Comparison of the h-Index Scores Among Pathogens Identified as Emerging Hazards in North America.

Disease surveillance must assess the relative importance of pathogen hazards. Here, we use the Hirsch index (h-index) as a novel method to identify and rank infectious pathogens that are likely to be a hazard to human health in the North American region. This bibliometric index was developed to quantify an individual's scientific research output and was recently used as a proxy measure for pathogen impact. Analysis of more than 3000 infectious organisms indicated that 651 were human pathogen species that had been recorded in the North American region. The h-index of these pathogens ranged from 0 to 584. The h-index of emerging pathogens was greater than non-emerging pathogens as was the h-index of frequently pathogenic pathogens when compared to non-pathogenic pathogens. As expected, the h-index of pathogens varied over time between 1960 and 2011. We discuss how the h-index can contribute to pathogen prioritization and as an indicator of pathogen emergence.

Surveillance of diarrhoea in small animal practice through the Small Animal Veterinary Surveillance Network (SAVSNET).

Using the Small Animal Veterinary Surveillance Network (SAVSNET), a national small animal disease-surveillance scheme, information on gastrointestinal disease was collected for a total of 76 days between 10 May 2010 and 8 August 2011 from 16,223 consultations (including data from 9115 individual dogs and 3462 individual cats) from 42 premises belonging to 19 UK veterinary practices. During that period, 7% of dogs and 3% of cats presented with diarrhoea. Adult dogs had a higher proportional morbidity of diarrhoea (PMD) than adult cats (P <0.001). This difference was not observed in animals <1 year old. Younger animals in both species had higher PMDs than adult animals (P < 0.001). Neutering was associated with reduced PMD in young male dogs. In adult dogs, miniature Schnauzers had the highest PMD. Most animals with diarrhoea (51%) presented having been ill for 2-4 days, but a history of vomiting or haemorrhagic diarrhoea was associated with a shorter time to presentation. The most common treatments employed were dietary modification (66% of dogs; 63% of cats) and antibacterials (63% of dogs; 49% of cats). There was variability in PMD between different practices. The SAVNET methodology facilitates rapid collection of cross-sectional data regarding diarrhoea, a recognised sentinel for infectious disease, and characterises data that could benchmark clinical practice and support the development of evidence-based medicine.

Using open-access taxonomic and spatial information to create a comprehensive database for the study of mammalian and avian livestock and pet infections.

What are all the species of pathogen that affect our livestock? As 6 out of every 10 human pathogens came from animals, with a good number from livestock and pets, it seems likely that the majority that emerge in the future, and which could threaten or devastate human health, will come from animals. Only 10 years ago, the first comprehensive pathogen list was compiled for humans; we still have no equivalent for animals. Here we describe the creation of a novel pathogen database, and present outputs from the database that demonstrate its value. The ENHanCEd Infectious Diseases database (EID2) is open-access and evidence-based, and it describes the pathogens of humans and animals, their host and vector species, and also their global occurrence. The EID2 systematically collates information on pathogens into a single resource using evidence from the NCBI Taxonomy database, the NCBI Nucleotide database, the NCBI MeSH (Medical Subject Headings) library and PubMed. Information about pathogens is assigned using data-mining of meta-data and semi-automated literature searches. Here we focus on 47 mammalian and avian hosts, including humans and animals commonly used in Europe as food or kept as pets. Currently, the EID2 evidence suggests that: • Within these host species, 793 (30.5%) pathogens were bacteria species, 395 (15.2%) fungi, 705 (27.1%) helminths, 372 (14.3%) protozoa and 332 (12.8%) viruses. • The odds of pathogens being emerging compared to not emerging differed by taxonomic division, and increased when pathogens had greater numbers of host species associated with them, and were zoonotic rather than non-zoonotic. • The odds of pathogens being zoonotic compared to non-zoonotic differed by taxonomic division and also increased when associated with greater host numbers. • The pathogens affecting the greatest number of hosts included: Escherichia coli, Giardia intestinalis, Toxoplasma gondii, Anaplasma phagocytophilum, Cryptosporidium parvum, Rabies virus, Staphylococcus aureus, Neospora caninum and Echinococcus granulosus. • The pathogens of humans and domestic animal hosts are characterised by 4223 interactions between pathogen and host species, with the greatest number found in: humans, sheep/goats, cattle, small mammals, pigs, dogs and equids. • The number of pathogen species varied by European country. The odds of a pathogen being found in Europe compared to the rest of the world differed by taxonomic division, and increased if they were emerging compared to not emerging, or had a larger number of host species associated with them.

