Sampling considerations for herd-level measurement of faecal Escherichia coli antimicrobial resistance in finisher pigs

Factors influencing the detection of antibacterial-resistant Escherichia coli in faecal samples from individual cattle

AIMS

To investigate whether on-farm antibacterial usage (ABU), environmental antibacterial-resistant (ABR) Escherichia coli prevalence, sampling and sample handling methodologies are associated with ABR E. coli positivity in individual faecal samples from dairy heifers.

METHODS AND RESULTS

Three hundred and sixty-four heifers from 37 farms were sampled via rectal or faecal pat sampling. Samples were stored at -80°C for variable periods before microbiological analysis. Data analysis was done through a multilevel, multivariable logistic regression approach. Individual rectal samples had increased odds of positivity for amoxicillin-, cefalexin- and tetracycline-resistant E. coli. Sample storage for 6-12 months was associated with decreased odds of finding amoxicillin- and tetracycline-resistant E. coli. On-farm ABU had little influence, and environmental ABR E. coli prevalence had no significant influence on the odds of sample-level positivity for ABR E. coli.

CONCLUSIONS

Sampling methodology and sample handling have a greater association than on-farm factors with the detection of ABR E. coli in individual faecal samples from dairy heifers.

SIGNIFICANCE AND IMPACT OF THE STUDY

Sampling and storage methodologies should be considered carefully at the point of designing ABR surveillance studies in livestock and their environments and, where possible, these methodologies should be standardized between and within future studies.

Informing Stewardship Measures in Canadian Food Animal Species through Integrated Reporting of Antimicrobial Use and Antimicrobial Resistance Surveillance Data-Part I, Methodology Development

This study explores methodologies for the data integration of antimicrobial use (AMU) and antimicrobial resistance (AMR) results within and across three food animal species, surveyed at the farm-level by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). The approach builds upon existing CIPARS methodology and principles from other AMU and AMR surveillance systems. Species level data integration involved: (1) standard CIPARS descriptive and temporal analysis of AMU/AMR, (2) synthesis of results, (3) selection of AMU and AMR outcomes for integration, (4) selection of candidate AMU indicators to enable comparisons of AMU levels between species and simultaneous assessment of AMU and AMR trends, (5) exploration of analytic options for studying associations between AMU and AMR, and (6) interpretation and visualization. The multi-species integration was also completed using the above approach. In addition, summarized reporting of internationally-recognized indicators of AMR (i.e., AMR adjusted for animal biomass) and AMU (mg/population correction unit, mg/kg animal biomass) is explored. It is envisaged that this approach for species and multi-species AMU-AMR data integration will be applied to the annual CIPARS farm-level data and progressively developed over time to inform AMU-AMR integrated surveillance best practices for further enhancement of AMU stewardship actions.

Robotic Antimicrobial Susceptibility Platform (RASP): a next-generation approach to One Health surveillance of antimicrobial resistance

Background

Surveillance of antimicrobial resistance (AMR) is critical to reducing its wide-reaching impact. Its reliance on sample size invites solutions to longstanding constraints regarding scalability. A robotic platform (RASP) was developed for high-throughput AMR surveillance in accordance with internationally recognized standards (CLSI and ISO 20776-1:2019) and validated through a series of experiments.

Methods

Experiment A compared RASP’s ability to achieve consistent MICs with that of a human technician across eight replicates for four Escherichia coli isolates. Experiment B assessed RASP’s agreement with human-performed MICs across 91 E. coli isolates with a diverse range of AMR profiles. Additionally, to demonstrate its real-world applicability, the RASP workflow was then applied to five faecal samples where a minimum of 47 E. coli per animal (239 total) were evaluated using an AMR indexing framework.

Results

For each drug–rater–isolate combination in Experiment A, there was a clear consensus of the MIC and deviation from the consensus remained within one doubling dilution (the exception being gentamicin at two dilutions). Experiment B revealed a concordance correlation coefficient of 0.9670 (95% CI: 0.9670–0.9670) between the robot- and human-performed MICs. RASP’s application to the five faecal samples highlighted the intra-animal diversity of gut commensal E. coli, identifying between five and nine unique isolate AMR phenotypes per sample.

Conclusions

While adhering to internationally accepted guidelines, RASP was superior in throughput, cost and data resolution when compared with an experienced human technician. Integration of robotics platforms in the microbiology laboratory is a necessary advancement for future One Health AMR endeavours.

