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In recent decades antimicrobial resistance has emerged as a growing public health concern. Surveillance is critical to managing antimicrobial resistance, especially in low- and middle-income countries. A recent report describes how scientists are using economic modeling to track antimicrobial resistance.

Evidence suggests that socioeconomic factors drive a country’s vulnerability to antimicrobial resistance. Some of these factors include income status, out-of-pocket health expenditures, and [poor governance and corruption](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0116746].

The authors of the report used economic data collected by the World Bank and antimicrobial resistance data from ResistanceMap, an interactive collection of maps and charts that describe antimicrobial use and resistance worldwide. They collected data for 75 combinations of pathogens and antibiotic classes representing antimicrobial resistance prevalence in 74 countries over nearly two decades ending in 2017.

They identified statistical relationships between antimicrobial resistance and socioeconomic status for nine pathogens resistant to 19 antibiotic classes. Their model accurately predicted antimicrobial resistance for six of the nine pathogens 78 to 86 percent of the time. Of particular interest were carbapenem-resistant Acinetobacter baumannii (a common hospital-acquired microbe) and cephalosporin-resistant Escherichia coli in the Middle East, sub-Saharan Africa, and the Pacific Islands.

This model shows promise as a means to predict antimicrobial resistance based on socioeconomic factors and adds to the arsenal of approaches to monitoring the problem. Such measures may benefit as many as 5 billion people worldwide.

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