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Weber MF, Aalberts M, Dijkstra T, Schukken YH. Predicting Positive ELISA Results in Dairy Herds with a Preferred Status in a Paratuberculosis Control Program. Animals (Basel) 2022; 12:ani12030384. [PMID: 35158707 PMCID: PMC8833702 DOI: 10.3390/ani12030384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/23/2022] [Accepted: 01/26/2022] [Indexed: 12/04/2022] Open
Abstract
Dairy herds participating in the Dutch milk quality assurance program for paratuberculosis are assigned a herd status on the basis of herd examinations by ELISA of individual serum or milk samples, followed by an optional confirmatory fecal PCR. Test-negative herds are assigned Status A; the surveillance of these herds consists of biennial herd examinations. Farmers falsely believing that their Status A herds are Map-free may inadvertently refrain from preventive measures. Therefore, we aimed to develop a predictive model to alert Status A farmers at increased risk of future positive ELISA results. Using data of 8566 dairy herds with Status A in January 2016, two logistic regression models were built, with the probabilities of ≥1 or ≥2 positive samples from January 2017–June 2019 as dependent variables, and province, soil type, herd size, proportion of cattle born elsewhere, time since previous positive ELISA results, and the 95th percentile of the S/P ratios in 2015–2016, as explanatory variables. As internal validation, both models were applied to predict positive ELISA results from January 2019–June 2021, in 8026 herds with Status A in January 2019. The model predicting ≥1 positive sample had an area under the receiver-operating-characteristics curve of 0.76 (95% CI: 0.75, 0.77). At a cut-off predicted probability πc = 0.40, 25% of Status A herds would be alerted with positive and negative predictive values of 0.52 and 0.83, respectively. The model predicting ≥2 positive samples had lower positive, but higher negative, predictive values. This study indicates that discrimination of Status A herds with high and low risks of future positive ELISA results is feasible. This might stimulate farmers with the highest risks to take additional measures to control any undetected Map infections.
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Affiliation(s)
- Maarten F. Weber
- Royal GD, P.O. Box 9, 7400 AA Deventer, The Netherlands; (M.A.); (T.D.); (Y.H.S.)
- Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 7, 3584 CL Utrecht, The Netherlands
- Correspondence:
| | - Marian Aalberts
- Royal GD, P.O. Box 9, 7400 AA Deventer, The Netherlands; (M.A.); (T.D.); (Y.H.S.)
| | - Thomas Dijkstra
- Royal GD, P.O. Box 9, 7400 AA Deventer, The Netherlands; (M.A.); (T.D.); (Y.H.S.)
| | - Ynte H. Schukken
- Royal GD, P.O. Box 9, 7400 AA Deventer, The Netherlands; (M.A.); (T.D.); (Y.H.S.)
- Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 7, 3584 CL Utrecht, The Netherlands
- Quantitative Veterinary Epidemiology, Department of Animal Sciences, Wageningen University, P.O. Box 338, 6700 AH Wageningen, The Netherlands
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Kanankege KST, Phelps NBD, Vesterinen HM, Errecaborde KM, Alvarez J, Bender JB, Wells SJ, Perez AM. Lessons Learned From the Stakeholder Engagement in Research: Application of Spatial Analytical Tools in One Health Problems. Front Vet Sci 2020; 7:254. [PMID: 32478109 PMCID: PMC7237577 DOI: 10.3389/fvets.2020.00254] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 04/16/2020] [Indexed: 01/06/2023] Open
Abstract
Stakeholder engagement in research is widely advocated as a tool to integrate diverse knowledge and perspectives in the management of health threats while addressing potential conflicts of interest. Although guidelines for stakeholder engagement exist in public health and environmental sciences, the feasibility of actionable decisions based on scientific analyses and the lessons learned from the stakeholder engagement in the process co-creation of knowledge have been rarely discussed in One Health literature and veterinary sciences. Risk maps and risk regionalization using spatiotemporal epidemiological/analytical tools are known to improve risk perception and communication. Risk maps are useful when informing policy and management decisions on quarantine, vaccination, and surveillance intended to prevent or control threats to human, animal, or environmental health interface (i.e., One Health). We hypothesized that researcher-stakeholder engagement throughout the research process could enhance the utility of risk maps; while identifying opportunities to improve data collection, analysis, interpretation, and, ultimately, implementation of scientific/evidence-based management and policy measures. Three case studies were conducted to test this process of co-creation of scientific knowledge, using spatiotemporal epidemiological approaches, all related to One Health problems affecting Minnesota. Our interpretation of the opportunities, challenges, and lessons learned from the process are summarized from both researcher and stakeholder perspectives. By sharing our experience we intend to provide an understanding of the expectations, realizations, and “good practices” we learned through this slow-moving iterative process of co-creation of knowledge. We hope this contribution benefits the planning of future transdisciplinary research related to risk map-based management of One Health problems.
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Affiliation(s)
- Kaushi S T Kanankege
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| | - Nicholas B D Phelps
- Department of Fisheries, Wildlife and Conservation Biology, College of Food, Agriculture and Natural Resource Sciences, University of Minnesota, Minneapolis, MN, United States.,Minnesota Aquatic Invasive Species Research Center, University of Minnesota, Minneapolis, MN, United States
| | - Heidi M Vesterinen
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| | - Kaylee M Errecaborde
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| | - Julio Alvarez
- Centro de Vigilancia Sanitaria Veterinaria (VISAVET), Universidad Complutense, Madrid, Spain.,Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense, Madrid, Spain
| | - Jeffrey B Bender
- Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Scott J Wells
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
| | - Andres M Perez
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United States
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