Globally, antimicrobial resistance is a growing public health concern. However, our understanding of the burden and regional patterns of antimicrobial resistance is limited by current methods. Complementary approaches to antimicrobial resistance surveillance are needed. We hypothesize that existing online antimicrobial resistance information can be aggregated for monitoring of regional antimicrobial resistance patterns.
We developed a web-based/mobile platform for aggregating, analyzing, and disseminating regional antimicrobial resistance indices. Antimicrobial resistance indices were reviewed and abstracted, respectively, by experienced curators. Antimicrobial resistance data was captured for 35 pre-specified bacterial species/types and 37 pre-specified common antimicrobials. Relevant variables collected included: year of index isolates, laboratory standards employed, specimen site, hospital site, and hospital/laboratory/surveillance body classification. To validate the antimicrobial resistance data, in the absence of regional comparators, United States and Canadian indices were aggregated and compared to existing national and state estimates. Measures of variability of antimicrobial susceptibility were determined for the United States and Canada to evaluate magnitudes of differences within countries.
Over 850 unique resistance indexes globally were also identified and abstracted, totaling over 5 million isolates, from 340 unique locations. Resistance index coverage spanned 41 countries, 6 continents, 43/50 U.S. States, and 8/10 Canadian provinces. When compared to reported values, aggregated resistance values for the United States and Canada for the years 2013 and 2014 demonstrated agreements ranging from 94-97%. For the United States, state-specific resistance estimates demonstrated an agreement of 92%. Large differences in antimicrobial resistance were seen within countries.
Using existing non-traditional data sources, we have developed a web-based platform for aggregating antimicrobial resistance indices to support monitoring of regional antimicrobial resistance patterns globally. This approach appears to generate comparable estimates to traditional surveillance estimates, and may be a useful approach in under-resourced regions.
J. Andre, None
Y. Ara, None
I. Bogoch, None
N. Daneman, None
A. Wang, None
M. Vavitsas, None
L. Castellani, None
J. Brownstein, None
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