Supplement Features HSR&D Research on Combating Antibiotic Resistance – A Growing Worldwide Crisis

CITATION: Matching Methods to Problems: Computational Science to Combat Antibiotic Resistance. Clinical Infectious Diseases. January 15, 2021;72(Suppl 1).

BACKGROUND:

Developed by HSR&D’s Informatics, Decision-Enhancement and Analytic Sciences Center (IDEAS) in Salt Lake City, UT, a special supplement titled “Matching Methods to Problems: Computational Science to Combat Antibiotic Resistance” has been published in Clinical Infectious Diseases. Antimicrobial resistance is a growing worldwide crisis, declared by the World Health Organization as "one of the principal threats to global public health today." The emergence and spread of antimicrobial resistance is a multi-faceted problem that spans all aspects of healthcare, as researchers address the unique challenges of infectious disease and antimicrobial resistance. However, much remains unknown about which factors influence the natural history of bacterial colonization, how pathogens are spread in the healthcare environment, and what the best strategies are in different healthcare settings to intervene and slow the spread of resistance. VA has had a long-standing commitment to cutting-edge research and practice in antimicrobial stewardship and infection prevention, all of which is made possible by its robust nationwide electronic medical record. The research included in this collection links the methodological strengths of data science and transmission modeling, harnessing the unique advantages of VA’s large datasets to estimate key parameters governing colonization and transmission of pathogens from person to person.

Articles in this supplement include, but are not limited to:

  • Christensen and colleagues conducted an advanced simulation of the complete adoption of recently released 2019 ATS/IDSA treatment guidelines for community acquired pneumonia, which provide revised recommendations for the use of empiric broad-spectrum antibiotics. Results demonstrate that adopting these guidelines would decrease empiric selection of anti-MRSA therapies from 27% to 1% on VA hospital wards and from 61% to 8% in ICUs.
  • Khader and colleagues constructed a model to estimate key parameters of difficile transmission dynamics in two specialty care units in a 565-bed tertiary-care hospital. They found that the two units had a similar prevalence of colonization at admission and overall. However, the acquisition rates, transmission rates, and median time to clearance differed substantially, suggesting that there may be different patient risk factors, or care factors that contribute to differential risk of transmission, clearance, or ability to detect C. difficile, such as antibiotic exposure. Khader and colleagues have used similar modeling techniques to examine MRSA transmission dynamics in VA hospitals and Community Living Centers (CLCs).
  • Nelson and colleagues estimated the cost of healthcare-associated infections (HAIs) occurring in a VA long-term care (LTC) setting. They found that while LTC costs were no different in patients with and without MRSA HAIs, these infections did lead to an increased risk of transfer to an acute care facility and an increase in acute care costs, highlighting the importance of understanding costs accrued across facilities.

IMPLICATIONS: This Supplement demonstrates how the union of data science and transmission modeling can produce work that addresses some of the more difficult questions regarding the transmission and spread of antimicrobial resistance in different healthcare settings, leading to insights that can provide clinicians and policymakers with useful inputs in decision-making.

Guest Editors for this Supplement are all part of IDEAS: Michael Rubin, MD, PhD, Richard Nelson, PhD, and Matthew Samore, PhD (Center Director).