Risk assessment is EFSA's core activity. The lack of quantitative data and standardized methodology to collect data for risk assessment may limit the use of formal and structured quantitative risk assessments and delay the production of accurate scientific opinions. The aim of this project was to develop a methodological framework to identify data needs in accordance with the risk questions and the relevant risk assessment methods. A review of AHAW panel opinions in the area of animal health, adopted between 2004 and 2010 (WP1) was conducted in order to identify and to categorize the most recurrent risk questions, the suitable risk assessment methods and data needs. Subsequently an inventory of the possible sources of required data and an assessment of their availability and accessibility were performed (WP2). Facts and metadata were distinguished from WP1 initial list of data needs and a metadata model for facts collection and documentation was then proposed (WP3). The outcomes and conclusions from the three first work packages was used to suggest a structured approach to collect data (facts and metadata) in regard to expected future risk questions. Based on four case studies related to Echinococcus multilocularis, E. granulosus, Porcine Reproductive and Respiratory Syndrome and Venezuelan Equine Encephalitis (WP4), we concluded that this approach could lead to more efficient preparatory data collection, and therefore enable more rapid response to new risk managers questions. KEY WORDSRisk assessment, risk questions, animal health, data, fact, metadata 1 EFSA-Q-2010-00903 2 Acknowledgement: This study was carried out within the framework of the EFSA art.36 project (CFP/EFSA/AHAW/2010/01). We warmly thank the ANSES, CVI and ULg consortium team. We are grateful to all the experts from Belgium, France and Netherlands, who helped evaluate the needed data related to WP4 case studies
Risk assessments are mostly carried out based on available data, which do not reflect all data theoretically required by experts to answer them. This study aimed at developing a methodology to assess data availability, accessibility and format, based on a scoring system and focusing on two diseases: Venezuelan equine encephalomyelitis (VEE), still exotic to Europe, and alveolar echinococcosis, caused by Echinococcus multilocularis (EM), endemic in several Member States (MSs). After reviewing 36 opinions of the EFSA-AHAW Panel on risk assessment of animal health questions, a generic list of needed data was elaborated. The methodology consisted, first, in implementing a direct and an indirect survey to collect the data needed for both case studies: the direct survey consisted in a questionnaire sent to contact points of three European MSs (Belgium, France and the Netherlands), and the organization of a workshop gathering experts on both diseases. The indirect survey, focusing on the three MSs involved in the direct survey plus Spain, relied on web searches. Secondly, a scoring system with reference to data availability, accessibility and format was elaborated, to, finally, compare both diseases and data between MSs. The accessibility of data was generally related to their availability. Web searches resulted in more data available for VEE compared to EM, despite its current exotic status in the European Union. Hypertext markup language and portable document files were the main formats of available data. Data availability, accessibility and format should be improved for research scientists/assessors. The format of data plays a key role in the feasibility and rapidness of data management and analysis, through a prompt compilation, combination and aggregation in working databases. Harmonization of data collection process is encouraged, according to standardized procedures, to provide useful and reliable data, both at the national and the international levels for both animal and human health; it would allow assessing data gaps through comparative studies. The present methodology is a good way of assessing the relevance of data for risk assessment, as it allows integrating the uncertainty linked to the quality of data used. Such an approach could be described as transparent and traceable and should be performed systematically.
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