Predictive Bayesian microbial dose-response assessment based on suggested self-organization in primary illness response: Cryptosporidium parvum

Demonstrating the Benefits of Predictive Bayesian Dose-Response Relationships Using Six Exposure Studies of Cryptosporidium parvum

A conventional dose-response function can be refitted as additional data become available. A predictive dose-response function in contrast does not require a curve-fitting step, only additional data and presents the unconditional probabilities of illness, reflecting the level of information it contains. In contrast, the predictive Bayesian dose-response function becomes progressively less conservative as more information is included. This investigation evaluated the potential for using predictive Bayesian methods to develop a dose-response for human infection that improves on existing models, to show how predictive Bayesian statistical methods can utilize additional data, and expand the Bayesian methods for a broad audience including those concerned about an oversimplification of dose-response curve use in quantitative microbial risk assessment (QMRA). This study used a dose-response relationship incorporating six separate data sets for Cryptosporidium parvum. A Pareto II distribution with known priors was applied to one of the six data sets to calibrate the model, while the others were used for subsequent updating. While epidemiological principles indicate that local variations, host susceptibility, and organism strain virulence may vary, the six data sets all appear to be well characterized using the Bayesian approach. The adaptable model was applied to an existing data set for Campylobacter jejuni for model validation purposes, which yielded results that demonstrate the ability to analyze a dose-response function with limited data using and update those relationships with new data. An analysis of the goodness of fit compared to the beta-Poisson methods also demonstrated correlation between the predictive Bayesian model and the data.

Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms

Following a request from EFSA, the Panel on Plant Protection Products and their Residues (PPR) developed an opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) models and their use in prospective environmental risk assessment (ERA) for pesticides and aquatic organisms. TKTD models are species- and compound-specific and can be used to predict (sub)lethal effects of pesticides under untested (time-variable) exposure conditions. Three different types of TKTD models are described, viz., (i) the 'General Unified Threshold models of Survival' (GUTS), (ii) those based on the Dynamic Energy Budget theory (DEBtox models), and (iii) models for primary producers. All these TKTD models follow the principle that the processes influencing internal exposure of an organism, (TK), are separated from the processes that lead to damage and effects/mortality (TD). GUTS models can be used to predict survival rate under untested exposure conditions. DEBtox models explore the effects on growth and reproduction of toxicants over time, even over the entire life cycle. TKTD model for primary producers and pesticides have been developed for algae, Lemna and Myriophyllum. For all TKTD model calibration, both toxicity data on standard test species and/or additional species can be used. For validation, substance and species-specific data sets from independent refined-exposure experiments are required. Based on the current state of the art (e.g. lack of documented and evaluated examples), the DEBtox modelling approach is currently limited to research applications. However, its great potential for future use in prospective ERA for pesticides is recognised. The GUTS model and the Lemna model are considered ready to be used in risk assessment.

Distributions of Autocorrelated First-Order Kinetic Outcomes: Illness Severity

Many complex systems produce outcomes having recurring, power law-like distributions over wide ranges. However, the form necessarily breaks down at extremes, whereas the Weibull distribution has been demonstrated over the full observed range. Here the Weibull distribution is derived as the asymptotic distribution of generalized first-order kinetic processes, with convergence driven by autocorrelation, and entropy maximization subject to finite positive mean, of the incremental compounding rates. Process increments represent multiplicative causes. In particular, illness severities are modeled as such, occurring in proportion to products of, e.g., chronic toxicant fractions passed by organs along a pathway, or rates of interacting oncogenic mutations. The Weibull form is also argued theoretically and by simulation to be robust to the onset of saturation kinetics. The Weibull exponential parameter is shown to indicate the number and widths of the first-order compounding increments, the extent of rate autocorrelation, and the degree to which process increments are distributed exponential. In contrast with the Gaussian result in linear independent systems, the form is driven not by independence and multiplicity of process increments, but by increment autocorrelation and entropy. In some physical systems the form may be attracting, due to multiplicative evolution of outcome magnitudes towards extreme values potentially much larger and smaller than control mechanisms can contain. The Weibull distribution is demonstrated in preference to the lognormal and Pareto I for illness severities versus (a) toxicokinetic models, (b) biologically-based network models, (c) scholastic and psychological test score data for children with prenatal mercury exposure, and (d) time-to-tumor data of the ED₀₁ study.

