Antibodies against Dictyocaulus viviparus major sperm protein in bulk tank milk: Association with clinical appearance, herd management and milk production.

The objective of this study was to conduct a comprehensive field survey using a Dictyocaulus viviparus major sperm protein ELISA on bulk tank milk samples from Belgian dairy herds to gain insights in: (1) the sensitivity (Se) and specificity (Sp) of the test under field conditions; (2) the value of the test to predict a future clinical lungworm outbreak; (3) its associations with milk production parameters and (4) its associations with herd management factors. A total of 1248 herds were sampled, with samplings occurring in the middle ("August") and towards the end ("October") of the grazing season. A completed questionnaire on potential risk factors and potentially lungworm-induced clinical signs was obtained from 587 farms and milk production records could be obtained from 343 herds. The median (25th-75th percentile) D. viviparus antibody level (ODR) was 0.25 (0.19-0.31) in "August" and 0.24 (0.19-0.32) in "October". At a threshold of 0.41 ODR, the Se and Sp were estimated using mixture models at 50 and 99%, respectively. At the same threshold, the positive and negative predictive value of the ELISA applied in "August" on the occurrence of farmer-reported lungworm symptoms in the period August-November were 65% and 69%, respectively. D. viviparus antibody levels were significantly higher in the north vs. the south of the country, in large herds and in herds that did not mow pastures or that frequently purchased new animals. An increase in the ELISA result of "August" over the interquartile range was associated with a drop in the annual average milk yield, milk protein% and milk fat% of -0.50kgcow-1day-1, 0.02 and 0.02, respectively. The relationships between the ELISA results in "October" and milk production parameters were also negative, but lower and non- or only marginally significant. We conclude that the bulk tank milk ELISA has a low value to predict lungworm disease on an individual farm based on a fixed sampling date in the middle of the grazing season. On the other hand, the test has been potential to detect subclinical production impacts and study risk factors through epidemiological surveys.

Climate-driven longitudinal trends in pasture-borne helminth infections of dairy cattle.

Helminth parasites of grazing ruminants are highly prevalent globally and impact negatively on animal productivity and food security. There is a growing concern that climate change increases helminth disease frequency and intensity. In Europe, these concerns stem from case reports and theoretical life cycle models assessing the effects of climate change scenarios on helminth epidemiology. We believe this study is the first to investigate climate-driven trends in helminth infections of cattle on a cohort of randomly selected farms. One thousand, six hundred and eighty dairy farms were monitored over an 8year period for the two major helminth infections in temperate climate regions and climate-driven trends were investigated by multivariable linear mixed models. The general levels of exposure to Fasciola hepatica decreased over the study period while those to Ostertagia ostertagi increased, and this could at least be partially explained by meteorological factors (i.e. the number of rainy (precipitation >1mm) and warm days (average daily temperature >10°C) in a year). The longitudinal trends varied according to the altitude and the agricultural region of the farm. This study shows that longitudinal epidemiological data from sentinel farms combined with meteorological datasets can significantly contribute to understanding the effects of climate on infectious disease dynamics. When local environmental conditions are taken into account, the effects of climate change on disease dynamics can also be understood at more local scales. We recommend setting up a longitudinal sampling strategy across Europe in order to monitor climate-driven changes in helminth disease risk to inform adaptation strategies to promote animal health and productivity.

Assessment of type I error rate associated with dose-group switching in a longitudinal Alzheimer trial.

In clinical trials, there always is the possibility to use data-driven adaptation at the end of a study. There prevails, however, concern on whether the type I error rate of the trial could be inflated with such design, thus, necessitating multiplicity adjustment. In this project, a simulation experiment was set up to assess type I error rate inflation associated with switching dose group as a function of dropout rate at the end of the study, where the primary analysis is in terms of a longitudinal outcome. This simulation is inspired by a clinical trial in Alzheimer's disease. The type I error rate was assessed under a number of scenarios, in terms of differing correlations between efficacy and tolerance, different missingness mechanisms, and different probabilities of switching. A collection of parameter values was used to assess sensitivity of the analysis. Results from ignorable likelihood analysis show that the type I error rate with and without switching was approximately the posited error rate for the various scenarios. Under last observation carried forward (LOCF), the type I error rate blew up both with and without switching. The type I error inflation is clearly connected to the criterion used for switching. While in general switching, in a way related to the primary endpoint, may impact the type I error, this was not the case for most scenarios in the longitudinal Alzheimer trial setting under consideration, where patients are expected to worsen over time.

Model based estimates of long-term persistence of inactivated hepatitis A vaccine-induced antibodies in adults.

