Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims—the ARIC Study.
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 (...
| Publicado en: | American Journal of Epidemiology Vol. 194; no. 12; pp. 3537 - 3549 |
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| Autores principales: | , , , , , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189866692&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189866692 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00029262 1X1 jtl: American Journal of Epidemiology issn: 00029262 maglogo: N pubinfo: dt: Dec2025 vid: 194 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 189866692 189866692 189866692 10.1093/aje/kwaf166 189866692 ppf: 3537 ppct: 12 formats: tig: atl: Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims—the ARIC Study. aug: au: Wang, Tiansheng Pate, Virginia Kim, Dae Hyun Power, Melinda C Garden, Gwenn Palta, Priya Knopman, David Jonsson-Funk, Michelle Stürmer, Til Kucharska-Newton, Anna M affil: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United StatesDepartment of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX 77204, United States sug: subj: Dementia Epidemiology Dementia Diagnosis Medicare Billing and Claims Algorithms Dementia Risk Factors Risk Assessment Instrument Construction Instrument Validation Human Funding Source Male Female Aged Prospective Studies Validation Studies Random Sample Sensitivity and Specificity Predictive Value of Tests Comparative Studies Dementia Classification Aged, 80 and Over Descriptive Statistics Data Analysis Software Neuropsychological Tests Scales Aged: 65+ years Aged, 80 & over Male Female ab: 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. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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