Potential risk‐factors affecting Salmonella sp. and Escherichia coli occurrence and distribution in Midwestern United States swine feed mills

2020 ◽  
Vol 129 (6) ◽  
pp. 1744-1750
Author(s):  
G. Magossi ◽  
E. Lambertini ◽  
L. Noll ◽  
J. Bai ◽  
C. Jones ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Escherichia Coli ◽  
Potential Risk ◽  
Midwestern United States ◽  
Factors Affecting ◽  
Potential Risk Factors ◽  
Swine Feed

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 ◽  
...  
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
The Netherlands ◽  
Potential Risk ◽  
Molecular Characteristics ◽  
Horse Population ◽  
Nationwide Study ◽  
Extended Spectrum ◽  
Animal Populations ◽  
Potential Risk Factors

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.

scholarly journals Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States

2021 ◽  
Vol 18 (13) ◽  
pp. 6832
Author(s):  
Han Yue ◽  
Tao Hu
Keyword(s):  
United States ◽  
Risk Factors ◽  
Mortality Rate ◽  
Population Density ◽  
Potential Risk ◽  
Interactive Effects ◽  
Geographical Detector ◽  
Spatial Risk ◽  
Potential Risk Factors ◽  
The Government

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.

scholarly journals Identification of Risk Factors for Invasive Extraintestinal Pathogenic Escherichia coli (ExPEC) Disease

2021 ◽  
Vol 1 (S1) ◽  
pp. s22-s22
Author(s):  
Erik Clarke ◽  
Jeroen Geurtsen ◽  
Bart Spiessens ◽  
Christel Chehoud
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Potential Risk ◽  
Multidrug Resistant ◽  
Older Age ◽  
Gradient Boosting ◽  
Common Data Model ◽  
Potential Risk Factors ◽  
Precise Relationship ◽  
And Gender

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

scholarly journals Identification of Risk Factors of Development and Operation of Roads in the Light of Surveying Work

2016 ◽  
Vol 62 (2) ◽  
pp. 183-190 ◽  
Author(s):  
D. Skorupka ◽  
M. Kowacka
Keyword(s):  
Risk Factors ◽  
Potential Risk ◽  
Factors Affecting ◽  
Potential Risk Factors ◽  
Construction Works

Abstract This article aims to identify potential risk factors affecting the implementation and synchronisation of surveying and construction works during building and operation of roads. The task was executed on the basis of literature studies and experience. The article is an introduction to the research that has been conducted by the authors on the reasonably precise index of factors which one may deal with during the implementation of facilities of this type. The raised issue is crucial for financial and time reasons, but what is important in the roads construction - also for social ones, as prolonged traffic disruption adversely affects the environment.

scholarly journals Motor Vehicle Fatalities during Memorial Day Weekends, 1981-2016

2019 ◽  
Author(s):  
Yuni Tang ◽  
Kendra L Ratnapradipa ◽  
Henry Xiang ◽  
Motao Zhu
Keyword(s):  
United States ◽  
Risk Factors ◽  
Potential Risk ◽  
Motor Vehicle ◽  
The United States ◽  
Traffic Fatalities ◽  
Fatality Risk ◽  
Rural And Urban ◽  
Increased Risk ◽  
Potential Risk Factors

Abstract Objective: Our aims were to determine which day(s) during the holiday had highest motor vehicle fatality risk compared to non-holiday travel and to identify potential risk factors. Results: Of 43,457 traffic fatalities studied, 15,292 (35%) occurred during the holiday, with Saturday being deadliest but Monday having highest odds. Both sexes, all years, age <65, drivers and passengers, rural and urban, and all regions in the United States were at increased risk during the holiday versus non-holiday periods.

scholarly journals Prevalence and Potential Risk Factors for Escherichia coli Isolated from Tibetan Piglets with White Score Diarrhea

2017 ◽  
Vol 50 (1) ◽  
Author(s):  
Hailong Dong ◽  
Hui Zhang ◽  
Kun Li ◽  
Khalid Mehmood ◽  
Mujeeb Ur Rehman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Potential Risk ◽  
Potential Risk Factors
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