The Center for Disease Dynamics, Economics & Policy

Background: Antibiotic resistance is a growing problem worldwide, but communicating this challenge to policymakers and non-experts is complicated by the multiplicity of bacterial pathogens and the distinct classes of antibiotics used to treat them. It is difficult, even for experts aware of the pharmacodynamics of antibiotics, to infer the seriousness of resistance without information on how commonly the antibiotic is being used and whether alternative antibiotics are available. Difficulty in aggregating resistance to multiple drugs to assess trends poses a further challenge to quantifying and communicating changes in resistance over time and across locations.

Methods: We developed a method for aggregating bacterial resistance to multiple antibiotics, creating an index comparable to the composite economic indices that measure consumer prices and stock market values. The resulting drug resistance index (DRI) and various subindices show antibiotic resistance and consumption trends in the USA but can be applied at any geographical level.

Findings: The DRI based on use patterns in 1999 for Escherichia coli rose from 0.25  (95% CI 0.23 to 0.26) to 0.30 (95% CI 0.29 to 0.32) between 1999 and 2006. However, the adaptive DRI, which includes treatment of baseline resistant strains with alternative agents, climbed from 0.25 to 0.27 (95% CI 0.25 to 0.28) during that period. In contrast, both the static-use and the adaptive DRIs for Acinetobacter spp. rose from 0.41 (95% CI 0.4 to 0.42) to 0.48 (95% CI 0.46 to 0.49) between 1999 and 2006.

Interpretation: Divergence between the static-use and the adaptive-use DRIs for E coli reflects the ability of physicians to adapt to increasing resistance. However, antibiotic use patterns did not change much in response to growing resistance to Acinetobacter spp. because physicians were unable to adapt; new drugs for Acinetobacter spp. are therefore needed. Composite indices that aggregate resistance to various drugs can be useful for assessing changes in drug resistance across time and space.