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Antimicrobial Use and Resistance
Michael A. Rubin1, Matthew H. Samore
1Division of Infectious Diseases, Department of Internal Medicine, University of Utah School of Medicine, 300 North 1900 East, Salt Lake City, UT 84132, USA. Michael.Rubin@hsc.utah.edu; Matthew.Samore@hsc.utah.edu
Abstract:
As antimicrobial use continues to rise, we are experiencing a concomitant rise in the prevalence of antimicrobial resistance. The precise relationship between use and resistance, however, has been challenging to define. Although the selection pressure exerted by antibiotic therapy appears to be the primary force promoting resistance, it is clear that the pathway to resistance is different for various organisms and antimicrobial agents. By understanding the mechanisms by which resistance emerges and spreads, it should be possible to design intervention strategies to slow or halt the process. This review summarizes some of our current understandings about the development and transmission of antibiotic-resistant bacteria, some of the control measures designed to interrupt the process, and how mathematical modeling can help us to better understand these complex pathways.
Insights
Antimicrobial resistance is increasing with use. Understanding resistance mechanisms and transmission is key to developing strategies, aided by mathematical modeling, to control this growing public health threat.
Area of Science:
- Microbiology
- Epidemiology
- Mathematical Biology
Background:
- Rising antimicrobial use correlates with increased antimicrobial resistance.
- The exact relationship between antimicrobial use and resistance development remains complex.
- Understanding resistance mechanisms is crucial for intervention.
Purpose of the Study:
- To review current knowledge on antibiotic-resistant bacteria development and transmission.
- To discuss control measures for interrupting resistance.
- To explore the role of mathematical modeling in understanding resistance pathways.
Main Methods:
- Literature review of antimicrobial resistance.
- Analysis of resistance emergence and spread mechanisms.
- Examination of mathematical modeling applications in antimicrobial resistance.
Main Results:
- Antibiotic therapy's selection pressure is a primary driver of resistance.
- Diverse organisms and agents exhibit different resistance pathways.
- Mathematical models offer insights into complex resistance dynamics.
Conclusions:
- Understanding resistance mechanisms is vital for designing effective control strategies.
- Intervention strategies are needed to slow or halt the rise of antimicrobial resistance.
- Mathematical modeling is a valuable tool for comprehending and combating antibiotic resistance.