View abstract Introduction:Infection prevention and control (IPC) is essential to reduce healthcare-associated infections in long-term care facilities (LTCFs). Its implementation faces unique challenges and is less studied than in acute care. This scoping review mapped barriers and facilitators to IPC implementation in LTCFs.Methods:Standardized procedures for scoping reviews were followed (eg, PRISMA-ScR). PubMed, Web of Science, CINAHL and PsycINFO were searched (2008–2025). Out of 965 articles, 21 studies were included. Data were charted and mapped to the (sub)domains of the Consolidated Framework for Implementation Research (CFIR).Results:Studies covered nursing homes, pediatric care, and disability care facilities in North America (n = 11), Europe (n = 8), Australia (n = 2) and Asia (n = 1). Most focused on hand hygiene and healthcare staff. Barriers and facilitators were identified across five CFIR domains. Intervention characteristics: Complex, untailored guidelines hindered use, while concise, context-specific protocols and adequate supplies enhanced adherence. Individual characteristics: Knowledge gaps, low risk perception, and competing priorities reduced adherence, whereas motivation, outbreak experience, and professional responsibility supported compliance. Inner setting: Staff shortages, high workload, and limited leadership were major barriers, while managerial engagement, supportive culture, and IPC champions promoted success of IPC. Outer setting: Limited policy support constrained implementation, whereas collaboration with public health agencies and peer networks facilitated it. Implementation process: limitations were lack of structured approaches; but ongoing training, peer feedback, and evaluation were key facilitators.Conclusion:Many barriers and facilitators towards IPC in LTCFs were identified that can inform future implementation strategies. Implementation processes were rarely considered and need attention in research and practice.

Background: Early mortality after long-term care facility (LTCF) admission is common; yet, prognostic tools are often derived from Western minimum dataset-based cohorts or require hospital electronic health record linkages that are unavailable at intake in many LTCFs. There is also limited evidence on explainable, admission-feasible machine learning–based prognostication in Asian LTCF settings. Objective: The aim of the study is to develop and temporally externally validate an interpretable machine learning model for predicting 6-month all-cause mortality among older adults newly admitted to LTCFs in Taiwan using routinely collected LTCF assessment data. Methods: We conducted a retrospective cohort study using the JUBO Long-Term Care Database, a nationwide private administrative registry covering 636 LTCFs (37.4% of the national LTCFs) in Taiwan. We included residents with first-time LTCF admission and prespecified nonoverlapping cohorts for temporal validation: development (January 1, 2020, to December 31, 2023; n=23,901) and external validation (January 1 to December 31, 2024; n=6216). The outcome measure was death within 180 days of admission. We compared a nonlinear ensemble model (hybrid of extreme gradient boosting and random forest [HybridXGBRF]) with 7 other algorithms, including tree-based and linear benchmarks. Discrimination (area under the receiver operating characteristic curve [AUROC]), classification metrics (accuracy, precision, recall, and ), and calibration (Brier score and calibration plots) were assessed. Model interpretability was examined using Shapley Additive Explanations. Results: In the development cohort, 5272 of 23,901 (22.1%) residents died within 180 days. In the 2024 temporal validation cohort, 1781 of 6216 (28.7%) residents died. In internal cross-validation, HybridXGBRF had the highest AUROC among the evaluated models (0.89, 95% CI 0.88‐0.89). In temporal validation, HybridXGBRF maintained strong discrimination (AUROC 0.90, 95% CI 0.89‐0.91), with an accuracy of 0.85 and an -score of 0.68. Calibration plots indicated close agreement between predicted and observed risks across most probability ranges, with mild divergences at higher predicted risks. Shapley Additive Explanations analysis identified frequent hospitalizations within 6 months, activities of daily living impairment, and weight loss as influential predictors. The model showed stable AUROC across sex and age strata (0.89‐0.90) and maintained high discrimination among residents with improving activities of daily living scores (AUROC 0.91, 95% CI 0.90‐0.92). Conclusions: An interpretable machine learning model using routinely collected Taiwanese LTCF assessment data achieved strong discrimination, acceptable calibration, and stable temporal validation performance without requiring hospital-based electronic health record linkage. The HybridXGBRF had the highest AUROC among the evaluated models, but performance differences from extreme gradient boosting were small. The model may serve as a risk-stratification tool to help identify residents who could benefit from structured care review, advance care planning, or palliative care assessment. Prospective implementation studies could determine its impact on care processes and resident- or family-centered outcomes.

YouTube video by Amelia Arsenic - Topic

YouTube video by Amelia Arsenic - Topic

RSV disproportionately impacts LTCF residents in both SNFs and ALFs, with these populations experiencing much higher hospitalization and mortality rates compared with CD older adults. Our findings und...

Aviation weather with TAF and METAR, Maps, hotels and aeronautical information for Kars airport (Turkey)

Aviation weather with TAF and METAR, Maps, hotels and aeronautical information for Kars airport (Turkey)

This study shows the high incidence of HAIs among LTCF residents in EU or EEA countries, with more than one in two residents experiencing at least one HAI, and with RTIs and UTIs accounting for almost...

INTRODUCTION Data for healthcare-associated infections (HAI) and antibiotic use in long-term care facilities (LTCF) in Switzerland are lacking but are necessary to take actions. AIM We aimed to estimate HAI prevalence and antibiotic use and to record existing structure and process indicators in the area of infection prevention/antibiotic use in Swiss LTCF. METHODS We invited all Swiss LTCF for this PPS in September 2024 using the adapted Healthcare-Associated Infections in European Long-Term Care Facilities (HALT)-4 protocol. The proportion of residents with HAI and systemic antibiotic treatment was calculated for a representative sample, stratified by language region and size. We assessed resident-level and institutional risk factors for HAI in all participating institutions, using random-effects logistic regression. RESULTS We included 94 LTCF (7,244 residents), whereof 49 LTCFs (3,375 residents) belonged to the representative sample. Median age of residents in the representative sample was 87 years (range: 36–107) and 2,334 (69.2%) were female. Prevalence of HAI was 2.2% (95% confidence interval (CI): 1.7–2.7); 2.7% (95% CI: 2.2–3.3) were receiving antibiotic treatment, with highest use in LTCF in French-speaking cantons (5.9%; 95% CI: 4.2–7.5). Urinary tract (46%) and respiratory infections (20%) were most common, aminopenicillins (26%) and nitrofurantoin (19%) the most commonly used antimicrobials. The strongest independent risk factor for HAI was presence of urinary catheters (adjusted odds ratio = 2.65; 95% CI: 1.71–4.11). DISCUSSION Prevalence of HAI and antibiotic use in Swiss LTCFs were comparable to the European average from 2023/24. There are regional differences in antibiotic consumption. Urinary catheterisation, potentially modifiable, was the most important risk factor for HAI.