Cleaning and disinfecting surfaces in hospitals and long-term care facilities for reducing hospital and facility-acquired bacterial and viral infections: A systematic review
Bibliographic record
Abstract
SUMMARY Background Multiply drug-resistant organisms (MDROs) in hospitals and long-term care facilities (LTCFs) of particular concern include meticillin-resistant S taphylococcus aureus (MRSA), vancomycin-resistant enterococcus, multidrug-resistant Acinetobacter species and extended spectrum beta-lactamase producing organisms. Respiratory viruses include influenza and SARS-CoV-2. Aim To assess effectiveness of cleaning and disinfecting surfaces in hospitals and LTCFs. Methods CINAHL, Cochrane CENTRAL Register of Controlled Trials, EMBASE, Medline, and Scopus searched inception to 28 June 2021, no language restrictions, for randomized controlled trials, cleaning, disinfection, hospitals, LTCFs. Abstracts and titles were assessed and data abstracted independently by two authors. Findings Of fourteen c-RCTs in hospitals and LTCFs, interventions in ten were focused on reducing patient infections of four MDROs and/or healthcare-associated infections (HAIs). In four c-RCTs patient MDRO and/or HAI rates were significantly reduced with cleaning and disinfection strategies including bleach, quaternary ammonium detergents, ultraviolet irradiation, hydrogen peroxide vapour and copper-treated surfaces or fabrics. Of three c-RCTs focused on reducing MRSA rates, one had significant results and one on Clostridioides difficile had no significant results. Heterogeneity of populations, methods, outcomes and data reporting precluded meta-analysis. Overall risk of bias assessment was low but high for allocation concealment, and GRADE assessment was low risk. No study assessed biofilms. Conclusions Ten c-RCTs focused on reducing multiple MDROs and/or HAIs and four had significant reductions. Three c-RCTs reported only patient MRSA colonization rates (one significant reductions), and one focused on Clostridioides difficile (no significant differences). Standardised primary and secondary outcomes are required for future c-RCTs including detailed biofilm cleaning/disinfection interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".