Short-Term Repeatability of Measurements of Antimicrobial Susceptibility of Escherichia Coli Isolated from Feces of Feedlot Cattle

Short-term stability of measurements of antimicrobial susceptibility of Escherichia coli isolated from feces of feedlot cattle is important in developing monitoring and surveillance programs. Frequent evaluations (i.e., daily) are resource intensive and in some situations may be impractical for long-term sampling protocols. Consequently, a point-in-time measurement will need to be used to represent conditions in the perisampling period. In this study, 30 fecal samples were collected from each of 6 cattle pens on a commercial cattle feedlot on 2 occasions separated by 48 hours. Escherichia coli was isolated from single and pooled samples. The isolates were tested for antimicrobial susceptibility against a panel of 17 antimicrobials. Resistance to 5 antimicrobials (ampicillin, nalidixic acid, streptomycin, sulfamethoxazole, and tetracycline) was detected in single and pooled samples from both sampling periods (days 1 and 3). The prevalence of isolates resistant to these 5 antimicrobials was 2% or higher in all treatment combinations except for pools obtained from day 3 samples. Lower levels of resistance to 6 more antimicrobials were detected inconsistently across the single and pooled samples. Logistic models constructed for the antimicrobials to which the E. coli isolates were most commonly resistant demonstrated that there were no significant differences between periods ( P > 0.10) and between single and pooled samples ( P > 0.20). The distribution of the number of antimicrobials to which isolates were resistant was consistent for the single samples across periods, but there appeared to be a lower prevalence of any resistance in day 1 pooled samples. A larger number of resistant phenotypes were detected in the single samples than in the pooled samples, and resistant phenotypes with prevalence of less than 2% were detected inconsistently across periods and single and pooled samples. Resistance to individual antimicrobials was consistent by all measures when the prevalence was at least 2%. Inconsistent results were obtained for antimicrobials to which resistance rarely occurred. The apparent inconsistencies do not appear to be related to external factors but rather to sampling intensity. Short-term stability is a plausible assumption under sampling strategies that are designed to detect specific levels of prevalence. However, when resistance levels fall below these levels, there will likely be fluctuations in the presence or absence of rare resistant phenotypes and in their prevalence and central tendency measures.

Sampling and Pooling Methods for Capturing Herd Level Antibiotic Resistance in Swine Feces using qPCR and CFU Approaches

The aim of this article was to define the sampling level and method combination that captures antibiotic resistance at pig herd level utilizing qPCR antibiotic resistance gene quantification and culture-based quantification of antibiotic resistant coliform indicator bacteria. Fourteen qPCR assays for commonly detected antibiotic resistance genes were developed, and used to quantify antibiotic resistance genes in total DNA from swine fecal samples that were obtained using different sampling and pooling methods. In parallel, the number of antibiotic resistant coliform indicator bacteria was determined in the same swine fecal samples. The results showed that the qPCR assays were capable of detecting differences in antibiotic resistance levels in individual animals that the coliform bacteria colony forming units (CFU) could not. Also, the qPCR assays more accurately quantified antibiotic resistance genes when comparing individual sampling and pooling methods. qPCR on pooled samples was found to be a good representative for the general resistance level in a pig herd compared to the coliform CFU counts. It had significantly reduced relative standard deviations compared to coliform CFU counts in the same samples, and therefore differences in antibiotic resistance levels between samples were more readily detected. To our knowledge, this is the first study to describe sampling and pooling methods for qPCR quantification of antibiotic resistance genes in total DNA extracted from swine feces.

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.

Quantifying antimicrobial resistance at veal calf farms

This study was performed to determine a sampling strategy to quantify the prevalence of antimicrobial resistance on veal calf farms, based on the variation in antimicrobial resistance within and between calves on five farms. Faecal samples from 50 healthy calves (10 calves/farm) were collected. From each individual sample and one pooled faecal sample per farm, 90 selected Escherichia coli isolates were tested for their resistance against 25 mg/L amoxicillin, 25 mg/L tetracycline, 0.5 mg/L cefotaxime, 0.125 mg/L ciprofloxacin and 8/152 mg/L trimethoprim/sulfamethoxazole (tmp/s) by replica plating. From each faecal sample another 10 selected E. coli isolates were tested for their resistance by broth microdilution as a reference. Logistic regression analysis was performed to compare the odds of testing an isolate resistant between both test methods (replica plating vs. broth microdilution) and to evaluate the effect of pooling faecal samples. Bootstrap analysis was used to investigate the precision of the estimated prevalence of resistance to each antimicrobial obtained by several simulated sampling strategies. Replica plating showed similar odds of E. coli isolates tested resistant compared to broth microdilution, except for ciprofloxacin (OR 0.29, p≤0.05). Pooled samples showed in general lower odds of an isolate being resistant compared to individual samples, although these differences were not significant. Bootstrap analysis showed that within each antimicrobial the various compositions of a pooled sample provided consistent estimates for the mean proportion of resistant isolates. Sampling strategies should be based on the variation in resistance among isolates within faecal samples and between faecal samples, which may vary by antimicrobial. In our study, the optimal sampling strategy from the perspective of precision of the estimated levels of resistance and practicality consists of a pooled faecal sample from 20 individual animals, of which 90 isolates are tested for their susceptibility by replica plating.

