New models for evaluation of radiation-induced lifetime cancer risk and its uncertainty employed in the UNSCEAR 2006 report

Recommendations on statistical approaches to account for dose uncertainties in radiation epidemiologic risk models

PURPOSE

Epidemiological studies of stochastic radiation health effects such as cancer, meant to estimate risks of the adverse effects as a function of radiation dose, depend largely on estimates of the radiation doses received by the exposed group under study. Those estimates are based on dosimetry that always has uncertainty, which often can be quite substantial. Studies that do not incorporate statistical methods to correct for dosimetric uncertainty may produce biased estimates of risk and incorrect confidence bounds on those estimates. This paper reviews commonly used statistical methods to correct radiation risk regressions for dosimetric uncertainty, with emphasis on some newer methods. We begin by describing the types of dose uncertainty that may occur, including those in which an uncertain value is shared by part or all of a cohort, and then demonstrate how these sources of uncertainty arise in radiation dosimetry. We briefly describe the effects of different types of dosimetric uncertainty on risk estimates, followed by a description of each method of adjusting for the uncertainty.

CONCLUSIONS

Each of the method has strengths and weaknesses, and some methods have limited applicability. We describe the types of uncertainty to which each method can be applied and its pros and cons. Finally, we provide summary recommendations and touch briefly on suggestions for further research.

Considering clonal hematopoiesis of indeterminate potential in space radiation risk analysis for hematologic cancers and cardiovascular disease

BACKGROUND

Expanding human presence in space through long-duration exploration missions and commercial space operations warrants improvements in approaches for quantifying crew space radiation health risks. Currently, risk assessment models for radiogenic cancer and cardiovascular disease consider age, sex, and tobacco use, but do not incorporate other modifiable (e.g., body weight, physical activity, diet, environment) and non-modifiable individual risk factors (e.g., genetics, medical history, race/ethnicity, family history) that may greatly influence crew health both in-mission and long-term. For example, clonal hematopoiesis of indeterminate potential (CHIP) is a relatively common age-related condition that is an emerging risk factor for a variety of diseases including cardiovascular disease and cancer. CHIP carrier status may therefore exacerbate health risks associated with space radiation exposure.

METHODS

In the present study, published CHIP hazard ratios were used to modify background hazard rates for coronary heart disease, stroke, and hematologic cancers in the National Aeronautics and Space Administration space radiation risk assessment model. The risk of radiation exposure-induced death for these endpoints was projected for a future Mars exploration mission scenario.

RESULTS

Here we show appreciable increases in the lifetime risk of exposure-induced death for hematologic malignancies, coronary heart disease, and stroke, which are observed as a function of age after radiation exposure for male and female crew members that are directly attributable to the elevated health risks for CHIP carriers.

CONCLUSIONS

We discuss the importance of evaluating individual risk factors such as CHIP as part of a comprehensive space radiation risk assessment strategy aimed at effective risk communication and disease surveillance for astronauts embarking on future exploration missions.

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

A generalisation of the method of regression calibration and comparison with the Bayesian 2-dimensional Monte Carlo method

For many cancer sites it is necessary to assess risks from low-dose exposures via extrapolation from groups exposed at moderate and high levels of dose. Measurement error can substantially alter the shape of this relationship and hence the derived population risk estimates. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, much attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. In this paper we test a Bayesian model averaging method, the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, that has been fairly recently proposed against a very newly proposed modification of the regression calibration method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. We also compared both methods against standard regression calibration and Monte Carlo maximum likelihood. The Bayesian 2DMC method performs poorly, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5%, particularly when the magnitudes of classical and Berkson error are both moderate to large (20%-50%). The method also produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. By comparison the extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the Bayesian 2DMC and all other methods. The bias of the predicted relative risk at a variety of doses is generally smallest for extended regression calibration, and largest for the Bayesian 2DMC method (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between the other two methods.

Radiation exposure and leukaemia risk among cohorts of persons exposed to low and moderate doses of external ionising radiation in childhood

BACKGROUND

Many high-dose groups demonstrate increased leukaemia risks, with risk greatest following childhood exposure; risks at low/moderate doses are less clear.

