Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning–based cohort study

Objective To estimate the prevalence of potential overtreatment of type 2 diabetes mellitus (T2DM) among older adults and to develop and compare predictive models to identify patient and physician characteristics associated with overtreatment. Design Population-based retrospective cohort study with predictive modelling. Setting A province-wide, publicly funded healthcare system in British Columbia, Canada, using linked administrative health claims data from 2016 to 2023. Participants Residents of long-term care facilities over age 65, and community-dwelling individuals over age 75, with a diagnosis of T2DM and a glycated haemoglobin (A1C) laboratory value ≤7.0%. Participants were required to have ≥365 days of continuous provincial health insurance coverage prior to their index A1C test. Patients receiving palliative care and those with missing physician information were excluded. Primary and secondary outcome measures Potential overtreatment of T2DM, defined a priori as overlapping prescriptions for ≥2 glucose-lowering medications or ≥1 insulin or sulfonylurea dispensing within 90 days after the index A1C test. Model performance outcomes included discrimination (area under the curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value). Performance metrics were calculated with 95% CIs using a 25% temporally distinct test dataset (2021–2023). No changes were made to outcome definitions after protocol development. Results Among 133 773 patients with an A1C≤7.0%, 38 074 (28.5%) were classified as overtreated. These patients had a mean age of 79.6 years, were 47% female, and had a median A1C of 6.4%. The gradient boost model was the best performing model overall, using a combination of expert-selected variables and data-driven variables, achieving an AUC of 0.87, sensitivity of 0.81 and negative predictive value of 0.89. The top predictors of overtreatment included use of blood glucose test strips, A1C test volume, polypharmacy, specialist involvement and measures of diabetes severity. Conclusions Overtreatment of T2DM was prevalent among older adults in our cohort. Machine learning algorithms that integrate clinical expertise with data-driven variable selection performed the best in predicting T2DM overtreatment. We identified several patient and physician characteristics as key contributors that may inform future clinical practice and quality improvement initiatives, although external validation is required before clinical implementation.