A method for calculating Bayesian uncertainties on internal doses resulting from complex occupational exposures.

Estimating uncertainties on doses from bioassay data is of interest in epidemiology studies that estimate cancer risk from occupational exposures to radionuclides. Bayesian methods provide a logical framework to calculate these uncertainties. However, occupational exposures often consist of many intakes, and this can make the Bayesian calculation computationally intractable. This paper describes a novel strategy for increasing the computational speed of the calculation by simplifying the intake pattern to a single composite intake, termed as complex intake regime (CIR). In order to assess whether this approximation is accurate and fast enough for practical purposes, the method is implemented by the Weighted Likelihood Monte Carlo Sampling (WeLMoS) method and evaluated by comparing its performance with a Markov Chain Monte Carlo (MCMC) method. The MCMC method gives the full solution (all intakes are independent), but is very computationally intensive to apply routinely. Posterior distributions of model parameter values, intakes and doses are calculated for a representative sample of plutonium workers from the United Kingdom Atomic Energy cohort using the WeLMoS method with the CIR and the MCMC method. The distributions are in good agreement: posterior means and Q(0.025) and Q(0.975) quantiles are typically within 20 %. Furthermore, the WeLMoS method using the CIR converges quickly: a typical case history takes around 10-20 min on a fast workstation, whereas the MCMC method took around 12-72 hr. The advantages and disadvantages of the method are discussed.

A Monte Carlo method for calculating Bayesian uncertainties in internal dosimetry.

This paper presents a novel Monte Carlo method (WeLMoS, Weighted Likelihood Monte-Carlo sampling method) that has been developed to perform Bayesian analyses of monitoring data. The WeLMoS method randomly samples parameters from continuous prior probability distributions and then weights each vector by its likelihood (i.e. its goodness of fit to the measurement data). Furthermore, in order to quality assure the method, and assess its strengths and weaknesses, a second method (MCMC, Markov chain Monte Carlo) has also been developed. The MCMC method uses the Metropolis algorithm to sample directly from the posterior distribution of parameters. The methods are evaluated and compared using an artificially generated case involving an exposure to a plutonium nitrate aerosol. In addition to calculating the uncertainty on internal dose, the methods can also calculate the probability distribution of model parameter values given the observed data. In other words, the techniques provide a powerful tool to obtain the estimates of parameter values that best fit the data and the associated uncertainty on these estimates. Current applications of the methodology, including the determination of lung solubility parameters, from volunteer and cohort data, are also discussed.

Internal dosimetry: towards harmonisation and coordination of research.

The CONRAD Project is a Coordinated Network for Radiation Dosimetry funded by the European Commission 6th Framework Programme. The activities developed within CONRAD Work Package 5 ('Coordination of Research on Internal Dosimetry') have contributed to improve the harmonisation and reliability in the assessment of internal doses. The tasks carried out included a study of uncertainties and the refinement of the IDEAS Guidelines associated with the evaluation of doses after intakes of radionuclides. The implementation and quality assurance of new biokinetic models for dose assessment and the first attempt to develop a generic dosimetric model for DTPA therapy are important WP5 achievements. Applications of voxel phantoms and Monte Carlo simulations for the assessment of intakes from in vivo measurements were also considered. A Nuclear Emergency Monitoring Network (EUREMON) has been established for the interpretation of monitoring data after accidental or deliberate releases of radionuclides. Finally, WP5 group has worked on the update of the existing IDEAS bibliographic, internal contamination and case evaluation databases. A summary of CONRAD WP5 objectives and results is presented here.

Evaluation of scattering factor values for internal dose assessment following the IDEAS guidelines: preliminary results.

The IDEAS Guidelines for the assessment of internal doses from monitoring data suggest default measurement uncertainties (i.e. scattering factors, SFs) to be used for different types of monitoring data. However, these default values were mainly based upon expert judgement. In this paper, SF values have been calculated for different radionuclides and types of monitoring data using real data contained in the IDEAS Internal Contamination Database. Results are presented.

Application of IDEAS guidelines: the IDEAS/IAEA intercomparison exercise on internal dose assessment.

As part of the EU Fifth Framework Programme IDEAS project 'General Guidelines for the Evaluation of Incorporation Monitoring Data', and in collaboration with the International Atomic Energy Agency, a new intercomparison exercise for the assessment of doses from intakes of radionuclides was organised. Several cases were selected, to cover a wide range of practices in the nuclear fuel cycle and medical applications. The cases were: (1) acute intake of HTO, (2) acute inhalation of the fission products 137Cs and 90Sr, (3) acute inhalation of 60Co, (4) repeated intakes of 131I, (5) intake of enriched uranium and (6) single intake of Pu isotopes and 241Am. This intercomparison exercise especially focused on the effect of the Guidelines proposed by the IDEAS project for harmonisation of internal dosimetry.

Uncertainties in doses from intakes of radionuclides assessed from monitoring measurements.

The evaluation of uncertainties in doses from intakes of radionuclides is one of the most difficult problems in internal dosimetry. In this paper, the process of assessing internal doses from monitoring measurements is reviewed and the major sources of uncertainty are discussed. Methods developed independently at HPA and at IRSN for the determination of uncertainties in internal doses assessed from monitoring measurements are described. Both use a Monte Carlo simulation approach. Results are described for three illustrative examples. An alternative method developed at the Los Alamos National Laboratory that uses Bayesian statistical methods is also described briefly.

Non-fundholding in Nottingham: a vision of the future.

The 1991 health service reforms introduced the internal market and grave individual fundholding practices budgets with which they could attempt to secure preferential access to secondary health care for their patients. In the view of many doctors this undermined the principle of equity on which the NHS was founded. In Nottingham 200 non-fundholding general practitioners have joined together to act in liaison with their purchasing health authority. A committed representative group of general practitioners can collectively offer more time and knowledge to the contracting process while minimising the impact on clinical workload. As a large purchaser with low management costs the group has secured access to quality secondary care which is equitably available to all patients, preventing the development of a local two tier service. Nottingham's non-fundholding model of commissioning is equitable and efficient.