Abstract
This study aimed to compare the FECPAKG2 and the McMaster techniques for counting of gastrointestinal nematode eggs in the faeces of alpacas using two floatation solutions (saturated sodium chloride and sucrose solutions). Faecal eggs counts from both techniques were compared using the Lin’s concordance correlation coefficient and Bland and Altman statistics. Results showed moderate to good agreement between the two methods, with better agreement achieved when saturated sugar is used as a floatation fluid, particularly when faecal egg counts are less than 1000 eggs per gram of faeces. To the best of our knowledge this is the first study to assess agreement of measurements between McMaster and FECPAKG2 methods for estimating faecal eggs in South American camelids.
Electronic supplementary material
The online version of this article (10.1186/s13071-018-2861-1) contains supplementary material, which is available to authorized users.
Keywords: McMaster technique, FECPAKG2, Nematodes, Faecal egg count, Alpaca
Letter to the Editor
Parasitic gastroenteritis caused by gastrointestinal nematodes (GINs) is responsible for significant clinical and subclinical problems in domesticated South American camelids (SACs), alpacas and llamas, resulting in economic losses arising from lowered production of fibre, meat and/or leather [1, 2]. Although no drugs are registered for use against GINs of SACs in Australia, anthelmintics are routinely used to deworm alpacas and llamas, mostly without assessment of worm burdens prior to treatment [3].
In SACs, the burden of GINs can be assessed using various coprological methods originally developed for domestic ruminants [4], with estimation of the number of GIN eggs per gram of faeces (faecal egg count, FEC). The most commonly used coprological method is the McMaster technique [5, 6]. The choice of floatation solution (saturated sugar or salt) is important for this technique because the type and specific gravity of floatation solutions can affect FEC results [5]. Previously, saturated sugar was found to be superior to salt as a floatation solution for the detection of some GINs in SACs as the rate of water loss and distortion of GIN eggs was slower using sugar [6, 7]. Although the McMaster technique has stood the test of time because it is a relatively simple and cheap procedure to carry out, it has a number of disadvantages, including difficulties identifying eggs when faeces are thick and dark, and high levels of technical skill and experience are required to identify and count nematode eggs of different species [5]. To overcome these limitations, other FEC estimation methods such as Kato-Katz®, FLOTAC® and FECPAK have been developed and validated in both humans and animals [5, 8, 9]. Recently, smartphone applications have been found to provide a more efficient approach for counting parasite eggs in the faeces of animals compared with the McMaster technique [10].
The FECPAK method is based on a modified McMaster technique with a minimum detection limit of 30–35 eggs per gram (EPG) of faeces [11]. The original FECPAK method was developed in New Zealand to provide a simple on-farm method for FEC estimation. The updated FECPAKG2 method uses a floatation-dilution approach similar to the McMaster technique, but involves capturing digital images of samples without the use of a microscope. Digital images of samples are stored, ready for assessment by trained technicians for identification and counting nematode eggs [12]. Each digital image remains available for reference and auditing purposes. Setting up the FECPAKG2 test does not require specialised laboratory equipment or technical skills, and preparation can be done easily in the field by a lay operator. As a result, large numbers of samples can be processed at one time and images analysed later.
The aim of this study was to compare FEC estimates of GINs in alpacas using FECPAKG2 and the McMaster technique using two floatation solutions (saturated sodium chloride and sucrose solutions).
Defining α = 0.05, a study power of 0.80 and agreement limits of ± 2100 EPG, we estimated that a sample size of 94 faecal samples was required to assess agreement between the two methods of measurement [13]. Briefly, fresh faecal samples (n = 94) were collected directly from the rectum of 3-month to 16-year-old Huacaya alpacas, from commercial farms (n = 10) in New South Wales, Queensland and Victoria, Australia. Samples were stored at 4 °C for up to seven days until the time of testing.
Each faecal sample was tested using modified McMaster technique [4, 6]. Briefly, four grams of faeces were soaked for 5–30 minutes in 11 ml of water in 60 ml plastic containers. The faecal slurry was then mixed with either 45 ml of saturated sodium chloride [specific gravity (SG) 1.20, Merck, Germany] or white sugar (SG 1.27, www.csrsugar.com.au) solution and homogenised using a metal spatula. After 30–45 minutes, a sample was drawn from the suspension using a sieve-top pipette (sieve aperture size 12 meshes per cm). Following agitation, the sample was introduced into two chambers of a Whitlock egg counting slide (www.whitlock.com.au) which was then placed on the stage of a compound light microscope. After five minutes, eggs were counted. The minimum detection limit using this method was 15 EPG.
All of the faecal samples that were processed using the McMaster technique were then processed using the FECPAKG2 (Techion Group Ltd., New Zealand; www.techiongroup.com) method using salt and sugar solution as per the manufacturer’s protocol. Four grams of faeces were selected for each sample.
