Farm and cow-level prevalence of bovine digital dermatitis on dairy farms in Taranaki, New Zealand.

AIMS: The aims of this cross-sectional study were to investigate the herd and cow-level prevalence of bovine digital dermatitis (BDD) in dairy farms in the northern Taranaki region of New Zealand, and to identify whether there was any spatial clustering of herds with the disease. METHODS: A survey of 224 dairy farms in the northern Taranaki region of New Zealand was undertaken from September 2014 to February 2015. Following training in robust criteria to confirm BDD visually, a technician inspected the rear feet of every milking cow on the farms during milking. The identity of cows with lesions and the feet involved were recorded. The proportion of cows affected among the inspected population (cow-level prevalence), the proportion of a herd affected (farm-level prevalence), and proportion of farms with ≥1 cow with lesions, were calculated. A bivariate K function analysis was then used to assess whether farms with ≥1 cow with lesions were clustered, after accounting for the distribution of the farms involved in the study. RESULTS: Bovine digital dermatitis lesions were observed on 143/224 (63.8 (95% CI=57.5-70.1)%) farms. Within-farm prevalence was 0% on 81 (36.2%) farms, between >0 and <3% on 120 (53.5%) farms, with a maximum prevalence of 12.7% on one farm. Overall, cow-level prevalence was 707/60,455 (1.2 (95% CI=0.9-3.0)%), and on affected farms was 707/41,116 (1.7 (95% CI=1.4-2.1)%). In affected cows, 268/707 (37.9%) had a lesion on left foot only, 262/707 (37.1%) on the right foot only and 177/707 (25.0%) on both feet. The K function analysis showed no evidence of clustering of farms with BDD. CONCLUSIONS: Bovine digital dermatitis was widespread among the survey farms, but there was no evidence that there was any clustering of herds with BDD. The cow-level prevalence on affected farms was much lower than reported elsewhere. CLINICAL RELEVANCE: Although the prevalence at the cow level was low, if these data are representative of other regions of New Zealand, BDD could easily become a major problem on dairy farms in New Zealand, as has been observed in other countries.

Building a foundation for 'One Health': an education strategy for enhancing and sustaining national and regional capacity in endemic and emerging zoonotic disease management.

The rapid global spread of diseases such as SARS, H5N1, and H1N1 influenza has emphasized the pressing need for trans-disciplinary collaboration and cross-border action, and has also exposed a serious deficit of capacity and coordination in dealing effectively with emerging disease threats. The need for capacity development is particularly acute in the developing world, which is the least well-equipped to respond adequately. Such capacity development can be achieved through education and the implementation of applied 'One Health' activities. This chapter describes the establishment of a 'One Health' capacity development program in South Asia, consisting of two phases. The first phase provides Masters level training for public health doctors and veterinarians, with a focus on epidemiology, and disease control. The second phase reinforces the postgraduate training by establishing a sustainable framework for the implementation of collaborative 'One Health' activities such as the development of multidisciplinary professional networks, implementation of applied zoonotic disease investigation projects, and support for continuing professional development. The objectives are to provide individual skills required to strengthen capacity; to develop an appreciation of the cross-cutting issues which affect human and animal health, set within an institutional context; and to facilitate the development of regional professional networks which will be instrumental in implementing 'One Health' activities.

A framework for the joint modeling of longitudinal diagnostic outcome data and latent infection status: application to investigating the temporal relationship between infection and disease.

For many diseases the infection status of individuals cannot be observed directly, but can only be inferred from biomarkers that are subject to measurement error. Diagnosis of infection based on observed symptoms can itself be regarded as an imperfect test of infection status. The temporal relationship between infection and marker outcomes may be complex, especially for recurrent diseases where individuals can experience multiple bouts of infection. We propose an approach that first models the unobserved longitudinal infection status of individuals conditional on relevant covariates, and then jointly models the longitudinal sequence of biomarker outcomes conditional on infection status and covariate information through time, thus resulting in a joint model for longitudinal infection and biomarker sequences. This model can be used to investigate the temporal dynamics of infection, and to evaluate the usefulness of biomarkers for monitoring purposes. Our work is motivated and illustrated by a longitudinal study of bovine digital dermatitis (BDD) on commercial dairy farms in North West England and North Wales, in which the infection of interest is Treponeme spp., and the biomarkers of interest are a continuous enzyme-linked immunosorbent assay test outcome and a dichotomous outcome, foot lesion status. BDD is known to be one of the possible causes of foot lesions in cows.

Prevalence studies for Theileria orientalis conducted during the early stages of the 2012 New Zealand epidemic of Theileria associated bovine anaemia.

