Association between intrahospital transfer and hospital-acquired infection in the elderly: a retrospective case–control study in a UK hospital network

Abstract

BackgroundIntrahospital transfers have become more common as hospital staff balance patient needs with bed availability. However, this may leave patients more vulnerable to potential pathogen transmission routes via increased exposure to contaminated surfaces and contacts with individuals.ObjectiveThis study aimed to quantify the association between the number of intrahospital transfers undergone during a hospital spell and the development of a hospital-acquired infection (HAI).MethodsA retrospective case–control study was conducted using data extracted from electronic health records and microbiology cultures of non-elective, medical admissions to a large urban hospital network which consists of three hospital sites between 2015 and 2018 (n=24 240). As elderly patients comprise a large proportion of hospital users and are a high-risk population for HAIs, the analysis focused on those aged 65 years or over. Logistic regression was conducted to obtain the OR for developing an HAI as a function of intrahospital transfers until onset of HAI for cases, or hospital discharge for controls, while controlling for age, gender, time at risk, Elixhauser comorbidities, hospital site of admission, specialty of the dominant healthcare professional providing care, intensive care admission, total number of procedures and discharge destination.ResultsOf the 24 240 spells, 2877 cases were included in the analysis. 72.2% of spells contained at least one intrahospital transfer. On multivariable analysis, each additional intrahospital transfer increased the odds of acquiring an HAI by 9% (OR=1.09; 95% CI 1.05 to 1.13).ConclusionIntrahospital transfers are associated with increased odds of developing an HAI. Strategies for minimising intrahospital transfers should be considered, and further research is needed to identify unnecessary transfers. Their reduction may diminish spread of contagious pathogens in the hospital environment.

Prevalence of Hospital Acquired Infection and Associated Factors Among Patients Admitted, at Hawassa University Comprehensive Specialized Hospital Getachew Melakie 1, Embialle Mengistie2, Tesfaye Ashenafi3, Busera Seman4 1′ 2’3 Hawassa University Dep

“…The reason for this high (11.9%) prevalence might be due to the comprehensive nature of current study, which involved all admitted patients including those in intensive care, with debilitating medical conditions, and had history of hospitalization, these might have contributed for the observed higher prevalence (11.9%); whereas these was not in the contrast study [8]. This is in line with a retrospective case-control study in UK, given that additional intra hospital transfer increased the odds of acquiring HAI by 9% [27].…”

Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study

“…patient admitted, discharged, followed by re-admission and a SARS-CoV-2-positive test sample three or more days post re-admission). Increased movement rate (bed/room/ward/sites moves) is known to be associated with increased risk of acquisition of HCAI, and has been investigated locally 31 , but did not significantly differ between control and HOCI in our data (Table 2). This suggests that the risk from movement rates alone is likely too general for HOCI, lacking specificity on move types, and is better captured via contact related metrics.…”

Effects of patient-level risk factors, departmental allocation and seasonality on intrahospital patient transfer patterns: network analysis applied on a Norwegian single-centre data set

“…Network analysis, however, examines the total number of transfers and does not capture their sequence in individual patients. 3 4 Moreover, in some healthcare systems, the format of patient location data does not facilitate the analysis of entire patient trajectories, and data validity may be poor. 1 …”

“…Hospitalised patients who are frequently transferred have increased risk of falls, delirium, prolonged hospital length of stay (LOS), healthcare-associated infections and mortality. 3–6 Characterisation of transfer patterns both at a systemic and an individual level may thus be relevant for understanding and revising healthcare service use. For organisational planning, smoothed, long-term transfer data showing major patient flows and seasonality are key.…”