METHODS

We conducted a pooled analysis of the major radiation-associated leukaemias (acute myeloid leukaemia (AML) with/without the inclusion of myelodysplastic syndrome (MDS), chronic myeloid leukaemia (CML), acute lymphoblastic leukaemia (ALL)) in ten childhood-exposed groups, including Japanese atomic bomb survivors, four therapeutically irradiated and five diagnostically exposed cohorts, a mixture of incidence and mortality data. Relative/absolute risk Poisson regression models were fitted.

RESULTS

Of 365 cases/deaths of leukaemias excluding chronic lymphocytic leukaemia, there were 272 AML/CML/ALL among 310,905 persons (7,641,362 person-years), with mean active bone marrow (ABM) dose of 0.11 Gy (range 0-5.95). We estimated significant (P < 0.005) linear excess relative risks/Gy (ERR/Gy) for: AML (n = 140) = 1.48 (95% CI 0.59-2.85), CML (n = 61) = 1.77 (95% CI 0.38-4.50), and ALL (n = 71) = 6.65 (95% CI 2.79-14.83). There is upward curvature in the dose response for ALL and AML over the full dose range, although at lower doses (<0.5 Gy) curvature for ALL is downwards.

DISCUSSION

We found increased ERR/Gy for all major types of radiation-associated leukaemia after childhood exposure to ABM doses that were predominantly (for 99%) <1 Gy, and consistent with our prior analysis focusing on <100 mGy.

A generalisation of the method of regression calibration

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient [Formula: see text] are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient [Formula: see text] are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for [Formula: see text] that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient [Formula: see text] are substantially upwardly biased.

A generalisation of the method of regression calibration

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient \(\alpha\) are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient \(\beta\) are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for \(\beta\) that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient \(\beta\) are substantially upwardly biased.

Comparison and multi-model inference of excess risks models for radiation-related solid cancer

In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose-response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

Galactic cosmic ray simulation at the NASA space radiation laboratory - Progress, challenges and recommendations on mixed-field effects

For missions beyond low Earth orbit to the moon or Mars, space explorers will encounter a complex radiation field composed of various ion species with a broad range of energies. Such missions pose significant radiation protection challenges that need to be solved in order to minimize exposures and associated health risks. An innovative galactic cosmic ray simulator (GCRsim) was recently developed at the NASA Space Radiation Laboratory (NSRL) at Brookhaven National Laboratory (BNL). The GCRsim technology is intended to represent major components of the space radiation environment in a ground analog laboratory setting where it can be used to improve understanding of biological risks and serve as a testbed for countermeasure development and validation. The current GCRsim consists of 33 energetic ion beams that collectively simulate the primary and secondary GCR field encountered by humans in space over the broad range of particle types, energies, and linear energy transfer (LET) of interest to health effects. A virtual workshop was held in December 2020 to assess the status of the NASA baseline GCRsim. Workshop attendees examined various aspects of simulator design, with a particular emphasis on beam selection strategies. Experimental results, modeling approaches, areas of consensus, and questions of concern were also discussed in detail. This report includes a summary of the GCRsim workshop and a description of the current status of the GCRsim. This information is important for future advancements and applications in space radiobiology.

Impact of uncertainties in exposure assessment on thyroid cancer risk among cleanup workers in Ukraine exposed due to the Chornobyl accident

A large excess risk of thyroid cancer was observed among Belarusian/Russian/Baltic Chornobyl cleanup workers. A more recent study of Ukraine cleanup workers found more modest excess risks of thyroid cancer. Dose errors in this data are substantial, associated with model uncertainties and questionnaire response. Regression calibration is often used for dose-error adjustment, but may not adequately account for the full error distribution. We aimed to examine the impact of exposure-assessment uncertainties on thyroid cancer among Ukrainian cleanup workers using Monte Carlo maximum likelihood, and compare with results derived using regression calibration. Analyses assessed the sensitivity of results to various components of internal and external dose. Regression calibration yielded an excess odds ratio per Gy (EOR/Gy) of 0.437 (95% CI - 0.042, 1.577, p = 0.100), compared with the EOR/Gy using Monte Carlo maximum likelihood of 0.517 (95% CI - 0.039, 2.035, p = 0.093). Trend risk estimates for follicular morphology tumors exhibited much more extreme effects of full-likelihood adjustment, the EOR/Gy using regression calibration of 3.224 (95% CI - 0.082, 30.615, p = 0.068) becoming ~ 50% larger, 4.708 (95% CI - 0.075, 85.143, p = 0.066) when using Monte Carlo maximum likelihood. Results were sensitive to omission of external components of dose. In summary, use of Monte Carlo maximum likelihood adjustment for dose error led to increases in trend risks, particularly for follicular morphology thyroid cancers, where risks increased by ~ 50%, and were borderline significant. The unexpected finding for follicular tumors needs to be replicated in other exposed groups.

