While the VHA methicillin-resistant Staphylococcus aureus (MRSA) Initiative has been successful in reducing infection rates, MRSA infections remain a significant problem for hospitalized Veterans. Efforts to identify the most effective and cost-efficient infection control strategies must continue. The challenge is to understand which interventions have the greatest impact, alone and in combination, and how best to implement them in a particular healthcare environment. In addition, the benefit of targeting long-term care facilities such as VA Community Living Centers (CLCs) in MRSA prevention and the importance of physician and nurse team behaviors in influencing various types of infection control efforts are unknown. The work described in this project will leverage the unique data available in VA as a result of the MRSA Initiative, VINCI databases, and multiple HSR&D studies (e.g., IIR 05-123, IIR 08-075, IIR 11-035, and CDA 07-022).
The goal of this study is to build a VISN-wide agent-based simulation model of MRSA transmission and control to compare the effectiveness and cost-effectiveness of various MRSA control strategies. Our specific objectives are to: 1) extend an existing hospital-level MRSA agent-based simulation model to include physician teams and behaviors, and expand into a VISN-wide model that includes VA CLCs; 2) use the extended model to compare the costs, impacts, and cost-effectiveness of MRSA infection control strategies, alone or in combination, and to assess the impact of varying levels of provider team uptake behaviors on strategy effectiveness; and 3) develop and disseminate a web-based version to be used by decision-makers at local, VISN, and national levels when making clinical and economic decisions about MRSA control strategies.
We will utilize existing systematic reviews, analyses of VA data, and findings including ethnographic observations from our other CREATE Projects to establish estimates of the key parameters needed for the model, such as: MRSA surveillance and prevalence data; duration, nature and frequency of healthcare worker visits to patient rooms in acute care and in VA CLCs; data on physician team attributes and behavioral measures and impact on patient outcomes; and costs associated with MRSA infection and various intervention components. These results will be incorporated into the simulation, which will then be used to assess the various strategies and factors that impact MRSA transmission through a series of simulation experiments. Traditional quantitative epidemiologic methods will be used to analyze simulation results, with a focus on MRSA incidence and transmission rates as outcomes. Dynamic cost-benefit analyses will be performed by projecting MRSA incidence rates and costs under the various alternative policies. Particular focus will be placed on reporting results based on hospital size, location (urban/rural), and type (acute care/CLC). Reports will also be generated for each VISN and VA facility that will rank optimal control strategies. The model will then be converted to a web-based version, with simple, graphical interface, to be deployed via the VA intranet to provide real-time facility- or VISN-specific estimated benefits, costs, and cost-effectiveness of the various MRSA control strategies. Users will be able to customize the simulation to reflect conditions at their facility or VISN in order to receive the most useful analyses for their setting of interest.
During the first three years of the project, our efforts have been spent predominantly on designing the simulation model and updating it to include additional types of VA facilities (such as CLCs), with the associated features and processes of these facilities. Part of this work is creating the infrastructure for the model to incorporate the findings from the other CREATE projects affiliated with this one. Many of those projects are now ending and will be supplying us with data and other results. As such, no findings have been generated yet.
We continue to expect the results of this "virtual" comparative effectiveness model to have great impact in improving VA quality of care, both for identifying targets for leveraging the investments of the current MRSA initiative and for informing future modifications. This proposal uniquely leverages past and current HSR&D-funded research, emerging VA data sources (VINCI), and the unique qualitative data collected in each of the other proposed CREATE projects through an integrated research-operations approach to comparative-effectiveness research, which will serve as a model for effective and rapid decision-making within VA. The findings from this project will have the potential to support VA policy decisions, and impact infection control policy and practice throughout the VA healthcare system.
External Links for this Project
Grant Number: I01HX001134-01
- Cheng Y, Sauer B, Zhang Y, Nickman NA, Jamjian C, Stevens V, LaFleur J. Adherence and virologic outcomes among treatment-naïve veteran patients with human immunodeficiency virus type 1 infection. Medicine. 2018 Jan 1; 97(2):e9430. [view]
- Stevens VW, Stenehjem DD, Patterson OV, Kamauu AWC, Yim YM, Morlock RJ, DuVall SL. Characterization and survival of patients with metastatic basal cell carcinoma in the Department of Veterans Affairs: a retrospective electronic health record review. Archives of dermatological research. 2018 Aug 1; 310(6):505-513. [view]
- Cheng Y, Nickman NA, Jamjian C, Stevens V, Zhang Y, Sauer B, LaFleur J. Predicting poor adherence to antiretroviral therapy among treatment-naïve veterans infected with human immunodeficiency virus. Medicine. 2018 Jan 1; 97(2):e9495. [view]
- Khader K, Munoz-Price LS, Hanson R, Stevens V, Keegan LT, Thomas A, Pezzin LE, Nattinger A, Singh S, Samore MH. Transmission Dynamics of Clostridioides difficile in 2 High-Acuity Hospital Units. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America. 2021 Jan 29; 72(Suppl 1):S1-S7. [view]
Infectious Diseases, Health Systems
Best Practices, Computer Simulations, Cost-Effectiveness, Guideline Development and Implementation, Practice Patterns/Trends, Predictive Modeling, Quality Improvement