Cost-effectiveness of a UK-based primary healthcare intervention: Improving Medicines use in People with Polypharmacy in Primary Care (IMPPP)
Bibliographic record
Abstract
1. Abstract The prescribing of multiple medicines to one individual, or polypharmacy, is increasingly common. While the use of multiple medications by a patient is often appropriate, in some cases prescribed medicines may not have the intended benefit and may even cause harm, and it is important to understand the clinical and economic implications of polypharmacy and interventions to optimise prescribing. In the UK, most ongoing clinical management of polypharmacy takes place in primary care. We estimated the cost-effectiveness of the Improving Medicines use in People with Polypharmacy in Primary Care (IMPPP) trial from a UK NHS perspective. IMPPP was a pragmatic, open-label, two-arm cluster-randomised trial across 37 English general practices, including 1,715 patients. The intervention comprised a structured, enhanced process for delivering patient-centred polypharmacy reviews, and was compared to control arm practices delivering usual care. Costs were derived from routine electronic health records including primary and secondary care service utilisation data whilst QALYs were estimated via SF-12v2. Follow-up was assessed at 6 months compared to pre-randomisation baseline. Cost-effectiveness was assessed using multilevel modelling with bias-corrected and accelerated bootstrapping to calculate 95% confidence intervals. Additional one-way sensitivity analyses were conducted to explore uncertainty. Adjusted mean QALYs were slightly higher in the intervention group (0.629) versus control (0.624), with a non-significant difference of 0.006 (95% CI: -0.002 to 0.014). Mean adjusted costs were also higher in the intervention group (£4166 vs. £3655), with a non-significant cost difference of £511 (95% CI: -£73 to £949). The probability of cost-effectiveness at National Institute for the Health and Care Excellence’s £20,000/QALY and £30,000/QALY thresholds were 6% and 12% respectively. Complete case analysis showed a £138 NHS cost reduction (95% CI: -£652 to £376) and a QALY gain of 0.012 (95% CI: 0.004 to 0.021). Polypharmacy medication review as conducted in the IMPPP trial is not cost-effective. This probably reflects multiple factors, including clinical effectiveness outcomes and key cost outcomes being relatively insensitive to the intervention.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".