Technical variability and required sample size of helminth egg isolation procedures: revisited

A standardised faecal collection protocol for intestinal helminth egg counts in Asian elephants, Elephas maximus

The quantitative assessment of parasite infection is necessary to measure, manage and reduce infection risk in both wild and captive animal populations. Traditional faecal flotation methods which aim to quantify parasite burden, such as the McMaster egg counting technique, are widely used in veterinary medicine, agricultural management and wildlife parasitology. Although many modifications to the McMaster method exist, few account for systematic variation in parasite egg output which may lead to inaccurate estimations of infection intensity through faecal egg counts (FEC). To adapt the McMaster method for use in sampling Asian elephants (Elephas maximus), we tested a number of possible sources of error regarding faecal sampling, focussing on helminth eggs and using a population of over 120 semi-captive elephants distributed across northern Myanmar. These included time of day of defecation, effects of storage in 10% formalin and 10% formol saline and variation in egg distribution between and within faecal boluses. We found no significant difference in the distribution of helminth eggs within faecal matter or for different defecation times, however, storage in formol saline and formalin significantly decreased egg recovery. This is the first study to analyse several collection and storage aspects of a widely-used traditional parasitology method for helminth parasites of E. maximus using known host individuals. We suggest that for the modified McMaster technique, a minimum of one fresh sample per elephant collected from any freshly produced bolus in the total faecal matter and at any point within a 7.5 h time period (7.30am-2.55 pm) will consistently represent parasite load. This study defines a protocol which may be used to test pre-analytic factors and effectively determine infection load in species which produce large quantities of vegetative faeces, such as non-ruminant megaherbivores.

A simple method to count total faecal Capillaria worm eggs in racing pigeons (Columba livia)

Capillaria columbae and C. longicollis are fine threadworms found in racing pigeons' small intestines that produce a characteristic lemon shaped bipolar egg. Clinically, capillariasis in racing pigeons can present as severe illness and it has been suggested that worms may affect race performance. A major aim of this study was to validate a cheap, simple to perform flotation technique for counting Capillaria worm eggs in racing pigeon droppings. Trials using reference samples of pigeon droppings laced with 348, 275 and 129 Capillaria eggs per gram, found a typical flotation method based on the modified Wisconsin technique to be inaccurate at counting worm eggs. The main sources of error were due to the loss of eggs in the faecal discard and insufficient flotation time. A new technique, using 0.15 g sample size and 8h flotation time resulted in significantly improved test accuracy. On average the new technique recovered 93% of eggs from reference samples with 129-348 epg concentration, recovering 197 times more eggs than the modified Wisconsin technique. Typical percentage error, as a measure of absolute reliablility, was 10% for the new technique and 50% for the modified Wisconsin technique. The regression line on a test-retest series of samples over a range of egg counts from 0 to 573 epg had a gradient of 0.96 (y=0.96x+6.28; r(2)=0.8408) for the new technique and 0.54 (y=0.54x+0.06; r(2)=0.4249) for the modified Wisconsin technique. The Pearson product moment correlations of the new technique and the modified Wisconsin technique were 0.92 and 0.65 respectively. As measures of relative reliability both the gradient of the regression line and the Pearson product moment correlation further suggested better repeatability of the new technique. It was concluded that the new technique would be an appropriate quantitative method of assessing worm egg burdens in racing pigeons.

Quantifying the sources of variability in equine faecal egg counts: implications for improving the utility of the method

The faecal egg count (FEC) is the most widely used means of quantifying the nematode burden of horses, and is frequently used in clinical practice to inform treatment and prevention. The statistical process underlying the FEC is complex, comprising a Poisson counting error process for each sample, compounded with an underlying continuous distribution of means between samples. Being able to quantify the sources of variability contributing to this distribution of means is a necessary step towards providing estimates of statistical power for future FEC and FECRT studies, and may help to improve the usefulness of the FEC technique by identifying and minimising unwanted sources of variability. Obtaining such estimates require a hierarchical statistical model coupled with repeated FEC observations from a single animal over a short period of time. Here, we use this approach to provide the first comparative estimate of multiple sources of within-horse FEC variability. The results demonstrate that a substantial proportion of the observed variation in FEC between horses occurs as a result of variation in FEC within an animal, with the major sources being aggregation of eggs within faeces and variation in egg concentration between faecal piles. The McMaster procedure itself is associated with a comparatively small coefficient of variation, and is therefore highly repeatable when a sufficiently large number of eggs are observed to reduce the error associated with the counting process. We conclude that the variation between samples taken from the same animal is substantial, but can be reduced through the use of larger homogenised faecal samples. Estimates are provided for the coefficient of variation (cv) associated with each within animal source of variability in observed FEC, allowing the usefulness of individual FEC to be quantified, and providing a basis for future FEC and FECRT studies.