Since 1982 there have been two epidemics of bovine anaemia associated with Theileria orientalis infection (TABA) in New Zealand. The latest and more serious epidemic started in 2012 and is associated with the T. orientalis (Ikeda) type. In 2013 the Ministry for Primary Industries (MPI) carried out several epidemiological investigations on the TABA epidemic, which included a series of on-farm herd prevalence studies. The aims of this paper are to present and summarise the results of these herd prevalence studies. The data were collected from March 2013 to September 2013 and comprised 26 farms. For 21/26 of these farms, a T. orientalis (Ikeda) positive index case of TABA precipitated the follow-up herd prevalence study. Each herd prevalence study involved a random sample of 10 cows for haematocrit, for piroplasm counts (number of Theileria infected RBCs per 1000 RBCs) and for PCR molecular diagnosis. Animals were diagnosed anaemic if the haematocrit ≤0.24. The results showed that for the index cases, the average haematocrit = 0.10 L/L and the average piroplasm count = 10.9 Theileria infected RBCs per 1000 RBCs, and for the herd prevalence studies the average haematocrit = 0.26 and the average piroplasm count = 6.9. The average herd prevalence of T. orientalis (Ikeda) PCR positive cattle and of anaemic cattle was 87% and 26% respectively. For dairy herds (n = 7 herds) it took, on average, 80 days for the within herd prevalence of anaemia to return to zero after the initial diagnosis. In conclusion the prevalence studies showed that infection rates and anaemia rates were already high in herds when the index case of TABA was observed. At the time, these prevalence studies significantly added to our understanding of the epidemiology of the evolving T. orientalis (Ikeda) epidemic and furthermore met the New Zealand MPI objective of being rapid and applied, fit for purpose and completed at a relatively low cost.

Farm level risk factors for bovine digital dermatitis in Taranaki, New Zealand: An analysis using a Bayesian hurdle model.

As part of a cross-sectional study of bovine digital dermatitis (BDD) in 60,455 cows across 224 herds, in Taranaki, a region of the North Island of New Zealand, from September 2014 to February 2015, questionnaires from 114 farms were analysed to identify the key management practices which affect the probability of a farm being affected by BDD and the prevalence of an individual cow being affected on affected farms. The data from the questionnaires were analysed using a Bayesian hurdle model. Two factors were identified as being associated with farm level infection status: (1) milking parlour type; farms which had rotary platforms were more likely to be recorded as having BDD than those which had herringbone parlours (odds ratio, OR, 3.19; 95% probability interval, PI, 1.31-8.51); and (2) young stock movement; farms whose young stock were reared on farms alongside heifers from other farms had a higher odds of being BDD positive than farms where heifers were kept separate (OR 4.15; 95% PI 1.39-15.27). Two factors were associated with an increase in the prevalence of BDD within affected farms: (1) farms which used outside staff to trim feet had a higher prevalence of BDD (prevalence ratio, PR, 3.13; 95% PI 1.25-7.29) than farms which did not use outside staff; and (2) farms examined in spring (September to November) had a higher prevalence of BDD (PR 2.16; 95% PI 1.05-4.43) than farms examined in summer (December to February).

Estimating the true prevalence of bovine digital dermatitis in taranaki, New Zealand using a bayesian latent class model.

A Bayesian latent class model was developed to estimate the true prevalence of bovine digital dermatitis (BDD) in Taranaki, New Zealand. This model allowed farms to have zero prevalence as well as also accounting for between farm heterogeneity that was conditional on whether a farm was positive for bovine digital dermatitis. The estimated true farm level prevalence was 68.9% (95% credible interval [CrI]: 50.0%-85.7%), while on infected farms the overall cow level prevalence (number of infected cows/total number of cows on infected farms) was 2.9% (95%CrI: 2.1%-4.3%). The sensitivity analyses suggested that the prevalence estimates were reasonably robust when the variation of the priors fell within the biologically plausible range. These results indicated that visual inspection of standing animals during milking was sufficiently accurate to identify infected farms. However, for every 100 animals identified through visual inspection, 84 animals with lesions were missed. In other words, 46% (calculated as 84/184) of true positives at the animal level could be missed by visual inspection. The high and robust specificity (99.9%, 95%CrI: 99.8%-99.9%) suggested that lesions reported as BDD were very unlikely to be false positives.

An assessment of the herd-level impact of the Theileria orientalis (Ikeda) epidemic of cattle in New Zealand, 2012-2013: a mixed methods approach.

AIM: To estimate incidence risk, cumulative mortality and case fatality rate within herds affected by bovine anaemia associated with Theileria orientalis infection (TABA), in New Zealand during the early phase of the epidemic (August 2012-September 2013). METHODS: A mixed methods approach was utilised to integrate data from various sources, including a detailed questionnaire carried out on 18 dairy farms which had experienced cases of TABA; a brief telephone survey of an additional 139 case farms; and data extracted from a Ministry for Primary Industries database for a further 42 case farms. The subsequent analysis determined incidence risk, cumulative mortality and case fatality rates for beef and dairy herds. RESULTS: Data were analysed from 196/263 (74%) known case farms at the date of closing the questionnaires. These farms contained 99,505 cattle; 2,847 animals were reported with clinical disease, and a further 590 animals were recorded as having died from TABA. The within-herd incidence risk, cumulative mortality and case fatality rate were consistent between the three data sources, did not differ between beef and dairy herds, and were estimated to be 0.97 (inter-quartile range (IQR) 0.36-2.07)%, 0.23 (IQR 0.00-0.66)% and 16.67 (IQR 0.00-33.33)%, respectively. There was substantial variability in the level of impact, with 22 farms severely affected (incidence risk >5% and cumulative mortality >5%). CONCLUSIONS: The mixed methods approach was effective in dealing with the disparate data sources. The inclusion of the majority of farms known to be affected at the time the questionnaires were performed implies that the information is likely to be representative. The collective outputs of the analyses represent the best estimate available of within-herd measures of disease frequency in the early phase of the epidemic in New Zealand. The limitations of the data imply that their primary application may be to inform the design of subsequent structured observational field studies. CLINICAL RELEVANCE: The results of this study provide information on the impact of TABA on cattle farms during the emergence and early spread of the disease, as well as for generating hypotheses on causal mechanisms and risk factors that may influence the course of disease.