Cardiovascular Disease Risk Modeling for Astronauts: Making the Leap From Earth to Space

NASA has recently completed several long-duration missions to the International Space Station and is solidifying plans to return to the Moon, with an eye toward Mars and beyond. As NASA pushes the boundaries of human space exploration, the hazards of spaceflight, including space radiation, levy an increasing burden on astronaut health and performance. The cardiovascular system may be especially vulnerable due to the combined impacts of space radiation exposure, lack of gravity, and other spaceflight hazards. On Earth, the risk for cardiovascular disease (CVD) following moderate to high radiation doses is well-established from clinical, environmental, and occupational exposures (largely from gamma- and x-rays). Less is known about CVD risks associated with high-energy charged ions found in space and increasingly used in radiotherapy applications on Earth, making this a critical area of investigation for occupational radiation protection. Assessing CVD risk is complicated by its multifactorial nature, where an individual's risk is strongly influenced by factors such as family history, blood pressure, and lipid profiles. These known risk factors provide the basis for development of a variety of clinical risk prediction models (CPMs) that inform the likelihood of medical outcomes over a defined period. These tools improve clinical decision-making, personalize care, and support primary prevention of CVD. They may also be useful for individualizing risk estimates for CVD following radiation exposure both in the clinic and in space. In this review, we summarize unique aspects of radiation risk assessment for astronauts, and we evaluate the most widely used CVD CPMs for their use in NASA radiation risk assessment applications. We describe a comprehensive dual-use risk assessment framework that supports both clinical care and operational management of space radiation health risks using quantitative metrics. This approach is a first step in using personalized medicine for radiation risk assessment to support safe and productive spaceflight and long-term quality of life for NASA astronauts.

Improving astronaut cancer risk assessment from space radiation with an ensemble model framework

A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty as well as model-form uncertainty associated with differing theoretical or empirical formalisms. Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future models, improve uncertainty quantification, and reduce the impact of subjective bias. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met for future space exploration missions, such as human missions to Mars. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets. Closer agreement is seen at the median ± one sigma due to the inherent similarities in available models. Identification of potential new models, epidemiological data, and methods for statistical correlation between predictive ensemble members are discussed. Alternate ways of communicating risk and acceptable uncertainty with respect to NASA's current permissible exposure limits are explored.

Lifetime Mortality Risk from Cancer and Circulatory Disease Predicted from the Japanese Atomic Bomb Survivor Life Span Study Data Taking Account of Dose Measurement Error

