Abstract
Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S. National Antimicrobial Resistance Monitoring System tested 14,418 chicken-associated Escherichia coli between 2004 and 2012 for resistance to 15 antimicrobials, resulting in >32,000 possible MDR patterns. We analyzed MDR patterns in this dataset with association rule mining, also called market-basket analysis. The association rules were pruned with four quality measures resulting in a <1% false-discovery rate. MDR rules were more stable across consecutive years than between slaughter and retail. Rules were decomposed into networks with antimicrobials as nodes and rules as edges. A strong subnetwork of beta-lactam resistance existed in each year and the beta-lactam resistances also had strong associations with sulfisoxazole, gentamicin, streptomycin and tetracycline resistances. The association rules concur with previously identified E. coli resistance patterns but provide significant flexibility for studying MDR in large datasets.
Highlights
Bacteria had antimicrobial resistance genes prior to the discovery and clinical use of antimicrobials in the 1940s, antimicrobial use (AMU) selects for antimicrobial resistance (AMR) in both pathogenic and non-pathogenic bacteria (Knapp et al, 2010)
Antimicrobial susceptibility testing data from Escherichia coli isolated from chicken carcasses since 2000 and from retail chicken meat since 2002 as part of National Antimicrobial Resistance Monitoring System (NARMS) surveillance is publicly available (Food and Drug Administration, 2016)
The support of most rules was small (Figure 2E), which is consistent with the low frequency of resistance to most antimicrobials (Table 2)
Summary
Bacteria had antimicrobial resistance genes prior to the discovery and clinical use of antimicrobials in the 1940s, antimicrobial use (AMU) selects for antimicrobial resistance (AMR) in both pathogenic and non-pathogenic bacteria (Knapp et al, 2010). Each instance of AMU selects for AMR directly by favoring the growth or persistence of phenotypically resistant bacteria in treated individuals (Lipsitch and Samore, 2002). AMU indirectly selects for AMR by increasing the prevalence of resistant phenotypes in a population, thereby increasing the risk of future resistant infections (Lipsitch and Samore, 2002). An example antimicrobial susceptibility dataset is given in Table 3; each isolate is considered a transaction and each antimicrobial is an item. An itemset is a combination of zero or more items (e.g., antimicrobial resistances). The NARMS datasets used in this study include at most 15 antimicrobials and 32,767 potential combinations of resistances
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