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ABSTRACT: Salmonella is a key human pathogen worldwide, most often associated with food poisoning incidences. There is a small number of predominant serotypes found in human cases. The role of exposure in the epidemiology of Salmonella can be explained using dose-response assessment both for infection and acute enteric illness. Dose-response studies are traditionally based on human challenge experiments but an alternative is to use outbreak data. Such data were collected from the published literature which included estimates of the dose ingested and the attack rate. Separate dose-response models for infection and illness given infection were fitted using a multi-level statistical framework. These models incorporated serotype and susceptibility as categorical covariates, and adjusted for heterogeneity in exposure. The results indicate that both the risk of infection and the risk of illness given infection increase with dose. The dose-response model incorporating data from all outbreaks had an infection ID50 of 7 CFU's and illness ID50 of 36 CFUs. This is indicative of much higher infectivity and pathogenicity compared with feeding studies of healthy human volunteers with laboratory adapted strains. No differences were found in the outbreak models between serotypes and susceptibility categories. However, for serotypes other than S. Enteritidis or S. Typhimurium, results indicate that a minor proportion of individuals exposed will not fall ill even at high doses. The dose-response relations indicate that outbreaks are associated with higher doses making it more likely to have a higher attack rate. Applications of the dose-response model in outbreak situations where either dose or attack rate is missing were successfully used to clarify the epidemiology. Finally, the dose-response models described here can be readily used in quantitative microbiological risk assessment to predict human infection and illness rates. A simple Excel spreadsheet implementing the model has been prepared and is available from the authors.
International journal of food microbiology 10/2010; 144(2):243-9. · 3.01 Impact Factor
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ABSTRACT: A human dose response model for Escherichia coli O157 would enable prediction of risk of infection to humans following exposure from either foodborne or environmental pathways. However, due to the severe nature of the disease, volunteer human dose response studies cannot be carried out. Surrogate models from Shigella fed to humans and E. coli O157 to rabbits have been utilised but are significantly different to one another. In addition data obtained by animal exposure may not be representative for human beings. An alternative approach to generating and validating a dose response model is to use quantitative data obtained from actual human outbreaks. This work collates outbreak data obtained from global sources and these are fitted using exponential and beta-Poisson models. The best fitting model was found to be the beta-Poisson model using a beta-binomial likelihood and the authors favour the exact version of this model. The confidence levels in this model encompass a previously published Shigella dose response model. The potential incorporation of this model into QMRAs is discussed together with applications of the model to help explain foodborne outbreaks.
International Journal of Food Microbiology 09/2005; 103(1):35-47. · 3.33 Impact Factor
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ABSTRACT: Salmonella is a key human pathogen worldwide, most often associated with food poisoning incidences. There is a small number of predominant serotypes found in human cases. The role of exposure in the epidemiology of Salmonella can be explained using dose–response assessment both for infection and acute enteric illness. Dose–response studies are traditionally based on human challenge experiments but an alternative is to use outbreak data. Such data were collected from the published literature which included estimates of the dose ingested and the attack rate. Separate dose–response models for infection and illness given infection were fitted using a multi-level statistical framework. These models incorporated serotype and susceptibility as categorical covariates, and adjusted for heterogeneity in exposure. The results indicate that both the risk of infection and the risk of illness given infection increase with dose. The dose–response model incorporating data from all outbreaks had an infection ID50 of 7 CFU's and illness ID50 of 36 CFUs. This is indicative of much higher infectivity and pathogenicity compared with feeding studies of healthy human volunteers with laboratory adapted strains. No differences were found in the outbreak models between serotypes and susceptibility categories. However, for serotypes other than S. Enteritidis or S. Typhimurium, results indicate that a minor proportion of individuals exposed will not fall ill even at high doses. The dose–response relations indicate that outbreaks are associated with higher doses making it more likely to have a higher attack rate. Applications of the dose–response model in outbreak situations where either dose or attack rate is missing were successfully used to clarify the epidemiology. Finally, the dose–response models described here can be readily used in quantitative microbiological risk assessment to predict human infection and illness rates. A simple Excel spreadsheet implementing the model has been prepared and is available from the authors.
International Journal of Food Microbiology.
