Prevalence of Healthcare-Associated Infections and Antimicrobial Use Among Adult Inpatients in Singapore Acute-Care Hospitals: Results From the First National Point Prevalence Survey
Bibliographic record
Abstract
BACKGROUND: We conducted a national point prevalence survey (PPS) to determine the prevalence of healthcare-associated infections (HAIs) and antimicrobial use (AMU) in Singapore acute-care hospitals. METHODS: Trained personnel collected HAI, AMU, and baseline hospital- and patient-level data of adult inpatients from 13 private and public acute-care hospitals between July 2015 and February 2016, using the PPS methodology developed by the European Centre for Disease Prevention and Control. Factors independently associated with HAIs were determined using multivariable regression. RESULTS: Of the 5415 patients surveyed, there were 646 patients (11.9%; 95% confidence interval [CI], 11.1%-12.8%) with 727 distinct HAIs, of which 331 (45.5%) were culture positive. The most common HAIs were unspecified clinical sepsis (25.5%) and pneumonia (24.8%). Staphylococcus aureus (12.9%) and Pseudomonas aeruginosa (11.5%) were the most common pathogens implicated in HAIs. Carbapenem nonsusceptibility rates were highest in Acinetobacter species (71.9%) and P. aeruginosa (23.6%). Male sex, increasing age, surgery during current hospitalization, and presence of central venous or urinary catheters were independently associated with HAIs. A total of 2762 (51.0%; 95% CI, 49.7%-52.3%) patients were on 3611 systemic antimicrobial agents; 462 (12.8%) were prescribed for surgical prophylaxis and 2997 (83.0%) were prescribed for treatment. Amoxicillin/clavulanate was the most frequently prescribed (24.6%) antimicrobial agent. CONCLUSIONS: This survey suggested a high prevalence of HAIs and AMU in Singapore's acute-care hospitals. While further research is necessary to understand the causes and costs of HAIs and AMU in Singapore, repeated PPSs over the next decade will be useful to gauge progress at controlling HAIs and AMU.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".