Risk factors for Escherichia coli bacteraemia: a population-based case-control study

Abstract

BackgroundEscherichia coli bacteraemia is associated with high morbidity and mortality and is increasing in incidence. This study investigated putative risk factors associated with development of E coli bacteraemia, to inform prevention strategies. MethodsA population-based case-control study was conducted from de-identified linked data sources in Wales. Pseudonymised blood microbiology culture reported from all Welsh laboratories between Jan 1, 2005, and Dec 31, 2011, were included in the Secure Anonymised Information Linkage databank and linked to other routinely collected health records. Cases were individuals with E coli-positive blood cultures. 20 sets of controls were randomly selected from Welsh demographic service data. Records were linked with Patient Episode Database for Wales (inpatient) and Welsh general practice data to identify risk and potential confounding factors, including demographic characteristics, comorbidity score, and clinical factors. Both cases and controls needed to be residents of Wales on the date the E coli-positive blood sample of the case was received (reference date), and to have lived in Wales during the 91 days before the reference date. Conditional logistic regression modelling was used to identify factors associated with developing bacteraemia. FindingsThere were 10 815 cases over 7 years and 216 300 controls. Factors associated with a particularly high risk of bacteraemia included renal infection (odds ratio 145·7, 95% CI 72·2–294·0), laboratory-confirmed urinary tract infection (21·5, 19·6–23·7), conditions with likely hospital antibiotics prescription (16·5, 15·0–18·0), and high comorbidity score category (16·0, 14·7–17·5). 12 other factors had significantly elevated risks. InterpretationThis study has identified particular categories at substantially higher risk of developing bacteraemia. This information will be used in developing targeted interventions. Continuous or repeated analysis could be used to evaluate effectiveness of the interventions. The use of comprehensive linked records avoids selection biases and creates matched population based controls. Privacy-protecting data linkage allows for efficient retrospective follow-up of patients. However, in studies with e-records, there can be incomplete control of confounding, despite the large number of data and datasets used. The presumed hospital prescribing variable was defined from a diagnosis that should result in treatment due to absence of hospital prescription data. FundingPublic Health Wales NHS Trust, Farr Institute.