Quantitative microbial risk assessment (QMRA)

Science Center Objects

Quantitative microbial risk assessment (QMRA) is a tool for estimating human health risks from exposure to pathogens via food, water, air, and other environmental routes.

Figure of example QMRA dose-response curves for three pathogens

Figure 1. Examples of dose-response relationships for three pathogens: Campylobacter jejuni (orange), enterohemorrhagic Escherichia coli (green), and Salmonella spp (blue). In this case, the human health outcome of interest is probability of illness (on the vertical axis).

Compared to epidemiology, QMRA provides an economical and practical alternative for estimating health risk and identifying influential risk factors. QMRA is typically described as a sequence of four steps:

Step 1. Hazard identification. Determine the pathogens and human health outcomes of concern.  Typically, the health outcomes considered are either infection or acute illness. The difference between the two is defined operationally; infection is the presence of a pathogen in the human body, while illness is characterized by specific symptoms (nausea, vomiting, etc.).

Step 2. Exposure assessment. Determine the pathways of exposure (air, food, etc.), and measure or model the pathogen exposure doses during defined exposure events. When available, direct measurements of pathogen levels are preferred over modeled levels because they result in more empirical estimates of the exposure dose.

Step 3. Dose-response assessment. Using existing outbreak records or laboratory dosing studies, determine the relationship between the exposure dose and the likelihood of the health outcome (fig. 1). In practice, these relationships are usually developed by professionals with specific expertise in fitting the necessary mathematical models, and LIDE’s task is to select the most appropriate relationship from a group of possible alternatives.

Step 4. Risk characterization. Using the exposure doses from Step 2 and the dose-response relationship from Step 3, calculate the likelihood of the health outcome (a.k.a., “risk”) defined in Step 1. Risk characterization is often conducted using Monte Carlo simulations, which help account for variability and uncertainty in estimated health risks. Monte Carlo simulations also allow for a sensitivity analysis that can identify risk factors (environmental factors, exposure variables, etc.) that most strongly influence risk.

LIDE’s unique combination of experience and technical capabilities covers all four steps outlined above, allowing for easy integration of the entire sequence into a complete QMRA.