Evidence map›Paper›PMID 30067775›Full record

ArticlePloS one2018

Temporal trends and predictors of antimicrobial resistance among Staphylococcus spp. isolated from canine specimens submitted to a diagnostic laboratory.

Julia G Conner, Jackie Smith, Erdal Erol, Stephan Locke, Erica Phillips, Craig N Carter, Agricola Odoi

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

21 citing papers in PubMed, 36 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Julia G ConnerBiomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, Tennessee, United States of America.
Jackie SmithVeterinary Diagnostic Laboratory, University of Kentucky, Lexington, Kentucky, United States of America.
Erdal ErolVeterinary Diagnostic Laboratory, University of Kentucky, Lexington, Kentucky, United States of America.
Stephan LockeVeterinary Diagnostic Laboratory, University of Kentucky, Lexington, Kentucky, United States of America.
Erica PhillipsVeterinary Diagnostic Laboratory, University of Kentucky, Lexington, Kentucky, United States of America.
Craig N CarterVeterinary Diagnostic Laboratory, University of Kentucky, Lexington, Kentucky, United States of America.
Agricola OdoiBiomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, Tennessee, United States of America.ORCID 0000-0001-5050-6737
University of Kentucky · USUniversity of Tennessee at Knoxville · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResistance to commonly used antimicrobials is a growing concern in both human and veterinary medicine. Understanding the temporal changes in the burden of the problem and identifying its determinants is important for guiding control efforts. Therefore, the objective of this study was to investigate temporal patterns and predictors of antimicrobial resistance among Staphylococcus spp. isolated from canine specimens submitted to the University of Kentucky Veterinary Diagnostic Laboratory (UKVDL) between 1993 and 2009.

methodsRetrospective data of 4,972 Staphylococcus isolates assessed for antimicrobial susceptibility using the disk diffusion method at the UKVDL between 1993 and 2009 were included in the study. Temporal trends were assessed for each antimicrobial using the Cochran-Armitage trend test. Logistic regression models were used to investigate predictors of antimicrobial resistance (AMR) and multidrug resistance (MDR).

resultsA total of 68.2% (3,388/4,972) Staphylococcus isolates were S. intermedius group (SIG), 18.2% (907/4,972) were coagulase-negative staphylococci (CoNS), 7.6% (375/4,972) were S. aureus, 5.8% (290/4,972) were S. hyicus, and S. schleiferi subsp. coagulans comprised 0.2% (12/4,972) of the isolates. The overall percentage of AMR and MDR were 77.2% and 25.6%, respectively. The highest levels of AMR were seen in CoNS (81.3%; 737/907), S. aureus (80.5%; 302/375), and SIG (77.6%; 2,629/3388). The lowest levels of AMR were observed in S. hyicus (57.9%; 168/290) and S. schleiferi subsp. coagulans (33.3%; 4/12). Overall, AMR and MDR showed significant (p<0.001) decreasing temporal trends. Significant temporal trends (both increasing and decreasing) were observed among 12 of the 16 antimicrobials covering 6 of the 9 drug classes assessed. Thus, significant increasing temporal trends in resistance were observed to β-lactams (p<0.001) (oxacillin, amoxicillin-clavulanate, cephalothin, and penicillin (p = 0.024)), aminoglycosides (p<0.001) (gentamicin, and neomycin), bacitracin (p<0.001), and enrofloxacin (p<0.001). In contrast, sulfonamide (p<0.001) (sulfadiazin) and tetracycline (p = 0.010) resistant isolates showed significant decreasing temporal trends in AMR. Staphylococcus spp., geographic region, and specimen source were significant predictors of both AMR and MDR.

conclusionsAlthough not unexpected nor alarming, the high levels of AMR to a number of antimicrobial agents and the increasing temporal trends are concerning. Therefore, continued monitoring of AMR among Staphylococcus spp. is warranted. Future studies will need to identify local factors responsible for the observed geographic differences in risk of both AMR and MDR.

Indexed as

AminoglycosidesAnimalsAnti-Bacterial Agentsbeta-LactamsDisk Diffusion Antimicrobial TestsDogsDrug Resistance, BacterialDrug Resistance, Multiple, BacterialLogistic ModelsRetrospective StudiesStaphylococcal InfectionsStaphylococcusTetracyclineAminoglycosidesAnti-Bacterial Agentsbeta-LactamsTetracycline

Identifiers

PMID30067775
PMCPMC6070192
OpenAlexW2886569573

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.