Hospital-acquired infections in paediatric medical wards at a tertiary hospital in KwaZulu-Natal, South Africa
Bibliographic record
Abstract
BACKGROUND: Hospital-acquired infections (HAIs) impact care and costs in hospitals across the globe. There are minimal data on HAIs in sub-Saharan Africa and data specific to paediatrics are especially limited. OBJECTIVE: To describe the incidence of HAIs in the paediatric medical units at Grey's Hospital, a tertiary government hospital in KwaZulu-Natal, South Africa. METHODS: The Infection Prevention and Control (IPC) team collects data on all laboratory-confirmed infections, including from paediatric patients in two medical units (52 beds), the paediatric intensive/high-care unit (PICU, 8 beds) and the neonatal intensive care unit (NICU, 23 beds). HAIs are defined as infections: (i) not present (active or incubating) at the time of admission, and (ii) with onset >48 h after hospital admission. Daily patient statistics allow calculation of infections per 100 admissions and infections per 1000 patient days. RESULTS: In the non-ICU setting, there were 7.1 and 7.0 HAIs per 100 admissions in 2013 and 2014, respectively. In the PICU, there were 20.4 and 15.3 HAIs per 100 admissions, while in the NICU there were 23.9 and 21.6 HAIs per 100 admissions in 2013 and 2014, respectively. In the non-ICU setting, there were 6.8 HAIs per 1000 patient days in both 2013 and 2014. In the PICU, there were 27.5 and 33.0 HAIs per 1000 patient days, while in the NICU, there were 20.3 and 21.5 HAIs per 1000 patient days in 2013 and 2014, respectively. CONCLUSION: HAIs in non-ICU paediatric wards were consistent with a number of point-prevalence studies performed outside Africa (e.g. Canada, Russia, U.K.). Rates of HAIs in the ICUs were higher than rates reported from the International Nosocomial Infection Control Consortium, and were substantially higher than rates reported in the United States. HAIs are serious and important, especially in ICUs, and may be relatively neglected in low- and middle-income settings. Improved surveillance will allow the development and evaluation of targeted interventions to improve care of patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".