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

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 ß-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 (ß-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.

Responses of farmers to introduction in England and Wales of pre-movement testing for bovine tuberculosis.

Pre-movement testing (PrMT) for bovine tuberculosis (bTB) was introduced in England and Wales in 2 phases starting in 2006. This study used questionnaires (n=800, response rate=31%) and analysis of national cattle movement records between January 2003 and February 2007 to investigate the impact of PrMT on specific farm management behaviours. A majority of farmers (65%) believed they had not changed their behaviour in response to PrMT; the main reported changes related to decisions regarding selling of cattle. There was evidence in the cattle movement data that introduction of PrMT resulted in reduction of movements of cattle between farms in those areas which must undertake PrMT. The buying behaviour reported by farmers reflected the tendency to buy locally but some farmers reported that others might be more willing to purchase animals from high-risk areas as a result of increased confidence due to PrMT. However, there was little evidence in the movement data of increased movements from high- to low-risk bTB areas following introduction of PrMT. Analysis of the cattle movement data found increased movement of single animals and decreased movement of large batches (>10) of animals (directly) between farms since the introduction of PrMT.

Double-blind evaluation and benchmarking of survival models in a multi-centre study.

Accurate modelling of time-to-event data is of particular importance for both exploratory and predictive analysis in cancer, and can have a direct impact on clinical care. This study presents a detailed double-blind evaluation of the accuracy in out-of-sample prediction of mortality from two generic non-linear models, using artificial neural networks benchmarked against a partial logistic spline, log-normal and COX regression models. A data set containing 2880 samples was shared over the Internet using a purpose-built secure environment called GEOCONDA (www.geoconda.com). The evaluation was carried out in three parts. The first was a comparison between the predicted survival estimates for each of the four survival groups defined by the TNM staging system, against the empirical estimates derived by the Kaplan-Meier method. The second approach focused on the accurate prediction of survival over time, quantified with the time dependent C index (C(td)). Finally, calibration plots were obtained over the range of follow-up and tested using a generalization of the Hosmer-Lemeshow test. All models showed satisfactory performance, with values of C(td) of about 0.7. None of the models showed a systematic tendency towards over/under estimation of the observed survival at tau=3 and 5 years. At tau=10 years, all models underestimated the observed survival, except for COX regression which returned an overestimate. The study presents a robust and unbiased benchmarking methodology using a bespoke web facility. It was concluded that powerful, recent flexible modelling algorithms show a comparative predictive performance to that of more established methods from the medical and biological literature, for the reference data set.

Double-blind comparison of survival analysis models using a bespoke web system.

The aim of this study was to carry out a comparison of different linear and non-linear models from different centres on a common dataset in a double-blind manner to eliminate bias. The dataset was shared over the Internet using a secure bespoke environment called geoconda. Models evaluated included: (1) Cox model, (2) Log Normal model, (3) Partial Logistic Spline, (4) Partial Logistic Artificial Neural Network and (5) Radial Basis Function Networks. Graphical analysis of the various models with the Kaplan-Meier values were carried out in 3 survival groups in the test set classified according to the TNM staging system. The discrimination value for each model was determined using the area under the ROC curve. Results showed that the Cox model tended towards optimism whereas the partial logistic Neural Networks showed slight pessimism.