Developing a network for small animal disease surveillance

Radford, Alan; Tierney, Áine P.; Coyne, Karen P.; Gaskell, Rosalind M.; Noble, P. J. W.; Dawson, Susan; Setzkorn, Christian; Jones, Philip H.; Buchan, Iain; Newton, J. R.; Bryan, J. G. E.

doi:10.1136/vr.c5180

Cited by 20 publications

(19 citation statements)

References 5 publications

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“…However, increasing use of standardised electronic reporting of such results may mean these valuable data become tractable to such analysis in the future. Another approach to surveying resistance data is to capture this directly from the labs undertaking the tests and this type of surveillance represents the second arm of the SAVSNET surveillance initiative (Radford and others 2010). …”

Section: Discussionmentioning

confidence: 99%

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Antibacterial prescribing patterns in small animal veterinary practice identified via SAVSNET: the small animal veterinary surveillance network

et al. 2011

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In this study, data from veterinary clinical records were collected via the small animal veterinary surveillance network (SAVSNET). Over a three-month period, data were obtained from 22,859 consultations at 16 small animal practices in England and Wales: 69 per cent from dogs, 24 per cent from cats, 3 per cent from rabbits and 4 per cent from miscellaneous species. The proportion of consults where prescribing of antibacterials was identified was 35.1 per cent for dogs, 48.5 per cent for cats and 36.6 per cent for rabbits. Within this population, 76 per cent of antibacterials prescribed were β-lactams, including the most common group of clavulanic acid-potentiated amoxicillin making up 36 per cent of the antibacterials prescribed. Other classes included lincosamides (9 per cent), fluoroquinolones and quinolones (6 per cent) and nitroimidazoles (4 per cent). Vancomycin and teicoplanin (glycopeptide class), and imipenem and meropenem (β-lactam class) prescribing was not identified. Prescribing behaviour varied between practices. For dogs and cats, the proportion of consults associated with the prescription of antibacterials ranged from 0.26 to 0.55 and 0.41 to 0.73, respectively.

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Section: Discussionmentioning

confidence: 99%

“…SAVSNET was set up in 2008 to meet a deficiency in companion animal surveillance (Radford and others 2010). SAVSNET's ethos is to use highly cost-effective information technology solutions to capture ethically approved data from veterinary surgeons in private practice.…”

Section: Methodsmentioning

confidence: 99%

Antibacterial prescribing patterns in small animal veterinary practice identified via SAVSNET: the small animal veterinary surveillance network

et al. 2011

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“…Weight and height measurements were deemed as biologically implausible by using a combination of cut-offs (e.g. A structured health surveillance program for UK companion animals through the collection of laboratory and veterinary clinical records [42] A large-scale network of more than 1000 veterinary hospitals based primarily in the US that store electronic records from veterinary consultations [43] A consortium of longitudinal studies based in the UK. The age of the cohort describes the mean plus or minus the standard deviation followed by the range of the age in years of individuals in the study.…”

Section: Error and Duplication Simulation In Closer Datamentioning

confidence: 99%

Is it time to stop sweeping data cleaning under the carpet? A novel algorithm for outlier management in growth data

et al. 2020

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All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage across different domains. A decision-making algorithm that modified or deleted growth measurements based on a combination of pre-defined cutoffs and logic rules was designed. Five data cleaning methods for growth were tested with and without the addition of the algorithm and applied to five different longitudinal growth datasets: four uncleaned canine weight or height datasets and one pre-cleaned human weight dataset with randomly simulated errors. Prior to the addition of the algorithm, data cleaning based on non-linear mixed effects models was the most effective in all datasets and had on average a minimum of 26.00% higher sensitivity and 0.12% higher specificity than other methods. Data cleaning methods using the algorithm had improved data preservation and were capable of correcting simulated errors according to the gold standard; returning a value to its original state prior to error simulation. The algorithm improved the performance of all data cleaning methods and increased the average sensitivity and specificity of the non-linear mixed effects model method by 7.68% and 0.42% respectively. Using non-linear mixed effects models combined with the algorithm to clean data allows individual growth trajectories to vary from the population by using repeated longitudinal measurements, identifies consecutive errors or those within the first data entry, avoids the requirement for a minimum number of data entries, preserves data where possible by correcting errors rather than deleting them and removes duplications intelligently. This algorithm is broadly applicable to data cleaning anthropometric data in different mammalian species and could be adapted for use in a range of other domains.

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“…Determining the accuracy of the electronic medical record (EMR) is vital to the progression of veterinary practice-based research, as many of the methods used involve collection of EMR data (Lund et al, 1999;Radford et al, 2010;O'Neill, 2013). Previous research has found that discrepancies within the EMR occur relatively commonly (Dean, 2010), suggesting that it cannot be assumed that the accuracy is high.…”

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confidence: 98%

Agreement between veterinary patient data collected from different sources

Robinson¹,

Cobb²,

Dean³

2015

The Veterinary Journal

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Determining the accuracy of the electronic medical record (EMR) is vital to the progress of practice-based research. The aim of this study was to determine the agreement between the EMR and other sources of signalment data. Data were gathered during direct observation of small animal consultations in eight veterinary practices. Breed, age, sex and neuter status were recorded, where available, from the EMR, owner and observer and then compared for agreement. Agreement was 'almost perfect' or 'strong' for 18/28 comparisons, although there was variation between the species. The results have implications for researchers collecting data from the EMR of first opinion practices. Future work could focus on the accuracy of other data obtained from the EMR.

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Developing a network for small animal disease surveillance

Cited by 20 publications

References 5 publications

Antibacterial prescribing patterns in small animal veterinary practice identified via SAVSNET: the small animal veterinary surveillance network

Antibacterial prescribing patterns in small animal veterinary practice identified via SAVSNET: the small animal veterinary surveillance network

Is it time to stop sweeping data cleaning under the carpet? A novel algorithm for outlier management in growth data

Agreement between veterinary patient data collected from different sources

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