Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims—the ARIC Study

Abstract There is an urgent need to improve dementia ascertainment robustness in real-world studies assessing drug effects on dementia risk. We developed algorithms to dementia identification algorithms using Medicare claims (inpatient/outpatient/prescription) from 3318 Visit 5 (2011-2013) and 1828 Visit 6 (2016-2017) participants of the Atherosclerosis Risk in Communities (ARIC) Study, validated against ARIC's rigorous syndromic dementia classification. Algorithm performance was compared to existing algorithms (Jain, Bynum, Lee). We further evaluated algorithms effectiveness in a 20% random Medicare sample aged ≥70 years who initiating liraglutide or dipeptidyl peptidase 4 inhibitors (DPP4i) to assess 3-year adjusted risk difference (aRD) for dementia. Our incident dementia algorithm required two dementia diagnostic codes within 1-year, or one dementia code plus a new dementia prescription within 90 days. It achieved a positive predictive value (PPV) of 69.2%, specificity of 99.0%, and sensitivity of 34.6% (population prevalence: 8.8%), comparable to extant algorithms (PPV, 58.7–68.6%; sensitivity 25.5–40.4%). Prevalent dementia algorithm (without requiring incident diagnoses/prescriptions) demonstrated similar performance. In the Medicare sample, dementia risk ranged from 3.0% to 12.5%, aRD comparing liraglutide to DPP4i varied −1.2% to −3.6%, with our algorithm closely matching the Bynum algorithm. Algorithm selection significantly impacts treatment effect estimates, highlighting its importance in in pharmacoepidemiologic research.