Surveillance of healthcare-acquired infections in the hospital has changed over the years because of technological advancements and introduction of new techniques from other areas (i.e. statistical process charts of HAIs which was derived from engineering quality control process). In this thesis, selected surveillance techniques and models based on non-clinical data sources are applied to investigate their utility in strengthening healthcare-acquired infection (HAI) surveillance in a tertiary hospital in Queensland, Australia. The research focuses on healthcare-acquired methicillin-resistant Staphylococcus aureus (HA-MRSA) because it is an important pathogen that causes higher morbidity and mortality than methicillin-susceptible S. aureus. This in turn increases the public health care costs associated with MRSA. HA-MRSA infections are monitored by the Australian States and Territories under separate healthcare agencies (i.e. Queensland’s Centre for Healthcare Acquired Infection and Prevention, Victoria’s VICNISS program).
Can the application of contemporary statistical methods that capture the spatial and temporal dynamics of antibiotic-resistant HAI enhance the detection of unusually high frequency occurrences of HAI and strengthen HAI surveillance in a Queensland tertiary hospital?
What are the non-clinical drivers of MRSA transmission within the hospital?
Non-clinical data sources used in this thesis included intensive care unit (ICU) staffing data; hospital patient administration admission data; and maps of the physical layout of the hospital. These data sources were combined with clinical surveillance data to identify potential drivers of MRSA transmission, such as staffing deficits, ward activity levels, exposure to other infected or colonised patients due to a shared environment, the hierarchical and spatial layouts of the hospital and numbers of patient admissions from high-risk community sources. The primary outcome variable of interest was newly acquired HA- MRSA. Regression models were used to identify potential risk factors in the transmission of HA-MRSA. Multi-level models were used to investigate the levels of the hospital hierarchy that corresponded to the greatest variation in MRSA acquisition risk and a spatial component was incorporated to investigate spatial clustering of HA-MRSA in the hospital.
The thesis identified that in a well-resourced ICU, there was no clear association between staffing levels and risk of MRSA acquisition (Chapter 4). In the hospital as a whole, patients were found to be at a greater risk of HA-MRSA if there was a MRSA infected/colonised patient in the same cubicle or ward two weeks prior (Chapters 5 and6).This suggested that environmental contamination is an important driver of MRSA acquisition risk. Additionally, the ward was the level of the hospital hierarchy at which most variation in MRSA acquisition risk occurred, possibly related to the relative homogeneity of patient case-mix within compared to between wards (Chapter 5). Finally, admissions from aged care facilities and hospital transfers were identified as predictors of the burden of HA-MRSA in the hospital (Chapter 7).
Re-assessment of the current strategies in infection control requires a combined focus on environmental cleaning together with hand hygiene rather than an overwhelming focus on the latter. Resources for MRSA surveillance and control should be targeted at wards and cubicles in the two weeks after a patient has been identified as infected or colonised. Community sources of MRSA highlighted an additional need for active surveillance of high-risk admissions in combination with active surveillance in selected high-risk wards. The use of active surveillance at specific high-risk admission points to the hospital may be useful in the control of MRSA. Based on current evidence, staffing policies and practices in the ICU should continue in order to prevent emergence of a greater MRSA burden.