The importance of sample size in the determination of a flock-level antimicrobial resistance profile for Escherichia coli in broilers

Determining herd- or flock-specific antimicrobial resistance profiles is important to guide therapeutic use of antimicrobials and to assess risk factors for the development and spread of antimicrobial resistance. As such, it is of utmost importance to optimize the sampling strategy for the determination of herd-specific antimicrobial resistance profiles. However, the multitude of prevalences measured at the same time as well as the presence of variation both at the level of the animal and the bacterial population of concern make it impossible to use conventional sample size determination methods. In this article, the use of bootstrapping techniques for sample size determination was explored. In particular, one-stage and two-stage bootstrap samplings were used to determine the optimal number of animals and the optimal number of isolates within one animal. Results show that focus should be on the number of animals sampled rather than on the number of isolates tested within one animal.

Virulence factors, antimicrobial resistance, and plasmid content of Escherichia coli isolated in swine commercial farms

Virulence factors and antimicrobial resistance patterns of Escherichia coli isolates were evaluated. A total of 80 E. coli isolates were evaluated, being 64 from clinical samples (intestinal content and fragments of organs from diarrheic piglets), seven from feces of clinically healthy piglets and sows, and nine environmental samples (five from facilities, two from feed, one from insect, and one from waste). Molecular characterization was performed by PCR detection of fimbriae and toxin genes and plasmid content determination. The isolates were also characterized according to their resistance or sensitivity to the following drugs: ampicillin, trimethoprim:sulfamethoxazole, tetracycline, amikacine, colistin, norfloxacin, florfenicol, enrofloxacin, cefalexin, trimethoprim, neomycin, chloramphenicol, and gentamicin. From 80 E. coli isolates, 53.8% were classified as enterotoxigenic E. coli (ETEC), 2.5% were shiga toxin-producing E. coli (STEC), and 43.8% showed a non specific pattern and were unclassified. One fecal isolate from non-diarrheic piglet was classified as ETEC by PCR. Clinical isolates showed resistance mainly for tetracycline and trimethoprim:sulfamethoxazole. Plasmidial DNA was observed in 70 isolates, being 78.5% of clinical isolates, 8.57% of non-diarrheic feces, and 12.8% of environment.

PATOTIPOS DE ESCHERICHIA COLI NA SUINOCULTURA E SUAS IMPLICAÇÕES AMBIENTAIS E NA RESISTÊNCIA AOS ANTIMICROBIANOS

RESUMO A suinocultura moderna tem propiciado a obtenção de índices produtivos positivos, entretanto tem predisposto os suínos a um grande número de doenças. A ocorrência dessas enfermidades estimulou o uso indiscriminado das drogas antimicrobianas na prevenção de infecções.Escherichia coli é um dos principais patógenos da suinocultura e se caracteriza pela alta resistência aos agentes antimicrobianos. A habilidade deste patógeno na transmissão horizontal da resistência aos antimicrobianos decorre de vários mecanismos genéticos e possui sérias implicações à saúde pública. Dentre os problemas associados à disseminação da resistência múltipla aos antimicrobianos, podemos citar a contaminação do homem e dos animais por bactérias patogênicas de difícil controle terapêutico, principalmente por meio dos alimentos e de ambiente contaminados. Esta revisão tem como objetivo abordar aspectos relevantes de E. coli relativos ao seu potencial patogênico em suínos e à sua resistência às drogas antimicrobianas. Além disso, também apresenta algumas das alternativas aos usos desses fármacos na suinocultura.

Using data on resistance prevalence per sample in the surveillance of antimicrobial resistance

OBJECTIVES

In most existing antimicrobial resistance monitoring programmes, one single bacterial colony from each collected sample is susceptibility tested against a panel of antimicrobials. Detecting the proportion of colonies resistant to different antimicrobials in each sample can provide quantitative data on antimicrobial resistance (resistance prevalence per sample).

