Use of laboratory data to reduce the time taken to detect new diseases: <scp>vida</scp> to FarmFile

Gibbens, J. C.; Robertson, Sara; Willmington, J. A.; Milnes, Ailsa; Ryan, J. B. M.; Wilesmith, J. W.; Cook, A. J. C.; David, GP

doi:10.1136/vr.162.24.771

Cited by 27 publications

(26 citation statements)

References 3 publications

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“…BPHS started in 2005 and provides frequent feedback of benchmarked results from targeted abattoir inspections to the participating producers and their herd veterinarians [8], helping to increase their awareness of the occurrence of subclinical diseases in their farms [11]. Since 1999, farm and laboratory data collected from voluntary laboratory submissions made across Great Britain, to APHA Regional Laboratories and Scottish Agricultural College’s Consultancy Division’s Disease Surveillance Centres has been aggregated in the FarmFile database [12]. Carcass and non-carcass submissions are submitted through private veterinary surgeons for laboratory testing and diagnostic investigation.…”

Section: Introductionmentioning

confidence: 99%

Pig Abattoir Inspection Data: Can It Be Used for Surveillance Purposes?

et al. 2016

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Statutory recording of carcass lesions at the abattoir may have significant potential as a resource for surveillance of livestock populations. Food Standards Agency (FSA) data in Great Britain are not currently used for surveillance purposes. There are concerns that the sensitivity of detection, combined with other issues, may make the outputs unreliable. In this study we postulate that FSA data could be used for surveillance purposes. To test this we compared FSA data with BPHS (a targeted surveillance system of slaughtered pigs) and laboratory diagnostic scanning surveillance (FarmFile) data, from mid-2008 to mid-2012, for respiratory conditions and tail bite lesions in pigs at population level. We also evaluated the agreement/correlation at batch level between FSA and BPHS inspections in four field trials during 2013. Temporal trends and regional differences at population level were described and compared using logistic regression models. Population temporal analysis showed an increase in respiratory disease in all datasets but with regional differences. For tail bite, the temporal trend and monthly patterns were completely different between the datasets. The field trials were run in three abattoirs and included 322 batches. Pearson’s correlation and Cohen’s kappa tests were used to assess correlation/agreement between inspections systems. It was moderate to strong for high prevalence conditions but slight for low prevalence conditions. We conclude that there is potential to use FSA data as a component of a surveillance system to monitor temporal trends and regional differences of chosen indicators at population level. At producer level and for low prevalence conditions it needs further improvement. Overall a number of issues still need to be addressed in order to provide the pig industry with the confidence to base their decisions on these FSA inspection data. Similar conclusions, at national level, may apply to other livestock sectors but require further evaluation of the inspection and data collection processes.

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Section: Introductionmentioning

confidence: 99%

Pig Abattoir Inspection Data: Can It Be Used for Surveillance Purposes?

et al. 2016

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“…Scanning surveillance has been defined as surveillance to monitor the health of defined populations in order to increase the likelihood that there will be timely detection of undefined or unexpected diseases, or of a change in the nature of an endemic disease. In Great Britain (GB), the FarmFile database has been used to collate epidemiological information on all clinical submissions received by the Veterinary Laboratories Agency (VLA) since 1998 and was developed to improve detection of new diseases (see [9] for a general review). Samples are submitted to VLA regional laboratories by veterinary practitioners for a variety of reasons, including investigation of clinical conditions on farm (diagnostic submissions) and further investigation of particular clinical conditions on farm (follow-up submissions).…”

Section: Introductionmentioning

confidence: 99%

“…Some submissions may not yield a definitive diagnosis and these are termed diagnosis not reached (DNR). New or emerging diseases are unlikely to have a diagnosis code, so an outbreak of a new or emerging disease is likely to appear as an increase in the number of DNRs initially [9]. …”

