A Simulation Model to Evaluate the Potential Impact of Disease-Modifying Treatments on Burden of Illness in Alzheimer’s Disease
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is a progressive neurodegenerative disease that places a substantial burden on patients, caregivers, and society. The advent of disease-modifying treatments (DMTs) would represent a major advancement in the management of AD, particularly in early AD. It is important to understand the potential value of these therapies to individuals and society. METHODS: A modeling framework was developed to estimate the potential clinical and economic burden of AD in the USA by simulating the impact, relative to that of usual care, of a DMT with hypothesized availability beginning from 2022. The model assessed AD epidemiology, disease progression, and burden of illness from 2020 to 2050. Model outcomes included the total number of Americans with mild cognitive impairment (MCI) due to AD and mild, moderate, or severe AD dementia in community or residential care settings and their associated care costs, including direct medical and non-medical costs for healthcare resource use and indirect costs for caregiving. RESULTS: A hypothetical DMT was compared to the usual care under different effect scenarios based on delay in onset of AD (1, 3, and 5 years) and DMT uptake (25%, 50%, and 100%). A delay in the onset of AD by 5 years would reduce the prevalence of AD in 2050 by 6%, 12%, and 25%, resulting in savings of $0.783, $1.566, and $3.132 trillion from 2022 to 2050 for the 25%, 50%, and 100% uptake scenarios, respectively. CONCLUSION: This analysis demonstrated that DMTs that provide even small delays in the onset of AD can lead to an increase in disease-free years and sizable savings in the cost of care, providing significant benefits to patients, caregivers, and society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".