Dosimetric measurement error is known to potentially bias the magnitude of the dose response, and can also affect the shape of dose response. In this report, generalized relative and absolute rate models are fitted to the latest Japanese atomic bomb survivor solid cancer, leukemia and circulatory disease mortality data (followed from 1950 through 2003), with the latest (DS02R1) dosimetry, using Bayesian techniques to adjust for errors in dose estimates and assessing other model uncertainties. Linear-quadratic models are fitted and used to assess lifetime mortality risks for contemporary UK, USA, French, Russian, Japanese and Chinese populations. For a test dose of 0.1 Gy absorbed dose weighted by neutron relative biological effectiveness, solid cancer, leukemia and circulatory disease mortality risks for a UK population using a generalized linear-quadratic relative rate model were estimated to be 3.88% Gy-1 [95% Bayesian credible interval (BCI): 1.17, 6.97], 0.35% Gy-1 (95% BCI: -0.03, 0.78) and 2.24% Gy-1 (95% BCI: -0.17, 13.76), respectively. Using a generalized absolute rate linear-quadratic model at 0.1 Gy, the lifetime risks for these three end points were estimated to be 3.56% Gy-1 (95% BCI: 0.54, 6.78), 0.41% Gy-1 (95% BCI: 0.01, 0.86) and 1.56% Gy-1 (95% BCI: -1.10, 7.21), respectively. There was substantial evidence of curvature for solid cancer (in particular, the group of solid cancers excluding lung, breast and stomach cancers) and leukemia, so that for solid cancer and leukemia, estimates of excess risk per unit dose were nearly doubled by increasing the dose from 0.01 to 1.0 Gy, with most of the increase occurring in the interval from 0.1 to 1.0 Gy. For circulatory disease, the dose-response curvature was inverse, so that risk per unit dose was nearly halved by going from 0.01 t o 1.0 Gy weighted absorbed dose, although there were substantial uncertainties. In general, there were higher radiation risks for females compared to males. This was true for solid cancer and circulatory disease overall, as well as for lung, breast, stomach and the group of other solid cancers, and was the case whether relative or absolute rate projection models were employed; however, for leukemia this pattern was reversed. Risk estimates varied somewhat between populations, with lower cancer risks in aggregate for China and Russia, but higher circulatory disease risks for Russia, particularly using the relative rate model. There was more pronounced variation for certain cancer sites and certain types of projection models, so that breast cancer risk was markedly lower in China and Japan using a relative rate model, but the opposite was the case for stomach cancer. There was less variation between countries using the absolute rate models for stomach cancer and breast cancer, but this was not the case for lung cancer and the group of other solid cancers, or for circulatory disease.

Analysis of Cataract in Relationship to Occupational Radiation Dose Accounting for Dosimetric Uncertainties in a Cohort of U.S. Radiologic Technologists

Cataract is one of the major morbidities in the U.S. population and it has long been appreciated that high and acutely delivered radiation doses of 1 Gy or more can induce cataract. Some more recent studies, in particular those of the U.S. Radiologic Technologists, have suggested that cataract may be induced by much lower, chronically delivered doses of ionizing radiation. It is well recognized that dosimetric measurement error can substantially alter the shape of the radiation dose-response relationship and thus, the derived study risk estimates, and can also inflate the variance of the estimates. In the current study, we evaluate the impact of uncertainties in eye-lens absorbed doses on the estimated risk of cataract in the U.S. Radiologic Technologists' Monte Carlo Dosimetry System, using both absolute and relative risk models. Among 11,345 cases we show that the inflation in the standard error for the excess relative risk (ERR) is generally modest, at most approximately 20% of the unadjusted standard error, depending on the model used for the baseline risk. The largest adjustment results from use of relative risk models, so that the ERR/Gy and its 95% confidence intervals change from 1.085 (0.645, 1.525) to 1.085 (0.558, 1.612) after adjustment. However, the inflation in the standard error of the excess absolute risk (EAR) coefficient is generally minimal, at most approximately 0.04% of the standard error.

Effect of Heterogeneity in Background Incidence on Inference about the Solid-Cancer Radiation Dose Response in Atomic Bomb Survivors

A recent analysis of solid cancer incidence in the Life Span Study of atomic bomb survivors (Hiroshima and Nagasaki, Japan) found evidence of a nonlinear, upwardly curving radiation dose response among males but not among females. Further analysis of this new and unexpected finding was necessary. We used two approaches to investigate this finding. In one approach, we excluded individual cancer sites or groups of sites from all solid cancers. In the other approach, we used joint analysis to allow for heterogeneity in background-rate parameters across groups of cancers with dissimilar trends in background rates. Exclusion of a few sites led to the disappearance of curvature among males in the remaining collection of solid cancers; some of these influential sites have unique features in their background age-specific incidence that are not captured by a background-rate model fit to all solid cancers combined. Exclusion of a few sites also led to an appearance of curvature among females. Misspecification of background rates can cause bias in inference about the shape of the dose response, so heterogeneity of background rates might explain at least part of the all solid cancer dose-response difference in curvature between males and females. We conclude that analysis based on all solid cancers as a single outcome is not the optimal method to assess radiation risk for solid cancer in the Life Span Study; joint analysis with suitable choices of cancer groups might be preferable by allowing for background-rate heterogeneity across sites while providing greater power to assess radiation risk than analyses of individual sites.