Section: Introductionmentioning

confidence: 99%

Use of spatiotemporal analysis of laboratory submission data to identify potential outbreaks of new or emerging diseases in cattle in Great Britain

et al. 2011

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BackgroundNew and emerging diseases of livestock may impact animal welfare, trade and public health. Early detection of outbreaks can reduce the impact of these diseases by triggering control measures that limit the number of cases that occur. The aim of this study was to investigate whether prospective spatiotemporal methods could be used to identify outbreaks of new and emerging diseases in scanning surveillance data. SaTScan was used to identify clusters of unusually high levels of submissions where a diagnosis could not be reached (DNR) using different probability models and baselines. The clusters detected were subjected to a further selection process to reduce the number of false positives and a more detailed epidemiological analysis to ascertain whether they were likely to represent real outbreaks.Results187,925 submissions of clinical material from cattle were made to the Regional Laboratory of the Veterinary Laboratories Agency (VLA) between 2002 and 2007, and the results were stored on the VLA FarmFile database. 16,925 of these were classified as DNRs and included in the analyses. Variation in the number and proportion of DNRs was found between syndromes and regions, so a spatiotemporal analysis for each DNR syndrome was done. Six clusters were identified using the Bernoulli model after applying selection criteria (e.g. size of cluster). The further epidemiological analysis revealed that one of the systemic clusters could plausibly have been due to Johne's disease. The remainder were either due to misclassification or not consistent with a single diagnosis.ConclusionsOur analyses have demonstrated that spatiotemporal methods can be used to detect clusters of new or emerging diseases, identify clusters of known diseases that may not have been diagnosed and identify misclassification in the data, and highlighted the impact of data quality on the ability to detect outbreaks. Spatiotemporal methods should be used alongside current temporal methods for analysis of scanning surveillance data. These statistical analyses should be followed by further investigation of possible outbreaks to determine whether cases have common features suggesting that these are likely to represent real outbreaks, or whether issues with the collection or processing of information have resulted in false positives.

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“…General wildlife disease surveillance is a means of maintaining vigilance against emerging wildlife-related diseases [8,9], but it produces data that are frequently biased [10]. These data are further characterised by the diversity of monitored parameters: species, pathogens, diagnoses, environmental characteristics, etc.…”

Section: Introductionmentioning

confidence: 99%

“…For efficient syndromic surveillance, it is necessary to group cases that share the same health indicators, in order to enhance the efficiency of event detection [14]. Health problems for which syndromic surveillance is used are either classified by bodily system [9,12,15,16] or focus on specific diseases, such as "influenza-like-illness" [17,18]. Syndrome definitions (groups of health indicators linked to these classifications) are either based on expert knowledge or on statistical classifications [12,13,19-21].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised clustering of wildlife necropsy data for syndromic surveillance

et al. 2010

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BackgroundThe importance of wildlife disease surveillance is increasing, because wild animals are playing a growing role as sources of emerging infectious disease events in humans. Syndromic surveillance methods have been developed as a complement to traditional health data analyses, to allow the early detection of unusual health events. Early detection of these events in wildlife could help to protect the health of domestic animals or humans. This paper aims to define syndromes that could be used for the syndromic surveillance of wildlife health data. Wildlife disease monitoring in France, from 1986 onward, has allowed numerous diagnostic data to be collected from wild animals found dead. The authors wanted to identify distinct pathological profiles from these historical data by a global analysis of the registered necropsy descriptions, and discuss how these profiles can be used to define syndromes. In view of the multiplicity and heterogeneity of the available information, the authors suggest constructing syndromic classes by a multivariate statistical analysis and classification procedure grouping cases that share similar pathological characteristics.ResultsA three-step procedure was applied: first, a multiple correspondence analysis was performed on necropsy data to reduce them to their principal components. Then hierarchical ascendant clustering was used to partition the data. Finally the k-means algorithm was applied to strengthen the partitioning. Nine clusters were identified: three were species- and disease-specific, three were suggestive of specific pathological conditions but not species-specific, two covered a broader pathological condition and one was miscellaneous. The clusters reflected the most distinct and most frequent disease entities on which the surveillance network focused. They could be used to define distinct syndromes characterised by specific post-mortem findings.ConclusionsThe chosen statistical clustering method was found to be a useful tool to retrospectively group cases from our database into distinct and meaningful pathological entities. Syndrome definition from post-mortem findings is potentially useful for early outbreak detection because it uses the earliest available information on disease in wildlife. Furthermore, the proposed typology allows each case to be attributed to a syndrome, thus enabling the exhaustive surveillance of health events through time series analyses.

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Use of laboratory data to reduce the time taken to detect new diseases: vida to FarmFile

Cited by 27 publications

References 3 publications

Pig Abattoir Inspection Data: Can It Be Used for Surveillance Purposes?

Pig Abattoir Inspection Data: Can It Be Used for Surveillance Purposes?

Use of spatiotemporal analysis of laboratory submission data to identify potential outbreaks of new or emerging diseases in cattle in Great Britain

Unsupervised clustering of wildlife necropsy data for syndromic surveillance

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