Problems with using the normal distribution--and ways to improve quality and efficiency of data analysis

Background

The Gaussian or normal distribution is the most established model to characterize quantitative variation of original data. Accordingly, data are summarized using the arithmetic mean and the standard deviation, by ± SD, or with the standard error of the mean, ± SEM. This, together with corresponding bars in graphical displays has become the standard to characterize variation.

Methodology/Principal Findings

Here we question the adequacy of this characterization, and of the model. The published literature provides numerous examples for which such descriptions appear inappropriate because, based on the “95% range check”, their distributions are obviously skewed. In these cases, the symmetric characterization is a poor description and may trigger wrong conclusions. To solve the problem, it is enlightening to regard causes of variation. Multiplicative causes are by far more important than additive ones, in general, and benefit from a multiplicative (or log-) normal approach. Fortunately, quite similar to the normal, the log-normal distribution can now be handled easily and characterized at the level of the original data with the help of both, a new sign, ^x/, times-divide, and notation. Analogous to ± SD, it connects the multiplicative (or geometric) mean * and the multiplicative standard deviation s* in the form * ^x/s*, that is advantageous and recommended.

Conclusions/Significance

The corresponding shift from the symmetric to the asymmetric view will substantially increase both, recognition of data distributions, and interpretation quality. It will allow for savings in sample size that can be considerable. Moreover, this is in line with ethical responsibility. Adequate models will improve concepts and theories, and provide deeper insight into science and life.

Comparison of three alternative methods for analysis of equine Faecal Egg Count Reduction Test data

The Faecal Egg Count Reduction Test (FECRT) is the most widely used method of assessing the efficacy of anthelmintics, and is the only in vivo technique currently approved for use with horses. Equine Faecal Egg Count (FEC) data are frequently characterised by a low mean, high variability, small sample size and frequent zero count observations. Accurate analysis of the data therefore depends on the use of an appropriate statistical technique. Analyses of simulated FECRT data by methods based on calculation of the empirical mean and variance, non-parametric bootstrapping, and Markov chain Monte Carlo (MCMC) are compared. The MCMC method consistently outperformed the other methods, independently of the distribution from which the data were generated. Bootstrapping produced notional 95% confidence intervals containing the true parameter as little as 40% of the time with sample sizes of less than 50. Analysis of equine FECRT data yielded inconclusive results in 53 of 63 (84%) datasets, suggesting that the routine use of prior sample size calculations should be adopted to ensure sufficient data are collected. The authors conclude that computationally intensive parametric methods such as MCMC be used for analysis of FECRT data with sample sizes of less than 50, in order to avoid erroneous inference about the true efficacy of anthelmintics in the field.

Counting coccidial oocysts in chicken faeces: A comparative study of a standard McMaster technique and a new rapid method

For the assessment of coccidial oocyst production by chickens, some modified form of the McMaster counting method is commonly used. The objective of this study was to evaluate a standard method and to compare it to a new, faster method, in which all the preparative stages before counting are carried out in the same container into which the original faecal sample was collected. A stock suspension containing purified oocysts of all seven valid Eimeria species that parasitize chickens was prepared, from which seven concentrations of oocyst suspensions were made. Since the faecal material in a sample influences the ability of oocysts to float up in a McMaster chamber, the new method was tested to establish the optimal amount of faeces in the original sample. Control oocyst suspensions containing no faeces were also tested, and three series of counts using the new method were compared with the standard McMaster method. The results were statistically analysed by agreement analysis. Repeatability and between-operator variation of both methods were also tested by agreement analysis. Counting by the standard McMaster method underestimated the true number of oocysts. The new method gave counts in agreement with the true number of oocysts if using 1 g of faeces per sample. With 2 g of faeces, counts were obtained that agreed with counts by the standard McMaster method. Both methods showed agreement between repeated measurements. The new method used on a sample containing 2 g of faeces provides a convenient alternative to the standard modified McMaster method. A 1-g faecal sample increases agreement with the true numbers of oocysts. Processing of a sample with the new method is about nine times faster than with the standard method.