Scientific data and theories for salmonellosis dose-response assessment

• Published in: Human and ecological risk assessment Vol. 23; no. 8; pp. 1857 - 1876
• Main Authors: Marks, Harry M., Coleman, Margaret E.
• Format: Journal Article
• Language: English
• Published: Boca Raton Taylor & Francis 17.11.2017; Taylor & Francis Ltd
• Subjects: "expansive" risk assessment; alternative theories; analytic-deliberative process; Animal models; Colonization; Correlation analysis; Cost benefit analysis; Decision analysis; Decisions; Dose-response effects; Illnesses; Judgments; Microorganisms; Outbreaks; Public health; Risk analysis; Risk assessment; Salmonellosis; Systematic errors; Uncertainty analysis
• ISSN: 1080-7039; 1549-7860
• DOI: 10.1080/10807039.2017.1352443

An "expansive" risk assessment approach is illustrated, characterizing dose-response relationships for salmonellosis in light of the full body of evidence for human and murine superorganisms. Risk assessments often require analysis of costs and benefits for supporting public health decisions. Decision-makers and the public need to understand uncertainty in such analyses for two reasons. Uncertainty analyses provide a range of possibilities within a framework of present scientific knowledge, thus helping to avoid undesirable consequences associated with the selected policies. And, it encourages the risk assessors to scrutinize all available data and models, thus helping avoid subjective or systematic errors. Without the full analysis of uncertainty, decisions could be biased by judgments based solely on default assumptions, beliefs, and statistical analyses of selected correlative data. Alternative data and theories that incorporate variability and heterogeneity for the human and murine superorganisms, particularly colonization resistance, are emerging as major influences for microbial risk assessment. Salmonellosis risk assessments are often based on conservative default models derived from selected sets of outbreak data that overestimate illness. Consequently, the full extent of uncertainty of estimates of annual number of illnesses is not incorporated in risk assessments and the presently used models may be incorrect.