Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-17T14:16:59.511Z Has data issue: false hasContentIssue false
  • English
  • Français

Statistical approach to measure the efficacy of anthelmintic treatment on horse farms

Published online by Cambridge University Press:  23 August 2007

A. N. VIDYASHANKAR ,
R. M. KAPLAN  and
S. CHAN
A. N. VIDYASHANKAR*
Affiliation:
Department of Statistical Science, Cornell University Ithaca, NY 14853-4201, USA
R. M. KAPLAN
Affiliation:
Department of Infectious Diseases College of Veterinary Medicine, University of Georgia Athens, GA 30602, USA
S. CHAN
Affiliation:
Department of Statistical Science, Cornell University Ithaca, NY 14853-4201, USA
*
*Corresponding author: Department of Statistical Science, Cornell University Ithaca, NY 14853-4201, USA. Tel: +607 255 3759. Fax: +607 255 9801. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Resistance to anthelmintics in gastrointestinal nematodes of livestock is a serious problem and appropriate methods are required to identify and quantify resistance. However, quantification and assessment of resistance depend on an accurate measure of treatment efficacy, and current methodologies fail to properly address the issue. The fecal egg count reduction test (FECRT) is the practical gold standard for measuring anthelmintic efficacy on farms, but these types of data are fraught with high variability that greatly impacts the accuracy of inference on efficacy. This paper develops a statistical model to measure, assess, and evaluate the efficacy of the anthelmintic treatment on horse farms as determined by FECRT. Novel robust bootstrap methods are developed to analyse the data and are compared to other suggested methods in the literature in terms of Type I error and power. The results demonstrate that the bootstrap methods have an optimal Type I error rate and high power to detect differences between the presumed and true efficacy without the need to know the true distribution of pre-treatment egg counts. Finally, data from multiple farms are studied and statistical models developed that take into account between-farm variability. Our analysis establishes that if inter-farm variability is not taken into account, misleading conclusions about resistance can be made.

Keywords

efficacyanthelmintic resistancehorsebeta-binomial modellogit-normal modelbootstrap methodserror ratespower
Type
Research Article
Information
Parasitology , Volume 134 , Issue 14 , December 2007 , pp. 2027 - 2039
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anderson, R. M. and Gordon, D. M. (1982). Processes influencing the distribution of parasite numbers within host populations with special emphasis on parasite-induced host mortalities. Parasitology 85, 373–398.CrossRefGoogle ScholarPubMed
Bauer, C., Merkt, J., Janke-Grimm, G. and Burger, H. (1986). Prevalence and control of benzimidazole-resistant small strongyles on German thoroughbred studs. Veterinary Parasitology 21, 189–203.CrossRefGoogle ScholarPubMed
Billingsley, P. (1995). Probability and Measure, 3rd Edn. John Wiley & Sons, Inc, New York.Google Scholar
Casella, G. and Berger, R. L. (2001). Statistical Inference. Duxbury, New York.Google Scholar
Coles, G. C., Bauer, C., Borgsteede, F. H. M., Geerts, S., Klei, T. R., Taylor, M. A. and Waller, P. J. (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. Veterinary Parasitology 44, 35–44.CrossRefGoogle ScholarPubMed
Cornell, S. (2005). Modelling nematode populations: 20 years of progress. Trends in Parasitology 21, 542–545.CrossRefGoogle ScholarPubMed
Craven, J., Bjorn, H., Henriksen, S. A., Nansen, P., Larsen, M. and Lendal, S. (1998). Survey of anthelmintic resistance on Danish horse farms, using 5 different methods of calculating fecal egg count reduction. Equine Veterinary Journal 30, 289–293.CrossRefGoogle Scholar
Crofton, H. D. (1971). A quantitative approach to parasitism. Parasitology 62, 179–193.CrossRefGoogle Scholar
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman & Hall, New York.CrossRefGoogle Scholar
Fulford, A. J. C. (1994). Dispersion and bias: Can we trust geometric means? Parasitology Today 10, 446–448.CrossRefGoogle ScholarPubMed
Grenfell, B. T., Wilson, K., Isham, V. S., Boyd, H. E. G. and Dietz, K. (1995). Modelling patterns of parasite aggregation in natural populations: Trichostrongylid nematode-ruminant interactions as a case study. Parasitology 111 (Suppl.), S135–S151.CrossRefGoogle ScholarPubMed
Kaplan, R. M. (2002). Anthelmintic resistance in nematodes of horses. Veterinary Research 33, 491–507.CrossRefGoogle ScholarPubMed
Kaplan, R. M. (2004). Drug resistance in nematodes of veterinary importance: a status report. Trends in Parasitology 20, 477–481.CrossRefGoogle ScholarPubMed
Kaplan, R. M., Klei, T. R., Lyons, E. T., Lester, G. D., French, D. D., Tolliver, S. C., Courtney, C. H., Vidyashankar, A. N. and Zhao, Y. (2004). Prevalence of anthelmintic resistant cyathostomes on horse farms. Journal of the American Veterinary Medical Association 225, 903–910.CrossRefGoogle ScholarPubMed
Klei, T. R. (1986). Laboratory diagnosis. In Veterinary Clinics of North America: Equine Practice (ed. Herd, R. P.), pp. 381–393. W.B. Saunders, Philadelphia, USA.Google Scholar
Lyons, E. T., Tolliber, S. C. and Drudge, J. H. (1983). Critical tests in equids with fenbendazole alone or combined with piperazine: particular reference to activity on benzimidazole-resistant small strongyles. Veterinary Parasitology 12, 91–98.CrossRefGoogle ScholarPubMed
McCulloch, C. E. and Searle, S. R. (2001). Generalized, Linear, and Mixed Models. Wiley Interscience, New York.Google Scholar
Morgan, E. R., Cavill, L., Curry, G. E., Wood, R. M. and Mitchell, E. S. E. (2005). Effects of aggregation and sample size on composite fecal egg counts in sheep. Veterinary Parasitology 131, 79–87.CrossRefGoogle ScholarPubMed
Pook, J. F., Power, M. L., Sangster, N. C., Hodgson, J. L. and Hodgson, D. R. (2002). Evaluation of tests for anthelmintic resistance in cyathostomes. Veterinary Parasitology 106, 331–343.CrossRefGoogle ScholarPubMed
SAS Publishing. (2004). SAS/STAT Users Guide, Version 9.1. SAS Institute Inc, Cary, NC, USA.Google Scholar
Shaw, D. J. and Dobson, A. P. (1995). Patterns of macroparasite abundance and aggregation in wildlife populations: a quantitative review. Parasitology 111 (Suppl.), S111–S133.CrossRefGoogle ScholarPubMed
Torgerson, P. R., Schnyder, M. and Hertzberg, H. (2005). Detection of anthelmintic resistance: a comparison of mathematical techniques. Veterinary Parasitology 128, 291–298.CrossRefGoogle ScholarPubMed
Varady, M., Konigova, A. and Corba, J. (2000). Benzimidazole resistance in equine cyathostomes in Slovakia. Veterinary Parasitology 94, 67–74.CrossRefGoogle ScholarPubMed
Warnick, L. (1992). Daily variability of equine fecal strongyle egg counts. The Cornell Veterinarian 82, 453–463.Google ScholarPubMed
Woods, T. F., Lane, T. J., Zeng, Q. Y. and Courtney, C. H. (1998). Anthelmintic resistance on horse farms in north central Florida. Equine Practice 20, 14–17.Google Scholar