METHODS

In this study, a total of 98 faecal samples from slaughter pigs were tested for tetracycline and sulphonamide resistance in Escherichia coli using the single colony method, and these results were compared with the results obtained using the resistance prevalence per sample method.

RESULTS

The results obtained by the resistance prevalence per sample method showed a lower occurrence of resistance. Tetracycline resistance in E. coli was found in 36.7% of the samples using the single colony method, while the mean tetracycline resistance prevalence was 22.5% using the resistance prevalence per sample method. Similarly, sulphonamide resistance was 32.7% using the single colony method and 19.6% when using the resistance prevalence per sample method. Although different estimates were obtained by each method, the correlation test and the regression model demonstrated that there is a significant association between the results obtained using both methods (P value <0.01) for both antimicrobials tested.

CONCLUSIONS

To support risk assessment and analysis of the association between consumption of antimicrobials and occurrence of resistance, there is a need to move towards a more quantitative approach when dealing with antimicrobial resistance in a population, and the resistance prevalence per sample method can provide some of this additional information.

Antimicrobial resistance of fecal Escherichia coli isolated from grow-finish pigs in 20 herds in Alberta and Saskatchewan

Escherichia coli (n = 1439), isolated from the feces of apparently healthy grow-finish pigs in 20 herds in Alberta and Saskatchewan, were tested for susceptibility to 16 antimicrobials. All isolates were susceptible to amikacin, ceftriaxone, and ciprofloxacin and less than 1% was resistant to amoxicillin-clavulanic acid, cefoxitin, ceftiofur, gentamicin, and nalidixic acid. Resistance was most common to tetracycline (66.8%), sulfamethoxazole (46.0%) and streptomycin (33.4%). Twenty-one percent of the isolates were susceptible to all drugs, while 57% were resistant to 2 or more antimicrobials. Unconditional associations between resistances provided insight into the potential for co-selection. Every resistance-outcome was associated with at least 2 other drug-resistances. These associations illustrate the propensity for resistance phenotypes to occur together and the importance of considering co-selection in antimicrobial use decisions. A 2nd analysis explored the associations between resistance phenotypes in E. coli and Salmonella spp. from the same herd. Only 2 resistances in Salmonella were associated with herd-level E. coli resistance, indicating that E. coli is a poor sentinel for Salmonella AMR within herds. Herd-level management, including antimicrobial use, could affect antimicrobial resistance. The intra-class correlation between isolates within herds ranged from 0.1 to 0.46, which confirmed resistance clustered within herds. This suggests herd-level interventions might mitigate antimicrobial resistance. Overall, these results reflect the on-farm selection pressure for resistance and the potential food-safety risk from near-market animals. These data provide a baseline for comparisons with future on-farm monitoring of antimicrobial resistance in E. coli.

Antimicrobial resistance in generic Escherichia coli isolated from swine fecal samples in 90 Alberta finishing farms

The objective of this study was to determine the prevalence of antimicrobial resistance in generic Escherichia coli isolates obtained from 90 Alberta finisher swine farms. Up to 5 isolates were obtained from each of 269 pooled fecal samples and were classified as susceptible or resistant according to Clinical and Laboratory Standards Institute guidelines. Of the 1322 isolates, 166 (12.6%) were susceptible to all 15 antimicrobials. No resistance to amikacin, ceftiofur, ceftriaxone, or ciprofloxacin, antimicrobials of importance in human medicine, was observed. Relatively low frequencies of resistance were observed to gentamicin (1.1%), amoxicillin/clavulanic acid (0.7%), and cefoxitin (0.7%). Higher frequencies of resistance were observed for tetracycline (78.9%), sulfisoxazole (49.9%), streptomycin (49.6%), ampicillin (30.6%), chloramphenicol (17.6%), kanamycin (10%), and trimethoprim/ sulfamethoxazole (6.4%). Among the isolates resistant to > or = 2 antimicrobial classes, 20.8%, 20.6%, 18.2%, 7.0%, 1.8%, 0.2%, and 0.2% were resistant to 2, 3, 4, 5, 6, 7, and 8 antimicrobials, respectively. The most common multidrug-resistance patterns (resistance to > or = 2 antimicrobial classes) were streptomycin-tetracycline (9.4%), streptomycin-sulfisoxazole-tetracycline (6.2%), and ampicillin-streptomycin-sulfisoxazole-tetracycline (6.1%). More clustering (higher intra-class correlation coefficients) in antimicrobial resistance was observed for isolates at the same visit than for isolates from different visits in the same farm, indicating that sampling more farms, testing fewer isolates per visits, and taking longer periods between visits may be appropriate and more efficient for a better understanding of potential shifts in resistance over time.

