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Updated: Feb 23, 2026

A Protocol to Characterize the Morphological Changes of Clostridium difficile in Response to Antibiotic Treatment
Published on: May 25, 2017
Assessing the burden of Clostridium difficile infections for hospitals
E Hebbinckuys1, J-P Marissal2, C Preda3
1Centre hospitalier de St Philibert, Lomme, France.
Background:
Nosocomial infections place a heavy burden on the healthcare system. However, quantifying the burden raises many questions, ranging from the way to accurately estimate the extra length of stay at hospital to defining and costing the preventive methods among the different care providers.
Aim:
To estimate the cost of nosocomial infection by C. difficile to inform the hospital managers.
Methods:
Multi-state modelling based on Markov processes and bootstrapping was used to derive individual estimates of the prolongation of stay at hospital associated with Clostridium difficile infection (CDI). Indicators of cost for hospitals were then computed, including an estimation of the productivity losses derived from diagnosis-related group (DRG)-based payment systems. Patients were aged ≥55 years, admitted in two hospital facilities in Lille, with and without an episode of CDI from January 1st, 2013 to September 15th, 2014.
Findings:
A total of 52 episodes were screened during the study period. The estimated mean cost of CDI was approximately €23,909 (SD: 17,458) for an extended length of hospital stay (N = 27). In the case of a reduced length of hospital stay (N = 25), the mean cost was approximately €-14,697 (SD: 16,936), which represents net savings for the hospitals. The main cost/savings driver was the productivity losses/gains resulting from the nosocomial infection. A sensitivity analysis showed that the main factor explaining the amount of costs or savings due to nosocomial infections was the length of the hospital stay.
Conclusion:
The notion of productivity gains in the case of deaths as a factor revealing the incompleteness of the payment systems is discussed, followed by the methodological issues associated with the statistical method used to control for temporality bias.
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