Healthcare-associated infections in older adults in long-term care: scoping review
DOI:
https://doi.org/10.29352/mill0228.42073Keywords:
Keywords: colonization; elderly; epidemiological surveillance; healthcare-associated infections; long-term care facilitiesAbstract
Introduction: Healthcare-associated infections (HAIs) significantly impact elderly residents of long-term care facilities (LTCFs). Diagnosing infections appearing 3-5 days post-admission is challenging, potentially misclassifying pre-existing conditions as HAIs.
Objective: To map and analyse the scientific evidence on the occurrence of HAIs in institutionalised older adults, focusing on the manifestation of infectious symptoms between the 3rd and 5th day after admission.
Methods: A scoping review was conducted according to the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR. The search was conducted in the PubMed, Scopus, and CINAHL Complete databases, covering publications between 2015 and 2025. The inclusion criteria included studies with a population of ≥60 years old, in the context of LTCFs, with data on early manifestations of infections. Of the 423 studies identified, 15 were included after screening and full reading.
Results: Common LTCF pathogens (E. coli, S. aureus, influenza, RSV) frequently exhibit incubation periods overlapping the 3-5 day window, challenging the ≥48-hour HAI diagnostic criterion. This overlap raises concerns about overestimating HAIs, skewing epidemiological data, and hindering effective infection control. The current definition may misclassify pre-existing conditions. A more nuanced approach, incorporating detailed patient history and microbiological data, is needed to differentiate between pre-existing and institutionally-acquired infections.
Conclusion: The ≥48-hour HAI criterion requires critical re-evaluation in geriatric LTCFs to improve diagnostic accuracy and guide appropriate infection control strategies. Future studies should investigate alternative diagnostic thresholds and explore the clinical utility of incorporating biomarkers of infection onset and leveraging AI-driven predictive models. Artificial intelligence could assist in identifying at-risk patients and optimizing resource allocation.
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