Source attribution of human toxoplasmosis: A quantitative microbiological risk assessment approach

Huifang Deng

Research output: ThesisDoctoral thesis 2 (Research NOT UU / Graduation UU)

Abstract

Toxoplasma gondii (T. gondii) is a protozoan parasite with a worldwide distribution. It can infect all warm-blooded animals, and is the causative agent of toxoplasmosis. The global disease burden of toxoplasmosis is substantial and therefore considered as a public health problem worldwide. Quantitative microbial risk assessment (QMRA) is a source attribution method, which aggregates all available data on different aspects to compare the importance of different sources of infection. This thesis focuses on the assessment of relative attribution of different meat products and soil to human T. gondii infection by using QMRA models. In chapter 1, the life cycle of T. gondii, source of human infection, and different source attribution methods are described. In chapter 2, an overview of existing mathematical models to study the transmission of T. gondii and their assessments of different control strategies is described. Due to the complicated life cycle of the parasite and the absence of field data, models do not always represent reality. In chapter 3, a study estimating the seroprevalence and associated risk factors in indoor-housed Dutch dairy goats to investigate whether a biosecurity intervention would be feasible for reducing the risk of toxoplasmosis in grazing animals is presented. The estimated low seroprevalence suggested that indoor housing seems reduce the exposure of goats to T. gondii. In chapter 4, a hierarchical meta-analysis was performed to estimate the adjusted seroprevalence in pregnant women and livestock in China. These results not only provided us accurate insight on T. gondii infection status in China, but also important data in the meat-borne QMRA model. In chapter 5, the QMRA model of meat-borne T. gondii infection in the Netherlands as described previously by Opsteegh et al. (2011) was updated. The updated model found that filet americain, spiced and smoked pork sausage and leg of mutton were the top three products associated with the highest number of predicted infections. In addition, salting had a great effect on T. gondii survival but does not necessarily inactive all T. gondii. The Dutch QMRA model was implemented for China in chapter 6. It identified pork as the main source of T. gondii infection, followed by poultry, small ruminants, ducks and cattle. Results from the QMRA models clearly show that the prevalence of T. gondii infection in livestock does not necessarily present an indication for the risk of infection or the importance of the different types of meat on a population level. In chapter 7, The risk associated with T. gondii exposure via soil intake in the Netherlands was estimated and a sensitive magnetic capture qPCR method was developed. Currently the model likely overestimates the risk of infection via soil, and several data gaps were identified. We recommend to collect specific data for different population groups in the future. In chapter 8, the values and limitations of the quantitative risk assessment approach for toxoplasmosis source attribution were discussed. The findings in this thesis can aid science-based decision-making on where to target interventions, thereby, in the end, reducing the disease burden of toxoplasmosis.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Utrecht University
Supervisors/Advisors
  • Heederik, Dick, Primary supervisor
  • van der Giessen, J.W.B., Co-supervisor, External person
  • Opsteegh, M., Co-supervisor
Award date8 Dec 2020
Place of PublicationUtrecht
Publisher
Print ISBNs978-94-6421-127-6
Electronic ISBNs978-94-6421-127-6
DOIs
Publication statusPublished - 8 Dec 2020

Keywords

  • Toxoplasma gondii
  • Quantitative microbial risk assessment
  • Source attribution
  • Seroprevalence
  • Risk factors
  • Salt inactivation
  • Magnetic capture qPCR
  • Bayesian hierarchical meta-analysis
  • Modelling
  • Zoonoses

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