Based on the data from the integrated Danish Salmonella surveillance in 1999, the researchers developed a mathematical model for quantifying the contribution of each of the major animal-food sources to human salmonellosis. The model was set up to calculate the number of domestic and sporadic cases caused by different Salmonella sero and phage types as a function of the prevalence of these Salmonella types in the animal-food sources and the amount of food source consumed. 

Mathematical models that estimate the proportion of foodborne illnesses attributable to food commodities at specific points in the food chain may be useful to risk managers and policy makers to formulate public health goals, prioritize interventions, and document the effectiveness of mitigations aimed at reducing illness. Using human surveillance data on laboratory-confirmed Salmonella infections from the Centers for Disease Control and Prevention and Salmonella testing data from U.S. Department of Agriculture Food Safety and Inspection Service's regulatory programs, the researchers developed a point-of-processing foodborne illness attribution model by adapting the Hald Salmonella Bayesian source attribution model. Key model outputs include estimates of the relative proportions of domestically acquired sporadic human Salmonella infections resulting from contamination of raw meat, poultry, and egg products processed in the United States from 1998 through 2003. 

The Food Safety Research Consortium sponsored the Food Attribution Data Workshop in October 2003 to discuss the virtues and limitations of these approaches and to identify future options for collecting food attribution data in the United States. This article summarizes workshop discussions and identifies challenges that affect progress in this critical component of a risk-based approach to improving food safety.

The intent of this guide is to provide the reader with insight into various tools and approaches used to track sources of fecal contamination impacting water quality in streams, rivers, lakes, and marine beaches. Descriptions of research and several case studies gathered through workshops, literature searches, and phone interviews are also provided. An effort was made to showcase programs, activities, and analyses that incorporated diverse Microbial Source Tracking (MST) approaches and tools.

This website includes annual reports for this Med Vet Net project on source attribution. To identify and prioritize effective food safety interventions, it is critical to attribute human zoonotic infections to the sources responsible. A wide variety of approaches and data for source attribution are used around the world and in the process of priority setting, it is very important to understand the differences and limitations of these approaches.

The objective of this project was to apply, compare, discuss and recommend the appropriate use of four different approaches: microbial subtyping, risk (exposure) assessment, analytical epidemiology and outbreak inverstigations. Salmonella and Campylobacter are selected as cases.

This document provides an overview on the methods for source attribution for human illness from foodborne microbiological hazards. This report illustrates attribution of human illness through microbiological subtyping, using summary outbreak data for source attribution, epidemiological studies for source attribution of sporadic cases, source attribution by comparative exposure or risk assessment and source attribution by expert elicitation. This reports concludes that the scientific and accurate attribution of foodborne illnesses to specific foods requires developing a comprehensive program that combines many of these methods. Such a system can be achieved with increased resources and cooperation among food safety institutions.

This website provides a focus on sources and pathogens, methodological options and unique benefits of source attribution.

Source attribution is described as the process of determining what proportion of a particular disease is acquired from a given source (e.g. chicken) and through a given pathway (e.g. food, water, person-to-person transmission).

This site explains that C-EnterNet, a comprehensive surveillance program in Canada, will approach source attribution mainly through the comparison of pathogen profiles (microbial source tracking).

C-EnterNet will focus on the pathogens known to have the greatest potential to cause enteric diseases, with particularly severe consequences for the youngest, oldest and immune-compromised members of the population.

This report describes an extension of NZFSA-funded studies carried out between 2005 and 2008, and should be read in conjunction with the 2008 report "Enhancing surveillance of potentially foodborne enteric diseases in New Zealand: Human campylobacteriosis inthe Manawatu."

In this paper, the authors summarize the application of extensions to the Hald, Dutch, and Island models for source attribution to the isolates from a sentinel collection site over a 4 year period, focusing on the temporal variation in the attribution through time.

In this paper, the authors describe a method that is able to attribute human cases to specific food items contained in complex foods. The model is based on data from investigations of Salmonella and Campylobacter outbreaks in the European Union in 2005 and 2006.

Using the dataset from the Manawatu-cited study, several risk attribution models and risk research tools have been developed and used to assess the relative contribution of food and non-food sources to the burden of human disease.

The purpose of this study was to explore the usefulness of foodborne outbreak data extracted from publicly available international electronic reports and publications to provide estimates of food attribution, to derive and compare these estimates between regions, while improving the understanding of the pathogen/food vehicle combination.

This study was conducted to explore the usefulness of three comprehensive Canadian foodborne outbreak data sets covering 30 years for estimating food attribution in cases of gastrointestinal illness, providing Canadian food attribution estimates from a historical perspective.

Here, the authors adapted and applied the Hald model to L. monocytogenes to determine whether such an approach may provide further insight and quantify the relative importance of food sources to listeriosis cases, including risk population groups such as the elderly.

In this article, the investigators develop a Bayesian model that attributes illness to specific sources and allows for a better estimation of the differences in the ability of Salmonella subtypes and food types to result in reported salmonellosis. The model accommodates data for multiple years and is based on the Danish Salmonella surveillance.

A Bayesian approach was developed by Hald et al. to estimate the contribution of different food sources to the burden of human salmonellosis in Denmark. This article describes the development of several modifications that can be used to adapt the model to different countries and pathogens.

Opinion adopted on January 24, 2008 regarding the quantitative risk assessment and evaluation of the relative contribution of different meat categories to cases of food-borne Salmonella infections in humans, taking into account the occurrence of the pathogen in the food chain, risk factors, food production flows and food preparation and consumption habits.