Literature DB >> 28830108

A Platform for Monitoring Regional Antimicrobial Resistance, Using Online Data Sources: ResistanceOpen.

Derek R MacFadden1,2, David Fisman1, Jeff Andre2, Yuki Ara2, Maimuna S Majumder2,3, Isaac I Bogoch1, Nick Daneman1, Annie Wang1, Marianna Vavitsas2, Lucas Castellani1, John S Brownstein2,4.   

Abstract

Background: Our understanding of the global burden of antimicrobial resistance is limited. Complementary approaches to antimicrobial resistance surveillance are needed.
Methods: We developed a Web-based/mobile platform for aggregating, analyzing, and disseminating regional antimicrobial resistance information. Antimicrobial resistance indices from existing but disparate online sources were identified and abstracted. To validate antimicrobial resistance data, in the absence of regional comparators, US 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.
Results: Over 850 resistance indices globally were identified and abstracted, totaling >5 million isolates, from 340 unique locations. Resistance index coverage spanned 41 countries, 6 continents, 43 of 50 US states, and 8 of 10 Canadian provinces. When compared to reported values, aggregated resistance values for the United States and Canada during 2013 and 2014 demonstrated agreements ranging from 94% to 97%. For the United States, state-specific resistance estimates demonstrated an agreement of 92%. Large differences in antimicrobial susceptibility were seen within countries. Conclusions: Using existing nontraditional data sources, we have developed a Web-based platform for aggregating antimicrobial resistance indices to support monitoring of regional antimicrobial resistance patterns.
© The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Keywords:  antibiotic resistance; antimicrobial resistance; big data; digital disease detection; digital surveillance; surveillance

Mesh:

Year:  2016        PMID: 28830108     DOI: 10.1093/infdis/jiw343

Source DB:  PubMed          Journal:  J Infect Dis        ISSN: 0022-1899            Impact factor:   5.226


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