Janice C Marceaux1,2, Jason R Soble1,3, Justin J F O'Rourke1, Alicia A Swan4, Margaret Wells5, Megan Amuan6, Hari Krishna Raju Sagiraju7, Blessen C Eapen5,8, Mary Jo Pugh6,7. 1. Psychology Service, South Texas Veterans Health Care System San Antonio, TX, USA. 2. Department of Neurology, University of Texas Health Science Center, San Antonio, TX, USA. 3. Psychiatry & Neurology, Neuropsychiatric Institute, University of Illinois College of Medicine, Chicago, IL, USA. 4. Department of Psychology, University of Texas at San Antonio, San Antonio, TX, USA. 5. Polytrauma Rehabilitation Center, South Texas Veterans Health Care System, San Antonio, TX, USA. 6. VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, USA. 7. Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA. 8. Department of Rehabilitation Medicine, UT Health San Antonio, San Antonio, TX, USA.
Abstract
OBJECTIVE: To determine the validity of diagnoses indicative of early-onset dementia (EOD) obtained from an algorithm using administrative data, we examined Veterans Health Administration (VHA) electronic medical records (EMRs). METHOD: A previously used method of identifying cases of dementia using administrative data was applied to a random sample of 176 cases of Post-9/11 deployed veterans under 65 years of age. Retrospective, cross-sectional examination of EMRs was conducted, using a combination of administrative data, chart abstraction, and review/consensus by board-certified neuropsychologists. RESULTS: Approximately 73% of EOD diagnoses identified using existing algorithms were identified as false positives in the overall sample. This increased to approximately 76% among those with mental health conditions and approximately 85% among those with mild traumatic brain injury (TBI; i.e. concussion). Factors related to improved diagnostic accuracy included more severe TBI, diagnosing clinician type, presence of neuroimaging data, absence of a comorbid mental health condition diagnosis, and older age at time of diagnosis. CONCLUSIONS: A previously used algorithm for detecting dementia using VHA administrative data was not supported for use in the younger adult samples and resulted in an unacceptably high number of false positives. Based on these findings, there is concern for possible misclassification in population studies using similar algorithms to identify rates of EOD among veterans. Further, we provide suggestions to develop an enhanced algorithm for more accurate dementia surveillance among younger populations.
OBJECTIVE: To determine the validity of diagnoses indicative of early-onset dementia (EOD) obtained from an algorithm using administrative data, we examined Veterans Health Administration (VHA) electronic medical records (EMRs). METHOD: A previously used method of identifying cases of dementia using administrative data was applied to a random sample of 176 cases of Post-9/11 deployed veterans under 65 years of age. Retrospective, cross-sectional examination of EMRs was conducted, using a combination of administrative data, chart abstraction, and review/consensus by board-certified neuropsychologists. RESULTS: Approximately 73% of EOD diagnoses identified using existing algorithms were identified as false positives in the overall sample. This increased to approximately 76% among those with mental health conditions and approximately 85% among those with mild traumatic brain injury (TBI; i.e. concussion). Factors related to improved diagnostic accuracy included more severe TBI, diagnosing clinician type, presence of neuroimaging data, absence of a comorbid mental health condition diagnosis, and older age at time of diagnosis. CONCLUSIONS: A previously used algorithm for detecting dementia using VHA administrative data was not supported for use in the younger adult samples and resulted in an unacceptably high number of false positives. Based on these findings, there is concern for possible misclassification in population studies using similar algorithms to identify rates of EOD among veterans. Further, we provide suggestions to develop an enhanced algorithm for more accurate dementia surveillance among younger populations.
Entities:
Keywords:
Dementia; TBI; epidemiology; health administrative data; veterans
Authors: Kristen Dams-O'Connor; Patrick S F Bellgowan; Roderick Corriveau; Mary Jo Pugh; Douglas H Smith; Julie A Schneider; Keith Whitaker; Henrik Zetterberg Journal: J Neurotrauma Date: 2021-12 Impact factor: 5.269
Authors: Michelle M Mielke; Jeanine E Ransom; Jay Mandrekar; Pierpaolo Turcano; Rodolfo Savica; Allen W Brown Journal: J Alzheimers Dis Date: 2022 Impact factor: 4.160
Authors: Eamonn Kennedy; Samin Panahi; Ian J Stewart; David F Tate; Elisabeth A Wilde; Kimbra Kenney; J Kent Werner; Jessica Gill; Ramon Diaz-Arrastia; Megan Amuan; Anne C Van Cott; Mary Jo Pugh Journal: Brain Inj Date: 2022-02-05 Impact factor: 2.167
Authors: William C Walker; Justin O'Rourke; Elisabeth Anne Wilde; Mary Jo Pugh; Kimbra Kenney; Clara Libby Dismuke-Greer; Zhining Ou; Angela P Presson; J Kent Werner; Jacob Kean; Deborah Barnes; Amol Karmarkar; Kristine Yaffe; David Cifu Journal: Brain Inj Date: 2022-02-02 Impact factor: 2.167
Authors: Ian J Stewart; Eduard Poltavskiy; Jeffrey T Howard; Jud C Janak; Warren Pettey; Lee Ann Zarzabal; Lauren E Walker; Carl A Beyer; Alan Sim; Ying Suo; Andrew Redd; Kevin K Chung; Adi Gundlapalli Journal: J Gen Intern Med Date: 2020-09-21 Impact factor: 5.128