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      Cost Attributable to Nosocomial Bacteremia. Analysis According to Microorganism and Antimicrobial Sensitivity in a University Hospital in Barcelona

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          Abstract

          Aim

          To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility.

          Methods

          We selected patients who developed nosocomial bacteremia caused by Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, or Pseudomonas aeruginosa. These microorganisms were analyzed because of their high prevalence and they frequently present multidrug resistance. A control group consisted of patients classified within the same all-patient refined-diagnosis related group without bacteremia. Our hospital has an established cost accounting system (full-costing) that uses activity-based criteria to analyze cost distribution. A logistic regression model was fitted to estimate the probability of developing bacteremia for each admission (propensity score) and was used for propensity score matching adjustment. Subsequently, the propensity score was included in an econometric model to adjust the incremental cost of patients who developed bacteremia, as well as differences in this cost, depending on whether the microorganism was multidrug-resistant or multidrug-sensitive.

          Results

          A total of 571 admissions with bacteremia matched the inclusion criteria and 82,022 were included in the control group. The mean cost was € 25,891 for admissions with bacteremia and € 6,750 for those without bacteremia. The mean incremental cost was estimated at € 15,151 (CI, € 11,570 to € 18,733). Multidrug-resistant P. aeruginosa bacteremia had the highest mean incremental cost, € 44,709 (CI, € 34,559 to € 54,859). Antimicrobial-susceptible E. coli nosocomial bacteremia had the lowest mean incremental cost, € 10,481 (CI, € 8,752 to € 12,210). Despite their lower cost, episodes of antimicrobial-susceptible E. coli nosocomial bacteremia had a major impact due to their high frequency.

          Conclusions

          Adjustment of hospital cost according to the organism causing bacteremia and antibiotic sensitivity could improve prevention strategies and allow their prioritization according to their overall impact and costs. Infection reduction is a strategy to reduce resistance.

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          Most cited references34

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          Overall burden of bloodstream infection and nosocomial bloodstream infection in North America and Europe.

          In this systematic review, we estimated the total number of episodes of bloodstream infection (BSI) and deaths from BSI per year in North America and Europe, using data from population-based settings. Then, we estimated the number of episodes and deaths from nosocomial BSI from population-based studies and nosocomial infection surveillance systems. We estimated 575 000-677 000 episodes of BSI per year in North America (536 000-628 000 in the USA and 40 000-49 000 in Canada) and 79 000-94 000 deaths (72 000-85 000 in the USA and 7000-9000 in Canada), using estimates from three population-based studies. We estimated over 1 200 000 episodes of BSI and 157 000 deaths per year in Europe, using estimates from one population-based study in each of the following countries: Denmark (9100 episodes and 1900 deaths), Finland (8700 episodes and 1100 deaths) and England (96 000 episodes and 12 000-19 000 deaths). There were substantial differences in estimates of nosocomial BSI between population-based and nosocomial infection surveillance data. BSI has a major impact on the morbidity and mortality of the general population, as it ranks among the top seven causes of death in all included countries in North America and Europe. However, it is difficult to obtain precise estimates of nosocomial BSI, owing to the limited number of studies. This review highlights the need for a greater focus on BSI research in order to reduce the overall burden of disease by improving the outcome of patients with BSI. It also emphasizes the role of infection control and prevention methods in reducing the burden of nosocomial BSI. ©2013 The Authors Clinical Microbiology and Infection ©2013 European Society of Clinical Microbiology and Infectious Diseases.
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            Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect.

            Observational studies often provide the only available information about treatment effects. Control of confounding, however, remains challenging. The authors compared five methods for evaluating the effect of tissue plasminogen activator on death among 6,269 ischemic stroke patients registered in a German stroke registry: multivariable logistic regression, propensity score-matched analysis, regression adjustment with the propensity score, and two propensity score-based weighted methods-one estimating the treatment effect in the entire study population (inverse-probability-of-treatment weights), another in the treated population (standardized-mortality-ratio weights). Between 2000 and 2001, 212 patients received tissue plasminogen activator. The crude odds ratio between tissue plasminogen activator and death was 3.35 (95% confidence interval: 2.28, 4.91). The adjusted odds ratio depended strongly on the adjustment method, ranging from 1.11 (95% confidence interval: 0.67, 1.84) for the standardized-mortality-ratio weighted to 10.77 (95% confidence interval: 2.47, 47.04) for the inverse-probability-of-treatment-weighted analysis. For treated patients with a low propensity score, risks of dying were high. Exclusion of patients with a propensity score of <5% yielded comparable odds ratios of approximately 1 for all methods. High levels of nonuniform treatment effect render summary estimates very sensitive to the weighting system explicit or implicit in an adjustment technique. Researchers need to be clear about the population for which an overall treatment estimate is most suitable.
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              Hospital and societal costs of antimicrobial-resistant infections in a Chicago teaching hospital: implications for antibiotic stewardship.