BACKGROUND: In this paper, we review the results of existing statistical models of the long-term persistence of hepatitis A vaccine-induced antibodies in light of recently available immunogenicity data from 2 clinical trials (up to 17 years of follow-up). METHODS: Healthy adult volunteers monitored annually for 17 years after the administration of the first vaccine dose in 2 double-blind, randomized clinical trials were included in this analysis. Vaccination in these studies was administered according to a 2-dose vaccination schedule: 0, 12 months in study A and 0, 6 months in study B (NCT00289757/NCT00291876). Antibodies were measured using an in-house ELISA during the first 11 years of follow-up; a commercially available ELISA was then used up to Year 17 of follow-up. Long-term antibody persistence from studies A and B was estimated using statistical models for longitudinal data. Data from studies A and B were modeled separately. RESULTS: A total of 173 participants in study A and 108 participants in study B were included in the analysis. A linear mixed model with 2 changepoints allowed all available results to be accounted for. Predictions based on this model indicated that 98% (95%CI: 94-100%) of participants in study A and 97% (95%CI: 94-100%) of participants in study B will remain seropositive 25 years after receiving the first vaccine dose. Other models using part of the data provided consistent results: ≥95% of the participants was projected to remain seropositive for ≥25 years. CONCLUSION: This analysis, using previously used and newly selected model structures, was consistent with former estimates of seropositivity rates ≥95% for at least 25 years.

A Bayesian, generalized frailty model for comet assays.

This paper proposes a flexible modeling approach for so-called comet assay data regularly encountered in preclinical research. While such data consist of non-Gaussian outcomes in a multilevel hierarchical structure, traditional analyses typically completely or partly ignore this hierarchical nature by summarizing measurements within a cluster. Non-Gaussian outcomes are often modeled using exponential family models. This is true not only for binary and count data, but also for, example, time-to-event outcomes. Two important reasons for extending this family are for (1) the possible occurrence of overdispersion, meaning that the variability in the data may not be adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of a hierarchical structure in the data, owing to clustering in the data. The first issue is dealt with through so-called overdispersion models. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. In the case of time-to-event data, one encounters, for example, the gamma frailty model (Duchateau and Janssen, 2007 ). While both of these issues may occur simultaneously, models combining both are uncommon. Molenberghs et al. ( 2010 ) proposed a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. Here, we use this method to model data from a comet assay with a three-level hierarchical structure. Although a conjugate gamma random effect is used for the overdispersion random effect, both gamma and normal random effects are considered for the hierarchical random effect. Apart from model formulation, we place emphasis on Bayesian estimation. Our proposed method has an upper hand over the traditional analysis in that it (1) uses the appropriate distribution stipulated in the literature; (2) deals with the complete hierarchical nature; and (3) uses all information instead of summary measures. The fit of the model to the comet assay is compared against the background of more conventional model fits. Results indicate the toxicity of 1,2-dimethylhydrazine dihydrochloride at different dose levels (low, medium, and high).

Joint modeling of hierarchically clustered and overdispersed non-gaussian continuous outcomes for comet assay data.

Multivariate longitudinal or clustered data are commonly encountered in clinical trials and toxicological studies. Typically, there is no single standard endpoint to assess the toxicity or efficacy of the compound of interest, but co-primary endpoints are available to assess the toxic effects or the working of the compound. Modeling the responses jointly is thus appealing to draw overall inferences using all responses and to capture the association among the responses. Non-Gaussian outcomes are often modeled univariately using exponential family models. To accommodate both the overdispersion and hierarchical structure in the data, Molenberghs et al. A family of generalized linear models for repeated measures with normal and conjugate random effects. Statistical Science 2010; 25:325-347 proposed using two separate sets of random effects. This papers considers a model for multivariate data with hierarchically clustered and overdispersed non-Gaussian data. Gamma random effect for the over-dispersion and normal random effects for the clustering in the data are being used. The two outcomes are jointly analyzed by assuming that the normal random effects for both endpoints are correlated. The association structure between the response is analytically derived. The fit of the joint model to data from a so-called comet assay are compared with the univariate analysis of the two outcomes.

Impact of the synbiotic combination of Lactobacillus casei shirota and oligofructose-enriched inulin on the fecal volatile metabolite profile in healthy subjects.

SCOPE: Hypothesis-driven approaches have mainly focused on the quantification of SCFAs as mediators of beneficial effects of synbiotics. However, the emergence of metabolite profiling strategies allows to evaluate the colonic metabolism from a top-down approach. In the present study, we evaluated the impact of a synbiotic combination on fecal metabolite profiles. METHODS AND RESULTS: A synbiotic combination (Lactobacillus casei Shirota cells+oligofructose-enriched inulin) was evaluated in nine healthy volunteers. Before the start, during and after 4-wk treatment, fecal samples were obtained. GC-MS technology was applied to analyze the volatile metabolites. Application of a Type III test revealed that the metabolite profiles from the three conditions were significantly different. We identified three volatile organic compounds, acetate, dimethyl trisulfide and ethyl benzene, which were significantly affected. The acetate levels increased, whereas the dimethyl trisulfide levels decreased during and after the intervention. For ethyl benzene only an effect during the synbiotic intervention period was observed. CONCLUSION: We report a detailed analysis of the influence of L. casei Shirota combined with oligofructose-enriched inulin on fermentation metabolites. Our results indicated a stimulation of saccharolytic fermentation and, importantly, a reduction of potentially toxic protein fermentation metabolites dimethyl trisulfide and ethyl benzene attended these effects.