Heterogeneity: a major factor influencing microbial exposure and risk assessment

Microbial risk assessment is dependent on several biological and environmental factors that affect both the exposure characteristics to the biological agents and the mechanisms of pathogenicity involved in the pathogen-host relationship. Many exposure assessment studies still focus on the location parameters of the probability distribution representing the concentration of the pathogens and/or toxin. However, the mean or median by themselves are insufficient to evaluate the adverse effects that are associated with a given level of exposure. Therefore, the effects on the risk of disease of a number of factors, including the shape parameters characterizing the distribution patterns of the pathogen in their environment, were investigated. The statistical models, which were developed to provide a better understanding of the factors influencing the risk, highlight the role of heterogeneity and its consequences on the commonly used risk assessment paradigm. Indeed, the heterogeneity characterizing the spatial and temporal distribution of the pathogen and/or the toxin contained in the water or food consumed is shown to be a major factor that may influence the magnitude of the risk dramatically. In general, the risk diminishes with higher levels of heterogeneity. This scheme is totally inverted in the presence of a threshold in the dose-response relationship, since heterogeneity will then have a tremendous impact, namely, by magnifying the risk when the mean concentration of pathogens is below the threshold. Moreover, the approach of this article may be useful for risk ranking analysis, regarding different exposure conditions, and may also lead to improved water and food quality guidelines.

An expert panel report of a proposed scientific model demonstrating the effectiveness of antibacterial handwash products

In 2005, a US Food and Drug Administration Nonprescription Drug Advisory Committee (NDAC) review of consumer antiseptic handwash product studies concluded that the data regarding existing products failed to demonstrate any association between specific log reductions of bacteria achieved by antiseptic handwashing and reduction of infection. The NDAC recommended that consumer antibacterial handwashing products should demonstrate a reduction in infection compared with non-antibacterial handwash products. In response to the NDAC review, a consumer product industry-sponsored expert panel meeting was held in October 2007 to review new methods for assessing the efficacy of antibacterial handwashes. The expert panel reviewed a newly proposed model for linking the effectiveness of antibacterial handwashing to infection reduction and made recommendations for conducting future studies designed to demonstrate the efficacy of antibacterial handwash formulations. The panel concluded that using the surrogate infection model to demonstrate efficacy has a sound scientific basis, that the use of Shigella flexneri as a test organism coupled with a modified hand contamination procedure is supported by published data, and that the model represents a realistic test for the efficacy of consumer antibacterial handwash products. This article summarizes the expert panel's deliberations, conclusions, and recommendations.

A gradient Markov chain Monte Carlo algorithm for computing multivariate maximum likelihood estimates and posterior distributions: mixture dose-response assessment

Multivariate probability distributions, such as may be used for mixture dose-response assessment, are typically highly parameterized and difficult to fit to available data. However, such distributions may be useful in analyzing the large electronic data sets becoming available, such as dose-response biomarker and genetic information. In this article, a new two-stage computational approach is introduced for estimating multivariate distributions and addressing parameter uncertainty. The proposed first stage comprises a gradient Markov chain Monte Carlo (GMCMC) technique to find Bayesian posterior mode estimates (PMEs) of parameters, equivalent to maximum likelihood estimates (MLEs) in the absence of subjective information. In the second stage, these estimates are used to initialize a Markov chain Monte Carlo (MCMC) simulation, replacing the conventional burn-in period to allow convergent simulation of the full joint Bayesian posterior distribution and the corresponding unconditional multivariate distribution (not conditional on uncertain parameter values). When the distribution of parameter uncertainty is such a Bayesian posterior, the unconditional distribution is termed predictive. The method is demonstrated by finding conditional and unconditional versions of the recently proposed emergent dose-response function (DRF). Results are shown for the five-parameter common-mode and seven-parameter dissimilar-mode models, based on published data for eight benzene-toluene dose pairs. The common mode conditional DRF is obtained with a 21-fold reduction in data requirement versus MCMC. Example common-mode unconditional DRFs are then found using synthetic data, showing a 71% reduction in required data. The approach is further demonstrated for a PCB 126-PCB 153 mixture. Applicability is analyzed and discussed. Matlab(®) computer programs are provided.

