Multidrug-resistant Gram-negative bacterial colonization in patients, carriage by healthcare workers and contamination of hospital environments in Ghana
Bibliographic record
Abstract
BACKGROUND: Patients already colonized with multidrug-resistant (MDR) Gram-negative bacteria (GNB) on admission to critical care units may be an important source of transmission of these bacteria in hospitals. We sought to determine the prevalence of MDR GNB colonization in patients, staff and the ward environment and to assess the risk factors for colonization of patients in wards. METHODS: The study was conducted from April 2021 to July 2021 in a teaching hospital in Ghana. MDR GNB were isolated from rectal, and hand swabs were taken from patients on admission and after 48 h. Swabs from HCW's hands and the ward environment were also taken. Risk factors for colonization with MDR GNB were assessed using univariate and multivariate analysis. RESULTS: MDR GNB rectal colonization rate among patients was 50.62% on admission and 44.44% after 48 h. MDR GNB were isolated from 6 (5.26%) and 24 (11.54%) of HCW's hand swabs and environmental swabs, respectively. Previous hospitalization (p-value = 0.021, OR, 95% CI= 7.170 (1.345-38.214) was significantly associated with colonization by MDR GNB after 48 h of admission. Age (21-30 years) (p-value = 0.022, OR, 95% CI = 0.103 (0.015-0.716) was significantly identified as a protective factor associated with a reduced risk of rectal MDR GNB colonization. CONCLUSION: The high colonization of MDR GNB in patients, the carriage of MDR GNB on HCW's hands, and the contamination of hospital environments highlights the need for patient screening and stringent infection prevention and control practices to prevent the spread of MDR GNB in hospitals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".