Skip to main content

The effect of analyst training on fecal egg counting variability

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Data availability

All data and materials are available upon request.

References

  • Auger J, Eustache F, Ducot B, Blandin T, Daudin M, Diaz I, El Matribi S, Gony B, Keskes L, Kolbezen M, Lamarte A, Lornage J, Nomal N, Pitaval G, Simon O, Virant-Klun I, Spira A, Jouannet P (2000) Intra- and inter-individual variability in human sperm concentration, motility, and vitality assessment during a workshop involving ten laboratories. Hum Reprod 15(11):2360-2368

  • Cain JL, Slusarewicz P, Rutledge MH, McVey MR, Wielgus KM, Zynda HM, Wehling LM, Scare JA, Steuer AE, Nielsen MK (2020) Diagnostic performance of McMaster, Wisconsin, and automated egg counting techniques for enumeration of equine strongyle eggs in fecal samples. Vet Parasitol 284:109199

    Article  Google Scholar 

  • Calvete C, Uriarte J (2013) Improving the detection of anthelmintic resistance: evaluation of faecal egg count reduction test procedures suitable for farm routines. Vet Parasitol 196:438–452

    Article  Google Scholar 

  • Carstensen H, Larsen L, Ritz C, Nielsen MK (2013) Daily variability of strongyle fecal egg counts in horses. J Equine Vet Sci 33(3):161–164

    Article  Google Scholar 

  • Coles GC, Bauer C, Borgsteede FHM, Geerts S, Klei TR, Taylor A, Waller PJ (1992) World Association for the Advancement of Veterinary Parasitology (W.A.A.V.P.) methods for the detection of anthelmintic resistance in nematodes of veterinary importance. Vet Parasitol 44:35–44

    CAS  Article  Google Scholar 

  • ESCCAP (2019) A guide to the treatment and control of equine gastrointestinal parasite infections. 2nd edition. European Scientific Counsel Companion Animal Parasites. https://www.esccap.org/uploads/docs/rtjqmu6t_0796_ESCCAP_Guideline_GL8_v7_1p.pdf. Accessed 5 October 2020

  • Kaplan R (2013) Recommendations for control of gastrointestinal nematode parasites in small ruminants: these ain’t your father’s parasites. Bovine Pr 47(2):97–109

    Google Scholar 

  • Kaplan RM, Nielsen MK (2010) An evidence-based approach to equine parasite control: it ain’t the 60s anymore. Equine Vet Educ 22:306–316

    Article  Google Scholar 

  • Kaplan R, Vidyashankar A (2012) An inconvenient truth: global worming and anthelmintic resistance. Vet Parasitol 186(1-2):70–78

    Article  Google Scholar 

  • Kenyon F, Jackson F (2012) Targeted flock/herd and individual ruminant treatment approaches. Vet Parasitol 186(1-2):10–17

    CAS  Article  Google Scholar 

  • Li Y, Zheng R, Wu Y, Chu K, Xu Q, Sun M, Smith ZJ (2019) A low-cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning. J Biophotonics 12(9):e201800410

    PubMed  Google Scholar 

  • Mariee N, Tuckrman E, Ali A, Li W, Laird S, Li TC (2012) The observer and cycle-to-cycle variability in the measurement of uterine natural killer cells by immunohistochemistry. J Reprod Imm 95:93–100

    CAS  Article  Google Scholar 

  • Mes THM, Eysker M, Ploeger HW (2007) A simple, robust and semi-automated parasite egg isolation protocol. Nat Prot 2:486–489

    CAS  Article  Google Scholar 

  • Nielsen MK, Mittel L, Grice A, Erskine M, Graves E, Vaala W, Tully RC, French D D, Bowman R, Kaplan RM (2019) AAEP parasite control guidelines. https://aaep.org/sites/default/files/Guidelines/AAEPParasiteControlGuidelines_0.pdf. Accessed 5 October 2020

  • Noel ML, Scare JA, Bellaw JL, Nielsen MK (2017) Accuracy and precision of mini-FLOTAC and McMaster techniques for determining equine strongyle egg counts. J Equine Vet Sci 48:1–6

    Article  Google Scholar 

  • O’Brien D (2018) Managing dewormer resistance. American Consortium for Small Ruminant Parasite Control. https://60f7303d-ac52-4cac-b7fb-6050f500b0b6.filesusr.com/ugd/6ef604_3981789ca4d34d74913834b1ea1b0b16.pdf. Accessed 12 October 2020

  • Peregrine AS, Molento MB, Kaplan RM, Nielsen MK (2014) Anthelmintic resistance in important parasites of horses: does it really matter? Vet Parasitol 201:1–8

    CAS  Article  Google Scholar 

  • Popović ZB, Thomas JD (2017) Assessing observer variability: a user’s guide. Cardiovasc Diagn Ther 7(3):317–324

    Article  Google Scholar 

  • Powell K, Kwee E, Nutter B, Herderick E, Paul P, Thut D, Boehm C, Muschler G (2016) Variability in subjective review of umbilical cord blood colony forming unit assay. Cytometry B Clin Cytom 90(6):517–524

    CAS  Article  Google Scholar 

  • R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/. Accessed 5 October 2020

  • Rendle D, Austin C, Bowen M, Cameron I, Furtado T, Hodgkinson J, McGorum B, Matthews J (2019) Equine de-worming: a consensus on current best practice. UK-Vet Equine 3:1):1–1)14

    Google Scholar 

  • Slusarewicz M, Slusarewicz P, Nielsen MK (2019) The effect of counting duration on quantitative fecal egg count test performance. Vet Par: X 2:100020

    Google Scholar 

  • Slusarewicz P, Pagano S, Mills C, Popa G, Chow KM, Mendenhall M, Rodgers DW, Nielsen MK (2016) Automated parasite faecal egg counting using fluorescence labelling, smartphone image capture and computational image analysis. Int J Parasitol 46:485–493

    Article  Google Scholar 

  • Suzuki CTN, Gomes JF, Falcão AX, Papa JP, Hoshino-Shimizu S (2013) Automatic segmentation and classification of human intestinal parasites from microscopy images. IEEE Trans Bio-med Eng 60:803–812

    Article  Google Scholar 

  • Vidyashankar AN, Hanlon BM, Kaplan RM (2012) Statistical and biological considerations in evaluating drug efficacy in equine strongyle parasites using fecal egg count data. Vet Parasitol 185:45–56

    CAS  Article  Google Scholar 

  • Yang YS, Park DK, Kim HC (2001) Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network. IEEE Trans Bio-med Eng 48:718–730

    CAS  Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Contributions

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.

Corresponding author

Correspondence to Jennifer L. Cain.

Ethics declarations

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.

Ethics approval

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

Consent to participate

N/A.

Consent for publication

N/A.

Code availability

N/A.

Additional information

Section Editor: Dante Zarlenga

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

ESM 1

(XLSX 17 kb)

ESM 2

(XLSX 9 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1007/s00436-021-07074-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00436-021-07074-2

Keywords

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