Secondary Logo

Journal Logo

Institutional members access full text with Ovid®

An Algorithm to Characterize a Dementia Population by Disease Subtype

Albrecht, Jennifer S., PhD*; Hanna, Maya, MPH; Randall, Rhonda L., DO; Kim, Dure, PharmD; Perfetto, Eleanor M., PhD, MS†,§,∥

Alzheimer Disease & Associated Disorders: April-June 2019 - Volume 33 - Issue 2 - p 118–123
doi: 10.1097/WAD.0000000000000295
Original Articles

Purpose: Identification of Alzheimer disease and related dementias (ADRD) subtypes is important for pharmacologic treatment and care planning, yet inaccuracies in dementia diagnoses make ADRD subtypes hard to identify and characterize. The objectives of this study were to (1) develop a method to categorize ADRD cases by subtype and (2) characterize and compare the ADRD subtype populations by demographic and other characteristics.

Methods: We identified cases of ADRD occurring during 2008 to 2014 from the OptumLabs Database using diagnosis codes and antidementia medication fills. We developed a categorization algorithm that made use of temporal sequencing of diagnoses and provider type.

Results: We identified 36,838 individuals with ADRD. After application of our algorithm, the largest proportion of cases were nonspecific dementia (41.2%), followed by individuals with antidementia medication but no ADRD diagnosis (15.6%). Individuals with Alzheimer disease formed 10.2% of cases. Individuals with vascular dementia had the greatest burden of comorbid disease. Initial documentation of dementia occurred primarily in the office setting (35.1%).

Discussion: Our algorithm identified 6 dementia subtypes and three additional categories representing unique diagnostic patterns in the data. Differences and similarities between groups provided support for the approach and offered unique insight into ADRD subtype characteristics.

*Department of Epidemiology and Public Health, University of Maryland School of Medicine

Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, Baltimore, MD

United Health Group, Minneapolis, MN

§National Health Council, Washington, DC

OptumLabs, Visiting Fellow, Cambridge, MA

Supported by AstraZeneca, Global CEO Initiative, Jansen, and OptumLabs, and Roche. No editorial service was provided. Dr. Albrecht was supported by the Agency for Healthcare Research and Quality [grant number K01HS024560].

The authors declare no conflicts of interest.

Reprints: Jennifer S. Albrecht, PhD, Department of Epidemiology and Public Health, University of Maryland School of Medicine, MSTF 334C, 10 S. Pine St., Baltimore, MD 21201 (e-mail:

Received August 3, 2018

Accepted December 21, 2018

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved