Tip of the Iceberg: Assessing the Global Socioeconomic Costs of Alzheimer’s Disease and Related Dementias and Strategic Implications for Stakeholders
Bibliographic record
Abstract
While it is generally understood that Alzheimer's disease (AD) and related dementias (ADRD) is one of the costliest diseases to society, there is widespread concern that researchers and policymakers are not comprehensively capturing and describing the full scope and magnitude of the socioeconomic burden of ADRD. This review aimed to 1) catalogue the different types of AD-related socioeconomic costs described in the literature; 2) assess the challenges and gaps of existing approaches to measuring these costs; and 3) analyze and discuss the implications for stakeholders including policymakers, healthcare systems, associations, advocacy groups, clinicians, and researchers looking to improve the ability to generate reliable data that can guide evidence-based decision making. A centrally emergent theme from this review is that it is challenging to gauge the true value of policies, programs, or interventions in the ADRD arena given the long-term, progressive nature of the disease, its insidious socioeconomic impact beyond the patient and the formal healthcare system, and the complexities and current deficiencies (in measures and real-world data) in accurately calculating the full costs to society. There is therefore an urgent need for all stakeholders to establish a common understanding of the challenges in evaluating the full cost of ADRD and define approaches that allow us to measure these costs more accurately, with a view to prioritizing evidence-based solutions to mitigate this looming public health crisis.
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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 | 0.038 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".