Economic analysis of healthcare-associated infection prevention and control interventions in medical and surgical units: systematic review using a discounting approach
Bibliographic record
Abstract
Nosocomial or healthcare-associated infections (HCAIs) are associated with a financial burden that affects both patients and healthcare institutions worldwide. The clinical best care practices (CBPs) of hand hygiene, hygiene and sanitation, screening, and basic and additional precautions aim to reduce this burden. The COVID-19 pandemic has confirmed these four CBPs are critically important prevention practices that limit the spread of HCAIs. This paper conducted a systematic review of economic evaluations related to these four CBPs using a discounting approach. We searched for articles published between 2000 and 2019. We included economic evaluations of infection prevention and control of Clostridioides difficile-associated diarrhoea, meticillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, and carbapenem-resistant Gram-negative bacilli. Results were analysed with cost-minimization, cost-effectiveness, cost-utility, cost-benefit and cost-consequence analyses. Articles were assessed for quality. A total of 11,898 articles were screened and seven were included. Most studies (4/7) were of overall moderate quality. All studies demonstrated cost effectiveness of CBPs. The average yearly net cost savings from the CBPs ranged from $252,847 (2019 Canadian dollars) to $1,691,823, depending on the rate of discount (3% and 8%). The average incremental benefit cost ratio of CBPs varied from 2.48 to 7.66. In order to make efficient use of resources and maximize health benefits, ongoing research in the economic evaluation of infection control should be carried out to support evidence-based healthcare policy decisions.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".