Who Benefits Most from Collaborative Dementia Care from a Patient and Payer Perspective? A Subgroup Cost-Effectiveness Analysis
Bibliographic record
Abstract
BACKGROUND: Dementia care management (DCM) aims to provide optimal treatment for people with dementia (PwD). Treatment and care needs are dependent on patients' sociodemographic and clinical characteristics and thus, economic outcomes could depend on such characteristics. OBJECTIVE: To detect important subgroups that benefit most from DCM and for which a significant effect on cost, QALY, and the individual cost-effectiveness could be achieved. METHODS: The analysis was based on 444 participants of the DelpHi-trial. For each subgroup, the probability of DCM being cost-effective was calculated and visualized using cost-effectiveness acceptability curves. The impact of DCM on individual costs and QALYs was assessed by using multivariate regression models with interaction terms. RESULTS: The probability of DCM being cost-effective at a willingness-to-pay of 40,000€ /QALY was higher in females (96% versus 16% for males), in those living alone (96% versus 26% for those living not alone), in those being moderately to severely cognitively (100% versus 3% for patients without cognitive impairment) and functionally impaired (97% versus 16% for patients without functional impairment), and in PwD having a high comorbidity (96% versus 26% for patients with a low comorbidity). Multivariate analyses revealed that females (b = -10,873; SE = 4,775, p = 0.023) who received the intervention had significantly lower healthcare cost. DCM significantly improved QALY for PwD with mild and moderate cognitive (b = +0.232, SE = 0.105) and functional deficits (b = +0.200, SE = 0.095). CONCLUSION: Patients characteristics significantly affect the cost-effectiveness. Females, patients living alone, patients with a high comorbidity, and those being moderately cognitively and functionally impaired benefit most from DCM. For those subgroups, healthcare payers could gain the highest cost savings and the highest effects on QALYs when DCM will be implemented.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.022 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".