title: Systematic review of expert elicitation methods as a tool for source attribution of enteric illness.
abstract: Expert elicitation is a useful tool to explore sources of uncertainty and to answer questions where data are expensive or difficult to collect. It has been used across a variety of disciplines and represents an important method for estimating source attribution for enteric illness. A systematic review was undertaken to explore published expert elicitation studies, identify key considerations, and to make recommendations for designing an expert elicitation in the context of enteric illness source attribution. Fifty-nine studies were reviewed. Five key themes were identified: the expert panel including composition and recruitment; the pre-elicitation material, which clarifies the research question and provides training in uncertainty and probability; the choice of elicitation tool and method (e.g., questionnaires, surveys, and interviews); research design; and analysis of elicited data. Careful consideration of these themes is critical in designing and implementing an expert elicitation in order to reduce bias and produce the best possible results. While there are various epidemiological and microbiological methods available to explore source attribution of enteric illness, expert elicitation provides an opportunity to identify gaps in our understanding and where such studies are not feasible or available, represents the only possible method for synthesizing knowledge about transmission.
authors.affiliation: Centre for Foodborne, Environmental, and Zoonotic Infectious Diseases , Public Health Agency of Canada, Guelph, Canada .
authors.forename: Ainslie J
authors.initials: AJ
authors.lastname: Butler
authors.forename: M Kate
authors.initials: MK
authors.lastname: Thomas
authors.forename: Katarina D M
authors.initials: KD
authors.lastname: Pintar
mesh_terms: D016208:Databases, Factual; D005506:Food Contamination; D005516:Food Microbiology; D005517:Foodborne Diseases / Q000175:diagnosis* / Q000382:microbiology*; D006801:Humans; D015201:Meta-Analysis as Topic; D035501:Uncertainty