Methods to account for uncertainties in exposure assessment in studies of environmental exposures

BACKGROUND

Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the effects of uncertainties in exposure estimation on risk estimates received a lot of attention in radiation epidemiology and in several studies of diet and air pollution. The objective of this narrative review is to examine the commonly used statistical approaches to account for exposure estimation errors in risk analyses and to suggest how each could be applied in environmental epidemiological studies.

MAIN TEXT

We review two main error types in estimating exposures in epidemiological studies: shared and unshared errors and their subtypes. We describe the four main statistical approaches to adjust for exposure estimation uncertainties (regression calibration, simulation-extrapolation, Monte Carlo maximum likelihood and Bayesian model averaging) along with examples to give readers better understanding of their advantages and limitations. We also explain the advantages of using a 2-dimensional Monte-Carlo (2DMC) simulation method to quantify the effect of uncertainties in exposure estimates using full-likelihood methods. For exposures that are estimated independently between subjects and are more likely to introduce unshared errors, regression calibration and SIMEX methods are able to adequately account for exposure uncertainties in risk analyses. When an uncalibrated measuring device is used or estimation parameters with uncertain mean values are applied to a group of people, shared errors could potentially be large. In this case, Monte Carlo maximum likelihood and Bayesian model averaging methods based on estimates of exposure from the 2DMC simulations would work well. The majority of reviewed studies show relatively moderate changes (within 100%) in risk estimates after accounting for uncertainties in exposure estimates, except for the two studies which doubled/tripled naïve estimates.

CONCLUSIONS

In this paper, we demonstrate various statistical methods to account for uncertain exposure estimates in risk analyses. The differences in the results of various adjustment methods could be due to various error structures in datasets and whether or not a proper statistical method was applied. Epidemiological studies of environmental exposures should include exposure-response analyses accounting for uncertainties in exposure estimates.

Probability Distribution of Dose and Dose-Rate Effectiveness Factor for use in Estimating Risks of Solid Cancers From Exposure to Low-Let Radiation

This paper presents an analysis to develop a subjective state-of-knowledge probability distribution of a dose and dose-rate effectiveness factor for use in estimating risks of solid cancers from exposure to low linear energy transfer radiation (photons or electrons) whenever linear dose responses from acute and chronic exposure are assumed. A dose and dose-rate effectiveness factor represents an assumption that the risk of a solid cancer per Gy at low acute doses or low dose rates of low linear energy transfer radiation, RL, differs from the risk per Gy at higher acute doses, RH; RL is estimated as RH divided by a dose and dose-rate effectiveness factor, where RH is estimated from analyses of dose responses in Japanese atomic-bomb survivors. A probability distribution to represent uncertainty in a dose and dose-rate effectiveness factor for solid cancers was developed from analyses of epidemiologic data on risks of incidence or mortality from all solid cancers as a group or all cancers excluding leukemias, including (1) analyses of possible nonlinearities in dose responses in atomic-bomb survivors, which give estimates of a low-dose effectiveness factor, and (2) comparisons of risks in radiation workers or members of the public from chronic exposure to low linear energy transfer radiation at low dose rates with risks in atomic-bomb survivors, which give estimates of a dose-rate effectiveness factor. Probability distributions of uncertain low-dose effectiveness factors and dose-rate effectiveness factors for solid cancer incidence and mortality were combined using assumptions about the relative weight that should be assigned to each estimate to represent its relevance to estimation of a dose and dose-rate effectiveness factor. The probability distribution of a dose and dose-rate effectiveness factor for solid cancers developed in this study has a median (50th percentile) and 90% subjective confidence interval of 1.3 (0.47, 3.6). The harmonic mean is 1.1, which implies that the arithmetic mean of an uncertain estimate of the risk of a solid cancer per Gy at low acute doses or low dose rates of low linear energy transfer radiation is only about 10% less than the mean risk per Gy at higher acute doses. Data were also evaluated to define a low acute dose or low dose rate of low linear energy transfer radiation, i.e., a dose or dose rate below which a dose and dose-rate effectiveness factor should be applied in estimating risks of solid cancers.