Prevalence and pattern of antimicrobial susceptibility inEscherichia coliisolated from pigs reared under antimicrobial-free and conventional production methods

OBJECTIVE

To determine and compare levels and patterns of antimicrobial resistance among Escherichia coli isolated from pigs on farms that did not use antimicrobial agents versus pigs produced under conventional methods.

DESIGN

Cross-sectional study. Sample Population-35 antimicrobial-free and 60 conventional swine farms.

PROCEDURES

Farms were visited once, and fecal samples were collected from 15 finisher pigs if available. One E coli isolate from each sample was tested for susceptibility pattern to 14 antimicrobial agents by use of microbroth dilution.

RESULTS

E coli isolates were recovered from 1,381 (97.1%) of 1,422 fecal samples. Herd size was significantly larger for conventional swine farms. Resistance to ceftriaxone, ciprofloxacin, or nalidixic acid was not observed on any of the 95 farms. Three isolates from 2 conventional farms were resistant to ceftiofur. Conventional farms had significantly higher levels of resistance to ampicillin, sulfamethoxazole, tetracycline, and chloramphenicol, compared with antimicrobial-free farms. Fourteen percent of E coli isolates were susceptible or had intermediate resistance to all the tested antimicrobial agents. The 3 most frequent patterns of multiple resistance were streptomycin-tetracycline, sulfamethoxazole-tetracycline, and kanamycin-streptomycin-sulfamethoxazole-tetracycline.

CONCLUSIONS AND CLINICAL RELEVANCE

Cessation of antimicrobial use did not appear to result in an immediate reduction in antimicrobial resistance in swine farms. Prospective studies of long-term antimicrobial usage and cessation are needed to estimate the extent to which food animal production may be contributing to antimicrobial drug resistance and might provide a direct measure of the rates of reversibility of antimicrobial drug resistance that might be achieved by curtailing antimicrobial usage.

Abundance and phenotypic diversity of Escherichia coli isolates with diminished susceptibility to expanded-spectrum cephalosporins in faeces from healthy food animals after slaughter

Antimicrobial resistance (AR) is an increasing phenomenon but its quantitative estimation remains controversial. The classical resistance percentage approach is not well suited to detect either emergence or low levels resistance. One option is to shift the focus from strains to hosts. This approach is applied to test for phenotypic diversity associated with diminished susceptibility to expanded-spectrum cephalosporins (DSESC) in faecal Escherichia coli from healthy food animals in Spain. We performed E. coli enumeration in faecal samples of broilers (82 pooled samples) and pigs (80 pooled samples) at the slaughterhouse level, using Coli-ID plates alone and supplemented with cefotaxime at two levels (1 and 8 microg/ml). Antimicrobial susceptibility of isolates was tested by the agar diffusion method. Clustering was carried out using these numerical values and Ward and UPGMA methods. When using plates supplemented with 1 microg/ml of cefotaxime for DSESC E. coli detection, 93% (76/82) of broiler pooled samples and 36% (29/80) pig pooled samples tested positive. When using 8 microg/ml of cefotaxime, 67% (55/82) of broilers and 13% (10/80) of pigs were positive. Nevertheless, the relative abundance of this phenotype was low in both animal species (range 0-4.3%). Irrespective of the clustering method (Ward or UPGMA), a noticeable phenotypic diversity was detected, especially from the plates containing 1 microg/ml of cefotaxime. We concluded that: (a) E. coli with phenotype DSESC are common in broilers and pigs but are less frequent in pigs, and (b) the host approach is the most appropriate method for antimicrobial resistance assessment when null or very low levels of antimicrobial resistant bacteria are expected.

