OBJECTIVE: To evaluate the cost-effectiveness of 3 alternative active screening strategies for methicillin-resistant Staphylococcus aureus (MRSA): universal surveillance screening for all hospital admissions, targeted surveillance screening for intensive care unit admissions, and no surveillance screening. DESIGN: Cost-effectiveness analysis using decision modeling. METHODS: Cost-effectiveness was evaluated from the perspective of an 800-bed academic hospital with 40,000 annual admissions over the time horizon of a hospitalization. All input probabilities, costs, and outcome data were obtained through a comprehensive literature review. Effectiveness outcome was MRSA healthcare-associated infections (HAIs). One-way and probabilistic sensitivity analyses were conducted. RESULTS: In the base case, targeted surveillance screening was a dominant strategy (ie, was associated with lower costs and resulted in better outcomes) for preventing MRSA HAI. Universal surveillance screening was associated with an incremental cost-effectiveness ratio of $14,955 per MRSA HAI. In one-way sensitivity analysis, targeted surveillance screening was a dominant strategy across most parameter ranges. Probabilistic sensitivity analysis also demonstrated that targeted surveillance screening was the most cost-effective strategy when willingness to pay to prevent a case of MRSA HAI was less than $71,300. CONCLUSION: Targeted active surveillance screening for MRSA is the most cost-effective screening strategy in an academic hospital setting. Additional studies that are based on actual hospital data are needed to validate this model. However, the model supports current recommendations to use active surveillance to detect MRSA.
OBJECTIVE: To evaluate the cost-effectiveness of 3 alternative active screening strategies for methicillin-resistant Staphylococcus aureus (MRSA): universal surveillance screening for all hospital admissions, targeted surveillance screening for intensive care unit admissions, and no surveillance screening. DESIGN: Cost-effectiveness analysis using decision modeling. METHODS: Cost-effectiveness was evaluated from the perspective of an 800-bed academic hospital with 40,000 annual admissions over the time horizon of a hospitalization. All input probabilities, costs, and outcome data were obtained through a comprehensive literature review. Effectiveness outcome was MRSA healthcare-associated infections (HAIs). One-way and probabilistic sensitivity analyses were conducted. RESULTS: In the base case, targeted surveillance screening was a dominant strategy (ie, was associated with lower costs and resulted in better outcomes) for preventing MRSA HAI. Universal surveillance screening was associated with an incremental cost-effectiveness ratio of $14,955 per MRSA HAI. In one-way sensitivity analysis, targeted surveillance screening was a dominant strategy across most parameter ranges. Probabilistic sensitivity analysis also demonstrated that targeted surveillance screening was the most cost-effective strategy when willingness to pay to prevent a case of MRSA HAI was less than $71,300. CONCLUSION: Targeted active surveillance screening for MRSA is the most cost-effective screening strategy in an academic hospital setting. Additional studies that are based on actual hospital data are needed to validate this model. However, the model supports current recommendations to use active surveillance to detect MRSA.
Authors: Richard E Nelson; Vanessa W Stevens; Karim Khader; Makoto Jones; Matthew H Samore; Martin E Evans; R Douglas Scott; Rachel B Slayton; Marin L Schweizer; Eli L Perencevich; Michael A Rubin Journal: Am J Prev Med Date: 2016-05 Impact factor: 5.043
Authors: Andie S Lee; Angelo Pan; Stephan Harbarth; Andrea Patroni; Annie Chalfine; George L Daikos; Silvia Garilli; José Antonio Martínez; Ben S Cooper Journal: BMC Infect Dis Date: 2015-02-27 Impact factor: 3.090
Authors: Virginia R Roth; Tara Longpre; Doug Coyle; Kathryn N Suh; Monica Taljaard; Katherine A Muldoon; Karamchand Ramotar; Alan Forster Journal: PLoS One Date: 2016-07-27 Impact factor: 3.240