THE MAYAK WORKER DOSIMETRY SYSTEM (MWDS-2013): TREATMENT OF UNCERTAINTY IN MODEL PARAMETERS

Different dose estimates have been produced for the Mayak PA workforce over recent years (DOSES-2000, DOSES-2005, MWDS-2008). The dosimetry system MWDS-2013 described here differs from previous analyses, in that it deals directly with uncertainty in the assumed model parameters. This paper details the way in which uncertainty is dealt with within MWDS-2013 to produce the final output represented by a multiple hyper-realisation of organ doses. More specifically, the paper describes: Application of the WeLMoS method to calculate Bayesian posterior probability distributions of organ doses.Extension of the WeLMoS method for dealing with multiple intake regimes.How shared and unshared parameters are dealt with using a multiple realisation method.A practical algorithm for the generation of multiple hyper-realisations.How to deal with uncertainty in the intake and the intake regime. The resulting multiple hyper-realisation contains all of the information required to take account of model parameter uncertainty and the effects of shared and unshared parameters in any epidemiological analysis, which uses this information, although it is acknowledged that in practice, certain data simplifications may be required to make such analyses tractable, and comparable to previous analyses. Such simplifications are outside the scope of this paper.

International organizations, risk assessment and research-Why, what and how

The process of setting radiation protection standards requires the interaction of a number of international and national organizations that in turn require the input of scientific and regulatory experts. Bill Morgan served in an expert capacity for several of these organizations particularly for the application of radiation biology data to risk assessment. He brought great enthusiasm and dedication to these committee efforts. In fact, he really enjoyed this type of service. The United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), for example, provides comprehensive reviews of the input data for radiation risk assessments. In this context, they do not conduct risk assessments. In Europe, a research component of the risk assessment process is provided by the Multidisciplinary European Low Dose Initiative (MELODI). Specific issue areas are identified for which additional research can aid in reducing uncertainty in risk assessments. The International Commission on Radiological Protection (ICRP) uses these types of input data to develop nominal cancer risk estimates as input data for establishing dose limits for the public and workers. A similar regulatory role is provided in the US by the National Council on Radiation Protection and Measurements (NCRP). The NCRP Reports address the underlying principles for setting regulatory dose limits for the US public and workers; these differ to a limited extent from those of ICRP. The implementation of dose limits is conducted by individual countries but with significant guidance by the International Atomic Energy Agency (IAEA) through its Basic Safety Standards. The role of other national and international organizations are discussed in this same framework.

A Bayesian Semiparametric Model for Radiation Dose-Response Estimation

In evaluating the risk of exposure to health hazards, characterizing the dose-response relationship and estimating acceptable exposure levels are the primary goals. In analyses of health risks associated with exposure to ionizing radiation, while there is a clear agreement that moderate to high radiation doses cause harmful effects in humans, little has been known about the possible biological effects at low doses, for example, below 0.1 Gy, which is the dose range relevant to most radiation exposures of concern today. A conventional approach to radiation dose-response estimation based on simple parametric forms, such as the linear nonthreshold model, can be misleading in evaluating the risk and, in particular, its uncertainty at low doses. As an alternative approach, we consider a Bayesian semiparametric model that has a connected piece-wise-linear dose-response function with prior distributions having an autoregressive structure among the random slope coefficients defined over closely spaced dose categories. With a simulation study and application to analysis of cancer incidence data among Japanese atomic bomb survivors, we show that this approach can produce smooth and flexible dose-response estimation while reasonably handling the risk uncertainty at low doses and elsewhere. With relatively few assumptions and modeling options to be made by the analyst, the method can be particularly useful in assessing risks associated with low-dose radiation exposures.

Levels of naturally occurring gamma radiation measured in British homes and their prediction in particular residences

Gamma radiation from natural sources (including directly ionising cosmic rays) is an important component of background radiation. In the present paper, indoor measurements of naturally occurring gamma rays that were undertaken as part of the UK Childhood Cancer Study are summarised, and it is shown that these are broadly compatible with an earlier UK National Survey. The distribution of indoor gamma-ray dose rates in Great Britain is approximately normal with mean 96 nGy/h and standard deviation 23 nGy/h. Directly ionising cosmic rays contribute about one-third of the total. The expanded dataset allows a more detailed description than previously of indoor gamma-ray exposures and in particular their geographical variation. Various strategies for predicting indoor natural background gamma-ray dose rates were explored. In the first of these, a geostatistical model was fitted, which assumes an underlying geologically determined spatial variation, superimposed on which is a Gaussian stochastic process with Matérn correlation structure that models the observed tendency of dose rates in neighbouring houses to correlate. In the second approach, a number of dose-rate interpolation measures were first derived, based on averages over geologically or administratively defined areas or using distance-weighted averages of measurements at nearest-neighbour points. Linear regression was then used to derive an optimal linear combination of these interpolation measures. The predictive performances of the two models were compared via cross-validation, using a randomly selected 70 % of the data to fit the models and the remaining 30 % to test them. The mean square error (MSE) of the linear-regression model was lower than that of the Gaussian-Matérn model (MSE 378 and 411, respectively). The predictive performance of the two candidate models was also evaluated via simulation; the OLS model performs significantly better than the Gaussian-Matérn model.

