Challenges with cost-utility analyses of behavioural interventions among older adults at risk for dementia
Bibliographic record
Abstract
BACKGROUND: Cognitive decline is one of the most prominent healthcare issues of the 21st century. Within the context of combating cognitive decline through behavioural interventions, physical activity is a promising approach. There is a dearth of health economic data in the area of behavioural interventions for dementia prevention. Yet, economic evaluations are essential for providing information to policy makers for resource allocation. It is essential we first address population and intervention-specific methodological challenges prior to building a larger evidence base. We use a cost-utility analysis conducted alongside the exercise for cognition and everyday living (EXCEL) study to illustrate methodological challenges specific to assessing the cost-effectiveness of behavioural interventions aimed at older adults at risk of cognitive decline. METHODS: A cost-utility analysis conducted concurrently with a 6-month, three-arm randomised controlled trial (ie, the EXCEL study) was used as an example to identify and discuss methodological challenges. RESULTS: Both the aerobic training and resistance training interventions were less costly than twice weekly balance and tone classes. In critically evaluating the economic evaluation of the EXCEL study we identified four category-specific challenges: (1) analysing costs; (2) assessing quality-adjusted life-years; (3) Incomplete data; and (4) 'Intervention' activities of the control group. CONCLUSIONS: Resistance training and aerobic training resulted in healthcare cost saving and were equally effective to balance and tone classes after only 6 months of intervention. To ensure this population is treated fairly in terms of claims on resources, we first need to identify areas for methodological improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.484 | 0.755 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".