The effect of subtherapeutic chlortetracycline on antimicrobial resistance in the fecal flora of swine

The aims of this research were to determine the association between inclusion of subtherapeutic chlortetracycline in the diets of swine and the prevalence and antimicrobial resistance of Salmonella; define the association between inclusion of subtherapeutic chlortetracycline and antimicrobial resistance in the aerobic Gram-negative fecal flora of swine; and estimate the proportion of total model variance attributable to farm, pig, and colony level effects. There was no association between subtherapeutic chlortetracycline exposure and Salmonella prevalence. There were increased odds for an aerobic Gram-negative fecal isolate to be resistant to ampicillin, ceftriaxone, and tetracycline if isolated from a pig that received chlortetracycline. There was a positive association between inclusion of subtherapeutic chlortetracycline in the diet and resistance to multiple antimicrobials. The proportion of total variance associated with farm, pig, and colony varied based on the resistance phenotype. Because farm-level variance contributed a small proportion to total variance in all models, effects of antimicrobial use interventions observed in this study may be predictive of anticipated impact of interventions on most swine farms. Resources for future investigations may be better allocated to sampling more pigs and more bacterial colonies per pig in relationship to the number of farms.

Use of a simulation model to evaluate sampling strategies for characterization of antimicrobial resistance in non–type-specificEscherichia coliisolated from dairy cows

OBJECTIVE

To evaluate various sampling strategies for potential use in measuring prevalence of antimicrobial susceptibility in cattle.

SAMPLE POPULATION

500 isolates of non-type-specific Escherichia coli (NTSEC) isolated from the feces of 50 cows from 2 dairy farms (25 cows/farm and 10 isolates/cow).

PROCEDURES

Diameters of inhibition zones for 12 antimicrobials were analyzed to estimate variation among isolates, cows, and farms and then used to determine sampling distributions for a stochastic simulation model to evaluate 4 sampling strategies. These theoretic sampling strategies used a total of 100 isolates in 4 allocations (1 isolate from 100 cows, 2 isolates from 50 cows, 3 isolates from 33 cows, or 4 isolates from 25 cows).

RESULTS

Analysis of variance composition revealed that 74.2% of variation was attributable to isolates, 18.5% to cows, and 7.3% to farms. Analysis of results of simulations suggested that when most of the variance was attributable to differences among isolates within a cow, culturing 1 isolate from each of 100 cows underestimated overall prevalence, compared with results for culturing more isolates per cow from fewer cows. When variance was not primarily attributable to differences among isolates, all 4 sampling strategies yielded similar results.

CONCLUSIONS AND CLINICAL RELEVANCE

It is not always possible to predict the hierarchical level at which clustering will have its greatest impact on observed susceptibility distributions. Results suggested that sampling strategies that use testing of 3 or 4 isolates/cow from a representative sample of all animals better characterize herd prevalence of antimicrobial resistance when impacted by clustering.

Patterns of antimicrobial resistance among commensal Escherichia coli isolated from integrated multi-site housing and worker cohorts of humans and swine

We examined antimicrobial resistance (AR) phenotypes among commensal Escherichia coli isolated from fecal matter of humans and swine housed in a semi-closed and uniquely integrated multi-site farrow-to-plate operation. Aggregate cohorts of humans consisted of (1) "control" groups of consumers, (2) groups of swine workers, and (3) groups of slaughter-plant workers. Analyses of cross-sectional AR data from 472 human and 376 swine isolates are presented. AR phenotypes among these isolates were compared by (1) host species, (2) facility location, (3) facility type, (4) housing (human) or production (swine) cohort, and (5) sample collection period and time of day. There were significant (p < 0.05) differences in isolates among host-species with swine uniformly at greater risk for (1) AR to four specific antimicrobials (kanamycin, streptomycin, sulfamethoxazole, tetracycline), and (2) multiple resistance phenotypes (p < 0.0001). Facility type and unit location were more often associated with AR differences among swine isolates than among human isolates. Swine production group was significantly associated with AR prevalence (p < 0.05) for nine antimicrobials; in general, purchased boars, suckling piglets, weaned piglets, and lactating sows were at higher risk of AR. There was no significant (p > 0.05) association of human occupational/consumer cohort with AR prevalence. Several unique AR phenotypes were detected in each of the human- and swine-intake groups. These data establish baseline characteristics for an on-going 3-year longitudinal study designed to further characterize AR phenotype and genotype in this population. Host-, facility-, and cohort-specific data demonstrate that sufficient prevalence differences exist to permit the future quantification of AR transmission, should it occur. Based on these cross-sectional data, occupational exposure to either swine-rearing or swine-slaughter facilities does not appear to be associated with the prevalence of phenotypic resistance among the commensal fecal E. coli isolated from this integrated system.

Evaluation of an antimicrobial resistance monitoring program for campylobacter in poultry by simulation

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.

