Detection of anthelmintic resistance: a comparison of mathematical techniques

• Published in: Veterinary parasitology 2005-03, Vol.128 (3), p.291-298
• Main Authors: Torgerson, P.R., Schnyder, M., Hertzberg, H.
• Format: Article
• Language: English
• Subjects: Animals; Anthelmintic resistance; anthelmintics; Anthelmintics - pharmacology; Binomial Distribution; Computer Simulation; detection; Drug Resistance; fecal egg count; Feces - parasitology; gastrointestinal nematodes; Haemonchiasis - drug therapy; Haemonchus - growth & development; Intestinal Diseases, Parasitic - drug therapy; Intestinal Diseases, Parasitic - parasitology; Likelihood Functions; mathematical models; Maximum likelihood model; Models, Biological; Negative binomial distribution; nematode infections; Parasite Egg Count - methods; Parasite Egg Count - veterinary; Sheep; Sheep Diseases - drug therapy; Sheep Diseases - parasitology; simulation models; statistical models; worm burden
• ISSN: 0304-4017; 1873-2550
• DOI: 10.1016/j.vetpar.2004.12.009

Anthelmintic resistance has become an increasing problem particularly to gastrointestinal tract nematodes and appropriate methods are required to detect this phenomenon so the correct action can be taken. This paper compares a number of mathematical techniques that are used to analyse data. The negative binomial distribution is a mathematical distribution used to model aggregated data and hence is suitable to model the intensity of parasite burden and the magnitude of the faecal egg counts. Maximum likelihood techniques are utilised to exploit this mathematical distribution to analyse the magnitude of the faecal egg count reduction and decline in the worm burden in response to anthelmintic treatment. Data from experimental groups of sheep described in the accompanying paper are used. In addition, simulated data sets of faecal egg counts were created using a random number generator following appropriate negative binomial distributions. The results demonstrate this statistical model can detect evidence of anthelmintic resistance with a faecal egg reduction test that otherwise might require a slaughter trial to demonstrate. In addition, the simulated data sets confirm that there is a significant probability of failure to detect low anthelmintic efficacy with commonly used mathematical techniques. Consequently, the use of maximum likelihood mathematical techniques with a negative binomial statistical model would aid in the early detection of anthelmintic resistance using faecal egg count reductions and result in a lower probability of inappropriately assigning an anthelmintic as effective.