Relative risk assessment of cruise ships biosolids disposal alternatives

A relative risk assessment of biosolids disposal alternatives for cruise ships is presented in this paper. The area of study encompasses islands and marine waters of the Caribbean Sea. The objective was to evaluate relative human health and ecological risks of (a) dewatering/incineration, (b) landing the solids for disposal, considering that in some countries land-disposed solids might be discharged in the near-shore environment untreated, and (c) deep ocean disposal. Input to the Bayesian assessment consisted of professional judgment based on available literature and modeling information, data on constituent concentrations in cruise ship biosolids, and simulations of constituent concentrations in Caribbean waters assuming ocean disposal. Results indicate that human health and ecological risks associated with land disposal and shallow ocean disposal are higher than those of the deep ocean disposal and incineration. For incineration, predicted ecological impacts were lower relative to deep ocean disposal before considering potential impacts of carbon emissions.

Quantitative health risk assessment of Cryptosporidium in rivers of southern China based on continuous monitoring

The concentrations of Cryptosporidium in the source water of several cities of Zhejiang Province, China were determined to be in the range of 0-17 oocysts/10 L in the rainy season in 2008, with a mean value of 7 oocysts/10 L. Based on the investigation data, comprehensive risk assessment of Cryptosporidium infection was performed by considering different water intake routes as well as water consumption. Intakes of unboiled tapwater (including drinking and tooth-brushing and food and dish washing) and source water (through swimming in rivers) were estimated to be 2.59-25.9 and 0.32-0.74 L/year-person, respectively. The mortality due to Cryptosporidium infection for people in this region, excluding HIV-infected patients, was calculated as 0-0.0146 per 10(5) persons using a conditional probability formula. Disability-adjusted life years (DALYs) were used to quantify the risk of Cryptosporidium infection, for which uncertainty was analyzed. For people who consumed conventionally treated water, the DALYs due to Cryptosporidium infection were 6.51 per 10(5) (95% CI: 2.16 × 10(-5)-22.35 × 10(-5)) persons, which were higher than a risk judged acceptable by some (1.97 × 10(-5) DALYs per year), and the risk for those consuming ozone-treated water became 0.0689 × 10(-5) DALYs per year. The major risk of infection resulted from swimming in the river. This study provides a method to establish the risk of Cryptosporidium infection and optimize the scheme for reducing the risk effectively, which is useful for the modification of water quality standards based on cost utility analysis given use of DALYs.

A Bayesian approach to analyzing ecotoxicological data

Standardized chronic toxicity tests are usually analyzed using a NOEC (no observed effect concentration) or ECx (x% effect concentration) calculation. However,these methods provide very little information for the material cost they entail. It has been proposed that biology-based methods, such as the DEBtox approach, would make better use of the data available. DEBtox deals with the energy balance between physiological processes, and gives insight on how a compound disturbs it. We propose that data analysis can be further improved by estimating the DEBtox parameters using the considerable expertise available in laboratories and/or the literature. The Bayesian inference appears to be an appropriate estimation method for this purpose, as this technique takes expertise into account as prior probability distribution for each parameter, and provides the corresponding posterior distributions given the data. From these posterior distributions, point estimates can easily be deduced, but also credible intervals which are ideal for use in risk assessment. In this paper, we demonstrate this approach through the analysis of two 21-day Daphnia reproduction tests.