Spread of nontyphoidal Salmonella in the beef supply chain in northern Tanzania: Sensitivity in a probabilistic model integrating microbiological data and data from stakeholder interviews (original) (raw)
Zadoks, R. N. et al. (2022) Spread of nontyphoidal Salmonella in the beef supply chain in northern Tanzania: Sensitivity in a probabilistic model integrating microbiological data and data from stakeholder interviews.Risk Analysis, 42(5), pp. 989-1006. (doi: 10.1111/risa.13826) (PMID:34590330)
Abstract
East Africa is a hotspot for foodborne diseases, including infection by nontyphoidal Salmonella (NTS), a zoonotic pathogen that may originate from livestock. Urbanization and increased demand for animal protein drive intensification of livestock production and food processing, creating risks and opportunities for food safety. We built a probabilistic mathematical model, informed by prior beliefs and dedicated stakeholder interviews and microbiological research, to describe sources and prevalence of NTS along the beef supply chain in Moshi, Tanzania. The supply chain was conceptualized using a bow tie model, with terminal livestock markets as pinch point, and a forked pathway postmarket to compare traditional and emerging supply chains. NTS was detected in 36 (7.7%) of 467 samples throughout the supply chain. After combining prior belief and observational data, marginal estimates of true NTS prevalence were 4% in feces of cattle entering the beef supply and 20% in raw meat at butcheries. Based on our model and sensitivity analyses, true NTS prevalence was not significantly different between supply chains. Environmental contamination, associated with butchers and vendors, was estimated to be the most likely source of NTS in meat for human consumption. The model provides a framework for assessing the origin and propagation of NTS along meat supply chains. It can be used to inform decision making when economic factors cause changes in beef production and consumption, such as where to target interventions to reduce risks to consumers. Through sensitivity and value of information analyses, the model also helps to prioritize investment in additional research.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Zadoks, Professor Ruth and Allan, Dr Kathryn and Chaters, Gemma and Cleaveland, Professor Sarah |
| Authors: | Zadoks, R. N., Barker, G. C., Benschop, J., Allan, K. J., Chaters, G., Cleaveland, S., Crump, J. A., Davis, M. A., Mmbaga, B. T., Prinsen, G., Thomas, K. T., Waldman, L., and French, N. P. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine |
| Journal Name: | Risk Analysis |
| Publisher: | Wiley |
| ISSN: | 0272-4332 |
| ISSN (Online): | 1539-6924 |
| Published Online: | 29 September 2021 |
| Copyright Holders: | Copyright © 2021 The Authors |
| First Published: | First published in Risk Analysis 42(5): 989-1006 |
| Publisher Policy: | Reproduced under a Creative Commons licence |
University Staff: Request a correction | Enlighten Editors: Update this record
Funder and Project Information
Leptospirosis in Tanzania; a study of the role of rodents in an emerging public health problem.
Sarah Cleaveland
096400/Z/11/Z
Institute of Biodiversity, Animal Health and Comparative Medicine
Zoonoses and Emerging Livestock Systems ZELS Reducing the risk to livestock and people programme associated studentships - ZELS-AS
Sarah Cleaveland
BB/N503563/1
Institute of Biodiversity, Animal Health and Comparative Medicine
Zoonoses and Emerging Livestock Systems ZELS Reducing the risk to livestock and people programme associated studentships - ZELS-AS
Sarah Cleaveland
BB/N503563/1
Institute of Biodiversity, Animal Health and Comparative Medicine
Deposit and Record Details
| ID Code: | 252885 |
|---|---|
| Depositing User: | Ms Jacqui Brannan |
| Datestamp: | 01 Oct 2021 10:32 |
| Last Modified: | 22 Sep 2022 10:13 |
| Date of acceptance: | 27 August 2021 |
| Date of first online publication: | 29 September 2021 |
| Date Deposited: | 1 October 2021 |
| Data Availability Statement: | No |