title: Time valuation of historical outbreak attribution data.
abstract: Human illness attribution is recognized as an important metric for prioritizing and informing food-safety decisions and for monitoring progress towards long-term food-safety goals. Inferences regarding the proportion of illnesses attributed to a specific commodity class are often based on analyses of datasets describing the number of outbreaks in a given year or combination of years. In many countries, the total number of pathogen-related outbreaks reported nationwide for an implicated food source is often fewer than 50 instances in a given year and the number of years for which data are available can be fewer than 10. Therefore, a high degree of uncertainty is associated with the estimated fraction of pathogen-related outbreaks attributed to a general food commodity. Although it is possible to make inferences using only data from the most recent year, this type of estimation strategy ignores the data collected in previous years. Thus, a strong argument exists for an estimator that could 'borrow strength' from data collected in the previous years by combining the current data with the data from previous years. While many estimators exist for combining multiple years of data, most either require more data than is currently available or lack an objective and biologically plausible theoretical basis. This study introduces an estimation strategy that progressively reduces the influence of data collected in past years in accordance with the degree of departure from a Poisson process. The methodology is applied to the estimation of the attribution fraction for Salmonella and Escherichia coli O157:H7 for common food commodities and the estimates are compared against two alternative estimators.
authors.affiliation: Risk Assessment and Analytics Staff,Office of Public Health Science,Food Safety and Inspection Service,USDA,Fort Collins,CO,USA.
authors.forename: E D
authors.initials: ED
authors.lastname: Ebel
authors.affiliation: Risk Assessment and Analytics Staff,Office of Public Health Science,Food Safety and Inspection Service,USDA,Fort Collins,CO,USA.
authors.forename: M S
authors.initials: MS
authors.lastname: Williams
authors.affiliation: Risk Assessment and Analytics Staff,Office of Public Health Science,Food Safety and Inspection Service,USDA,Fort Collins,CO,USA.
authors.forename: N J
authors.initials: NJ
authors.lastname: Golden
authors.affiliation: Risk Assessment and Analytics Staff,Office of Public Health Science,Food Safety and Inspection Service,USDA,Fort Collins,CO,USA.
authors.forename: W D
authors.initials: WD
authors.lastname: Schlosser
authors.affiliation: Leidos, Incorporated,Reston,VA,USA.
authors.forename: C
authors.initials: C
authors.lastname: Travis
mesh_terms: D004196:Disease Outbreaks / Q000706:statistics & numerical data; D004927:Escherichia coli Infections / Q000453:epidemiology* / Q000382:microbiology; D019453:Escherichia coli O157 / Q000502:physiology*; D005516:Food Microbiology / Q000379:methods*; D005517:Foodborne Diseases / Q000453:epidemiology* / Q000382:microbiology; D006801:Humans; D008962:Models, Theoretical; D012475:Salmonella / Q000502:physiology*; D012478:Salmonella Food Poisoning / Q000453:epidemiology / Q000382:microbiology; D013997:Time Factors
keywords: Attributable fraction; Bayesian inference; Salmonella; time series