scholarly journals TeamMate: A Longitudinal Study of New Zealand Working Farm Dogs. III. Factors Affecting the Risk of Dogs Being Lost from the Workforce

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1602
Author(s):  
Katja E. Isaksen ◽  
Lori Linney ◽  
Helen Williamson ◽  
Elizabeth J. Norman ◽  
Nick J. Cave ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Acute Injury ◽  
Age Group ◽  
Factors Affecting ◽  
Regression Modelling ◽  
Physical Examinations ◽  
Livestock Farmers ◽  
Logistic Regression Modelling ◽  
Improve Health

Working farm dogs are essential to many livestock farmers. Little is known about factors that influence dogs’ risk of being lost from work. This paper explores risk factors for farm dogs being lost through death, euthanasia and retirement. All enrolled dogs were working and a minimum of 18 months old. Five data collection rounds were performed over four years. Data about dogs were collected from owners and dogs were given physical examinations by veterinarians. Dogs that were lost from work were counted and owner-reported reasons for loss were recorded. Multivariable logistic regression modelling was used to investigate risk factors for loss. Of 589 dogs, 81 were lost from work. Of these, 59 dogs died or were euthanized and 22 were retired. Farm dogs tended to reach advanced ages, with 38% being 10 years or older when last examined. Acute injury or illness was the most commonly owner-reported reason for loss. Age group (p < 0.0001) and lameness (p = 0.04, OR = 1.8) significantly affected dogs’ risk of being lost. These results expand our knowledge about factors that affect health, welfare and work in farm dogs. Further investigation into reasons for lameness may help improve health and welfare in working farm dogs.

scholarly journals TeamMate: A Longitudinal Study of Health in Working Farm Sogs. III. Factors Affecting the Risk of Death, Euthanasia or Retirement from Work

2021 ◽  
Author(s):  
Katja E. Isaksen ◽  
Lori Linney ◽  
Helen Williamson ◽  
Elizabeth J. Norman ◽  
Nick J. Cave ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Injury ◽  
Age Group ◽  
Factors Affecting ◽  
Risk Of Death ◽  
Regression Modelling ◽  
Physical Examinations ◽  
Livestock Farmers ◽  
Logistic Regression Modelling ◽  
Improve Health

Working farm dogs are essential to many livestock farmers. Little is known about factors that influence dogs&rsquo; risk of being lost from work. This paper explores risk factors for farm dogs being lost through death, euthanasia and retirement. All enrolled dogs were working and minimum 18 months old. Five data collection rounds were done over four years. Data about dogs were collected from owners and dogs were given physical examinations by veterinarians. Dogs that were lost from work were counted and owner-reported reasons for loss were recorded. Multivariable logistic regression modelling was used to investigate risk factors for loss. Of 589 dogs, 81 were lost from work. Of these, 59 dogs died or were euthanized and 22 were retired. Farm dogs tended to reach high ages, with 38% being 10 years or older when last examined. Acute injury or illness was the most commonly owner-reported reason for loss. Age group (P &amp;lt; 0.0001) and lameness (P = 0.04, OR = 1.8) significantly affected dogs&rsquo; risk being lost. These results expand our knowledge about factors that affect health, welfare and work in farm dogs. Further investigation into reasons for lameness may help improve health and welfare in working farm dogs.

scholarly journals Efficiency of slaughterhouse surveillance for the detection of bovine tuberculosis in cattle in Northern Ireland

2016 ◽  
Vol 145 (5) ◽  
pp. 995-1005 ◽  
Author(s):  
A. V. PASCUAL-LINAZA ◽  
A. W. GORDON ◽  
L. A. STRINGER ◽  
F. D. MENZIES
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Northern Ireland ◽  
Bovine Tuberculosis ◽  
Post Mortem ◽  
Univariable Analysis ◽  
Eradication Programme ◽  
Risk Status ◽  
Regression Modelling ◽  
Logistic Regression Modelling

