Actividades a Analizar Dentro del Plan de Manejo Ambiental

3.2.1.1 Selection of farms and sample collection

A sample size calculation showed that to have 80% power and 95% confidence to detect an odds ratio of 0.5 for a fluke infected cow having TB compared to a fluke uninfected cow, and with a TB prevalence within herds of 5% and a fluke prevalence of 30%, 71 bTB positive animals and 1420 bTB negative animals would be required

(http://powerandsamplesize.com/Calculators, Table 3.1). This assumed that the animals were not clustered within herds, however, as bTB is a relatively rare event and positive animals are removed from the herd prior to the next test, this is a reasonable assumption as long as herds all have a recent history of bTB, and other factors such as herd size and month of sample collection are taken into consideration. From previous discussions with veterinary surgeons in high risk areas, it was estimated that reaching this sample size might require testing 10-20 purposively selected herds.

Table 3.1. Details of the sample size calculation

Odds ratio of TB in fluke infected vs. fluke uninfected 0.5 Expected percentage of controls exposed to fluke 30%

Expected bTB prevalence 5%

Power 80%

Alpha 5%

Number of bTB negative animals required 1420 Number of bTB positive animals required 71

Veterinary surgeons with practices in high risk areas for bTB were asked to identify farmers who might be willing to participate in the study. Farmers were then contacted and given information about the study, before they decided whether to take part. Participating farms were visited between November 2013 and December 2014, at the time of the herd TB test, and all cattle over the age of 24 months were blood sampled from the tail vein. This was done under Home Office licence PIL 40/3621 and was approved by the University of Liverpool Ethics Committee ref VREC290. The TB test results (clear, reactor or IR) were obtained from the veterinary surgeon. For the purposes of this study, all of the results were used according to the interpretation (standard or severe) applied at the time of the test. Due to the lower than expected prevalence of bTB in the sampled cattle, the required

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number of bTB cases was not reached, as time and funds did not allow further herds to be tested.

3.2.1.2 Testing of samples

At the University of Liverpool, the samples were centrifuged at 1000 g for 20 minutes and the serum separated off for testing. Serum was used to test for F. hepatica antibodies using an excretory-secretory ELISA (Salimi-Bejestani et al., 2005, see Chapter 2 for details). Results obtained were a percent positivity (PP) of a known positive control, and PP values of 15 or above were considered to be positive.

An IgG isotype ELISA was used to compare F. hepatica specific IgG1 and IgG2 ratios in four reactors and four control (clear) animals. This was done using the same method as for the serum ELISA, with the following modifications: a separate ELISA plate was used for each isotype, and samples were added to the plate in decreasing concentrations. Polyclonal IgG1 and IgG2 conjugates were used (Sheep anti-bovine IgG1 horse radish peroxidase (HRP) and Sheep anti-bovine IgG2 HRP, Biorad, catalogue nrs AAI21 and AAI22). The conjugate concentrations were optimised using a checkerboard, and were 1 in 20,000 for IgG1 and 1 in 2500 for IgG2.

The mean optical density (OD) of the negative control was subtracted from the OD of the test samples, and the concentration of the last positive titre was recorded for each sample on each plate. This was used to calculate the ratio of IgG1 to IgG2 for each of the samples.

3.2.1.3 Analysis

Analysis was performed on both reactor and IR animals using R (R Core Team, 2011). Plots, summary statistics and logistic regression models were used to compare bTB diagnoses between fluke infected and uninfected animals. The month of sample collection modelled as a sinusoidal function (cos(2π*month/12) + sin(2π*month/12)), the age, sex and breed of cattle, and herd size and type were included as explanatory variables. These were added in different combinations and the best model was considered to be that with the lowest AIC. A longitudinal analysis was performed on data from one farm which was sampled three times.

Bootstrapping analysis was performed on the collected data to estimate the power of the current study and the sample size that would have been required under the observed bTB prevalence:

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yi (the probability of a cow I being diagnosed with bTB) was simulated for each cow in the

dataset as follows:

yi was drawn from a Bernoulli distribution of πi , where 𝑙𝑜𝑔𝑖𝑡(𝜋𝑖) = 𝛼 + 𝑥𝑖𝛽

Logit (πi ) is the log odds of cow i having bTB, α is the log odds of a fluke negative cow

having bTB, xi is the fluke positivity (0 = negative, 1 = positive) of cow i, and β is the co-

efficient for fluke infection.

A generalised linear regression model was constructed using these simulated values of Yi.

The simulation and model were run 1000 times and the p value of β recorded each time. The proportion of significant β values is an estimate of the power.

Values of α and β were varied. α depends on the prevalence of bTB whilst β depends on the magnitude of the effect of fluke.

This method was first used to calculate the power of the dataset to detect a significant difference with the values of α and β as found in the observed data (the output of the logistic regression model). Next, the effect size that could be detected by the sample size reached in the current study was estimated, and finally, by sampling with replacement from the collected dataset, the required sample size needed to detect the observed effect size at the observed prevalence was calculated.