The arithmetic means of EPG were calculated for faecal egg counts obtained using the two methods and differences in arithmetic means were tested using the Wilcoxon signed rank test. A P-value < 0.05 was considered as statistically significant. Differences in EPG estimates assessed using the McMaster and FECPAKG2 methods using sugar and salt solutions were statistically significant (P-values 0.003 and 0.001 for salt and sugar solutions, respectively). Due to the highly skewed distribution of the FEC data, individual FECs were log transformed [log10(EPG + 15)] [14], and agreement of the FEC estimates using the McMaster and FECPAKG2 methods assessed using the Lin’s concordance correlation coefficient [15] and Bland-Altman plot [16]. To provide interpretable Bland and Altman plots, limits of agreement for the log-transformed data were calculated and transformed back to the original scale using the approach described by Euser et al. [17]. These limits of agreement were plotted on the original scale using conventional Bland and Altman plots. Statistical analyses were carried out using the epiR package [18] implemented in R [19].
A total of 64–89% faecal samples was test-positive using the McMaster and FECPAKG2 methods (Table 1). However, when saturated salt solution was used, more samples had at least more than one EPG using the McMaster technique (89%; 81/91) compared with FECPAKG2 (77%; 70/91). Using sugar solution, more samples had at least more than one EPG using the McMaster technique (73%; 69/94) compared with FECPAKG2 (64%; 60/94; Table 1). There was a significant difference (P = 0.03) in the proportions of positive samples tested by both methods when salt solution was used (Table 1).
Table 1.
Floatation solution | Method | % Test-positive samples (proportion) | P-value | Arithmetic mean EPG ± SE | 95% CI | Range of EPG |
---|---|---|---|---|---|---|
Salt | McMaster | 89 (81/91) | 0.03 | 335 ± 62 | 211–458 | 0–3435 |
FECPAKG2 | 77 (70/91) | 438 ± 83 | 273–603 | 0–5180 | ||
Sugar | McMaster | 73 (69/94) | 0.16 | 448 ± 138 | 174–723 | 0–10,515 |
FECPAKG2 | 64 (60/94) | 280 ± 86 | 109–450 | 0–6930 |
Abbreviations: EPG eggs per gram of faeces, SE standard error of the mean, CI confidence interval
Lin’s concordance correlation coefficient was greater when sugar solution was used as a floatation fluid compared with salt (0.84, 95% CI: 0.77–0.89 and 0.78, 95% CI: 0.68–0.85, respectively) (see Additional file 1). These metrics are supported by the Bland-Altman plots shown in Fig. 1 where FEC differences for sugar are more tightly clustered around zero (Fig. 1b) compared with salt (Fig. 1a), particularly when mean EPGs were less than 1000. For the relatively small numbers of samples where mean EPGs were greater than 2500, differences in the two methods were greater for sugar (Fig. 1b).
To the best of our knowledge this is the first study to assess agreement between SAC FECs estimated using the McMaster and FECPAKG2 methods. Our results show moderate to good agreement between the two methods, with better agreement achieved when saturated sugar is used as a floatation fluid, particularly when FECs are less than 1000 EPG. The advantages of the FECPAKG2 method are that it does not require specialised laboratory equipment or highly trained staff on farm, and images are stored online for perpetuity.
Additional file
Acknowledgments
We are grateful to alpaca farmers across Australia who provided faecal samples for this study.
Funding
The financial assistance for this project was provided by the AgriFutures Australia and the Australian Alpaca Association. MHR is a grateful recipient of the Australian Postgraduate Award through the University of Melbourne and the PhD top-up scholarship from AgriFutures Australia.
Availability of data and materials
All data generated or analysed during this study are included in this published article and its supplementary information file.
Abbreviations
- CCC
Concordance correlation coefficient
- EPG
Eggs per gram of faeces
- FEC
Faecal egg count
- GINs
Gastrointestinal nematodes
- SACs
South American camelids
Authors’ contributions
AJ, SW and GM conceived the idea and provided feedback on the manuscript. MHR, MAS and AJ designed the study. MHR conducted the laboratory work, analyzed the data and drafted the manuscript. MAS helped in statistical analyses and sample size calculation. MAS, JLV and AJDC commented on the structure of manuscript and provided critical intellectual input. All authors read and approved the final manuscript.
Ethics approval
Faecal samples used in this study were available from a project approved by the Animal Ethics Committee (AEC no. 1413412.1) of the University of Melbourne.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Footnotes
Electronic supplementary material
The online version of this article (10.1186/s13071-018-2861-1) contains supplementary material, which is available to authorized users.
Contributor Information
Mohammed H. Rashid, Email: mrashid@student.unimelb.edu.au
Mark A. Stevenson, Email: mark.stevenson1@unimelb.edu.au
Shea Waenga, Email: shea@techiongroup.co.nz.
Greg Mirams, Email: Greg@techiongroup.co.nz.
Angus J. D. Campbell, Email: a.campbell@unimelb.edu.au
Jane L. Vaughan, Email: vicuna@me.com
Abdul Jabbar, Email: jabbara@unimelb.edu.au.
