Article

# Within-sample and between-sample variation of antimicrobial resistance in fecal Escherichia coli isolates from pigs.

Section of Epidemiology, National Veterinary Institute, N-0033 Oslo, Norway.

Microbial Drug Resistance (Impact Factor: 2.36). 02/2002; 8(4):385-91. DOI: 10.1089/10766290260469660 Source: PubMed

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**ABSTRACT:**An ideal national resistance monitoring program should deliver a precise estimate of the resistance situation for a given combination of bacteria and antimicrobial at a low cost. To achieve this, decisions need to be made on the number of samples to be collected at each of different possible sampling points. Existing methods of sample size calculation can not be used to solve this problem, because sampling decisions do not only depend on the prevalence of resistance and sensitivity and specificity of resistance testing, but also on the prevalence of the bacteria, and test characteristics of isolation of these bacteria. Our aim was to develop a stochastic simulation model that optimized a national resistance monitoring program, taking multi-stage sampling, imperfect sensitivity and specificity of diagnostic tests, and cost-effectiveness considerations into account. The process of resistance testing of Campylobacter spp. isolated from cloacal swab samples from poultry was modeled using a Markov Chain Monte Carlo model. Different sampling scenarios on the number of flocks to be tested, the number of birds from each flock, and the number of campylobacter colonies submitted to susceptibility testing were evaluated regarding the precision of the resulting prevalence estimate. Precision of the prevalence estimate was defined as the absolute difference between apparent and true prevalence of resistance. A partial budget approach was utilized to find the most cost-effective combination of samples to obtain a defined precision of the prevalence estimate. For a sampling scenario testing 100 flocks, five birds per flock, and one campylobacter colony per sample, the median error of the prevalence estimate was 2.5%, and 95% of the simulations resulted in an error of 7% or less. When the total number of samples was kept constant, maximizing the number of flocks tested, and only testing one bird per flock resulted in the most precise prevalence estimate. Submitting more than one campylobacter colony to resistance testing did not improve the prevalence estimate. Partial budget analysis indicated that the most cost-effective strategy was testing of two birds per flock, and submitting one colony per sample to resistance testing.Preventive Veterinary Medicine 09/2005; 70(1-2):29-43. · 2.39 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**In 2006, the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) Farm Program was implemented in sentinel grower-finisher swine herds in Québec, Ontario, Manitoba, Saskatchewan and Alberta. Herds were visited 1-3 times annually. Faecal samples were collected from pens of close-to-market (CTM) weight (>80 kg) pigs and antimicrobial use (AMU) data were collected via questionnaires. Samples were cultured for generic Escherichia coli and Salmonella and tested for antimicrobial susceptibility. This paper describes the findings of this program between 2006 and 2008. Eighty-nine, 115 and 96 herds participated in this program in 2006, 2007 and 2008 respectively. Over the 3 years, antimicrobial resistance (AMR) levels remained consistent. During this period, resistance to one or more antimicrobials was detected in 56-63% of the Salmonella spp. isolates and 84-86% of E. coli isolates. Resistance to five or more antimicrobials was detected in 13-23% of Salmonella and 12-13% of E. coli. Resistance to drugs classified as very important to human health (Category I) by the Veterinary Drug Directorate (VDD), Health Canada, was less than or equal to 1% in both organisms. AMU data were provided by 100 herds in 2007 and 95 herds in 2008. Nine herds in 2007 and five herds in 2008 reported no AMU. The most common route of antimicrobial administration (75-79% of herds) was via feed, predominantly macrolides/lincosamides (66-68% of herds). In both 2007 and 2008, the primary reasons given for macrolide/lincosamide use were disease prevention, growth promotion and treatment of enteric disease. The Category I antimicrobials, ceftiofur and virginiamycin were not used in feed or water in any herds in 2008, but virginiamycin was used in feed in two herds in 2007. Parenteral ceftiofur was used in 29 herds (29%) in 2007 and 20 herds (21%) in 2008. The reasons for ceftiofur use included treatment of lameness, respiratory disease and enteric disease.Zoonoses and Public Health 11/2010; 57 Suppl 1:71-84. · 2.09 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**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.PLoS ONE 01/2014; 9(1):e87147. · 3.53 Impact Factor

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