              Organisms resistant to antimicrobials continue to emerge and spread. This study was performed to measure the medical and societal cost attributable to antimicrobial-resistant infection (ARI). A sample of high-risk hospitalized adult patients was selected. Measurements included ARI, total cost, duration of stay, comorbidities, acute pathophysiology, Acute Physiology and Chronic Health Evaluation III score, intensive care unit stay, surgery, health care-acquired infection, and mortality. Hospital services used and outcomes were abstracted from electronic and written medical records. Medical costs were measured from the hospital perspective. A sensitivity analysis including 3 study designs was conducted. Regression was used to adjust for potential confounding in the random sample and in the sample expanded with additional patients with ARI. Propensity scores were used to select matched control subjects for each patient with ARI for a comparison of mean cost for patients with and without ARI. In a sample of 1391 patients, 188 (13.5%) had ARI. The medical costs attributable to ARI ranged from $18,588 to $29,069 per patient in the sensitivity analysis. Excess duration of hospital stay was 6.4-12.7 days, and attributable mortality was 6.5%. The societal costs were $10.7-$15.0 million. Using the lowest estimates from the sensitivity analysis resulted in a total cost of $13.35 million in 2008 dollars in this patient cohort. The attributable medical and societal costs of ARI are considerable. Data from this analysis could form the basis for a more comprehensive evaluation of the cost of resistance and the potential economic benefits of prevention programs.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                7 April 2016
                2016
                : 11
                : 4
                : e0153076
                Affiliations
                [1 ]IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
                [2 ]Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
                [3 ]School of Nursing, Hospital del Mar, Barcelona, Spain
                [4 ]Department of Epidemiology and Evaluation, Hospital del Mar, Barcelona, Spain
                [5 ]Redissec (Red de Investigación en Servicios Sanitarios en enfermedades crónicas), Madrid, Spain
                [6 ]Department of Pharmacy, Hospital del Mar, Barcelona, Spain
                University College London, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MR PC RT FC. Performed the experiments: MR PC RT EG-A FC. Analyzed the data: MR PC RT EG-A FC. Contributed reagents/materials/analysis tools: MR PC EG-A FC. Wrote the paper: MR PC RT MS EG-A XC SG FC.

                Article
                PONE-D-15-25709
                10.1371/journal.pone.0153076
                4824502
                27055117
                497e2114-eecc-4cf5-9bca-8a6a602b1a19
                © 2016 Riu et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 June 2015
                : 23 March 2016
                Page count
                Figures: 0, Tables: 5, Pages: 12
                Funding
                The authors have no support or funding to report.
                Categories
                Research Article
                Medicine and Health Sciences
                Infectious Diseases
                Bacterial Diseases
                Bacteremia
                Medicine and Health Sciences
                Infectious Diseases
                Nosocomial Infections
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Pseudomonas Aeruginosa
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Pseudomonas Aeruginosa
                Biology and Life Sciences
                Organisms
                Bacteria
                Pseudomonas
                Pseudomonas Aeruginosa
                Biology and Life Sciences
                Microbiology
                Microbial Control
                Antimicrobial Resistance
                Medicine and Health Sciences
                Pharmacology
                Antimicrobial Resistance
                Biology and Life Sciences
                Organisms
                Bacteria
                Klebsiella
                Klebsiella Pneumoniae
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Klebsiella
                Klebsiella Pneumoniae
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Klebsiella
                Klebsiella Pneumoniae
                Medicine and Health Sciences
                Infectious Diseases
                Bacterial Diseases
                Escherichia Coli Infections
                Biology and life sciences
                Organisms
                Bacteria
                Staphylococcus
                Staphylococcus aureus
                Methicillin-resistant Staphylococcus aureus
                Biology and life sciences
                Microbiology
                Medical microbiology
                Microbial pathogens
                Bacterial pathogens
                Staphylococcus
                Staphylococcus aureus
                Methicillin-resistant Staphylococcus aureus
                Medicine and health sciences
                Pathology and laboratory medicine
                Pathogens
                Microbial pathogens
                Bacterial pathogens
                Staphylococcus
                Staphylococcus aureus
                Methicillin-resistant Staphylococcus aureus
                Medicine and Health Sciences
                Health Care
                Health Care Facilities
                Hospitals
                Custom metadata
                All relevant data are within the paper and its Supporting Information file.

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