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Abstract Title:

A Systematic Meta-Analysis of Toxoplasma gondii Prevalence in Food Animals in the United States.

Abstract Source:

Foodborne Pathog Dis. 2016 Feb 8. Epub 2016 Feb 8. PMID: 26854596

Abstract Author(s):

Miao Guo, Abhinav Mishra, Robert L Buchanan, Jitender P Dubey, Dolores E Hill, H Ray Gamble, Jeffrey L Jones, Abani K Pradhan

Article Affiliation:

Miao Guo

Abstract:

Toxoplasma gondii is a widely distributed protozoan parasite. The Centers for Disease Control and Prevention reported that T. gondii is one of three pathogens (along with Salmonella and Listeria), that together account for>70% of all deaths due to foodborne illness in the United States. Food animals are reservoirs for T. gondii and act as one of the sources for parasite transmission to humans. Based on limited population-based data, the Food and Agriculture Organization/World Health Organization estimated that approximately 22% of human T. gondii infections are meatborne. The objective of the current study was to conduct a systematic meta-analysis to provide a precise estimation of T. gondii infection prevalence in food animals produced in the United States. Four databases were searched to collect eligible studies. Prevalence was estimated in six animal categories (confinement-raised market pigs, confinement-raised sows, non-confinement-raised pigs, lamb, goats, and non-confinement-raised chickens) by a quality-effects model. A wide variation in prevalence was observed in each animal category. Animals raised outdoors or that have outdoor access had a higher prevalence as compared with animals raised indoors. T. gondii prevalence in non-confinement-raised pigs ranked the highest (31.0%) followed by goats (30.7%), non-confinement-raised chickens (24.1%), lambs (22.0%), confinement-raised sows (16.7%), and confinement-raised market pigs (5.6%). These results indicate that T. gondii-infected animals are a food safety concern. The computed prevalence can be used as an important input in quantitative microbial risk assessment models to further predict public health burden.

Study Type : Meta Analysis

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Sayer Ji
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