Differentiation Between Early and Severe Stages of Dementia in Claims Data Based on Diagnosis, Prescription, and Utilization Patterns

Moritz Platen; Maresa Buchholz; Anika Rädke; Eva Gläser; Audrey Iskandar; Neeltje van den Berg; Wolfgang Hoffmann; Bernhard Michalowsky

doi:10.1007/s40120-025-00778-y

Differentiation Between Early and Severe Stages of Dementia in Claims Data Based on Diagnosis, Prescription, and Utilization Patterns

Neurol Ther. 2025 Aug;14(4):1589-1608. doi: 10.1007/s40120-025-00778-y. Epub 2025 Jun 12.

Authors

Moritz Platen¹, Maresa Buchholz², Anika Rädke², Eva Gläser², Audrey Iskandar², Neeltje van den Berg³, Wolfgang Hoffmann^{2

3}, Bernhard Michalowsky²

Affiliations

¹ German Center for Neurodegenerative Diseases (DZNE), Site Greifswald, Ellernholzstrasse 1-2, 17489, Greifswald, Germany. moritz.platen@dzne.de.
² German Center for Neurodegenerative Diseases (DZNE), Site Greifswald, Ellernholzstrasse 1-2, 17489, Greifswald, Germany.
³ Section Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald (UMG), Ellernholzstrasse 1-2, 17489, Greifswald, Germany.

PMID: 40504345
DOI: 10.1007/s40120-025-00778-y

Abstract

Introduction: Claims data typically lack clinical parameters such as dementia severity, limiting insights into disease progression and related healthcare utilization and costs. Although diagnoses, prescriptions, and utilization patterns may serve as proxies, their validity is unclear. This study aimed to identify and validate these parameters to distinguish early from severe dementia stages.

Methods: Baseline data from 737 patients with dementia were analyzed. Dementia severity was assessed using the Mini-Mental State Examination and classified as early (≥ 27), mild (20-26), and moderate to severe (0-19). Healthcare utilization was recorded via structured interviews. Diagnoses, long-term care levels, and prescribed medications were extracted from physicians' files. Ordinal logistic regression evaluated associations between predictors and severity, with average marginal effects (AME) quantifying impact. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed for key predictors.

Results: Among the sample (56% female patients, mean age 80), 18% were in the early stages, 43% mild, and 39% moderate to severe. Antipsychotic prescriptions (odds ratio (OR) 3.40, 95% confidence interval (CI) 1.94-5.95), antidementia drugs (OR 2.31, 95% CI 1.56-3.40), and higher long-term care levels (OR 5.59, 95% CI 2.23-13.99 for level ≥ 4) were associated with advanced severity. AME analysis revealed that antipsychotic use reduced early-stage probability by 14% and increased severe-stage probability by 21%. Similarly, antidementia drugs lowered early-stage probability by 9% and raised severe-stage probability by 13%. Increasing care levels were associated with a 2-16% decline in early-stage probability and a 3-34% rise in severe-stage probability. The combined model showed high specificity (99.6%) and PPV (84.6%) for severe dementia, but sensitivity and NPV for early stage were low.

Conclusion: Antidementia drugs, antipsychotics, and long-term care level serve as robust predictors of moderate to severe dementia, whereas early-stage detection remains challenging. Future studies should validate these markers and explore additional predictors to improve early detection in claims data.

Keywords: Alzheimer’s disease; Antidementia drug treatment; Antipsychotics; Claims data; Dementia; Dementia severity; Healthcare utilization; Real-world data; Real-world evidence.