Henrik Stryhn

The use of multilevel models to evaluate sources of variation in reproductive performance in dairy cattle in Reunion Island.

Sources of variation in measures of reproductive performance in dairy cattle were evaluated using data collected from 3207 lactations in 1570 cows in 50 herds from five geographic regions of Reunion Island (located off the east coast of Madagascar). Three continuously distributed reproductive parameters (intervals from calving-to-conception, calving-to-first-service and first-service-to-conception) were considered, along with one Binomial outcome (first-service-conception risk). Multilevel models which take into account the hierarchical nature of the data were used to fit all models. For the overall measure of calving-to-conception interval, 86% of the variation resided at the lactation level with only 7, 6 and 2% at the cow, herd and regional levels, respectively. The proportion of variance at the herd and cow levels were slightly higher for the calving-to-first-service interval (12 and 9%, respectively) - but for the other two parameters (first-service-conception risk and first-service-to-conception interval), >90% of the variation resided at the lactation level. For the three continuous dependent variables, comparison of results between models based on log-transformed data and Box-Cox-transformed data suggested that minor departures from the assumption of normality did not have a substantial effect on the variance estimates. For the Binomial dependent variable, five different estimation procedures (penalised quasi-likelihood, Markov-Chain Monte Carlo, parametric and non-parametric bootstrap estimates and maximum-likelihood) yielded substantially different results for the estimate of the cow-level variance.

Evaluation of anti-Ostertagia ostertagi antibodies in individual milk samples as decision parameter for selective anthelmintic treatment in dairy cows

The aim of this study was to explore whether anti-Ostertagia ostertagi antibody levels measured by ELISA in individual milk samples hold promise as a decision parameter for either herd-level decisions or selective anthelmintic treatments by investigating (1) the relationship between individual and bulk-tank milk ELISA results; (2) the relationships of individual milk ELISA results with non-parasitic cow factors and (3) the relationship between individual milk ELISA results and the milk production response after anthelmintic treatment. Twelve farms were randomly allocated to a whole-herd treatment with eprinomectin or a placebo in October 2004 and individual milk samples and a bulk-tank milk sample were collected 1 month before and 1 month after treatment. Linear mixed models were used to investigate the associations of ELISA results with (a) breed, actual milk production, lactation stage, somatic cell count, age and sampling month and (b) the milk production response after anthelmintic treatment. There was a reasonable correlation between the mean individual and bulk-tank milk ELISA results (r=0.72). Individual cow ELISA results increased with higher lactation number and were lower in November than September. The associations with the other non-parasitic factors were weak and not significant. Milk yield responses to anthelmintic treatment were greater when treatment was given in early lactation and increased with the pre-treatment ELISA result and cow age. However, these latter two interaction terms were not significant when they were put in the model together. We conclude that (1) O. ostertagi ELISA results from individual milk samples may provide more information on the herds parasitic status than a single bulk-tank milk result; (2) lactation number should be taken into account when interpreting ELISA results from individual milk samples and (3) the value of the O. ostertagi antibody level in individual cow milk samples to predict individual production responses after anthelmintic treatment remains equivocal.

Using pseudogold standards and latent-class analysis in combination to evaluate the accuracy of three diagnostic tests

We previously reported our use of latent-class models to estimate the sensitivity (Se) and specificity (Sp) for each of three tests used to monitor farmed salmon for infectious salmon anaemia virus (ISAv). Those tests were reverse-transcriptase polymerase chain reaction (RT-PCR), virus isolation (VI), and an indirect immunofluorescent-antibody test (IFAT). We used tissues from 403 salmon from four populations presumed to have different prevalence of ISAv. However, no formal evaluation of the assumptions of conditional independence and constant accuracy had been carried out. In our present study, we adjusted that and used two pseudogold standards (a composite reference standard and a study pseudogold), as indicative of the true health status of each fish. The assumption of constant accuracy across populations was evaluated using separate random-effects logistic-regression models for fish classified as D+ or D- (disease positive or negative, according to the pseudogold standards) with study population included in the model to determine if it affected the probability of a positive test result. Where there was evidence of variation in test accuracy across populations, the issue was further investigated using separate latent-class models with informative priors for each study population. Our results suggested that only one PCR test had an accuracy that varied across populations. The assumption of conditional independence among tests was first evaluated using log-linear models of D+ and D- fish with significant interaction between test results indicative of conditional dependence. Latent-class models which incorporated up to two pairs of between-test dependencies were also fit using Bayesian methods. The two approaches showed considerable evidence of dependence between IFAT and VI and some evidence of dependence between one PCR and IFAT. Results obtained from both maximum-likelihood models and from Bayesian analyses of models allowing for conditional dependence between two pairs of tests were consistent with those obtained with the pseudogold standards. The results suggest that pseudogold standards can help in choosing a correct dependence structure and should be used in combination with latent-class models.

A scenario tree model for the Canadian Notifiable Avian Influenza Surveillance System and its application to estimation of probability of freedom and sample size determination

In 2008, Canada designed and implemented the Canadian Notifiable Avian Influenza Surveillance System (CanNAISS) with six surveillance activities in a phased-in approach. CanNAISS was a surveillance system because it had more than one surveillance activity or component in 2008: passive surveillance; pre-slaughter surveillance; and voluntary enhanced notifiable avian influenza surveillance. Our objectives were to give a short overview of two active surveillance components in CanNAISS; describe the CanNAISS scenario tree model and its application to estimation of probability of populations being free of NAI virus infection and sample size determination. Our data from the pre-slaughter surveillance component included diagnostic test results from 6296 serum samples representing 601 commercial chicken and turkey farms collected from 25 August 2008 to 29 January 2009. In addition, we included data from a sub-population of farms with high biosecurity standards: 36,164 samples from 55 farms sampled repeatedly over the 24 months study period from January 2007 to December 2008. All submissions were negative for Notifiable Avian Influenza (NAI) virus infection. We developed the CanNAISS scenario tree model, so that it will estimate the surveillance component sensitivity and the probability of a population being free of NAI at the 0.01 farm-level and 0.3 within-farm-level prevalences. We propose that a general model, such as the CanNAISS scenario tree model, may have a broader application than more detailed models that require disease specific input parameters, such as relative risk estimates.