Mr Angus McLure1, Dr Archie Clements1, Dr Martyn Kirk1, Dr Kathryn Glass1
1Research School of Population Health, ANU, Acton, Australia
Introduction
Clostridium difficile infections (CDIs) are classified as hospital-acquired if the onset of symptoms was more than two days after hospital admission or less than four weeks after discharge. The incubation period for C. difficile is often more than two days but usually much less than four weeks, which may result in significant misclassification. We assess this method of classification and identify potential improvements.
Methods
We simulated C. difficile transmission in the community including a hospitalised sub-population to determine the time from admission to onset of symptoms for CDIs acquired during hospitalisation and CDIs acquired in the community. We determined the best classification strategy for distinguishing hospital-acquired and community-acquired cases for a range of plausible scenarios.
Results
In our base scenario, the common two-day classification had good sensitivity, but poor specificity to identify CDIs acquired in the current hospitalisation, overestimating their incidence by 100%. A six-day cut-off accurately estimated the proportion of CDIs acquired during the current hospitalisation and proportion of CDIs acquired prior to admission. In the sensitivity analysis, a two-day cut-off overestimated the incidence of CDIs acquired in the current hospitalisation by 30-350%, with the greatest error in scenarios with low within-hospital transmission.
Conclusion
The commonly used two-day cut-off for classifying hospital-acquired CDIs has poor specificity and systematically overestimates the proportion of infections acquired in hospital. We recommend that infection control practitioners use a cut-off of at least 5 days when reporting the incidence of hospital acquired CDIs.
Biography:
Angus McLure is a PhD candidate at the Research School of Population Health, ANU. He completed is undergraduate studies in science and pure and applied mathematics at the Australian National University. His doctoral research uses mathematical models to understand the transmission dynamics of Clostridium difficile in hospitals and communities, assess existing practices and evaluate potential control measures.