A trial-based cost-effectiveness analysis of antibiotic prescription strategies for non-complicated respiratory tract infections in children
Bibliographic record
Abstract
BACKGROUND: Antibiotic prescription for respiratory tract infections (RTIs) in children attending primary care centres is almost double that predicted according to bacterial prevalence. Delayed antibiotic prescription (DAP) is designed to deploy a more rational use of antibiotics. While studies have evaluated DAP efficacy and safety for children with RTIs, little research has been conducted on the economic implications. METHODS: Our trial compared cost-effectiveness for DAP, immediate antibiotic prescription (IAP), and no antibiotic prescription (NAP) for children aged 2-14 years with acute uncomplicated RTIs attended to in 39 primary care centres in Spain. The main outcome was the incremental cost-effectiveness ratio (ICER), measured in euros per gained quality-adjusted life days (QALDs). Net monetary benefit (NMB) was also calculated as a tool for decision making. The analysis was performed from a societal perspective for a time horizon of 30 days, and included healthcare direct costs, non-healthcare direct and indirect costs, and the antimicrobial resistance (AMR) cost. RESULTS: DAP was the most cost-effective strategy, even when the cost of AMR was included. QALD values for the three strategies were very similar. IAP compared to DAP was more costly (109.68 vs 100.90 euros) and similarly effective (27.88 vs 27.94 QALDs). DAP compared to NAP was more costly (100.90 vs 97.48 euros) and more effective (27.94 vs. 27.82 QALDs). The ICER for DAP compared to NAP was 28.84 euros per QALD. The deterministic sensitivity analysis indicated that non-healthcare indirect costs had the greatest impact on the ICER. The cost-effectiveness acceptability curve showed that DAP was the preferred option in approximately 81.75% of Monte Carlo iterations, assuming a willingness-to-pay value of 82.2 euros per gained QALD. CONCLUSIONS: When clinicians are in doubt about whether an antibiotic is needed for children with RTIs attending PC centres, those treated with the DAP strategy will have slightly better efficiency outcomes than those treated with IAP because its costs are lower than those of IAP. DAP is also the most cost-effective strategy over a time horizon of 30 days if AMR is considered, despite higher short-term costs than NAP. However, if in the long term the costs of AMR are larger than estimated, NAP could also be an alternative strategy. TRIAL REGISTRATION: This trial has been registered at www. CLINICALTRIALS: gov (identifier NCT01800747; Date: 28/02/2013 (retrospectively registered).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".