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The effect of analyst training on fecal egg counting variability


Fecal egg counts (FECs) are essential for veterinary parasite control programs. Recent advances led to the creation of an automated FEC system that performs with increased precision and reduces the need for training of analysts. However, the variability contributed by analysts has not been quantified for FEC methods, nor has the impact of training on analyst performance been quantified. In this study, three untrained analysts performed FECs on the same slides using the modified McMaster (MM), modified Wisconsin (MW), and the automated system with two different algorithms: particle shape analysis (PSA) and machine learning (ML). Samples were screened and separated into negative (no strongylid eggs seen), 1–200 eggs per gram of feces (EPG), 201–500 EPG, 501–1000 EPG, and 1001+ EPG levels, and ten repeated counts were performed for each level and method. Analysts were then formally trained and repeated the study protocol. Between analyst variability (BV), analyst precision (AP), and the proportion of variance contributed by analysts were calculated. Total BV was significantly lower for MM post-training (p = 0.0105). Additionally, AP variability and analyst variance both tended to decrease for the manual MM and MW methods. Overall, MM had the lowest BV both pre- and post-training, although PSA and ML were minimally affected by analyst training. This research illustrates not only how the automated methods could be useful when formal training is unavailable but also how impactful formal training is for traditional manual FEC methods.

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The authors would like to thank Paul Slusarewicz and MEP Equine Solutions for providing technical support and materials for this study.

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Authors and Affiliations



Study conception and design were performed by Jennifer L. Cain, Kerri T. Peters, and Martin K. Nielsen. Data collection was performed by Jennifer L. Cain, Kerri T. Peters, Parul Suri, and Amber Roher. Data analysis was performed by Jennifer L. Cain and Matthew H. Rutledge. Writing of original draft preparation and writing were done by Jennifer L. Cain and Martin K. Nielsen with all other authors were responsible for reviewing, editing, and approving the final version. Jennifer L. Cain supervised Kerri T. Peters, Parul Suri, and Amber Roher in the laboratory and Martin K. Nielsen oversaw the entire project.

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Correspondence to Jennifer L. Cain.

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Conflict of interest

Dr. Martin Nielsen holds stock in MEP Equine Solutions, a company that is manufacturing an automated parasite egg counting technique. None of the other authors have any conflicts to declare.

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Sample collection was conducted in accordance with the University of Kentucky’s Institutional Animal Care and Use Committee (IACUC) protocol number 2012-1046.

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Section Editor: Dante Zarlenga

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Cain, J.L., Peters, K.T., Suri, P. et al. The effect of analyst training on fecal egg counting variability. Parasitol Res 120, 1363–1370 (2021).

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  • Fecal egg count
  • McMaster
  • Wisconsin
  • Automated
  • Horse
  • Analyst