scholarly journals Fecal Carriage of Extended-Spectrum-β-Lactamase/AmpC-Producing Escherichia coli in Horses

2020 ◽  
Vol 86 (8) ◽  
Author(s):  
Joost Hordijk ◽  
Evangelia Farmakioti ◽  
Lidwien A. M. Smit ◽  
Birgitta Duim ◽  
Haitske Graveland ◽  
...  

ABSTRACT A nationwide study on the occurrence of extended-spectrum β-lactamase (ESBL)/AmpC in nonhospitalized horses in the Netherlands was performed. Molecular characterization was done, and questionnaires were analyzed to identify factors associated with carriage. In total, 796 horse owners were approached; 281 of these submitted a fecal sample from their horse(s), resulting in 362 samples. All samples were cultured qualitatively in Luria-Bertani (LB) broth and subsequently on MacConkey agar, both supplemented with 1 mg/liter cefotaxime (LB+ and MC+). Positive samples were subsequently cultured quantitatively on MC+. Initial extended-spectrum-β-lactamase (ESBL)/AmpC screening was performed by PCR, followed by whole-genome sequencing on selected strains. Associations between ESBL/AmpC carriage and questionnaire items were analyzed using a univariate generalized estimating equation (GEE) regression analysis, followed by a multiple GEE model for relevant factors. In total, 39 of 362 samples (11%) were determined to be positive for ESBL/AmpC. blaCTX-M-1-carrying isolates were obtained from 77% of positive samples (n = 30). Other ESBL/AmpC genes observed included blaCTX-M-2, blaCTX-M-14, blaCTX-M-15, blaCTX-M-32, blaSHV-12, blaCMY-2, and blaACT-10. A high association between the presence of blaCTX-M-1 and IncHI1 plasmids was observed (46% of samples; n = 18). Based on core genome analysis (n = 48 isolates), six Escherichia coli clusters were identified, three of which represented 80% of the isolates. A negative association between ESBL/AmpC carriage and horses being in contact with other horses at a different site was observed. The presence of a dog on the premises and housing in a more densely human-populated region were positively associated. IMPORTANCE Extended-spectrum β-lactamases (ESBLs) are widespread in human and animal populations and in the environment. Many different ESBL variants exist. The dissemination of ESBLs within and between populations and the environment is also largely influenced by genetic mobile elements (e.g., plasmids) that facilitate spread of these ESBLs. In order to identify potential attributable ESBL sources for, e.g., the human population, it is important to identify the different ESBL variants, the bacteria carrying them, and the potential risk factors for ESBL carriage from other potential sources. This nationwide study focuses on ESBL carriage in the open horse population and investigated the molecular characteristics, geographical distribution throughout the Netherlands, and potential risk factors for fecal ESBL carriage in horses. These data can be used for future attribution studies in order to reduce potential transmission of ESBL-producing bacteria between sources.

2016 ◽  
Vol 51 (11) ◽  
pp. 1509-1523 ◽  
Author(s):  
Terese Sara Hoej Joergensen ◽  
Solvej Maartensson ◽  
Else Helene Ibfelt ◽  
Martin Balslev Joergensen ◽  
Ida Kim Wium-Andersen ◽  
...  

2014 ◽  
Vol 69 (10) ◽  
pp. 2669-2675 ◽  
Author(s):  
P. M. C. Huijbers ◽  
E. A. M. Graat ◽  
A. P. J. Haenen ◽  
M. G. van Santen ◽  
A. van Essen-Zandbergen ◽  
...  

