Cost‐saving analysis for adopting Anti‐Amyloid Therapies for Alzheimer's Disease
Bibliographic record
Abstract
BACKGROUND: Anti-amyloid drugs hold promise in delaying clinical stages of dementia, though they may be rarely used in Low and Middle-Income Countries (LMIC) due to treatment costs. Herein, we aimed to evaluate the potential cost-savings associated with the use of Donanemab for Alzheimer's Disease (AD) in the perspective of the Brazilian healthcare system. METHOD: We conducted a systematic review of studies estimating the costs of dementia stages in Brazil between 1997 and 2024. Key terms included costs and cost analyses; Alzheimer's Disease; Dementia; and Brazil. Costs of Donanemab were sourced from industry reports, and a cost-saving analysis compared healthcare costs for managing mild and moderate AD. The treatment's efficacy was evaluated based on performance in the Clinical Dementia Rating scale, considering that the treatment is able to delay the progression between mild and moderate dementia stages by an average of 5.3 months. RESULT: Three studies evaluating costs of dementia in Brazil were retrieved in the systematic review (Figure 1), with a cost associated with dementia ranging from USD 536.27 to USD 1,376.28. Monthly costs for mild, moderate, and severe dementia stages were estimated at USD 1,000, USD 1,700, and USD 1,372, respectively (Table 1). The cost of Donanemab treatment (18 months) was estimated at USD 48,696, along with approximately USD 720 for the six magnetic resonance imaging exams required during the treatment period. Reducing progression from a patient with mild to moderate dementia stage for 5.3 additional months can reduce the cost of approximately USD 3,710 (USD 700 per month). For the total 18-month treatment period, the cost saving threshold to make anti-amyloid economically viable in Brazil would be estimated around USD 210 monthly, or a total cost of USD 3,780. CONCLUSION: Anti-amyloid treatment in Brazil can collaborate to reduce Dementia costs in Brazil and, potentially, become a cost-effective strategy. Transposing the results presented here to the estimated number of AD patients in Brazil of 1.2 million people emphasizes the potential economic implications in terms of public policies and the healthcare system as a whole.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".