While healthcare-associated infection (HAI) surveillance in Singapore’s acute care hospitals is well established, comparable data from long-term acute care hospitals (LTACHs), known as community hospitals, are limited. We conducted the first national point prevalence survey (PPS) to determine HAI prevalence in Singapore LTACHs.

A standardized protocol adapted from the Singapore acute care hospital HAI PPS protocol, which was adapted from the European Centre for Disease Prevention and Control protocol for acute care hospitals, was implemented across 9 LTACHs. Adult inpatients present in participating wards at 8 a.m. on the survey day were included. Data on patient characteristics, invasive devices, and active infections were collected from electronic medical records. Factors associated with HAIs were determined using multivariate logistic regression.

Among 1,546 patients, 86 had 94 HAIs, corresponding to a prevalence of 5.6% (95% confidence interval [CI]: 4.5–6.8). The most common infection types were unspecified sepsis (36.2%), urinary tract infection (UTI) (24.5%), and surgical site infection (18.1%). Microbiological confirmation was available for 25.5% of HAIs, with Enterobacterales (54.8%) most frequently identified. Presence of central vascular catheter (odds ratio [OR]: 3.88; 95% CI: 1.66–9.08) and peripheral vascular catheter (OR: 10.48; 95% CI: 6.45–17.03) were associated with HAIs, while larger hospital size (≥250 beds) was associated with reduced odds (OR: 0.62; 95% CI: 0.39–0.99).

This inaugural PPS establishes the baseline for HAIs in Singapore’s LTACHs. Findings suggest broad infection prevention initiatives may provide the greatest benefit sector-wide, while the high UTI burden highlights an opportunity for targeted prevention strategies.

Hand hygiene (HH) is the most effective way to prevent healthcare-associated infections (HAIs) and the spread of antimicrobial-resistant pathogens. HH compliance is particularly relevant in Greece, which has among the highest rates of HAIs and antimicrobial resistance in the European Union. The aim of our study was to assess current HH practices in Greece and to improve compliance.

Prospective observational quasi-experimental (before-after) study.

Eight public adult tertiary hospitals.

“Physicians, Nurses, and Ancillary Healthcare Workers.”

Three time periods were defined: the baseline period (December 2021–June 2022), intervention period (July 2022–January 2023), and postintervention period (February 2023–May 2024). An improvement bundle was implemented. A minimum of 30 observations per department per month of HH opportunities were required. Trained observers conducted observations using a standardized data collection tool. Interrupted time-series analysis was performed.

38,069 HH opportunities were observed. Overall compliance was 48.5%. Glove use without proper HH was observed in 72.9% of opportunities. Alcohol-based hand rub was used for HH by 57.8% of HCW who conducted HH. Compliance improved from 37.3% in the baseline period to 53.3% after the intervention (16% increase, P < .001). Improved compliance was observed in 9/13 (69%) intensive care units. Of 10 participating medical and surgical wards, improved compliance was observed in 9 (90%) units, and improvement ranged from 7.2% to 28.3%

Overall, we found low compliance rates and gaps in HH practices in Greek hospitals. A quality improvement intervention improved overall HH compliance. This is the largest study of HH compliance in Greek hospitals to date.

Under current National Healthcare and Safety Network (NHSN) methodology for CLABSI reporting, patients with multiple, concurrent central lines are counted as one line day, potentially underestimating true central line exposure.

To evaluate the impact of NHSN central line reporting guidelines on CLABSI rates, benchmarking, and quality improvement outcomes across a multi-hospital health system.

Retrospective observational study.

Electronic health record (EHR) data was collected for 7,645 inpatients across three adult acute care and one pediatric acute care hospital from January 2024 through December 2024. Additional data from the academic medical center’s intensive care units (ICU) was collected from January 2025 to July 2025. Reported and actual central line days were compared across hospitals and care settings.

Actual line days totaled 113,529 compared to 90,789 reported line days with significantly different proportions across hospitals (P < .001). The reduction in CLABSI rate calculation when using actual line days was largest at higher-acuity settings, with the academic medical center ICUs demonstrating a decrease from 1.54 to 1.03 per 1,000 line days, a 33% reduction. Evaluation of a quality improvement initiative revealed significant improvement across all three ICUs when using actual line days, compared to only two ICUs under reported data alone.

Current NHSN reporting methodology systematically underestimates central line exposure at higher-acuity hospitals, impacting CLABSI benchmarking and obscuring quality improvement initiatives. Health systems should consider monitoring both reported and actual line days when evaluating performance or the impact of quality improvement initiatives.

An automated CLuster AleRt system (CLAR) is employed at our hospital to detect clusters of relevant microorganisms. Alerts are subsequently assessed by infection prevention and control (IPC) experts to determine their epidemiological relevance. This study compares expert assessment of CLAR alerts with genotyping results.

