Bacterial Contamination of Intensive Care Units, Sana’a City, Yemen, 2019
Bibliographic record
Abstract
Background Bacterial contamination of the intensive care unit (ICU) is one of the risk factors responsible for the high incidence of nosocomial infections that can significantly increase the mortality among ICU patients. Objective The aim of this study was to investigate the bacterial contamination and pathogen isolates from the ICU environment of hospitals in Sana’a city, Yemen. Methods A descriptive cross-sectional study was performed in Sana’a city hospitals from December 5 to December 15, 2019. All hospitals that frequently reported mortality among ICU patients were included. A sterile swab moistened in sterile normal saline was used for sample collection. Seven ICU sites were targeted, including the patient’s bed, bedside table, masks of the oxygen-supplying apparatus, intravenous (IV) stand, door knob, wall, and floor, and two samples from each site were collected. The samples were transported to the National Center of Public Health Laboratory for microbiological culture. Results A total of 112 swabs were collected from the ICUs of eight hospitals. Among these, 87 (77.7%) yielded positive bacterial growth and 109 bacterial strains were isolated, including 62.4% (n=68) gram-positive and 37.6% (n=41) gram-negative bacteria. Coagulase-negative Staphylococcus, Staphylococcus aureus, and Bacillus cereus were the predominant gram-positive bacteria isolated, which accounted for 27.5% (n=30), 21.1% (n=23), and 10.1% (n=11) of all 109 bacterial isolates, respectively. Klebsiella species, Pseudomonas species, and Acinetobacter were the main gram-negative isolates obtained, accounting for 12.8% (n=14), 12.8% (n=14), and 11.9% (n=13) of all 109 bacterial isolates, respectively. The common contaminated sites were the patients’ beds/bedside tables (40/109 strains, 36.7%), floors (24/109 strains, 22.0%), walls (15/109 strains, 13.8%), and masks of the oxygen-supplying apparatus (12/109 strains, 11.0%). The door knobs and IV stands were contaminated by 9 strains, representing 8.3% of the total isolated bacteria. Conclusions The contamination of ICU environments was high and patients’ surroundings were the most contaminated areas. Implementations of strict quality standards of hygienic practices and effective cleaning of inanimate surfaces by the hospitals’ infection control units along with periodic monitoring by the health authority are highly recommended.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".