Cost-Effectiveness of Including a Nurse Specialist in the Treatment of Urinary Incontinence in Primary Care in the Netherlands
Bibliographic record
Abstract
OBJECTIVE: Incontinence is an important health problem. Effectively treating incontinence could lead to important health gains in patients and caregivers. Management of incontinence is currently suboptimal, especially in elderly patients. To optimise the provision of incontinence care a global optimum continence service specification (OCSS) was developed. The current study evaluates the costs and effects of implementing this OCSS for community-dwelling patients older than 65 years with four or more chronic diseases in the Netherlands. METHOD: A decision analytic model was developed comparing the current care pathway for urinary incontinence in the Netherlands with the pathway as described in the OCSS. The new care strategy was operationalised as the appointment of a continence nurse specialist (NS) located with the general practitioner (GP). This was assumed to increase case detection and to include initial assessment and treatment by the NS. The analysis used a societal perspective, including medical costs, containment products (out-of-pocket and paid by insurer), home care, informal care, and implementation costs. RESULTS: With the new care strategy a QALY gain of 0.005 per patient is achieved while saving €402 per patient over a 3 year period from a societal perspective. In interpreting these findings it is important to realise that many patients are undetected, even in the new care situation (36%), or receive care for containment only. In both of these groups no health gains were achieved. CONCLUSION: Implementing the OCSS in the Netherlands by locating a NS in the GP practice is likely to reduce incontinence, improve quality of life, and reduce costs. Furthermore, the study also highlighted that various areas of the continence care process lack data, which would be valuable to collect through the introduction of the NS in a study setting.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".