How to Choose Target Facilities in a Region to Implement Carbapenem-resistant Enterobacteriaceae Control Measures

Abstract

Background When trying to control regional spread of antibiotic-resistant pathogens such as carbapenem-resistant Enterobacteriaceae (CRE), decision makers must choose the highest-yield facilities to target for interventions. The question is, with limited resources, how best to choose these facilities. Methods Using our Regional Healthcare Ecosystem Analyst–generated agent-based model of all Chicago metropolitan area inpatient facilities, we simulated the spread of CRE and different ways of choosing facilities to apply a prevention bundle (screening, chlorhexidine gluconate bathing, hand hygiene, geographic separation, and patient registry) to a resource-limited 1686 inpatient beds. Results Randomly selecting facilities did not impact prevalence, but averted 620 new carriers and 175 infections, saving $6.3 million in total costs compared to no intervention. Selecting facilities by type (eg, long-term acute care hospitals) yielded a 16.1% relative prevalence decrease, preventing 1960 cases and 558 infections, saving $62.4 million more than random selection. Choosing the largest facilities was better than random selection, but not better than by type. Selecting by considering connections to other facilities (ie, highest volume of discharge patients) yielded a 9.5% relative prevalence decrease, preventing 1580 cases and 470 infections, and saving $51.6 million more than random selection. Selecting facilities using a combination of these metrics yielded the greatest reduction (19.0% relative prevalence decrease, preventing 1840 cases and 554 infections, saving $59.6 million compared with random selection). Conclusions While choosing target facilities based on single metrics (eg, most inpatient beds, most connections to other facilities) achieved better control than randomly choosing facilities, more effective targeting occurred when considering how these and other factors (eg, patient length of stay, care for higher-risk patients) interacted as a system.

Modeling Interventions to Reduce the Spread of Multidrug-Resistant Organisms Between Health Care Facilities in a Region

“…However, the specific facilities targeted for interventions can alter the regional value. 43 Thus, understanding patient-sharing patterns can help facilities coordinate to increase both direct and indirect benefits. Without this knowledge, a facility implementing highly successful interventions could garner benefits, but facilities that do not implement interventions may experience only limited benefits.…”

“…Furthermore, studies have reported that regional MDRO interventions (eg, registry, CHG bathing, and screening of interfacility transfers) are cost-saving. 43 , 50 …”

“…As the results of this and previous studies suggest, coordinating infection prevention efforts can maximize benefits for regional and individual facilities. However, the specific facilities targeted for interventions can alter the regional value . Thus, understanding patient-sharing patterns can help facilities coordinate to increase both direct and indirect benefits.…”

“…Notably, the costs associated with MDRO infection are often substantially higher than the costs of interventions (eg, CHG wipes are approximately $5.50-$7.50 per bath, gloves and gowns are approximately $0.84 per use, and educational and printing materials for an intervention campaign are approximately $4606). Furthermore, studies have reported that regional MDRO interventions (eg, registry, CHG bathing, and screening of interfacility transfers) are cost-saving …”

How Long-Term Acute Care Hospitals Can Play an Important Role in Controlling Carbapenem-Resistant Enterobacteriaceae in a Region: A Simulation Modeling Study

“…Using our previously described software platform, Regional Healthcare Ecosystem Analyst (18), we generated an agent-based model of all 462 health-care facilities serving adult inpatients in the Chicago metropolitan region and the patients moving among these facilities and surrounding communities to simulate CRE spread (10,19). Web Appendix 1 provides model details as well as all model inputs (Web Table 1, available at https://academic.oup.com/ aje) and data sources; Web Figure 1 shows the flow of patients through the model.…”