Burden of Healthcare-Associated Infections in a Pediatric Intensive Care Setting Before, During, and After the Pandemic
Bibliographic record
Abstract
Background: Adult rates of non-COVID-19-related healthcare-associated infection (HAI) initially decreased and subsequently increased during the COVID-19 pandemic. Little is known about pediatric HAI rates during this period. Methods: A retrospective review of HAIs was conducted for patients admitted to the intensive care unit (PICU) at a pediatric tertiary care hospital between January 1, 2019 and November 30, 2023. Patients who spent ≥48 hours in the PICU were included. Surgical site infections were excluded. Data were obtained from infection surveillance reports; each HAI was reviewed for validity and attribution based on National Healthcare Safety Network definitions. HAIs were grouped into 3 time periods: pre-pandemic (January 2019-February 2020), pandemic (March 2020- February 2022), and post-pandemic (March 2022-November 2023). Infection rate ratios were calculated for pre-pandemic and post-pandemic periods. Results: Among 2,959 PICU patients admitted during the study period, there were 60 HAI events (4.78 per 1,000 patient days). Rates generally remained steady throughout with slight increases and decreases between time periods (Table 1). There was no significant difference in CAUTI, CLABSI, or HAVRI rates noted in the PICU between pre-pandemic and post-pandemic periods despite a significantly higher device utilization ratio in the post-pandemic period for both urinary catheters and central lines (IRR, 0.89; p < 0 .05; 95% CI, 0.82-0.97). The most frequent HAI in all time periods was CAUTI. Conclusion: Unlike reports from adult centers, no significant variation between time periods was noted for HAIs in our pediatric center. Despite numerous COVID-19-related changes in infection prevention and control measures and contexts throughout the study period, HAI rates remained stable. This may be due in part to the lower burden of critically ill COVID-19 pediatric patients compared to adult populations. Additionally, this could indicate resiliency and consistency in practice among pediatric providers throughout the pandemic. Further evaluation of pediatric HAIs in the context of the COVID-19 pandemic may reveal practices that could be replicated elsewhere to control HAI rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".