How to Choose Target Facilities in a Region to Implement Carbapenem-resistant Enterobacteriaceae Control Measures
| Autor: | Mary K. Hayden, Robert A. Weinstein, Joel Welling, Shawn T. Brown, William E Trick, Sarah M. Bartsch, Leslie E Mueller, Sarah K Kemble, Michael Y. Lin, Jim Leonard, Kruti Doshi, Bruce Y. Lee |
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| Rok vydání: | 2020 |
| Předmět: |
Microbiology (medical)
medicine.medical_specialty Bathing Total cost media_common.quotation_subject Control (management) Psychological intervention Carbapenem-resistant enterobacteriaceae 030501 epidemiology 03 medical and health sciences 0302 clinical medicine Hygiene Acute care Health care Humans Medicine 030212 general & internal medicine Ecosystem media_common Chicago Cross Infection business.industry Enterobacteriaceae Infections medicine.disease Major Articles and Commentaries Carbapenem-Resistant Enterobacteriaceae Infectious Diseases Medical emergency 0305 other medical science business |
| Zdroj: | Clin Infect Dis |
| ISSN: | 1537-6591 1058-4838 |
| DOI: | 10.1093/cid/ciaa072 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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