Zecconi Alfonso, Scali Federico, Bonizzi Luigi, Ferrari Nicola, Ferrero Filippo, Grillo Guido, Lanfranchi Paolo, Mortarino Michele, Sala Vittorio, Taloni Dalila, Frazzi Piero
Dipartimento di Medicina Veterinaria (DIMEVET), Università degli Studi di Milano, Via Celoria 10, 20133 Milan, Italy.
Vet Ital. 2019 Jun 30;55(2):113-121. doi: 10.12834/VetIt.172.518.2.
In this study we developed a model for risk prioritisation and characterisation focused on zoonoses and food safety for diseases of interest in veterinary public health at a regional level in Italy. A previous model (Discontools) based on scorecards was used as a basis to develop the new model. A Formalised Consensus Process approach involving academics and veterinary officers was used to develop scorecards and relative form and guidelines. Scorecards include several areas of interest, with different categories and coefficient of importance. The following areas were identified: relevance of the disease, socio-economic impact, impact on public health, impact on trade, impact on animal welfare, control tools. A guide and a form were finalised in order to fill scorecards. Scorecards were filled by consulting available data, literature, and expert opinions. Among bovine diseases, mastitis (Salmonella aureus) showed the highest score; Q fever was the highest among small ruminants; among swine diseases the highest was salmonellosis; while among other animal diseases, toxoplasmosis had the highest score. The approach described in this study is designed to aid professionals in risk prioritisation, decision-making, and to improve disease control systems at a regional level in Italy. It also facilitates risk characterisation in different backgrounds and the identification of data holes in specific areas of interest for the diseases considered. This approach is conceived to aid professionals in risk prioritization, decision-making and to improve disease control systems at a regional level. It also allows to perform risk characterization in different backgrounds and to identify lacks of data in specific areas of interest for the diseases considered.
在本研究中,我们开发了一种风险优先级排序和特征描述模型,该模型聚焦于意大利区域层面兽医公共卫生领域人畜共患病和食品安全相关的目标疾病。之前一个基于记分卡的模型(Discontools)被用作开发新模型的基础。采用了一种涉及学者和兽医官员的形式化共识流程方法来制定记分卡以及相关的表格和指南。记分卡包括几个感兴趣的领域,具有不同的类别和重要系数。确定了以下领域:疾病的相关性、社会经济影响、对公众健康的影响、对贸易的影响、对动物福利的影响、控制手段。最终确定了一份指南和一种表格以便填写记分卡。通过查阅现有数据、文献和专家意见来填写记分卡。在牛病中,乳腺炎(金黄色葡萄球菌感染)得分最高;在小反刍动物疾病中,Q热得分最高;在猪病中,沙门氏菌病得分最高;而在其他动物疾病中,弓形虫病得分最高。本研究中描述的方法旨在帮助专业人员进行风险优先级排序、决策,并改善意大利区域层面的疾病控制系统。它还便于在不同背景下进行风险特征描述,并识别所考虑疾病特定感兴趣领域的数据漏洞。这种方法旨在帮助专业人员进行风险优先级排序、决策,并改善区域层面的疾病控制系统。它还允许在不同背景下进行风险特征描述,并识别所考虑疾病特定感兴趣领域的数据缺失情况。