Hospitalization costs due to healthcare-associated infections: An analysis of propensity score matching

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BackgroundOn January 7, 2019, we observed an outbreak of healthcare-associated infection (HAI) caused by Carbapenem-resistant Acinetobacter baumannii (CRAB) in the neurosurgical intensive care unit (NSICU). A follow-up epidemiological investigation was conducted, and an emergency response was initiated. We aimed to study the clonal transmission of CRAB and its possible source.MethodsA matched case-control (1:2) study was performed to identify the possible predisposing factors. A multifaceted intervention was implemented to control the outbreak. We collected environmental samples from patients’ rooms and living area of the staff. CRAB isolates were tested for genetic relatedness by Pulsed-Field Gel Electrophoresis (PFGE).ResultsEnvironmental sampling showed that a faucet aerator was contaminated with A. baumannii. Molecular typing revealed the only outbreak strain, which was isolated from tracheal aspirate cultures of the first case of community-acquired infection and 3 cases of HAI. In environmental samples, the outbreak strain was found only in the faucet aerator of the dining room. This CRAB outbreak was discovered in time, and further progress of this outbreak was prevented through a pre-set emergency response procedure.ConclusionsThe faucet aerator acted as a reservoir for bacteria in the outbreak, and contamination of the faucet aerator might have occurred from splashes originating from handwashing by the healthcare workers (HCWs). In high-risk areas, such as NSICU, the faucet aerators should not be used during an outbreak or they should be regularly cleaned and disinfected. The start-up criteria for the emergency response played a key role in controlling the CRAB outbreak, and its settings should be discussed more widely.

Section: Methodsmentioning

confidence: 99%

“…② To avoid “time bias,” the selected controls should have sufficient length of hospitalization stay [17, 18]. Therefore, controls were patients who had been in the NSICU for an interval at least as long as that between the time of NSICU admission and isolation of CRAB for the case patient;…”

Section: Methodsmentioning

confidence: 99%

Faucet aerators as a reservoir for Carbapenem-resistant Acinetobacter baumannii: a healthcare-associated infection outbreak in a neurosurgical intensive care unit

Xiang

Fang

et al. 2019

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“…Currently, there are few economic studies about HAI in Mainland China. Studies from Hubei and Sichuan demonstrated that the attributable cost of HAI were US$6173.02 and $2439.77 per case, respectively [13,14]. Another Chinese study of catheter-related bloodstream infections indicated the total cost attributable was US $3528.6 per case [15].…”

mentioning

confidence: 99%

Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections

Zhang

Johnston

et al. 2020

Background: Hospital-acquired bloodstream infection (BSI) is associated with high morbidity and mortality and increases patients' length of stay (LOS) and hospital charges. Our goals were to calculate LOS and charges attributable to BSI and compare results among different models. Methods: A retrospective observational cohort study was conducted in 2017 in a large general hospital, in Beijing. Using patient-level data, we compared the attributable LOS and charges of BSI with three models: 1) conventional non-matching, 2) propensity score matching controlling for the impact of potential confounding variables, and 3) risk set matching controlling for time-varying covariates and matching based on propensity score and infection time. Results: The study included 118,600 patient admissions, 557 (0.47%) with BSI. Six hundred fourteen microorganisms were cultured from patients with BSI. Escherichia coli was the most common bacteria (106, 17.26%). Among multidrug resistant bacteria, carbapenem-resistant Acinetobacter baumannii (CRAB) was the most common (42, 38.53%). In the conventional non-matching model, the excess LOS and charges associated with BSI were 25.06 days (P < 0.05) and US$22041.73 (P < 0.05), respectively. After matching, the mean LOS and charges attributable to BSI both decreased. When infection time was incorporated into the risk set matching model, the excess LOS and charges were 16.86 days (P < 0.05) and US$15909.21 (P < 0.05), respectively. Conclusion: This is the first study to consider time-dependent bias in estimating excess LOS and charges attributable to BSI in a Chinese hospital setting. We found matching on infection time can reduce bias.

“…This is called time bias, which is a confounding factor in HAI-related epidemiological studies that needs to be considered for balance [9]. There are many ways to balance time bias, as mentioned in previous studies, such as matching, propensity scores, and COX regression [10,11].…”

Section: Introductionmentioning

confidence: 99%

There is no dose–response relationship between allogeneic blood transfusion and healthcare-associated infection: a retrospective cohort study

Xiang

Lin

et al. 2021

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Background The association between allogeneic blood transfusion and healthcare-associated infection (HAI) is considered dose-dependent. However, this association may be confounded by transfusion duration, as prolonged hospitalization stay increases the risk of HAI. Also, it is not clear whether specific blood products have different dose–response risks. Methods In this retrospective cohort study, a logistic regression was used to identify confounding factors, and the association between specific blood products and HAI were analyzed. Then Cox regression and restricted cubic spline regression was used to visualize the hazard of HAI per transfusion product. Results Of 215,338 inpatients observed, 4.16% were transfused with a single component blood product. With regard to these transfused patients, 480 patients (5.36%) developed a HAI during their hospitalization stay. Logistic regression showed that red blood cells (RBCs) transfusion, platelets transfusion and fresh-frozen plasmas (FFPs) transfusion were risk factors for HAI [odds ratio (OR) 1.893, 95% confidence interval (CI) 1.656–2.163; OR 8.903, 95% CI 6.646–11.926 and OR 1.494, 95% CI 1.146–1.949, respectively]. However, restricted cubic spline regression analysis showed that there was no statistically dose–response relationship between different transfusion products and the onset of HAI. Conclusions RBCs transfusion, platelets transfusion and FFPs transfusion were associated with HAI, but there was no dose–response relationship between them.