Statistical models of health risk due to microbial contamination of foods

Between 6 million and 33 million cases of food-related illness are estimated to occur in the United States each year, with about 5000 episodes resulting in death. Growing concerns about the safety of food prompted the National Food Safety Initiative of 1997, the goal of which is to reduce the incidence of illness caused by food-borne pathogens. A key component of the food safety initiative is the improvement of farm-to-table risk assessment capabilities, including the development of improved dose-response models for estimating risk. When sufficient data are available, allowable contamination levels of specific micro-organisms in food are established using dose-response models to predict risk at very low doses based on experimental data at much higher doses. This necessitates having reliable models for setting allowable exposures to food-borne pathogens. While only limited data on relatively few micro-organisms that occur in food are available at present for dose-response modeling and risk estimation, still none of the two-parameter models proposed so far, including the popular Beta-Poisson (BP) model, appears to be completely satisfactory for describing and fitting all of the present data (Holcomb et al., 1999). The Weibull–Gamma (WG) model is the only three-parameter model that has been proposed to date. In this paper, new competitive three-parameter models are derived, using a formulation that can be parameterized to represent statistical variation with respect to the dose of micro-organism received by the host and the hosts susceptibility to infection. Parameters of the models are estimated using the maximum likelihood method. Experimental data on several common microbial contaminants in food are used to illustrate the methodology.     

环境内分泌干扰物低剂量-效应研究进展

环境内分泌干扰物(EDCs)的低剂量-效应已成为生态毒理学界的研究热点.环境内分泌干扰物在接近或低于无可见不良效应浓度水平(NOAEL)时仍可诱发生物效应并存在非单调剂量-效应关系,这对现行的动物繁殖和发育毒理学检测规程以及环境内分泌干扰物风险评价的理论和方法提出了挑战.此外,由于环境中内分泌干扰物呈低剂量长期暴露的特征,研究低剂量-效应对正确进行生态风险评价具有重大的科学意义.在总结国内外相关研究的基础上,对当前环境内分泌干扰物低剂量-效应的研究进展进行了综述.     

Effects of sampling error and temporal correlations in population growth on process variance estimators

Estimates of a population’s growth rate and process variance from time-series data are often used to calculate risk metrics such as the probability of quasi-extinction, but temporal correlations in the data from sampling error, intrinsic population factors, or environmental conditions can bias process variance estimators and detrimentally affect risk predictions. It has been claimed (McNamara and Harding, Ecol Lett 7:16–20, 2004) that estimates of the long-term variance that incorporate observed temporal correlations in population growth are unaffected by sampling error; however, no estimation procedures were proposed for time-series data. We develop a suite of such long-term variance estimators, and use simulated data with temporally autocorrelated population growth and sampling error to evaluate their performance. In some cases, we get nearly unbiased long-term variance estimates despite ignoring sampling error, but the utility of these estimators is questionable because of large estimation uncertainty and difficulties in estimating correlation structure in practice. Process variance estimators that ignored temporal correlations generally gave more precise estimates of the variability in population growth and of the probability of quasi-extinction. We also found that the estimation of probability of quasi-extinction was greatly improved when quasi-extinction thresholds were set relatively close to population levels. Because of precision concerns, we recommend using simple models for risk estimates despite potential biases, and limiting inference to quantifying relative risk; e.g., changes in risk over time for a single population or comparative risk among populations.     

GIS-basierte Modellierung von Gewässerimmissionen

Goal and Scope

Environmental assessment of aquatic micro pollutants should consider the spatial and temporal variability of emission, transport and transformation. Simulation models coupled with Geographic Information Systems (GIS) provide digital maps of concentration patterns caused by the overlay of multipoint and diffuse emissions and natural attenuation processes in river basins. The paper gives an overview on GIS-based models for river basins and demonstrates the applicability by using some illustrating examples with GREAT-ER.

Main Features

Georeferenced models have several advantages: visualization of concentration patterns, investigation of spatial and temporal concentration profiles, analysis of exceedance of environmental quality standards, embedding in integrated river basin management systems.

Results and Discussion

GIS-based models allow a more realistic assessment. Monitoring programmes should be designed to deliver appropriate measured data for the evaluation and improvement of models.

Recommendation and Perspectives

The combination of digital maps, simulation models and environmental monitoring would provide better approaches for the risk assessment and water quality management of aquatic micro pollutants.     

Korrelation meteorologischer und pflanzenphänologischer Indikatoren des Klimawandels

Goal and Scope

While the rise of the air temperature as part of the global climatic change seems to be widely assured, questions about the effects of this phenomenon in ecological systems get more and more interesting. In this sense this article shows by the example of monitoring sites in Baden-Württemberg the correlation of air temperature measurements with data on the phenology of selected plants.

Methods

To this end the data on air remperature and plant phenology which are gathered from spatial dislocated measurement sites were correlated by prior application of Kriging interpolation. In addition, geostatistics are ssed to analyze and cartographically depict the spatial structure of the phenology in spring and in summer.

Results and Conclusion

The statistical analysis reveals a significant relationship between the rising air temperature and the early beginning phenological phases like blooming or fruit maturation. From 1991 to 1999 spring time as indicated by phenological phases begins up to 14 days earlier than from 1961 to 1990. As proved by geostatistics, this holds true for the whole territory of Baden-Württemberg.

