Septicaemia, E. coli bacteraemia, epidemiology, risk factors, public health
Introduction:
Bacteraemia is of public health importance due to the high morbidity and mortality associated with this condition. Numbers and rates of E. coli bacteraemia in Wales have risen substantially over the last 10 years and it is clear that interventions aimed at preventing the spread of E. coli and the development of bacteraemia need to be
introduced to interrupt this upward trend. Public Health Wales have been requested to undertake an investigation
into the rise of E. coli bacteraemia by the Chief Medical Officer for Wales. Anonymised, routinely collected
administrative data stored in the Secure Anonymised Information Linkage (SAIL) databank will be used to provide
descriptive and risk factor analysis.
Methods:
Anonymised blood microbiology culture data reported between 2005 and 2011 are included in the SAIL databank. E. coli bacteraemia cases have been linked with Welsh demographic service (WDS) data to obtain address information, week of birth and gender. All potential controls are randomly selected from WDS. Three different methods were used to identify controls: 1) the cases and controls had a Welsh address on the date the E. coli blood sample of the case was received (reference date), and both cases and controls lived in Wales during the 91 days before the reference date and controls did not have an E. coli blood culture sample during the 91 days prior 2) Method one was extended to also match on age and sex 3) method 2 was extended by additionally matching on GP practice. All cases and controls in these three groups have been linked with the patient episode database for Wales (PEDW), Welsh general practice data, emergency department dataset, outpatient data and Welsh index of multiple deprivation (WIMD) to flag the relative risk factors.
Results:
Logistic regression and conditional logistic regression modelling techniques have been used to identify risk factors for developing E. coli bacteraemia. All three models show that kidney infection, urine infection, likely hospital antibiotics prescription and high comorbidity score are the risk factors with the highest odds ratios. For group 1, the odds of a patient with a high comorbidity score are 16 times the odds of a patient with a low comorbidity score the odds of a patient who had likely antibiotics prescription from hospital within 3 months are 16.5 times the odds of a patient who did not the odds of a patient who had an urine infection within 3 months are 21.5 times the odds of a patient who did not the odds of a patient who had kidney infection within 3 months are 145.7 times the odds of a patient who did not.
Conclusions:
Determining the factors associated with the development of E. coli bacteraemia will allow patients at highest risk to be identified. If these risk factors are modifiable, then preventive interventions can be introduced to reduce the number of potential cases of E. coli bacteraemia.