Review
Risk factors for healthcare-associated urinary tract infection and their applications in surveillance using hospital administrative data: a systematic review

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Summary

Background

Healthcare-associated urinary tract infections (HCA UTI) account for a large proportion of hospital infections, with recently launched surveillance in the UK focusing on reducing catheter-associated urinary tract infections. However, a wealth of administrative information already collected routinely by hospitals is currently not used to its maximum potential for surveillance.

Aim

To quantify the evidence base of HCA UTI risk factors and to determine their potential for shaping and informing innovative surveillance tools using local hospital data.

Methods

A systematic literature review was undertaken to find established risks for HCA UTI. Population-attributable risk percentages (PAR%) were calculated for these risk factors, generating a hierarchy of risks. Administrative hospital data were subsequently interrogated for these quantified risks.

Findings

Over 30% of the risk factors identified from the systematic literature review were independent predictors of infection. The highest PAR% was associated with urinary catheterization, with the calculation that 79.3% of UTI would be prevented if catheterization was not performed. PAR% calculations were performed for 60% of the independent predictors for HCA UTI. Sixty-five percent of the identified independent risk factors were found to be coded within the administrative hospital dataset, including urinary catheterization.

Conclusion

This work has quantified established HCA UTI risks and demonstrates that there is potential for more effective use of administrative hospital data for risk monitoring and surveillance of HCA UTI.

Introduction

Healthcare-associated urinary tract infections (HCA UTI) contribute a significant burden to healthcare services globally.1 In the UK, it is estimated that the National Health Service (NHS) incurs 66,160 cases of HCA UTI per year, costing £68 million in excess bed-days.2 In the 2006 UK and Republic of Ireland prevalence survey of healthcare-associated infections (HCAI), UTI accounted for 19.9% of HCAI, second in frequency only to gastrointestinal infections (20.6%).3 The high rate of UTI within hospitals is commonly attributed to the use of urinary catheters, with 31.8% of the NHS patient population having undergone urinary catheterization or bladder instrumentation on the day of the survey or in the preceding seven days.4

Traditional surveillance has long been established as an essential component to any infection prevention and control programme,5 despite the fact that it can be costly to implement and time consuming to conduct.6 In the UK, there are mandatory surveillance systems for several HCAI, such as Clostridium difficile (since January 2004) and meticillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (since early 2001).7, 8 Surveillance of catheter-associated urinary tract infections (CA UTI) is now performed as part of an initiative launched by the UK Department of Health in January 2011 in order to reduce patient harm.9 However, despite the high burden that HCA UTI represents to the NHS, this surveillance scheme is voluntary, focuses exclusively on CA UTI, and is still in its infancy. The potential utility of innovative HCA UTI surveillance tools is apparent, with current work focusing on the development of automated systems to identify UTI using traditional clinical definitions.10

Hospitals routinely collect and store large amounts of administrative data, including information on admissions, microbiology, diagnoses and procedures.11 Although this wealth of data has not been widely exploited for HCAI surveillance, integrative approaches towards the more effective use of administrative data are being pioneered.11, 12, 13 Determining the utility of established risk factors for HCA UTI within administrative hospital data presents the opportunity to develop and test innovative surveillance tools with the potential to enhance traditional surveillance of UTI within the healthcare environment.

The aim of this study was to quantify the existing evidence base of risk factors for HCA UTI, create a hierarchy of risk, identify these risks within administrative hospital data, and assess the potential of this data source for informing novel syndromic surveillance tools.

Section snippets

Systematic literature review

A systematic literature review was conducted to establish an evidence base of risk factors for HCA UTI. EMBASE, PubMed/MEDLINE, Web of Science and the Cochrane Database were searched using ‘UTI’, ‘urinary tract infection’ and ‘risk’ as the search terms. The Cochrane Database was included to assess whether or not a comprehensive literature review on HCA UTI risk factors had been undertaken previously. The search strategy and inclusion criteria were discussed and agreed between the authors, and

Results

The systematic literature search returned 4109 unique papers. One percent were excluded for not being in English, 89% had titles that were not relevant to the study question, and 6% were excluded as irrelevant after abstract review. The results retrieved from the Cochrane Database returned no relevant studies. In total, 154 studies underwent full-text review and 23 were ultimately included in the study (Figure 1). Of the 23 papers included, three papers were case–control studies,20, 21, 22 13

Discussion

The purpose of this study was to quantify the scientific evidence base on risk factors for HCA UTI, and to apply this to locally available administrative hospital data to establish the utility of this data source for further research activities in the development of surveillance tools, including risk stratification and the generation of automated syndromic HCA UTI algorithms.

The systematic literature review returned 23 relevant studies, with 96 significant and 43 independent HCA UTI risk

Conflict of interest statement

None declared.

Funding sources

The UK Clinical Research Collaboration funds the National Centre for Infection Prevention and Management at Imperial College London, and also supports the UK National Institute for Health Research Biomedical Research Centre funding scheme. The Dr Foster Unit at Imperial is largely funded by a research grant from Dr Foster Intelligence (an independent health service research organization) and is affiliated with the Centre for Patient Safety and Service Quality at Imperial College Healthcare NHS

Acknowledgements

The authors wish to thank the Infection Prevention and Control, Pathology and Microbiology departments and the Information Technology teams within Imperial College NHS Trust for their collaboration.

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