SUMMARYPost-mortem examination continues to play an important surveillance role in the bovine tuberculosis (bTB) eradication programme in Northern Ireland. It is estimated that 18–28% of new bTB herd breakdowns are disclosed by the detection of bTB lesions in animals routinely slaughtered. The purpose of this study was to compare the performance of different slaughterhouses in Northern Ireland in detecting bTB-lesioned animals at routine slaughter (LRS) and to apply the findings to maximize the sensitivity of bTB slaughterhouse surveillance. Univariate statistical analysis on cattle slaughtered in Northern Ireland during 2011–2013 revealed that the risk of LRS disclosure varied between slaughterhouses, ranging from 0·08% to 0·54%. Furthermore, the risk of confirmation of these LRS as bTB varied between slaughterhouses, ranging from 57·9% to 72·4%. Logistic regression modelling of selected risk factors found that the risk of LRS disclosure increased with age, and was higher in purchased animals, during winter months, in animals coming from high bTB incidence areas and in animals slaughtered from herds with a bTB restriction in the last 2–3 years. Adjusting for these selected factors, the risk of LRS disclosure and bTB confirmation changed very little from the univariable analysis, suggesting that differences in disclosure risks between slaughterhouses were likely to be due to factors related to the slaughterhouses, rather than to the risk status of the animals presented. Examination of procedures within these slaughterhouses is recommended to identify ways that could increase the sensitivity of their bTB surveillance.

scholarly journals Canine dystocia in 50 UK first-opinion emergency-care veterinary practices: prevalence and risk factors

2017 ◽  
Vol 181 (4) ◽  
pp. 88-88 ◽  
Author(s):  
D. G. O'Neill ◽  
A. M. O'Sullivan ◽  
E. A. Manson ◽  
D. B. Church ◽  
A. K. Boag ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Emergency Care ◽  
Data Resource ◽  
Regression Modelling ◽  
Factors Analysis ◽  
Welfare Issue ◽  
Clinical Records ◽  
French Bulldog ◽  
Logistic Regression Modelling

Dystocia can represent a major welfare issue for dogs of certain breeds and morphologies. First-opinion emergency-care veterinary caseloads represent a useful data resource for epidemiological research because dystocia can often result in emergency veterinary care. The study analysed a merged database of clinical records from 50 first-opinion emergency-care veterinary practices participating in the VetCompass Programme. Multivariable logistic regression modelling was used for risk factors analysis. There were 701 dystocia cases recorded among 18,758 entire female dogs, resulting in a dystocia prevalence of 3.7 per cent (95 per cent CI 3.5–4.0 per cent). Breeds with the highest odds of dystocia compared with crossbred bitches were French Bulldog (OR: 15.9, 95 per cent CI 9.3 to 27.2, P<0.001), Boston Terrier (OR: 12.9, 95 per cent CI 5.6 to 29.3, P<0.001), Chihuahua (OR: 10.4, 95 per cent CI 7.0 to 15.7, P<0.001) and Pug (OR: 11.3, 95 per cent CI 7.1 to 17.9, P<0.001). Bitches aged between 3.0 and 5.9 years had 3.1 (95 per cent CI 2.6 to 3.7, P<0.001) times the odds of dystocia compared with bitches aged under 3.0years. Certain breeds, including some brachycephalic and toy breeds, appeared at high risk of dystocia. Opportunities to improve this situation are discussed.

Telemedicine during the COVID-19 pandemic

2021 ◽  
Vol 103-B (6 Supple A) ◽  
pp. 196-204
Author(s):  
Jeffrey Shi Chen ◽  
Daniel B. Buchalter ◽  
Chelsea S. Sicat ◽  
Vinay K. Aggarwal ◽  
Matthew S. Hepinstall ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Tertiary Hospital ◽  
Ordinal Logistic Regression ◽  
Rapid Expansion ◽  
Regression Modelling ◽  
Widespread Implementation ◽  
Physical Examinations ◽  
Audiovisual Quality ◽  
Question Survey ◽  
Logistic Regression Modelling

