Cost-Effectiveness Analysis of the Use of Probiotics for the Prevention of<i>Clostridium difficile</i>–Associated Diarrhea in a Provincial Healthcare System
Bibliographic record
Abstract
OBJECTIVE To conduct a full economic evaluation assessing the costs and consequences related to probiotic use for the primary prevention of Clostridium difficile-associated diarrhea (CDAD). DESIGN Cost-effectiveness analysis using decision analytic modeling. METHODS A cost-effectiveness analysis was used to evaluate the risk of CDAD and the costs of receiving oral probiotics versus not over a time horizon of 30 days. The target population modeled was all adult inpatients receiving any therapeutic course of antibiotics from a publicly funded healthcare system perspective. Effectiveness estimates were based on a recent systematic review of probiotics for the primary prevention of CDAD. Additional estimates came from local data and the literature. Sensitivity analyses were conducted to assess how plausible changes in variables impacted the results. RESULTS Treatment with oral probiotics led to direct costs of CDN $24 per course of treatment per patient. On average, patients treated with oral probiotics had a lower overall cost compared with usual care (CDN $327 vs $845). The risk of CDAD was reduced from 5.5% in those not receiving oral probiotics to 2% in those receiving oral probiotics. These results were robust to plausible variation in all estimates. CONCLUSIONS Oral probiotics as a preventive strategy for CDAD resulted in a lower risk of CDAD as well as cost-savings. The cost-savings may be greater in other healthcare systems that experience a higher incidence and cost associated with CDAD. Infect Control Hosp Epidemiol 2016;37:1079-1086.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".