How Much Are We Willing to Pay to Prevent A Fall? Cost-Effectiveness of a Multifactorial Falls Prevention Program for Community-Dwelling Older Adults
Bibliographic record
Abstract
This study examined the cost-effectiveness of a multifactorial falls prevention program and estimated the trade-off between the extra costs of such a program and the additional reduction of unintentional falls. Cost-effectiveness was evaluated using the traditional incremental cost-effectiveness ratio (ICER) and the net benefit regression framework (NBRF). Using the NBRF, decision making was formalized by incorporating values of willingness to pay (WTP) a priori. The results failed to provide evidence that a multifactorial falls prevention program was cost-effective. Participant adherence to recommendations ranged from low (41.3%), to moderate (21.1%), to high (37.6%). A future challenge is to understand more clearly the relationship between the community-dwelling older adult, potentially modifiable risks for falls, adherence to multifactorial risk factor recommendations, costs, and resulting effects of falls prevention practices. Future economic evaluations of falls prevention interventions remain necessary and should consider the NBRF so that regression tools can facilitate cost-effectiveness analysis.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".