Assessing the impact of a home-based stroke rehabilitation programme: a cost-effectiveness study
Bibliographic record
Abstract
Background: Stroke is often a severe and debilitating event that requires ongoing rehabilitation. The Community Stroke Rehabilitation Teams (CSRTs) offer home-based stroke rehabilitation to individuals for whom further therapy is unavailable or inaccessible. The objective of this study was to evaluate the cost-effectiveness of the CSRT programme compared with a “Usual Care” cohort.Methods: We collected data on CSRT clients from January 2012 to February 2013. Comparator data were derived from a study of stroke survivors with limited access to specialised stroke rehabilitation. Literature-derived values were used to inform a long-term projection. Using Markov modelling, we projected the model for 35 years in six-month cycles. One-way, two-way, and probabilistic sensitivity analyses were performed. Results were discounted at 3% per year.Results: Results demonstrated that the CSRT programme has a net monetary benefit (NMB) of $43,655 over Usual Care, and is both less costly and more effective (incremental cost = −$17,255; incremental effect = 1.65 Quality Adjusted Life Years [QALYs]). Results of the probabilistic sensitivity analysis revealed that incremental cost-effectiveness of the CSRT programme is superior in 100% of iterations when compared to Usual Care.Conclusions: The study shows that CSRT model of care is cost-effective, and should be considered when evaluating potential stroke rehabilitation delivery methods.Implications for RehabilitationOngoing rehabilitation following stroke is imperative for optimal recovery.Home-based specialised stroke rehabilitation may be an option for individuals for whom ongoing rehabilitation is unavailable or inaccessible.The results of this study demonstrated that home-based rehabilitation is a cost-effective means of providing ongoing rehabilitation to individuals who have experienced a stroke.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".