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We analyzed the efficacy, cost, and cost-effectiveness of predictive decision-support systems based on surveillance interventions to reduce the spread of carbapenem-resistant Enterobacteriaceae (CRE). We developed a computational model that included patient movement between acute-care hospitals (ACHs), long-term care facilities (LTCFs), and communities to simulate the transmission and epidemiology of CRE. A comparative cost-effectiveness analysis was conducted on several surveillance strategies to detect asymptomatic CRE colonization, which included screening in ICUs at select or all hospitals, a statewide registry, or a combination of hospital screening and a statewide registry. We investigated 51 ACHs, 222 LTCFs, and skilled nursing facilities, and 464 ZIP codes in the state of Maryland. The model was informed using 2013-2016 patient-mix data from the Maryland Health Services Cost Review Commission. This model included all patients that were admitted to an ACH. On average, the implementation of a statewide CRE registry reduced annual CRE infections by 6.3% (18.8 cases). Policies of screening in select or all ICUs without a statewide registry had no significant impact on the incidence of CRE infections. Predictive algorithms, which identified any high-risk patient, reduced colonization incidence by an average of 1.2% (3.7 cases) without a registry and 7.0% (20.9 cases) with a registry. Implementation of the registry was estimated to save $572,000 statewide in averted infections per year. Although hospital-level surveillance provided minimal reductions in CRE infections, regional coordination with a statewide registry of CRE patients reduced infections and was cost-effective.


Gary Lin, Katie K Tseng, Oliver Gatalo, Diego A Martinez, Jeremiah S Hinson, Aaron M Milstone, Scott Levin, Eili Klein, CDC Modeling Infectious Diseases in Healthcare Program. Cost-effectiveness of carbapenem-resistant Enterobacteriaceae (CRE) surveillance in Maryland. Infection control and hospital epidemiology. 2022 Sep;43(9):1162-1170

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PMID: 34674791

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