References
- 1.Leguia G. The epidemiology and economic impact of llama parasites. Parasitol Today. 1991;7:54–55. doi: 10.1016/0169-4758(91)90190-Y. [DOI] [PubMed] [Google Scholar]
- 2.Windsor RHS, Teran M, Windsor RS. Effects of parasitic infestation on the productivity of alpacas (Lama pacos) Trop Anim Health Prod. 1992;24:57–62. doi: 10.1007/BF02357238. [DOI] [PubMed] [Google Scholar]
- 3.Jabbar A, Campbell AJD, Charles JA, Gasser RB. First report of anthelmintic resistance in Haemonchus contortus in alpacas in Australia. Parasit Vectors. 2013;6:243. doi: 10.1186/1756-3305-6-243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Gordon HM, Whitlock H. A new technique for counting nematode eggs in sheep faeces. J Counc Sci Ind Res. 1939;12:50–52. [Google Scholar]
- 5.Ballweber LR, Beugnet F, Marchiondo AA, Payne PA. American Association of Veterinary Parasitologists’ review of veterinary fecal flotation methods and factors influencing their accuracy and use - is there really one best technique? Vet Parasitol. 2014;204:73–80. doi: 10.1016/j.vetpar.2014.05.009. [DOI] [PubMed] [Google Scholar]
- 6.Cebra CK, Stang BV. Comparison of methods to detect gastrointestinal parasites in llamas and alpacas. J Am Vet Med Assoc. 2008;232:733–741. doi: 10.2460/javma.232.5.733. [DOI] [PubMed] [Google Scholar]
- 7.Cringoli G, Rinaldi L, Veneziano V, Capelli G, Scala A. The influence of flotation solution, sample dilution and the choice of McMaster slide area (volume) on the reliability of the McMaster technique in estimating the faecal egg counts of gastrointestinal strongyles and Dicrocoelium dendriticum in sheep. Vet Parasitol. 2004;123:121–131. doi: 10.1016/j.vetpar.2004.05.021. [DOI] [PubMed] [Google Scholar]
- 8.Bosco A, Rinaldi L, Maurelli MP, Musella V, Coles GC, Cringoli G. The comparison of FLOTAC, FECPAK and McMaster techniques for nematode egg counts in cattle. Acta Parasitol. 2014;59:625–628. doi: 10.2478/s11686-014-0282-7. [DOI] [PubMed] [Google Scholar]
- 9.Levecke B, Behnke JM, Ajjampur SSR, Albonico M, Ame SM, Charlier J, et al. A comparison of the sensitivity and fecal egg counts of the McMaster egg counting and Kato-Katz thick smear methods for soil-transmitted helminths. PLOS Negl Trop Dis. 2011;5:e1201. doi: 10.1371/journal.pntd.0001201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Saeed MA, Jabbar A. Smart diagnosis of parasitic diseases by use of smartphones. J Clin Microbiol. 2018;56:e01469–e01417. doi: 10.1128/JCM.01469-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Presland SL, Morgan ER, Coles GC. Counting nematode eggs in equine faecal samples. Vet Rec. 2005;156:208–210. doi: 10.1136/vr.156.7.208. [DOI] [PubMed] [Google Scholar]
- 12.Sowerby SJ, Crump JA, Johnstone MC, Krause KL, Hill PC. Smartphone microscopy of parasite eggs accumulated into a single field of view. Am J Trop Med Hyg. 2016;94:227–230. doi: 10.4269/ajtmh.15-0427. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lu MJ, Lu MJ, Zhong WH, Liu YX, Miao HZ, Li YC, Ji MH. Sample size for assessing agreement between two methods of measurement by Bland-Altman method. Int J Biostat. 2016;12. doi:10.1515/ijb-2015-0039. [DOI] [PubMed]
- 14.Nodtvedt A, Dohoo I, Sanchez J, Conboy G, DesCoteaux L, Keefe G, Leslie K, Campbell J. The use of negative binomial modelling in a longitudinal study of gastrointestinal parasite burdens in Canadian dairy cows. Can J Vet Res. 2002;66:249–257. [PMC free article] [PubMed] [Google Scholar]
- 15.Lin L, Hedayat AS, Sinha B, Yang M. Statistical methods in assessing agreement: models, issues, and tools. J Am Stat Assoc. 2002;97:257–270. doi: 10.1198/016214502753479392. [DOI] [Google Scholar]
- 16.Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud. 2010;47:931–936. doi: 10.1016/j.ijnurstu.2009.10.001. [DOI] [PubMed] [Google Scholar]
- 17.Euser A, Dekker F, le Cessie S. A practical approach to Bland-Altman plots and variation coefficients for log transformed variables. J Clin Epidem. 2008;61:978–982. doi: 10.1016/j.jclinepi.2007.11.003. [DOI] [PubMed] [Google Scholar]
- 18.Stevenson M, Nunes T, Heuer C, Marshall J, Sanchez J, Thornton R, et al. epiR: Tools for the Analysis of Epidemiological Data. Melbourne: Faculty of Veterinary and Agricultural Sciences, The University of Melbourne; 2018. [Google Scholar]
- 19.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2017. https://www.R-project.org/.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data generated or analysed during this study are included in this published article and its supplementary information file.