Business intelligence tools to optimize the appropriateness of the diagnostic process for clinical and epidemiologic purposes in a multicenter veterinary pathology service.

Laboratory tests provide essential support to the veterinary practitioner, and their use has grown exponentially. This growth is the result of several factors, such as the eradication of historical diseases, the occurrence of multifactorial diseases, and the obligation to control endemic and epidemic diseases. However, the introduction of novel techniques is counterbalanced by economic constraints, and the establishment of evidence- and consensus-based guidelines is essential to support the pathologist. Therefore, we developed standardized protocols, categorized by species, type of production, age, and syndrome at the Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), a multicenter institution for animal health and food safety. We have 72 protocols in use for livestock, poultry, and pets, categorized as, for example, "bovine enteric calf", "rabbit respiratory", "broiler articular". Each protocol consists of a panel of tests, divided into 'mandatory' and 'ancillary', to be selected by the pathologist in order to reach the final diagnosis. After autopsy, the case is categorized into a specific syndrome, subsequently referred to as a syndrome-specific panel of analyses. The activity of the laboratories is monitored through a web-based dynamic reporting system developed using a business intelligence product (QlikView) connected to the laboratory information management system (IZILAB). On a daily basis, reports become available at general, laboratory, and case levels, and are updated as needed. The reporting system highlights epidemiologic variations in the field and allows verification of compliance with the protocols within the organization. The diagnostic protocols are revised annually to increase system efficiency and to address stakeholder requests.

Biological, Chemical, and Nutritional Food Risks and Food Safety Issues From Italian Online Information Sources: Web Monitoring, Content Analysis, and Data Visualization.

BACKGROUND: With rapid evolution of the internet and web 2.0 apps, online sources have become one of the main channels for most people to seek food risk information. Thus, it would be compelling to analyze the coverage of online information sources related to biological, chemical, and nutritional food risks, and related safety issues, to understand the type of content that online readers are exposed to, possibly influencing their perceptions. OBJECTIVE: The aim of this study was to identify the types of online sources that are predominantly covering this theme, and the topics that have received the most attention in terms of coverage and engagement on social media. METHODS: We performed an analysis of big data related to food risks by combining web monitoring techniques, content analysis, and data visualization of a large amount of unstructured text. Using a dictionary-based approach, a web monitoring app was instructed to automatically collect web content referring to the food risk and safety field. Data were retrieved from March 2017 to February 2018. The validated corpus (N=12,163) was subject to automatic and manual content analysis. Results were combined with descriptive statistics extracted from Web-Live and processed with Qlik Sense. RESULTS: Nutritional risks and news about outbreaks, controls, and alerts were the most widely covered topics. Thematic sources devoted major attention to nutritional topics, whereas national sources covered food risks, especially during food emergencies. Regarding engagement on social media, readers' interest was higher for nutritional topics and animal welfare. Although traditional sources still publish a great amount of content related to food risks and safety, new mediators have emerged as alternative sources for food risk information. CONCLUSIONS: This mixed methodological approach was demonstrated to be a useful means for obtaining an accurate characterization of the online discourse on food risks, and can provide insight into how the monitored sources contribute to the process of risk communication.

Food Risks on the Web: Analysis of the 2017 Fipronil Alert in the Italian Online Information Sources.

In the summer of 2017, several European Union Member States were involved in a food alert caused by the presence of fipronil pesticide residues in chicken eggs. The food alert became a major news and received wide coverage both in the mass media and on the Internet. This article describes a study that analyzed how the Italian online information sources represented the fipronil alert, using web monitoring techniques and both manual and automatic content analysis methods. The results indicate that the alert was amplified because general news media could represent the alert within the frame of a political scandal, and because different social actors exploited the case. However, online information sources correctly communicated that the risks for consumers were low, reporting mainly what was officially communicated by the Italian health authorities. The study provides empirical evidence on how the online information sources represent food risks and food alerts and offers useful indications for health authorities in charge of the public communication of food risks.

Survey, characterization and antimicrobial susceptibility of Clostridium difficile from marine bivalve shellfish of North Adriatic Sea.

Clostridium difficile is a major cause of infectious diarrhea associated to healthcare settings. Community-acquired infections are increasingly reported in the last decade and exposure other than to symptomatic patients rather to contaminated foods or animals is feasible. Occurrence of C. difficile in shellfish raises concern because spores can survive the cooking temperatures given that shellfish is often consumed poorly cooked or raw. Aim of our study was to investigate whether shellfish represents a reservoir of C. difficile human PCR-ribotypes (RTs). 702 shellfish samples of farmed and wild bivalve mollusc species were collected over the 2015-2017 period in North Adriatic Italian Sea to investigate contamination with C. difficile and characterize the isolates in terms of genotypic variability and antimicrobial resistance profile. C. difficile was detected in 16.9% (CI: 14.1%-19.8%) samples: 11.6% mussels and 23.2% clams. Compared to mussels, clams were significantly associated with detection of C. difficile (OR = 2.4, P < 0.01). Overall 113 C. difficile isolates were genotyped and 75 (66.4%) were toxigenic. Fifty-three different RTs were identified. 40.7% C. difficile isolates were among the RTs most commonly involved in human infection in Europe. The profile of antimicrobial susceptibility was determined by E-test; microbiological resistance was frequent against clindamycin (17%), erythromycin (23%), rifampicin (8.8%) and moxifloxacin (10.6%). All isolates were susceptible to metronidazole and one showed MIC > ECOFF for vancomycin. C. difficile strains showed high variety in RTs, most of them already detected in other animals or known as highly virulent and epidemic in humans. These results prompt towards investigating on specific risk mitigation measures against C. difficile and are preliminary for any source attribution and risk assessment study.