The health and cost burden of antibiotic resistant and susceptible Escherichia coli bacteraemia in the English hospital setting: A national retrospective cohort study

Abstract

Introduction: Antibiotic resistance poses a threat to public health and healthcare systems. Escherichia coli causes more bacteraemia episodes in England than any other bacterial species. This study aimed to estimate the burden of E. coli bacteraemia and associated antibiotic resistance in the secondary care setting.

Materials and methods: This was a retrospective cohort study, with E. coli bacteraemia as the main exposure of interest. Adult hospital in-patients, admitted to acute NHS hospitals between July 2011 and June 2012 were included. English national surveillance and administrative datasets were utilised. Cox proportional hazard, subdistribution hazard and multistate models were constructed to estimate rate of discharge, rate of in-hospital death and excess length of stay, with a unit bed day cost applied to the latter to estimate cost burden from the healthcare system perspective.

Results: 14,042 E. coli bacteraemia and 8,919,284 non-infected inpatient observations were included. E. coli bacteraemia was associated with an increased rate of in-hospital death across all models, with an adjusted subdistribution hazard ratio of 5.88 (95% CI: 5.62-6.15). Resistance was not found to be associated with in-hospital mortality once adjusting for patient and hospital covariates. However, resistance was found to be associated with an increased excess length of stay. This was especially true for third generation cephalosporin (1.58 days excess length of stay, 95% CI: 0.84-2.31) and piperacillin/tazobactam resistance (1.23 days (95% CI: 0.50-1.95)). The annual cost of E. coli bacteraemia was estimated to be £14,346,400 (2012 £), with third-generation cephalosporin resistance associated with excess costs per infection of £420 (95% CI: 220-630).

Conclusions: E. coli bacteraemia places a statistically significant burden on patient health and the hospital sector in England. Resistance to front-line antibiotics increases length of stay; increasing the cost burden of such infections in the secondary care setting.

Fig 1. Linked dataset cleaning.

*Variables needed for spell creation were HES identifier (‘extract_hesid’), admission date (‘admidate’), provider code, (‘procodet’), start of episode (‘epistart’), end of episode (‘epiend’) and method of discharge (‘dismeth’). **The spell was coded as being related to the exposure if specimen was less 14 days before admission or occurred during admission. *** Those that had not experienced infection prior to or on day 45 were treated as non-exposed patients and censored at day 45 (which causes the move of patients from exposed to unexposed category (n = 232)), this is due to our analysis being performed within the first 45 days of a patient’s hospital spell.

Fig 2. Multistate model schematic for excess length of stay estimation.

Potential health states are depicted by the boxes and the direction in which they may travel depicted via arrow directionality. State 0 = “Admission, No Infection”, State 1 = “Infection of Interest” with 1a relating to infections with resistant E. coli, 1b infections with susceptible E. coli and composite state 1c being all E. coli infections, state 2a = “In-hospital Death” and state 2b = “Discharge Alive”. States 2a and 2b, together, form the LoS endpoint, 2c.

Fig 3. Excess length of Stay of E. coli Bacteraemia.

Excess days associated with an exposure were calculated in comparison to “non-infected” non-exposed, i.e. patients in hospital without an E. coli bacteraemia. Error bars represent the 95% confidence intervals derived from bootstrapping. **“Resistant to 1 or more” refers to being resistant to at least one of the tested antibiotics (ciprofloxacin, third generation cephalosporins, gentamicin, piperacillin/tazobactam and carbapenems). Abbreviations: 3GC; third-generation cephalosporin, E. coli; Escherichia coli, pip/taz; piperacillin/tazobactam.