Aims The COVID-19 pandemic led to a swift adoption of telehealth in orthopaedic surgery. This study aimed to analyze the satisfaction of patients and surgeons with the rapid expansion of telehealth at this time within the division of adult reconstructive surgery at a major urban academic tertiary hospital. Methods A total of 334 patients underging arthroplasty of the hip or knee who completed a telemedicine visit between 30 March and 30 April 2020 were sent a 14-question survey, scored on a five-point Likert scale. Eight adult reconstructive surgeons who used telemedicine during this time were sent a separate 14-question survey at the end of the study period. Factors influencing patient satisfaction were determined using univariate and multivariate ordinal logistic regression modelling. Results A total of 68 patients (20.4%) and 100% of the surgeons completed the surveys. Patients were “Satisfied” with their telemedicine visits (4.10/5.00 (SD 0.98)) and 19 (27.9%) would prefer telemedicine to in-person visits in the absence of COVID-19. Multivariate ordinal logistic regression modelling revealed that patients were more likely to be satisfied if their surgeon effectively responded to their questions or concerns (odds ratio (OR) 3.977; 95% confidence interval (CI) 1.260 to 13.190; p = 0.019) and if their visit had a high audiovisual quality (OR 2.46; 95% CI 1.052 to 6.219; p = 0.042). Surgeons were “Satisfied” with their telemedicine experience (3.63/5.00 (SD 0.92)) and were “Fairly Confident” (4.00/5.00 (SD 0.53)) in their diagnostic accuracy despite finding the physical examinations to be only “Slightly Effective” (1.88/5.00 (SD 0.99)). Most adult reconstructive surgeons, seven of eight (87.5%) would continue to use telemedicine in the future. Conclusion Telemedicine emerged as a valuable tool during the COVID-19 pandemic. Patients undergoing arthroplasty and their surgeons were satisfied with telemedicine and see a role for its use after the pandemic. The audiovisual quality and the responsiveness of physicians to the concerns of patients determine their satisfaction. Future investigations should focus on improving the physical examination of patients through telemedicine and strategies for its widespread implementation. Cite this article: Bone Joint J 2021;103-B(6 Supple A):196–204.

scholarly journals Proportion and risk factors for death by euthanasia in dogs in the UK

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Camilla Pegram ◽  
Carol Gray ◽  
Rowena M. A. Packer ◽  
Ysabelle Richards ◽  
David B. Church ◽  
...  
Keyword(s):  
Risk Factors ◽  
Poor Quality ◽  
Clinical Factors ◽  
Sampling Frame ◽  
Additional Risk ◽  
Factors Associated ◽  
Regression Modelling ◽  
The Uk ◽  
Logistic Regression Modelling

AbstractThe loss of a pet can be particularly distressing for owners, whether the method of death is euthanasia or is unassisted. Using primary-care clinical data, this study aimed to report the demographic and clinical factors associated with euthanasia, relative to unassisted death, in dogs. Method of death (euthanasia or unassisted) and clinical cause of death were extracted from a random sample of 29,865 dogs within the VetCompass Programme from a sampling frame of 905,544 dogs under UK veterinary care in 2016. Multivariable logistic regression modelling was used to evaluate associations between risk factors and method of death. Of the confirmed deaths, 26,676 (89.3%) were euthanased and 2,487 (8.3%) died unassisted. After accounting for confounding factors, 6 grouped-level disorders had higher odds in euthanased dogs (than dogs that died unassisted), using neoplasia as the baseline. The disorders with greatest odds included: poor quality of life (OR 16.28), undesirable behaviour (OR 11.36) and spinal cord disorder (OR 6.00). Breed, larger bodyweight and increasing age were additional risk factors for euthanasia. The results highlight that a large majority of owners will face euthanasia decisions and these findings can support veterinarians and owners to better prepare for such an eventuality.

scholarly journals An improved medical admissions risk system using multivariable fractional polynomial logistic regression modelling

QJM ◽  
2009 ◽  
Vol 103 (1) ◽  
pp. 23-32 ◽  
Author(s):  
B. Silke ◽  
J. Kellett ◽  
T. Rooney ◽  
K. Bennett ◽  
D. O’Riordan
Keyword(s):  
Logistic Regression ◽  
Regression Modelling ◽  
Fractional Polynomial ◽  
Logistic Regression Modelling

scholarly journals Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Expert Opinion ◽  
Logistic Regression Model ◽  
Population Based ◽  
Regression Modelling ◽  
Preterm Babies ◽  
Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

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