Attributable length of hospital stay and mortality associated with healthcare associated infections in Queensland, Australia

Dr Xing Lee1, Dr Teresa Wozniak2, Professor Nicholas Graves1

1Queensland University Of Technology, Kelvin Grove, Australia
2Menzies School of Health Research, Darwin, Australia


Introduction: Hospitalised patients who acquired an infection have longer length of stay (LOS) and a higher risk of death, which is compounded if the causative organism is resistant to therapy. Quantifying the health impact of these infections is challenging due to the time-dependent nature of these infections and not including temporal information of events leads to biased estimates.

Methods: We analysed LOS and mortality associated with infections with one of five clinically important organisms (Staphylococcus aureus, Enterococcus faecium, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa) in all Queensland public hospital admissions between 2012 and 2016 using a case-cohort study design. Unbiased estimates of attributable LOS and mortality for each infection type were obtained using multistate modelling adapted to the case-cohort design.

Results: Preliminary investigations showed differences in attributable LOS and mortality by organisms and sites of infection. For instance, MRSA BSI infections were estimated to increase LOS by 5.4 (standard error (SE): 0.6) days compared with non-infected patients, while MRSA UTI infections increased LOS by 2.8 (SE: 0.6) days. Resistant K. pneumoniae and P. aeruginosa UTIs were estimated to extend LOS by 2.3 (0.4) and 2.7 (0.2) days, respectively, while resistant E. coli UTIs shorten LOS by 0.5 (0.7) days. Certain estimates have large uncertainty due to small numbers of infection reported.

Conclusion: We derived unbiased estimates of the additional burden of resistant and susceptible infections in Queensland. Such estimates are important to inform infection control and prevention programs, health policies and studies to determine the economic burden of resistance.


Xing Lee is a Research Fellow in Statistics with the Australian Centre for Health Services Innovations (AusHSI) based at Queensland University of Technology.

Xing completed his PhD in 2017 with the NHMRC-funded Centre of Research Excellence in Reducing Healthcare Associated Infections (CRE-RHAI), where he investigated statistical and simulation modelling of hospital pathogen transmission, with a focus on environmental contamination.

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