Infection Control & Hospital Epidemiology
Table 1. Parameter Estimates and Distributions for
E.coli Infections Used in the Economic Model Variable
Estimate (95% UI)
E. coli bloodstream infection Rate (susceptible þ resistant) per 10,000 patient days
Mortality, 30-day all-cause hospital onset Probability of ceftriaxone resistance Additional LoS_3GCR, days Mortality, 3GCR (hazard ratio)
Excess cost of antibiotics per infection
E. coli urinary tract infection Rate (susceptible þ resistant) per 10,000 patient days
Probability of ceftriaxone resistance
Additional LoS for resistant infection, days Mortality hazard ratio, resistant infection Excess cost of antibiotics per infection
6.11 (5.83–6.36) 0.2112
0.0546 (0.045–0.065) 4.89 (1.11–8.68)13 1.63 (1.13–2.35)13
þ$256.26 (N/A)
78.5 (77.55–79.56) 0.045 (0.043–0.049) N/A N/A N/A
Note. UI, uncertainty interval; LoS, length of stay; 3GCR, third-generation cephalosporin resistant; N/A, not available.
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Distribution Type (Parameters)
β (0.000611–0.0000136) Fixed value used β (0.0546 –0.0051) γ (4.89–1.93)
Log-normal (0.488–0.187) Fixed value used
β (0.00781–0.0000487) β (0.0457–0.00153)
Healthcare-associated E. coli,K. pneumoniae, P. aeruginosa, E. faecium and S. aureus infections
Good-quality published estimates of health impact and treatment
Rate of infection (per10,000 patient bed days) and Probability of resistance
Antibiotic choice, dose and duration for resistant and susceptible infections
Cost of treating resistant minus susceptible patients (BSI only)
Additional days spent in hospital due to AMR
Excess risk of death due to AMR (HR)
Excess treatment cost per infection
Total number of resistant infections in Australian hospitals in 2014
Excess risk of death due to AMR (HR)
Excess deaths due to AMR
Excess LOS
Value of a hospital bed-day
Total cost of AMR (AUD$)
Fig. 1. A schematic representing the parameters of the economic model used for the study.
stay. The cost of a hospital bed day was the product of multiplying excess days in hospital for each of the five organismsby the value of a hospital bed. The methods used for all of these estimations are reported in subsequent sections of this article. The total cost of AMRfor this study wasa measure ofnumberof resistant infection, excess treatment costs, and additional hospital stay. Excess deaths, as a measure of the number of resistant infections multiplied by the risk of death from AMR, were not included in the total cost of AMR. Uncertainties in the model parameters were included by fitting
prior statistical distributions and making 10,000 randompicks from all distributions. The method ofmoments19 was used to estimate the model parameters for γ and β distribution (Tables 1–5). A γ distri- bution was fitted to LoS data to reflect the skew typical of this type of information, and a β distribution to describe uncertainty about the true value of a rate and resistance probability. A log-normal distri- bution was used formortality measures (Tables 1–5). The results of the simulations show the uncertainty in estimates. Themodel was developedwith the advice froman advisory com- mittee made up of experts from the hospital, medical community,
and health department. All members had expertise in infectious dis- eases, epidemiology, and infection control. Selection of organisms was basedon the availability and completeness ofpublished evidence aswell the advice fromthe advisory
committee.The final selection of organismincludedhealthcare-associated (community- andhospital- onset) infections of the blood, urinary tract, and respiratory tract caused by any one of the five selected organisms: ceftriaxone- resistant E. coli (including extended-spectrum β-lactamases (ESBL); ceftriaxone-resistant K. pneumoniae (including ESBL); ceftazidime-resistantP. aeruginosa; vancomycin-resistant E. faecium; and methicillin-resistant S. aureus (MRSA). Organisms such as Acinetobacter baumanii, Mycobacterium tuberculosis, Neisseria meningitis, N. gonorrhoea, Haemophilus influenza,and human immunodeficiency virus were excluded following detailed review, primarily due to currently low incidence rates in Australia and/or a limited availability of information. Community-onset infections could not be adequately identified or appropriately dichotomized from infections that presented within a healthcare setting and were therefore analyzed together with the hospital-acquired infections.
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