Cost Attributable to Nosocomial Bacteremia. Analysis According to Microorganism and Antimicrobial Sensitivity in a University Hospital in Barcelona
To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility. We selected patients who developed nosocomial bacteremia caused by Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, or Pseudomonas aerugino...
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| Published in | PloS one Vol. 11; no. 4; p. e0153076 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
07.04.2016
Public Library of Science (PLoS) |
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| Abstract | To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility.
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.
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.
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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| AbstractList | To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility. 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. A total of 571 admissions with bacteremia matched the inclusion criteria and 82,022 were included in the control group. The mean cost was [euro] 25,891 for admissions with bacteremia and [euro] 6,750 for those without bacteremia. The mean incremental cost was estimated at [euro] 15,151 (CI, [euro] 11,570 to [euro] 18,733). Multidrug-resistant P. aeruginosa bacteremia had the highest mean incremental cost, [euro] 44,709 (CI, [euro] 34,559 to [euro] 54,859). Antimicrobial-susceptible E. coli nosocomial bacteremia had the lowest mean incremental cost, [euro] 10,481 (CI, [euro] 8,752 to [euro] 12,210). Despite their lower cost, episodes of antimicrobial-susceptible E. coli nosocomial bacteremia had a major impact due to their high frequency. 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. AIMTo calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility.METHODSWe 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.RESULTSA 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.CONCLUSIONSAdjustment 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. 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. 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. 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 [euro] 25,891 for admissions with bacteremia and [euro] 6,750 for those without bacteremia. The mean incremental cost was estimated at [euro] 15,151 (CI, [euro] 11,570 to [euro] 18,733). Multidrug-resistant P. aeruginosa bacteremia had the highest mean incremental cost, [euro] 44,709 (CI, [euro] 34,559 to [euro] 54,859). Antimicrobial-susceptible E. coli nosocomial bacteremia had the lowest mean incremental cost, [euro] 10,481 (CI, [euro] 8,752 to [euro] 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. To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility. 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. 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. 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. |
| Audience | Academic |
| Author | Riu, Marta Grau, Santiago Garcia-Alzorriz, Enric Castells, Xavier Cots, Francesc Sala, Maria Chiarello, Pietro Terradas, Roser |
| AuthorAffiliation | University College London, UNITED KINGDOM 6 Department of Pharmacy, Hospital del Mar, Barcelona, Spain 1 IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain 4 Department of Epidemiology and Evaluation, Hospital del Mar, Barcelona, Spain 2 Universitat Autònoma de Barcelona (UAB), Barcelona, Spain 5 Redissec (Red de Investigación en Servicios Sanitarios en enfermedades crónicas), Madrid, Spain 3 School of Nursing, Hospital del Mar, Barcelona, Spain |
| AuthorAffiliation_xml | – name: 3 School of Nursing, Hospital del Mar, Barcelona, Spain – name: 1 IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain – name: 4 Department of Epidemiology and Evaluation, Hospital del Mar, Barcelona, Spain – name: University College London, UNITED KINGDOM – name: 5 Redissec (Red de Investigación en Servicios Sanitarios en enfermedades crónicas), Madrid, Spain – name: 6 Department of Pharmacy, Hospital del Mar, Barcelona, Spain – name: 2 Universitat Autònoma de Barcelona (UAB), Barcelona, Spain |
| Author_xml | – sequence: 1 givenname: Marta surname: Riu fullname: Riu, Marta organization: Universitat Autònoma de Barcelona (UAB), Barcelona, Spain – sequence: 2 givenname: Pietro surname: Chiarello fullname: Chiarello, Pietro organization: Universitat Autònoma de Barcelona (UAB), Barcelona, Spain – sequence: 3 givenname: Roser surname: Terradas fullname: Terradas, Roser organization: School of Nursing, Hospital del Mar, Barcelona, Spain – sequence: 4 givenname: Maria surname: Sala fullname: Sala, Maria organization: Redissec (Red de Investigación en Servicios Sanitarios en enfermedades crónicas), Madrid, Spain – sequence: 5 givenname: Enric surname: Garcia-Alzorriz fullname: Garcia-Alzorriz, Enric organization: IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain – sequence: 6 givenname: Xavier surname: Castells fullname: Castells, Xavier organization: Redissec (Red de Investigación en Servicios Sanitarios en enfermedades crónicas), Madrid, Spain – sequence: 7 givenname: Santiago surname: Grau fullname: Grau, Santiago organization: Department of Pharmacy, Hospital del Mar, Barcelona, Spain – sequence: 8 givenname: Francesc surname: Cots fullname: Cots, Francesc organization: IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27055117$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2016 Public Library of Science 2016 Riu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2016 Riu et al 2016 Riu et al |
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| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 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. |
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| Snippet | To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility.
We selected... Aim To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility. Methods... To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility. We selected... AIMTo calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility.METHODSWe... AIM:To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility.... Aim To calculate the incremental cost of nosocomial bacteremia caused by the most common organisms, classified by their antimicrobial susceptibility. Methods... |
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| SubjectTerms | Accounting Aged Aged, 80 and over Antibiotics Antiinfectives and antibacterials Bacteremia Bacteremia - economics Bacteremia - microbiology Bacteremia - therapy Bacteria Bias Biology and Life Sciences Catheters Causes of Cost analysis Costs and Cost Analysis Criteria Cross Infection - economics Cross Infection - microbiology Cross Infection - therapy Drug resistance E coli Econometrics Economic aspects Escherichia coli Female Health care costs Hospitalization - economics Hospitals Hospitals, Teaching Humans Klebsiella Male Medicine and Health Sciences Microorganisms Middle Aged Mortality Multidrug resistance Nosocomial infection Nosocomial infections Patients Pseudomonas aeruginosa Regression analysis Regression models Retrospective Studies Sensitivity Sensitivity analysis Spain Staphylococcus aureus Statistical analysis |
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| Title | Cost Attributable to Nosocomial Bacteremia. Analysis According to Microorganism and Antimicrobial Sensitivity in a University Hospital in Barcelona |
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