Hot topics, Healthcare associated infection, antimicrobial resistance, urinary tract infection, E. coli bacteraemia, risk factors, antimicrobial treatment, epidemiology, surveillance, health protection, public health
Background:
A steady increase rates of Escherichia coli bacteraemia has been observed across the United Kingdom over the last decade. In England, this resulted in an extension of Public Health England’s mandatory healthcare associated infection surveillance programme to include the collection of all cases of E. coli bacteraemia, from June 2011.
Trends in bacteraemia have been monitored in Wales since 2005, E. coli is the leading causative organism every year. The numbers and rates of E. coli bacteraemia increase year on year. In 2013, the Chief Medical Officer for Wales requested a Root Cause Analysis into the rise of E. coli bacteraemia in Wales.
Aim:
To examine risk factors for developing E. coli bacteraemia (ECB) post E. coli urinary tract infection (UTI), a population based record linkage study.
Methods:
1. Study Design: Population-based retrospective cohort using linked health data analysis for public health investigation.
2. Dataset preparation:
• SAIL is an anonymous data linkage system that holds routinely collected data for research
• Anonymised blood and urine data (2005-2011) from Public Health Wales microbiology database included in SAIL
• Encrypted Anonymous Linking Field (ALF_E) allows electronic linkage of microbiology data to other routinely collected administrative datasets held in SAIL.
3. Dataset linkage
• Dataset of E. coli urine culture records with antimicrobial sensitivities created.
• Outcome variable; urine dataset linked to E. coli blood culture dataset to identify patients who developed E. coli bacteraemia (ECB) within 14 days post-UTI.
• Dataset linked at patient-level by ALFe to datasets within the SAIL databank; general practice, hospital inpatient, and demographic datasets.
• Comorbidity index score generated per patient based on NHS Information Services’ Charlson Comorbidity Index methodology adapted to local coding by Bottle & Aylin (2011) Journal of Clinical Epidemiology 64 (2011) 1426- 1433
4. Data Analysis:
Exposure variable;
A new variable was created to assess the adequacy of a patients’ antibiotic treatment compared to urine culture antimicrobial sensitivity profile. Based on the six different treatment types the treatment was defined as adequate, inadequate or unknown. For instance, treatment was classified as inadequate if the culture was resistant (or intermediate) to Trimethoprim or; adequate if the treatment category was Trimethoprim and the culture was sensitive to Trimethoprim or; unknown if the sensitivity information was missing.
To identify when a patient consulted their GP, event dates from the GP data between the 14 days before and the 14 days after the date the urine was received were extracted. Details of inpatient stays were extracted from PEDW inpatients data. The study population were classified into the following treatment types:
a) Consulted GP, treated and admitted
b) Consulted GP, not treated and admitted
c) Consulted GP, treated and not admitted
d) Consulted GP, not treated and not admitted
e) No GP consultation
Analysis was restricted to patients continuously resident in Wales 3 years prior to the urine culture specimen date to assess comorbidities, restricted to the first UTI per person & first treatment per infection (n=79,000). UTI cases with unknown treatment adequacy were removed from the final logistic regression analysis (n=78,472). This dataset was linked to the anonymised E. coli blood culture dataset from the same time period to compare the odds of patients developing ECB within 14 days post-UTI.
Results:
1. Summary statistics:
Of those that developed ECB 14 days post-UTI (528); 30.5% did not consult their GP for their UTI; 69.5% consulted their GP (58.7% did not receive treatment, 8% received inadequate treatment, 2.8% received adequate treatment)
2. Univariate logistic regression
Significant unadjusted variables; Gender, Older age, Moderate & high comorbidity, No GP consultation, No GP treatment, Inadequate treatment. The odds of a patient aged 65 years or older developing E. coli bacteraemia are 2.7 times the odds of a patient aged less than 65 years developing E. coli bacteraemia.
3. Multivariate logistic regression
Significant adjusted variables; Risk factors; male gender, High comorbidity, No GP consultation, No GP treatment, Inadequate treatment. Interaction terms were included in the multivariate model as there is significant effect modification between gender and comorbidity
Controlling for other covariates, the odds of a patient who received inadequate treatment developing E. coli bacteraemia are 7.5 times the odds of a patient who received adequate treatment developing E. coli bacteraemia; the odds of a patient who received no GP treatment developing E. coli bacteraemia are 10.6 times the odds of a patient who received adequate treatment developing E. coli bacteraemia; the odds of a patient who did not have a GP consultation developing E. coli bacteraemia are 14 times the odds of a patient who received adequate treatment developing E. coli bacteraemia.
4. Risk profiles
The predicted probability of a patient developing ECB post-UTI by risk profile.
a) Lowest risk- Female, no/low comorbidity, adequate treatment.
b) Highest risk- Male, high comorbidity, no GP consultation
Females with a low comorbidity index value who receive adequate treatment for their UTI are at least risk of developing ECB (0.03%).
Males with a high comorbidity index value who do not consult their GP for treatment of UTI are at greatest risk of developing ECB (5.4%).
Conclusion:
Developing E. coli bacteraemia post- E. coli UTI is a rare event; however E. coli urinary tract infection is a common occurrence.
The increase in E. coli bacteraemia is a growing public health concern; this study identifies some modifiable risk factors that could reduce the risk of patients with an E. coli UTI developing bacteraemia.