm called hiearachial clustering to a dataset of medications that had been dispensed to older adults living with dementia in Ontario, Canada. This resulted in six prescribing clusters which reflect underlying differences in comorbidity profiles. |
| Entry Date(s): | Date Created: 20250910 Date Completed: 20250930 Latest Revision: 20260521 |
| Update Code: | 20260521 |
| DOI: | 10.1007/s40266-025-01228-y |
| PMID: | 40928601 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1179-1969 |
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| DOI: | 10.1007/s40266-025-01228-y |