by Ali Siddiqui
According to the background research done by `Ben A. Smith et al. temperatures and total annual precipitation across most of North America are expected to rise. They suggest that these expected changes would impact areas negatively especially if those areas are developing and population is increasing. The negative impacts arise from how food and water contamination can be increased by the longer survival of both old and new pathogens as well as the extended peak season for many microbial diseases. An example they provided was of Salmonella infections, which correlated with rising global air temperatures in most continents besides Europe, negated the rise in infections by using human intervention through public health. This intervention was implemented once a quantities microbial risk assessment (QMRA) model was utilized. QMRA models are typically developed assuming historical/static climate conditions. These researchers suggest, however, that adding climate change factors in food/water safety QRMA models make them increasingly complex due to the varying range of relevancy of variables. According to the authors, a framework is necessary in order to better understand the large data alongside the QRMA model elements so as to asses the potential impacts of changing climate variables on public health.
The framework proposed is subdivided into three categories: knowledge synthesis, data storage and access, and stochastic QMRA modeling. These subdivisions allow for identifying and establishing relationships between climate variables and safety elements, applying data sets across risk models, and integrating data in an effective mathematical fashion. The researchers have developed three case studies in different regions of Canada and analyzed climate impacts (current and projected), hazards, commodity production and processing, and population demographics and behaviors. They analyzed ochratoxin A (in wheat grown in Saskatchewan), Vibrio parahaemolyticus (oysters in coastal British Columbia), and Giardia in drinking water of a generic northern community. The researchers then subjected each of these case studies to a food/water safety model, where they attempted to understand the projected change in concentrations of the ochratoxin A, Vibrio parahaemolyticus, and Giardia.
To ensure the effectiveness of their models, data extracted from primary literature, government reports, and existing databases were categorized in case-specific and crosscutting databases. Case specific databases were analyzed as point estimates directly put into their Analytica software-modeling program. Cross cutting data like regional air and water temperatures were used to inform their QMRA models. The Analytica studies were subjected to multiple simulations sometimes over 1000 that changed slightly each time to represent different situations. All these different representations therefore served as potential data points for the overall framework, which could then be applicable in many different situations.
Ultimately, the researchers created a framework which provides a platform to asses varying climate factors, their risk, and provide mitigation options to reduce food and waterborne diseases using risk models.
Smith, Ben. Ruthman, Todd., Sparling, Erik. Auld, Heather, Corner, Neil, Young, Ian, Lammerding, Anna M., Fazil, Aamir. 2014. “A risk modeling framework to evaluate the impacts of climate change and adaption on food and water safety.” Food Research International 61, 1-8. http://www.sciencedirect.com/science/article/pii/S0963996914004724