Pest risk assessment of insects in sea cargo containers

A survey of the floors of 3001 empty sea cargo containers in storage was undertaken to estimate the quarantine risk of importing exotic insect pests into Australia, with special reference to pests of timber. More than 7400 live and dead insects were collected from 1174 containers. No live infestations of timber-feeding insects were recorded, but feeding damage detected in one floor indicates a low risk of importing colonies of timber pests in containers. The survey collection of dead insects demonstrates that containers are regularly exposed to economically important quarantinable insects, including timber pests (bostrichids, curculionids, cerambycids, siricids and termites), agricultural pests (including Adoretus sinicus, Adoretus sp., Carpophilus obsoletus and Philaenus spumarius), and nuisance pests (vespids and Solenopsis sp.). Stored product pests were found in more than 10% of containers. The assessment of pest risk associated with shipping containers is discussed in terms of the quantity and quality of opportunities for exotic insects to establish via this pathway.

The design of hazard risk assessment matrices for ranking occupational health risks and their application in mining and minerals processing

Two hazard risk assessment matrices for the ranking of occupational health risks are described. The qualitative matrix uses qualitative measures of probability and consequence to determine risk assessment codes for hazard-disease combinations. A walk-through survey of an underground metalliferous mine and concentrator is used to demonstrate how the qualitative matrix can be applied to determine priorities for the control of occupational health hazards. The semi-quantitative matrix uses attributable risk as a quantitative measure of probability and uses qualitative measures of consequence. A practical application of this matrix is the determination of occupational health priorities using existing epidemiological studies. Calculated attributable risks from epidemiological studies of hazard-disease combinations in mining and minerals processing are used as examples. These historic response data do not reflect the risks associated with current exposures. A method using current exposure data, known exposure-response relationships and the semi-quantitative matrix is proposed for more accurate and current risk rankings.

Coal dust exposures in the longwall mines of New South Wales, Australia: a respiratory risk assessment

This paper presents an analysis of personal respirable coal dust measurements recorded by the Joint Coal Board in the underground longwall mines of New South Wales from 1985 to 1999. A description of the longwall mining process is given. In the study, 11 829 measurements from 33 mines were analysed and the results given for each occupation, for seven occupational groups, for individual de-identified mines and for each year of study. The mean respirable coal dust concentration for all jobs was 1.51 mg/m(3) (SD 1.08 mg/m(3)). Only 6.9% of the measurements exceeded the Australian exposure standard of 3 mg/m(3). Published exposure-response relationships were used to predict the prevalence of progressive massive fibrosis and the mean loss of FEV1, after a working lifetime (40 years) of exposure to the mean observed concentration of 1.5 mg/m(3). Prevalences of 1.3 and 2.9% were predicted, based on data from the UK and the USA, respectively. The mean loss of FEV1 was estimated to be 73.7 ml.

Development and evaluation of the Clinician Suicide Risk Assessment Checklist

This paper describes the development and evaluation of a new instrument – the Clinician Suicide Risk Assessment Checklist (CSRAC). The instrument assesses the clinician’s competency in three areas: clinical interviewing, assessment of specific suicide risk factors, and formulating a management plan. A draft checklist was constructed by integrating information from 1) literature review 2) expert clinician focus group and 3) consultation with experts. It was utilised in a simulated clinical scenario with clinician trainees and a trained actor in order to test for inter-rater agreement. Agreement was calculated and the checklist was re-drafted with the aim of maximising agreement. A second phase of simulated clinical scenarios was then conducted and inter-rater agreement was calculated for the revised checklist. In the first phase of the study, 18 of 35 items had inadequate inter-rater agreement (60%>), while in the second phase, using the revised version, only 3 of 39 items failed to achieve adequate inter-rater agreement. Further evidence of reliability and validity are required. Continued development of the CSRAC will be necessary before it can be utilised to assess the effectiveness of risk assessment training programs.

Science-policy in environmental and health risk assessment: if we cannot do without, can we do better?

How can empirical evidence of adverse effects from exposure to noxious agents, which is often incomplete and uncertain, be used most appropriately to protect human health? We examine several important questions on the best uses of empirical evidence in regulatory risk management decision-making raised by the US Environmental Protection Agency (EPA)'s science-policy concerning uncertainty and variability in human health risk assessment. In our view, the US EPA (and other agencies that have adopted similar views of risk management) can often improve decision-making by decreasing reliance on default values and assumptions, particularly when causation is uncertain. This can be achieved by more fully exploiting decision-theoretic methods and criteria that explicitly account for uncertain, possibly conflicting scientific beliefs and that can be fully studied by advocates and adversaries of a policy choice, in administrative decision-making involving risk assessment. The substitution of decision-theoretic frameworks for default assumption-driven policies also allows stakeholder attitudes toward risk to be incorporated into policy debates, so that the public and risk managers can more explicitly identify the roles of risk-aversion or other attitudes toward risk and uncertainty in policy recommendations. Decision theory provides a sound scientific way explicitly to account for new knowledge and its effects on eventual policy choices. Although these improvements can complicate regulatory analyses, simplifying default assumptions can create substantial costs to society and can prematurely cut off consideration of new scientific insights (e.g., possible beneficial health effects from exposure to sufficiently low 'hormetic' doses of some agents). In many cases, the administrative burden of applying decision-analytic methods is likely to be more than offset by improved effectiveness of regulations in achieving desired goals. Because many foreign jurisdictions adopt US EPA reasoning and methods of risk analysis, it may be especially valuable to incorporate decision-theoretic principles that transcend local differences among jurisdictions.

Robustness of mathematical models for biological systems

The robustness of mathematical models for biological systems is studied by sensitivity analysis and stochastic simulations. Using a neural network model with three genes as the test problem, we study robustness properties of synthesis and degradation processes. For single parameter robustness, sensitivity analysis techniques are applied for studying parameter variations and stochastic simulations are used for investigating the impact of external noise. Results of sensitivity analysis are consistent with those obtained by stochastic simulations. Stochastic models with external noise can be used for studying the robustness not only to external noise but also to parameter variations. For external noise we also use stochastic models to study the robustness of the function of each gene and that of the system.