Recommendation and Perspective

The effects of the rise of air temperature should be examined not only by monitoring of biological individuals as for example plants, but on ecosystem level, too. The German ecosystem research and the environmental monitoring should be supplemented by the study of the effects of the climatic change in ecosystems. Because air temperature and humidity have a great influence on the temporal and spatial distribution of pathogen carriers (vectors) and pathogens mapping of the determinants of vector and pathogen distribution in space and time should be done in order to identify hot spots for risk assessment and further detailed epidemiological studies.     

A controversy re-visited: Is the coccinellid Adalia bipunctata adversely affected by Bt toxins?

Background

The Cefic Mixtures Industry Ad-hoc Team (MIAT) has investigated how risks from combined exposures can be effectively identified and managed using concepts proposed in recent regulatory guidance, new advances in risk assessment, and lessons learned from a Cefic-sponsored case study of mixture exposures.

Results

A series of tools were created that include: a decision tree, a system for grouping exposures, and a graphical tool (the MCR-HI plot). The decision tree allows the division of combined exposures into different groups, exposures where one or more individual components are a concern, exposures that are of low concern, and exposures that are a concern for combined effects but not for the effects of individual chemicals. These tools efficiently use available data, identify critical data gaps for combined assessments, and prioritize which chemicals require detailed toxicity information. The tools can be used to address multiple human health endpoints and ecological effects.

Conclusion

The tools provide a useful approach for assessing risks associated with combined exposures to multiple chemicals.     

Expositionsszenarien nach REACH

Object and Background

Production and use of substances and formulations can result in exposures on humans and the environment. In October 2003, the European Commissions presented a proposal for the registration, evaluation, assessment and restriction of chemicals (REACH). Assessment of exposures will become an essential element of REACH for certain substances. For many manufacturers and users of substances and formulations these tasks are new. Pilot projects to REACH have shown so far that solutions and instruments for these tasks which meet the requirements of real life situations have yet to be developed.

Discussion and Conclusions

Within the REACH proposal, exposure scenarios are a main element of the chemical safety assessment. This article outlines the contents of exposure scenarios. It reflects experiences with exposure scenarios and discusses possibilities of standardization. It covers exposure steps/categories and possibilities to build product groups. Non-branch specific types of exposure situations are an important element of exposure scenarios. Therefore, exposure steps/categories should be contained in exposure scenarios. Nevertheless, these elements structure only a specific part of the information contained in exposure scenarios. Hence they cannot replace exposure scenarios. Exposure categories do not reveal the uses, the processes and the activities connected with the intended use of substances. These information are important parts of a complete exposure scenario. They are the basis for the following step of risk characterisation. In addition, they provide the necessary transparency regarding the use patteern of substances.     

Behavioural toxicity in zebrafish (Danio rerio) exposed to waterborne zinc and chromium(VI)

This study is to investigate the behavioural responses of zebrafish to the toxic effects of waterborne zinc and chromium(VI). Swimming performance of fish was tracked by a real-time toxicity bio-monitoring system and transformed into data to calculate individual and fish school behavioural endpoints. Behavioural responses under metals were in accordance with the Stepwise Behavioural Response model. Significant and concentration-dependent effects on swimming speed and turning times were found at concentrations equal to or higher than 3.5 mg L^-1 for zinc and 0.7 mg L^-1 for chromium. Swimming height significantly declined in concentrations equal to or higher than 3.5 mg L^-1 under zinc exposure, while for the chromium treatment, it significantly increased in lower concentration exposures and decreased in higher concentration exposures. Average distance and dispersion of fish school decreased at the beginning but there were no obvious changes during the whole exposure period. Behavioural responses under metal exposures were time-dependent: avoidance (increased speed and turning times and decreased average distance and dispersion) might be the first response and followed by height changes. Changes in behavioural endpoints ‘swimming speed and height’ can be used as early stress responses for acute aquatic metal contamination as part of ecological risk assessment.     

Transport and dynamics of toxic pollutants in the natural environment and their effect on human health: research gaps and challenge

The source–pathway–receptor (SPR) approach to human exposure and risk assessment contains considerable uncertainty when using the refined modelling approaches to pollutant transport and dispersal, not least in how compounds of concern might be prioritised, proxy or indicator substances identified and the basic environmental and toxicological data collected. The impact of external environmental variables, urban systems and lifestyle is still poorly understood. This determines exposure of individuals and there are a number of methods being developed to provide more reliable spatial assessments. Within the human body, the dynamics of pollutants and effects on target organs from diffuse, transient sources of exposure sets ambitious challenges for traditional risk assessment approaches. Considerable potential exists in the application of, e.g. physiologically based pharmacokinetic (PBPK) models. The reduction in uncertainties associated with the effects of contaminants on humans, transport and dynamics influencing exposure, implications of adult versus child exposure and lifestyle and the development of realistic toxicological and exposure data are all highlighted as urgent research needs. The potential to integrate environmental with toxicological models provides the next phase of research opportunity and should be used to drive empirical and model assessments.     

A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of \(\text {NO}_{x}\) in the Los Angeles area during a 10 year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated \(R^2\) of approximately \(0.7\) at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.