MODELLING THE PAN-EUROPEAN ECONOMIC BURDEN OF ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Background: Recent advances open the opportunity of altering the course of Alzheimer's disease (AD) through lifestyle-based modifications and novel therapies. Ensuring that society is investing limited budgets in the interventions that have the greatest potential to generate tangible impact will require tools to guide policymakers. Objectives: To build on previous studies to develop an economic model that estimates the societal burden of AD and evaluates the potential impact of novel interventions in six large European countries. Design: AD progression was modelled using a published Markov structure with a 40-year time horizon to estimate lifetime costs and life years in a cohort aged 65 years and above diagnosed with mild cognitive impairment due to AD (MCI-AD) in 2020. Demographic projections were utilized to estimate the prevalence of MCI-AD up to 2100, total corresponding costs and life years. The model allows a comparison of costs associated with the introduction of a hypothetical new disease-modifying therapy that slows disease progression between MCI-AD and all AD-Dementia stages as well as a 'delayed onset' scenario where disease progression is halted at the MCI-AD stage, potentially occurring, for example, through lifestyle-based modifications. Results: The 2022 present value of total lifetime costs for this cohort moving through all disease stages is ~€1.2T. Approximately 80% of the present value of lifetime costs in our model are driven by informal care and non-medical direct costs. Our model suggests that a 25% and 50% reduction in disease progression compared to natural history could translate into a present value of cost savings of €33.7B and €72.7B. Halting MCI-AD progression for 3 years with no therapeutic effect thereafter resulted in a present value cost savings of €84.7B in savings. Conclusions: Our data further suggest that early intervention via disease-modifying therapies or lifestyle-based modifications in AD could result in cost savings for society. Additionally, our findings reinforce the importance of accounting for the full value of innovative interventions, management and care paradigms, including their potential impact on direct, indirect and intangible costs impacting patients, their care partners and health and social care systems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".