Compatibility between livestock databases used for quantitative biosecurity response in New Zealand.

AIM: To characterise New Zealand's livestock biosecurity databases, and investigate their compatibility and capacity to provide a single integrated data source for quantitative outbreak analysis. METHODS: Contemporary snapshots of the data in three national livestock biosecurity databases, AgriBase, FarmsOnLine (FOL) and the National Animal Identification and Tracing Scheme (NAIT), were obtained on 16 September, 1 September and 30 April 2014, respectively, and loaded into a relational database. A frequency table of animal numbers per farm was calculated for the AgriBase and FOL datasets. A two dimensional kernel density estimate was calculated for farms reporting the presence of cattle, pigs, deer, and small ruminants in each database and the ratio of farm densities for AgriBase versus FOL calculated. The extent to which records in the three databases could be matched and linked was quantified, and the level of agreement amongst them for the presence of different species on properties assessed using Cohen's kappa statistic. RESULTS: AgriBase contained fewer records than FOL, but recorded animal numbers present on each farm, whereas FOL contained more records, but captured only presence/absence of animals. The ratio of farm densities in AgriBase relative to FOL for pigs and deer was reasonably homogeneous across New Zealand, with AgriBase having a farm density approximately 80% of FOL. For cattle and small ruminants, there was considerable heterogeneity, with AgriBase showing a density of cattle farms in the Central Otago region that was 20% of FOL, and a density of small ruminant farms in the central West Coast area that was twice that of FOL. Only 37% of records in FOL could be linked to AgriBase, but the level of agreement for the presence of different species between these databases was substantial (kappa>0.6). Both NAIT and FOL shared common farm identifiers which could be used to georeference animal movements, and there was a fair to substantial agreement (kappa 0.32-0.69) between these databases for the presence of cattle and deer on properties. CONCLUSIONS: The three databases broadly agreed with each other, but important differences existed in both species composition and spatial coverage which raises concern over their accuracy. Importantly, they cannot be reliably linked together to provide a single picture of New Zealand's livestock industry, limiting the ability to use advanced quantitative techniques to provide effective decision support during disease outbreaks. We recommend that a single integrated database be developed, with alignment of resources and legislation for its upkeep.

Diagnostic assessment without cut-offs: application of serology for the modelling of bovine digital dermatitis infection.

Bovine digital dermatitis (BDD) is an epidermitis which is a leading cause of infectious lameness. The only recognized diagnostic test is foot inspection, which is a labour-intensive procedure. There is no universally recognized, standardized lesion scoring system. As small lesions are easily missed, foot inspection has limited diagnostic sensitivity. Furthermore, interpretation is subjective, and prone to observer bias. Serology is more convenient to carry out and is potentially a more sensitive indicator of infection. By carrying out 20 serological assays using lesion-associated Treponema spp. isolates, three serogroups were identified. The reliability of the tests was established by assessing the level of agreement and the concordance correlation coefficient. Subsequently, an ELISA suitable for routine use was developed. The benchmark of diagnostic test validation is conventionally the determination of the key test parameters, sensitivity and specificity. This requires the imposition of a cut-off point. For serological assays with outcomes on a continuous scale, the degree by which the test result differs from this cut-off is disregarded. Bayesian statistical methodology has been developed which enables the assay result also to be interpreted on a continuous scale, thereby optimizing the information inherent in the test. Using a cross-sectional study dataset carried out on 8 representative dairy farms in the UK, the probability of infection, P(I), of each individual animal was estimated in the absence of a 'Gold Standard' by modelling I as a latent variable which was determined by lesion status, L as well as serology, S. Covariate data (foot hygiene score and age) were utilized to estimate P(L) when no lesion inspection was performed. Informative prior distributions were elicited where possible. The model was utilized for predictive inference, by computing estimates of P(I) and P(L) independently of the data. A more detailed and informative analysis of the farm-level distribution of infection could thus be performed. Also, biases associated with the subjective interpretation of lesion status were minimized. Model outputs showed that young stock were unlikely to be infected, whereas cows tended to have high or low probabilities of being infected. Estimates of probability of infection were considerably higher for animals with lesions than for those without. Associations were identified between both covariates and probability of infection in cows, but not in the young stock. Under the condition that the model assumptions are valid for the larger population, the results of this work can be generalized by predictive inference.

Spatial and spatio-temporal analysis of Salmonella infection in dairy herds in England and Wales.

Using data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0.001) and borderline evidence was found for spatial clustering of S. Typhimurium (P=0.077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.