Sampling strategies in antimicrobial resistance monitoring: evaluating how precision and sensitivity vary with the number of animals sampled per farm.

Because antimicrobial resistance in food-producing animals is a major public health concern, many countries have implemented antimicrobial monitoring systems at a national level. When designing a sampling scheme for antimicrobial resistance monitoring, it is necessary to consider both cost effectiveness and statistical plausibility. In this study, we examined how sampling scheme precision and sensitivity can vary with the number of animals sampled from each farm, while keeping the overall sample size constant to avoid additional sampling costs. Five sampling strategies were investigated. These employed 1, 2, 3, 4 or 6 animal samples per farm, with a total of 12 animals sampled in each strategy. A total of 1,500 Escherichia coli isolates from 300 fattening pigs on 30 farms were tested for resistance against 12 antimicrobials. The performance of each sampling strategy was evaluated by bootstrap resampling from the observational data. In the bootstrapping procedure, farms, animals, and isolates were selected randomly with replacement, and a total of 10,000 replications were conducted. For each antimicrobial, we observed that the standard deviation and 2.5-97.5 percentile interval of resistance prevalence were smallest in the sampling strategy that employed 1 animal per farm. The proportion of bootstrap samples that included at least 1 isolate with resistance was also evaluated as an indicator of the sensitivity of the sampling strategy to previously unidentified antimicrobial resistance. The proportion was greatest with 1 sample per farm and decreased with larger samples per farm. We concluded that when the total number of samples is pre-specified, the most precise and sensitive sampling strategy involves collecting 1 sample per farm

Prevalence of tested resistance genes among isolated Enterococci.

<p>Prevalence of antimicrobial resistance genes tested among <i>E</i>. <i>faecalis</i> and <i>E</i>. <i>faecium</i> isolated from retail poultry products collected in 5 major Japanese cities between July and August 2012.</p><p>Prevalence of tested resistance genes among isolated Enterococci.</p

Posterior marginal log odds ratios for parameters.

<p>Posterior estimates for remaining arcs obtained from the final ABN model after bootstrapping. The median and 95% credibility intervals for each parameter estimate are shown.</p><p>^aMIC for dihydrostreptomycin: ≥128 μg/mL and ≤512 μg/mL.</p><p>^bMIC for dihydrostreptomycin: >512 μg/mL.</p><p>^cMIC for oxytetracycline: ≥16 μg/mL and ≤64 μg/mL.</p><p>^dMIC for oxytetracycline: >64 μg/mL.</p><p>Posterior marginal log odds ratios for parameters.</p

Unraveling Antimicrobial Resistance Genes and Phenotype Patterns among <i>Enterococcus faecalis</i> Isolated from Retail Chicken Products in Japan

<div><p>Multidrug-resistant enterococci are considered crucial drivers for the dissemination of antimicrobial resistance determinants within and beyond a genus. These organisms may pass numerous resistance determinants to other harmful pathogens, whose multiple resistances would cause adverse consequences. Therefore, an understanding of the coexistence epidemiology of resistance genes is critical, but such information remains limited. In this study, our first objective was to determine the prevalence of principal resistance phenotypes and genes among <i>Enterococcus faecalis</i> isolated from retail chicken domestic products collected throughout Japan. Subsequent analysis of these data by using an additive Bayesian network (ABN) model revealed the co-appearance patterns of resistance genes and identified the associations between resistance genes and phenotypes. The common phenotypes observed among <i>E</i>. <i>faecalis</i> isolated from the domestic products were the resistances to oxytetracycline (58.4%), dihydrostreptomycin (50.4%), and erythromycin (37.2%), and the gene <i>tet</i>(L) was detected in 46.0% of the isolates. The ABN model identified statistically significant associations between <i>tet</i>(L) and <i>erm</i>(B), <i>tet</i>(L) and <i>ant</i>(6)-Ia, <i>ant</i>(6)-Ia and <i>aph</i>(3’)-IIIa, and <i>aph</i>(3’)-IIIa and <i>erm</i>(B), which indicated that a multiple-resistance profile of tetracycline, erythromycin, streptomycin, and kanamycin is systematic rather than random. Conversely, the presence of <i>tet</i>(O) was only negatively associated with that of <i>erm</i>(B) and <i>tet</i>(M), which suggested that in the presence of <i>tet</i>(O), the aforementioned multiple resistance is unlikely to be observed. Such heterogeneity in linkages among genes that confer the same phenotypic resistance highlights the importance of incorporating genetic information when investigating the risk factors for the spread of resistance. The epidemiological factors that underlie the persistence of systematic multiple-resistance patterns warrant further investigations with appropriate adjustments for ecological and bacteriological factors.</p></div

Final globally optimal additive Bayesian network model after adjustment for over-fitting.

<p>Final additive Bayesian network model after removing arcs that appeared in <50% of bootstrappings for the interrelationships between selected antimicrobial resistance genes and phenotypes. Solid lines and dashed lines represent positive and negative associations between variables, respectively. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121189#pone.0121189.g002" target="_blank">Fig. 2</a> lists the variable names.</p