Evidence map›Paper›PMID 20082072›Full record

ArticleMicrobial ecology2010

Evidence for the clustering of antibacterial resistance phenotypes of enterococci within integrated poultry companies.

Eve Pleydell, Lynn Rogers, Errol Kwan, Nigel French

Abstract read
PubMed Publisher
In one paragraph

Article in Microbial ecology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Eve PleydellInstitute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11 222, Palmerston North, New Zealand. pleydell@liverpool.ac.uk
Lynn Rogers
Errol Kwan
Nigel French

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

From July to December 2006, a panel of 401 enterococci was isolated from carcass rinse samples collected in five poultry processing plants in New Zealand. Agar diffusion assays for nine antibacterial drugs were used to obtain a resistance phenotype for each isolate. Hierarchical clustering techniques and diversity indices showed a high diversity of resistance phenotypes within each plant, with populations of Enterococcus faecalis showing greater heterogeneity than Enterococcus faecium. Bayesian modelling identified three clusters of phenotype patterns within the panel: the E. faecium isolates showed a high probability of containing two distinct clusters, whilst the E. faecalis isolates all grouped together to form the third cluster. The validity of these three clusters was examined using pairwise fixation indices and analysis of variance. Comparing the three clusters to the structure of the participating companies showed that resistance phenotypes for E. faecium isolated from processing plants that were geographically separated but were operated by the same integrated poultry company were more similar than E. faecium isolated from unconnected companies. Company-level management factors, such as the routine use of antibacterial drugs and the genetic line of birds reared, mirrored the structure of these clusters, thus indicating that company-level factors were the dominant selective pressures upon resistance phenotypes across all operating units within these integrated poultry companies.

Indexed as

Drug Resistance, BacterialFood ContaminationFood MicrobiologyAnimalsBayes TheoremCluster AnalysisConsumer Product SafetyEnterococcusMeat-Packing IndustryMicrobial Sensitivity TestsModels, StatisticalNew ZealandPhenotypePoultry

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.