Bayesian methods for estimating pathogen prevalence within groups of animals from faecal-pat sampling

Pathogens such as Escherichia coli O157:H7 and Campylobacter spp. have been implicated in outbreaks of food poisoning in the UK and elsewhere. Domestic animals and wildlife are important reservoirs for both of these agents, and cross-contamination from faeces is believed to be responsible for many human outbreaks. Appropriate parameterisation of quantitative microbial-risk models requires representative data at all levels of the food chain. Our focus in this paper is on the early stages of the food chain-specifically, sampling issues which arise at the farm level. We estimated animal-pathogen prevalence from faecal-pat samples using a Bayesian method which reflected the uncertainties inherent in the animal-level prevalence estimates. (Note that prevalence here refers to the percentage of animals shedding the bacteria of interest). The method offers more flexibility than traditional, classical approaches: it allows the incorporation of prior belief, and permits the computation of a variety of distributional and numerical summaries, analogues of which often are not available through a classical framework. The Bayesian technique is illustrated with a number of examples reflecting the effects of a diversity of assumptions about the underlying processes. The technique appears to be both robust and flexible, and is useful when defecation rates in infected and uninfected groups are unequal, where population size is uncertain, and also where the microbiological-test sensitivity is imperfect. We also investigated the determination of the sample size necessary for determining animal-level prevalence from pat samples to within a pre-specified degree of accuracy.

Comparison of sampling techniques for measuring the antimicrobial susceptibility of enteric Escherichia coli recovered from feedlot cattle

OBJECTIVE

To evaluate the effectiveness of various sampling techniques for determining antimicrobial resistance patterns in Escherichia coli isolated from feces of feedlot cattle.

SAMPLE POPULATION

Fecal samples obtained from 328 beef steers and 6 feedlot pens in which the cattle resided.

PROCEDURE

Single fecal samples were collected from the rectum of each steer and from floors of pens in which the cattle resided. Fecal material from each single sample was combined into pools containing 5 and 10 samples. Five isolates of Escherichia coli from each single sample and each pooled sample were tested for susceptibility to 17 antimicrobials.

RESULTS

Patterns of antimicrobial resistance for fecal samples obtained from the rectum of cattle did not differ from fecal samples obtained from pen floors. Resistance patterns from pooled samples differed from patterns observed for single fecal samples. Little pen-to-pen variation in resistance prevalence was observed. Clustering of resistance phenotypes within samples was detected.

CONCLUSIONS AND CLINICAL RELEVANCE

Studies of antimicrobial resistance in feedlot cattle can rely on fecal samples obtained from pen floors, thus avoiding the cost and effort of obtaining fecal samples from the rectum of cattle. Pooled fecal samples yielded resistance patterns that were consistent with those of single fecal samples when the prevalence of resistance to an antimicrobial was > 2%. Pooling may be a practical altemative when investigating patterns of resistance that are not rare. Apparent clustering of resistance phenotypes within samples argues for examining fewer isolates per fecal sample and more fecal samples per pen.

Antimicrobial susceptibilities of Escherichia coli strains from a turkey operation

OBJECTIVE

To measure minimum inhibitory concentrations (MIC) of 17 antimicrobials for Escherichia coli isolates from a turkey operation and assess whether small samples provide precise estimates of geometric mean MIC.

DESIGN

Prospective study.

SAMPLE POPULATION

105 clinical isolates from birds and 1,104 fecal isolates from 20 flocks (poults and finisher hens).

PROCEDURE

A Mueller-Hinton broth dilution panel was used to measure MIC, and MIC of fecal and clinical isolates were compared. We drew random samples of 5,10, 15, 20, 25, 30, 35, 40, and 45 isolates from each finisher flock and between 100 and 105 isolates from 5, 7, 10, and 20 flocks. Antimicrobial usage was determined for enrolled flocks.

RESULTS

Six of 12 poult and 18 of 20 finisher flocks had been treated with antimicrobials, often for respiratory illnesses consistent with colibacillosis. All birds received gentamicin at the hatchery. More fecal than clinical isolates were resistant to ampicillin; however, more clinical isolates were resistant to ciprofloxacin, gentamicin, and sulfamethoxazole. Precise estimates of geometric mean MIC for flocks were obtained when > or = 15 fecal isolates were obtained per flock and, for the operation, when 105 isolates were obtained from > or = 7 flocks.

CONCLUSION AND CLINICAL RELEVANCE

Antimicrobial usage was common and may have contributed to the resistance patterns of isolates. With a modest allocation of laboratory resources, producers can monitor antimicrobial susceptibilities of clinical and fecal E coli to manage risks of antimicrobial usage and resistance.

