Probabilistic microsimulation to examine the cost-effectiveness of hospital admission screening strategies for carbapenemase-producing enterobacteriaceae (CPE) in the United Kingdom

Abstract

Abstract Background Antimicrobial resistance has been recognised as a global threat with carbapenemase- producing-Enterobacteriaceae (CPE) as a prime example. CPE has similarities to COVID-19 where asymptomatic patients may be colonised representing a source for onward transmission. There are limited treatment options for CPE infection leading to poor outcomes and increased costs. Admission screening can prevent cross-transmission by pre-emptively isolating colonised patients. Objective We assess the relative cost-effectiveness of screening programmes compared with no- screening. Methods A microsimulation parameterised with NHS Scotland date was used to model scenarios of the prevalence of CPE colonised patients on admission. Screening strategies were (a) two-step screening involving a clinical risk assessment (CRA) checklist followed by microbiological testing of high-risk patients; and (b) universal screening. Strategies were considered with either culture or polymerase chain reaction (PCR) tests. All costs were reported in 2019 UK pounds with a healthcare system perspective. Results In the low prevalence scenario, no screening had the highest probability of cost-effectiveness. Among screening strategies, the two CRA screening options were the most likely to be cost-effective. Screening was more likely to be cost-effective than no screening in the prevalence of 1 CPE colonised in 500 admitted patients or more. There was substantial uncertainty with the probabilities rarely exceeding 40% and similar results between strategies. Screening reduced non-isolated bed-days and CPE colonisation. The cost of screening was low in relation to total costs. Conclusion The specificity of the CRA checklist was the parameter with the highest impact on the cost-effectiveness. Further primary data collection is needed to build models with less uncertainty in the parameters.