A Risk Sciences International Case Study
Risk Model Framework for Foodborne Campylobacteriosis via Poultry Meat Pathway
Client: Ministry for Primary Industries (New Zealand)
Listing of the client in no way affirms the client's support, sponsorship, or validation in any form of Risk Sciences International or the RSI staff member(s) who conducted this project during their stay with RSI or prior to joining the company. This case study is displayed for informative purposes only to demonstrate the capacity of RSI staff members. This case study reveals no proprietary information or information deemed sensitive.
Risk Model Framework for Foodborne Campylobacteriosis via Poultry Meat Pathway
To support national public health goals, RSI was tasked with developing a quantitative microbiological risk assessment (QMRA) model to estimate the human health risks associated with the consumption of chicken in New Zealand. The core objective was to quantify the relationship between Campylobacter contamination on poultry carcasses at the point of chilling and the subsequent incidence of foodborne illness among consumers.
The work unfolded in two distinct phases. The first phase focused on evaluating the feasibility and practicality of such a model. This included a review of international and domestic risk assessment models for Campylobacter in chicken, an analysis of available data sources (including the National Microbiological Database), and the formulation of a pathway model structure. The proposed pathway accounted for multiple stages, including post-chill processing, retail handling, consumer practices (storage, handling, and cooking), and health outcomes. The model also integrated burden-of-illness estimates using Disability-Adjusted Life Years (DALYs), including acute illness and long-term sequelae such as Guillain-Barré syndrome.
Following a review checkpoint, the second phase involved developing the probabilistic risk assessment model itself. This model was built to be transparent, modifiable, and aligned with New Zealand-specific data where possible. It supported multiple simulation platforms and was designed to facilitate the testing of various assumptions, including alternative dose-response curves and regulatory targets. The model included capacity for sensitivity and uncertainty analysis to evaluate the impact of key parameters and explore the effects of policy changes on public health outcomes.
The resulting framework and model aimed to provide the analytical foundation needed to assess whether a proposed 20% reduction in campylobacteriosis could be achieved by modifying regulatory targets, particularly those relating to contamination levels post-chill.
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