Protocol for a systematic review of economic analyses of nosocomial infection prevention and control interventions in OECD hospitals
Bibliographic record
Abstract
Background Nosocomial infections (NIs) are associated with extra treatment costs, medical complications, reduction of quality of life and mortality. This systematic review intends to consolidate the evidence on the economic evaluation of four clinical best practices (CBPs) related to NI prevention and control interventions: hand hygiene, hygiene and sanitation, admission screening and basic and additional precautions. It will measure the return on investment of these CBPs. Methods and analysis Electronic searches will be conducted on MEDLINE, CINAHL, EMBASE, Cochrane, Web of Science and JSTOR. OpenGrey will also be consulted for articles from 2000 to 2018, published in English or French. The population includes studies undertaken in medical or surgical units of hospitals of the Organisation for Economic Co-operation and Development countries. Studies will report the prevention and control of Clostridium difficile- associated diarrhoea, methicillin-resistant Staphylococcus aureus , vancomycin-resistant enterococci and carbapenem-resistant Gram-negative bacilli. Interventions evaluating any of the four CBPs will be included. The design of articles will fall within randomised clinical trials, quasi-experimental, case-control, cohort, longitudinal and cross-sectional studies. Outcomes will include incremental cost-effectiveness ratio, incremental cost per quality-adjusted life-year, incremental cost per disability-adjusted life year and the incremental cost-benefit ratio, net costs and net cost savings. Two authors will independently screen studies, extract data and assess risk of bias using the Scottish Intercollegiate Guidelines, the Drummond Economic Evaluation criteria and the Cochrane criteria for Systematic Reviews of Interventions. Consolidated Health Economic Evaluation Reporting Standards will be used for data extraction. All values will be adjusted to Canadian dollars ($C) indexed to 2019 using the discount rates (3%, 5% and 8%) for sensitivity analyses. This review will demonstrate the effectiveness of the CBPs in prevention and control of NIs. Decision-makers will thus have evidence to facilitate sound decision-making according to the financial gains generated. Ethics and dissemination The results of this systematic review will be published in a peer-reviewed journal and presented at a relevant scientific conference. Ethical approval is not required because the data we will use do not include individual patient data.
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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 | 0.110 | 0.157 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.025 | 0.021 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.141 | 0.018 |
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".