Retrospective observational study

We analyzed all CLAR alerts and genotyping reports generated during routine IPC activities between 08/2019 and 12/2024. CLAR alerts were classified relevant/not relevant based on epidemiological assessment by IPC physicians. Selected isolates were genotyped when transmission was suspected. Where multiple alerts and genotyping reports were available for an epidemiological cluster, only the first alert with genotyped isolates and the first corresponding genotyping report were considered, in order to emulate conditions at the onset of potential outbreaks. Genotyping was performed either by whole-genome sequencing (cgMLST) or repetitive-sequence-based polymerase chain reaction (rep-PCR). We calculated the proportion of CLAR alerts with ≥1 genotypically clustering isolate and performed a multivariable regression analysis to identify parameters associated with a higher likelihood of concordance.

We identified 116 relevant CLAR alerts corresponding to first genotyping reports (n = 77 cgMLST, n = 39 rep-PCR) of epidemiological clusters. Overall, these alerts contained 253 genotyped isolates. Genotypic clustering of ≥1 CLAR alert isolate was confirmed for 87 CLAR alerts (75.0%). A higher proportion of concordance with genotyping was seen for relevant alerts in neonatology (93.8%) and with carbapenem-resistant Enterobacterales (94.4%), also confirmed by multivariable analysis.

Expert-assessed epidemiological relevance of CLAR alerts showed promising concordance with genomic findings and may inform future machine learning-based predictions.

Wastewater-based epidemiology (WBE) offers pooled biological samples of defined populations, complementing clinical surveillance for infectious diseases. Hospitals are hotspots for antimicrobial resistance (AMR). We performed a scoping review assessing hospital wastewater for AMR monitoring.

We searched Medline, Embase, Biosis, Web of Science, and biorxiv.org from inception to May 5, 2026, for studies using hospital wastewater to monitor AMR. The primary outcome was AMR detection and concordance with traditional surveillance. Concordance was reported by resistance class, and reported as strain- or gene-level, as discordant, or as not assessed. Secondary outcomes included sampling methods, and laboratory techniques.

Of 4,695 screened studies, 36 from 22 countries met inclusion criteria (high-income 53%, upper-middle-income 42%, and lower-middle-income 5%). Traditional surveillance used mostly routine diagnostics (58%); only 39% of studies aligned temporally with wastewater sampling. Extended-Spectrum Beta-Lactamase-producing organisms showed strain-level concordance in 2/6 studies, and gene-level concordance in 4/6; carbapenem resistance showed strain-level concordance in 2/9 and gene-level concordance in 4/9. Concordance was present for vancomycin-resistant enterococci (2/3), while inconsistent for Methicillin-resistant Staphylococcus aureus (0/2) and Candida auris (1/2). One study quantified correlation statistically, finding strong association (Spearman’s rho = .88). Wastewater sampling methods included grab sampling (39%), composite sampling (36%), and was unreported (25%). Most studies combined culture-based (89%) and molecular methods (PCR 75%, WGS 42%, metagenomics 17%), mostly targeting Gram-negatives (83%).

Hospital WBE showed variable concordance with traditional surveillance, but methodological heterogeneity and limited temporal alignment constrain comparisons. Standardized frameworks and patient-level studies, particularly from low- and middle-income countries, are needed.

The Surgical Care Improvement Project (SCIP) was developed and implemented nationally from 2005 to 2015 partly to improve surgical antimicrobial prophylaxis processes. SCIP’s manual review process was costly yet led to high compliance rates in the Veterans Administration (VA). Our objective was to develop an electronic compliance algorithm to replicate the SCIP metric for timely discontinuation of antimicrobial prophylaxis (INF-3) in cardiac surgeries.

This retrospective cohort study matched manually adjudicated INF-3 data from the VA External Peer Review Program (EPRP) with 2006–2015 cardiac surgeries in the Corporate Data Warehouse (CDW). Algorithm development used EPRP data as the gold standard, supplemented with manual review, to apply SCIP-defined electronic exclusions and flag cases with appropriate antimicrobial discontinuation. Algorithm validation used manual review of compliant (n = 90) and non-compliant cases (n = 210) among cardiac surgeries not in EPRP.

The algorithm utilized CDW prophylactic versus therapeutic antimicrobial drug names and timestamps for drug administration to reliably exclude cases with appropriate antimicrobial use before flagging INF-3 compliance/non-compliance. In the EPRP-CDW development sample (n = 29,431), the algorithm-measured INF-3 compliance rate was 96.2% versus EPRP’s rate of 95.9%. In validation (n = 10,131), sensitivity was 92.1% (95%CI: 88.0%, 96.2%) and specificity was 97.0% (95%CI: 93.6%, 100%) compared to manual review. The algorithm exclusion rate in CDW cardiac surgeries was 18.5%—primarily for antimicrobial pharmacy administration indicative of pre-or postoperative infection (7,360/52,310 14.1%).

The SCIP INF-3 cardiac electronic algorithm effectively replicated the EPRP manual review process and has the potential to extend surveillance of appropriate surgical antimicrobial prophylaxis.