Multi-National, Cross-Sectional Survey of Healthcare Resource Utilization in Patients with All Stages of Cognitive Impairment, Analyzed by Disease Severity, Country, and Geographical Region
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is one of the most disabling conditions worldwide and the disease burden increases with the aging global population. There are only a few prospective studies using real-world data to support effective healthcare resource utilization (HCRU) in AD. OBJECTIVE: To confirm the association between HCRU and AD severity in a real-world population, including patients with all cognitive impairment (CI) severities. METHODS: Data were drawn from a multi-national, cross-sectional survey of physicians and their consulted patients with all stages (very mild, mild, moderate, and severe) of CI including AD conducted in France, Germany, Italy, Spain, UK, US, and Canada. Elements of HCRU including medical consultations, professional caregiver hours, hospitalization, and institutionalization were compared between CI severity subgroups, and by country and region. RESULTS: 6,143 CI patients were included with very mild (n = 659), mild (n = 2,473), moderate (n = 2,603), and severe (n = 408) dementia. HCRU increased with increasing CI severity (p < 0.001) for the majority of elements measured. Further analyses of overall and regional populations also confirmed significant increases in most HCRU elements with increasing disease severity. The general trend toward increased HCRU with increased CI severity was also seen in individual countries. Individual country data appeared to indicate that earlier intervention decreased hospitalizations and full-time institutionalization at the later (more severe) disease stages. CONCLUSION: Our findings confirmed that HCRU increases with increasing CI severity. Effective intervention in early disease could therefore reduce or delay incurring greater HCRU costs associated with more severe disease. Further studies are needed to confirm this hypothesis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".