2021 ◽  
Vol 1 (S1) ◽  
pp. s22-s22
Author(s):  
Erik Clarke ◽  
Jeroen Geurtsen ◽  
Bart Spiessens ◽  
Christel Chehoud

Background: A pathogenic group of invasive extraintestinal pathogenic (ExPEC) Escherichia coli possess the ability to infect normally sterile body sites and cause severe invasive ExPEC disease (IED). ExPEC is a leading cause of bacteremia and sepsis worldwide and is associated with older age and multidrug-resistant infections. Janssen Vaccines & Prevention is developing a novel multivalent glycoconjugate vaccine to prevent IED. We aimed to use an unbiased approach, with no prespecified potential risk factors, using machine-learning models, to screen for and identify IED risk factors for further validation. Methods: We used a patient-level prediction study design to model the probability of a patient developing IED within 14 days to 1 year from a given date based on their prior 2 years of health records. We used the Optum EHR database (~98 million subjects) in the common data model (CDM) format, with health features encoded in the following categories: conditions, procedures, drugs, healthcare visits, recent laboratory measurements, and age and gender. A gradient boosting model (XGBoost) was used with Shapley additive explanation (SHAP) values to identify which features were most important to the model’s decisions and to characterize precisely the relationship between features and outcomes (binary or continuous). Results: Study participants were aged ≥60 years at index with no previously recorded IED. Of ~6,500,000 cases included, ~8,000 had IED during the prediction window. We found that having ≥1 urinary tract infection (UTI) in the retrospective period increased the model’s probability of predicting IED for that patient, with more frequent or more recent UTIs increasing IED prediction chance (Figure 1). Higher age linearly increased the model’s likelihood of predicting that a patient would develop IED. The model also identified ≥1 inpatient or ER visit and laboratory values indicative of renal or immune dysfunction to be correlated with increased IED risk. This methodology is a generalizable approach to screening for potential risk factors for an outcome using EHR databases; it requires little to no prespecification of the health factors or precise relationship between the factors and outcome. Conclusions: Using a new, impartial methodology (with no prespecification), older age and a history of UTIs were key predictive features for IED, factors previously identified through traditional analysis, confirming the validity of the methodology. Novel features, including recent hospitalization, were shown to increase IED risk relative to existing criteria. Our findings may be used to inform the clinical development of preventive strategies.Funding: Janssen Research and DevelopmentDisclosures: None


2011 ◽  
Vol 140 (7) ◽  
pp. 1185-1192 ◽  
Author(s):  
Y. DOORDUYN ◽  
W. VAN PELT ◽  
A. H. HAVELAAR

SUMMARYIn 2009, a 1-year retrospective survey was performed in The Netherlands to estimate the incidence and the disease burden of infectious intestinal disease (IID) in the community, to study the selection of patients consulting a general practitioner and to identify potential risk factors for IID in the community. A questionnaire was sent to 6000 persons selected at random from the population registries of 28 municipalities, with 500 persons being approached per month. A total of 1975 (33%) persons participated. The incidence rate of IID was 964/1000 person-years. Potential risk factors associated with IID in the community were young age (0–4 years) [odds ratio (OR) 3·9, 95% confidence interval (CI) 1·5–10·5], having asthma as a child (OR 3·4, 95% CI 1·1–10·3) and use of gastric acid suppressive medication by persons aged ⩾45 years (OR 2·8, 95% CI 1·4–5·6). Of the 146 cases with IID, 11 (8%) consulted a physician. Cases with a long duration of symptoms, blood in the stool, children with IID and cases with a low level of education were more likely to consult a physician. Two cases had a stool sample taken and one was admitted to hospital. In conclusion, IID is common and has a significant burden of illness in The Netherlands. Our data indicate that about 15·9 million episodes of IID occur in The Netherlands per year. The incidence rate is substantially higher than the rate of 283/1000 person-years as estimated in 1999 in The Netherlands. This is probably largely due to the retrospective nature of the present study and, to a lesser extent, to differences in case definitions.


Author(s):  
Barbara Hinney ◽  
Daniel Sperling ◽  
Susan Kars-Hendriksen ◽  
Marlies Olde Monnikhof ◽  
Steven Van Colen ◽  
...  

2017 ◽  
Vol 100 (1) ◽  
pp. 562-571 ◽  
Author(s):  
I.M.G.A. Santman-Berends ◽  
M.A. Gonggrijp ◽  
J.J. Hage ◽  
A.E. Heuvelink ◽  
A. Velthuis ◽  
...  

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