Identifying deprescribing profiles in older adults from 14 countries using the Patient Deprescribing Typology
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background and Objectives Categorizing older adults by their medication and deprescribing beliefs may support tailored communication and shared decision-making. We aimed to identify latent classes of patients using the Patient Deprescribing Typology. Research Design and Methods This cross-sectional study used survey responses from 1,131 primary care patients (≥65 years, ≥5 medications) in 14 countries between May 2022 and December 2023. A latent class analysis was conducted using the 4-item Patient Deprescribing Typology, which captures older adults’ views on medication importance, learning styles, decision-making preferences, and attitudes toward stopping medications. A multinomial logit model was used to compare the latent classes. Results Among the 1,131 participants, 55.2% (n = 624) were female, and on average, participants were taking 7 regular medications (SD = 2.3). Three latent classes were identified: (1) Would consider deprescribing (n = 760, 67.2%), (2) Attached to medications (n = 249, 22.0%), and (3) Defers decision-making to others (n = 122, 10.8%). Compared with participants who Would consider deprescribing, those Attached to medications were significantly less likely to want to deprescribe one of their specific medications (odds ratio [OR] = 0.04, 95% CI: 0.02-0.09, p < .001). Participants who Defer decision-making to others reported lower trust in their GP (OR = 0.47, 95% CI: 0.32-0.67). Discussion and Implications While most participants expressed openness to deprescribing, their preferences for information sources, perceived medication importance, and decision-making roles varied. Our findings highlight the need to account for patient differences in deprescribing attitudes. Typologies offer a practical basis for personalized communication and patient-centered interventions.
Source: Innovation in Aging
Language: English
Subfield: Geriatrics and Gerontology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,977
Awards: Intersection of Pragmatic Clinical Trials and Implementation Science: Optimizing Prescribing in the Primary Care Setting Using Behaviorally-Based Health Information Technology Interventions, One size does not fit all: achieving patient-centred deprescribing through evidence-based, individualised interventions
OpenAlex ID: W4416733515
