Recent animal studies using nonhuman primates and guinea pigs have both estimated LD₅₀s of approximately 10⁷ Listeria monocytogenes colony forming units (cfu). The FAO/WHO estimated a human LD₅₀ of 1.9 × 10⁶ cfu based on data from a pregnant woman consuming contaminated soft cheese. The researchers reevaluated risk based on dose-response curves from pregnant rhesus monkeys and guinea pigs. Using standard risk assessment methodology including hazard identification, exposure assessment, hazard characterization, and risk characterization, risk was calculated based on the new dose-response information. To compare models, researchers looked at mortality rate per serving at predicted doses ranging from 10⁻⁴ to 10¹² L. monocytogenes cfu. 

OpenEpi is free and open source software for epidemiologic statistics. It can be run from a web server or downloaded and run without a web connection. A server is not required. OpenEpi provides statistics for counts and measurements in descriptive and analytic studies, stratified analysis with exact confidence limits, matched pair and person-time analysis, sample size and power calculations, random numbers, sensitivity, specificity and other evaluation statistics, R x C tables, chi-square for dose-response, and links to other useful sites.

This study describes an analytical framework that permits quantitative consideration of variability and uncertainty in microbial hazard characterization. Second-order modeling that used two-dimensional Monte Carlo simulation and stratification into homogeneous population subgroups was applied to integrate uncertainty and variability.  

The results indicate that uncertainty associated with dose-response modeling has a dominating influence on the analytical outcome. In contrast, inclusion of the age factor has a limited impact. While the advocacy of more closely modeling variability in hazard characterization is warranted, the characterization of key sources of uncertainties and their consistent propagation throughout a microbial risk assessment actually appear of greater importance.

Q fever is a zoonotic disease caused by the intracellular gram-negative bacterium Coxiella burnetii (C. burnetii), which only multiplies within the phagolysosomal vacuoles. Q fever may manifest as acute or chronic disease. The acute form is generally not fatal and manifestes as self-controlled febrile illness. Chronic Q fever is usually characterized by endocarditis. Many animal models, including humans, have been studied for Q fever infection through various exposure routes. The studies considered different endpoints including death for animal models and clinical signs for human infection. In this article, animal experimental data available in the open literature were fit to suitable dose-response models using maximum likelihood estimation. 

Microbial food safety risk assessment models can often at times be simplified by eliminating the need to integrate a complex dose-response relationship across a distribution of exposure doses. This is possible if exposure pathways lead to pathogens at exposure that consistently have a small probability of causing illness. In this situation, the probability of illness will follow an approximately linear function of dose. This article discusses a methodology for how to determine if a linear dose-response is in fact appropriate. 

The WHO/IPCS Risk Assessment Toolkit (RA Toolkit) project aims to make the international tools on chemical risk assessment more readily accessible, especially to relevant stakeholders in developing countries and countries with economies in transition.

This website includes records of the meeting introducing the pilot phase of the draft WHO Risk Assessment Toolkit in Thailand, Malaysia and China (Bangkok meeting, July 2009).

The RA Toolkit is organized into sections that provide:

-An overview of chemical RA, including the RA framework and uses of RA;

-Generic road maps for how to conduct RA;

-Listing of international resources that are useful for conducting RAs; and

-Examples of how that information can be applied to a RA question, including case-specific roadmaps.

A Bayesian network model was developed to integrate diverse types of data to conduct an exposure-dose-response assessment for benzene-induced acute myeloid leukemia (AML). The network approach was used to evaluate and compare individual biomarkers and quantitatively link the biomarkers along the exposure-disease continuum. The network was used to perform the biomarker-based dose-response analysis, and various other approaches to the dose-response analysis were conducted for comparison.