Changes in the prevalence of resistant Escherichia coil in cattle receiving subcutaneously injectable oxytetracycline in addition to in-feed chlortetracycline compared with cattle receiving only in-feed chlortetracycline

Information about the prevalence of antibiotic resistance in commensal enteric bacteria is of interest because these bacteria are potential indicators of selection pressure on enteric bacteria and represent a reservoir of resistance genes in potentially pathogenic bacteria. This study reports changes in the prevalence of resistance to antibiotics in commensal Escherichia coli from cattle receiving either subcutaneously injectable oxytetracycline in addition to in-feed chlortetracycline or only in-feed chlortetracycline. Resistance to 19 antibiotics was examined. The use of injectable oxytetracycline in addition to in-feed chlortetracycline was significantly associated (P < 0.05) with an increase in the prevalence of resistance only to chloramphenicol and sulfisoxazole.

Pathogenic and Fecal Escherichia coli Strains from Turkeys in a Commercial Operation

The biochemical phenotypes and antimicrobial susceptibility patterns of 105 clinical Escherichia coli isolates from flocks with colibacillosis in a turkey operation were compared with 1104 fecal E. coli isolates from 20 flocks in that operation. Clinical isolates and 194 fecal isolates with biochemical phenotypes or minimum inhibitory concentrations for gentamicin and sulfamethoxazole similar to clinical isolates were tested for somatic antigens and the potential virulence genes hylE, iss, tsh, and K1. The predominant biochemical phenotype of clinical isolates contained 21 isolates including 14 isolates belonging to serogroup 078 with barely detectable beta-D-glucuronidase activity. Thirty-five fecal isolates had biochemical phenotypes matching common phenotypes of clinical isolates. Sixty-six (63%) clinical isolates exhibited intermediate susceptibility or resistance to gentamicin and sulfamethoxazole compared with 265 (24%) fecal isolates (P < 0.001). Seventy-seven clinical isolates reacted with O-antisera, of which 51 (66%) belonged to the following serogroups: O1, O2, O8, O25, O78, O114, and O119. In comparison, 8 of 35 (23%) fecal isolates subtyped on the basis of biochemical phenotype belonged to these serogroups and four of 167 (2%) fecal isolates subtyped on the basis of their antimicrobial resistance patterns belonged to these serogroups. Iss, K1, and tsh genes were detected more often among clinical isolates than these fecal isolates (P < 0.05). In summary, a small subgroup of E. coli strains caused most colibacillosis infections in this operation. These strains existed at low concentration in normal fecal flora of healthy turkeys in intensively raised flocks. The data suggest that colibacillosis in turkey operations may be due to endogenous infections caused by specialized pathogens.

Longitudinal study of Salmonella enterica in growing pigs reared in multiple-site swine production systems

Intensive longitudinal investigations of breeding and growing pig populations in two multiple-site swine production systems were conducted in NC, USA. Five cohorts of sows and individually identified growing pigs from their litters were serially sampled in order to determine the prevalence and serotypes of Salmonella enterica in each stage of production based on fecal culture. In addition to fecal samples, feed and environmental samples were obtained. Fifteen different serotypes were isolated from the two systems, the most frequently isolated serotypes were S. typhimurium var Mbandaka and S. typhimurium var Copenhagen. Pig prevalence estimates ranged from 0 to 48.1%. Environmental contamination was frequently encountered despite cleaning and disinfection. Feed was rarely (2/800, 0.25%) identified as S. enterica positive. We observed highly variable patterns of S. enterica prevalence and serotype profiles within cohorts over time and among cohorts within systems. These observations indicate that point estimates of S. enterica prevalence and serotypes cannot be considered as reliable indicators of the S. enterica status of farms, and that uncontrolled studies of interventions to control S. enterica may yield misleading results. These findings are critical to the design of epidemiological studies of S. enterica on swine farms and may suggest that cohort level, as opposed to farm or company level events or management practices, may be important as potential risk factors for S. enterica fecal shedding in market age pigs.

What is antibiotic resistance and how can we measure it?

Antibiotic resistance is being found with increasing frequency in both pathogenic and commensal bacteria of humans and animals. Quantifying resistance within and between bacterial and host populations presents scientists with complex challenges in terms of laboratory methodologies and sampling design. Here, we discuss, from an epidemiological perspective, how antibiotic resistance can be defined and measured and the limitations of current approaches.