The Increase in Animal Mortality Risk following Exposure to Sparsely Ionizing Radiation Is Not Linear Quadratic with Dose

INTRODUCTION

The US government regulates allowable radiation exposures relying, in large part, on the seventh report from the committee to estimate the Biological Effect of Ionizing Radiation (BEIR VII), which estimated that most contemporary exposures- protracted or low-dose, carry 1.5 fold less risk of carcinogenesis and mortality per Gy than acute exposures of atomic bomb survivors. This correction is known as the dose and dose rate effectiveness factor for the life span study of atomic bomb survivors (DDREFLSS). It was calculated by applying a linear-quadratic dose response model to data from Japanese atomic bomb survivors and a limited number of animal studies.

METHODS AND RESULTS

We argue that the linear-quadratic model does not provide appropriate support to estimate the risk of contemporary exposures. In this work, we re-estimated DDREFLSS using 15 animal studies that were not included in BEIR VII's original analysis. Acute exposure data led to a DDREFLSS estimate from 0.9 to 3.0. By contrast, data that included both acute and protracted exposures led to a DDREFLSS estimate from 4.8 to infinity. These two estimates are significantly different, violating the assumptions of the linear-quadratic model, which predicts that DDREFLSS values calculated in either way should be the same.

CONCLUSIONS

Therefore, we propose that future estimates of the risk of protracted exposures should be based on direct comparisons of data from acute and protracted exposures, rather than from extrapolations from a linear-quadratic model. The risk of low dose exposures may be extrapolated from these protracted estimates, though we encourage ongoing debate as to whether this is the most valid approach. We also encourage efforts to enlarge the datasets used to estimate the risk of protracted exposures by including both human and animal data, carcinogenesis outcomes, a wider range of exposures, and by making more radiobiology data publicly accessible. We believe that these steps will contribute to better estimates of the risks of contemporary radiation exposures.

Impact of Uncertainties in Exposure Assessment on Thyroid Cancer Risk among Persons in Belarus Exposed as Children or Adolescents Due to the Chernobyl Accident

Background

The excess incidence of thyroid cancer in Ukraine and Belarus observed a few years after the Chernobyl accident is considered to be largely the result of ¹³¹I released from the reactor. Although the Belarus thyroid cancer prevalence data has been previously analyzed, no account was taken of dose measurement error.

Methods

We examined dose-response patterns in a thyroid screening prevalence cohort of 11,732 persons aged under 18 at the time of the accident, diagnosed during 1996–2004, who had direct thyroid ¹³¹I activity measurement, and were resident in the most radio-actively contaminated regions of Belarus. Three methods of dose-error correction (regression calibration, Monte Carlo maximum likelihood, Bayesian Markov Chain Monte Carlo) were applied.

Results

There was a statistically significant (p<0.001) increasing dose-response for prevalent thyroid cancer, irrespective of regression-adjustment method used. Without adjustment for dose errors the excess odds ratio was 1.51 Gy⁻ (95% CI 0.53, 3.86), which was reduced by 13% when regression-calibration adjustment was used, 1.31 Gy⁻ (95% CI 0.47, 3.31). A Monte Carlo maximum likelihood method yielded an excess odds ratio of 1.48 Gy⁻ (95% CI 0.53, 3.87), about 2% lower than the unadjusted analysis. The Bayesian method yielded a maximum posterior excess odds ratio of 1.16 Gy⁻ (95% BCI 0.20, 4.32), 23% lower than the unadjusted analysis. There were borderline significant (p = 0.053–0.078) indications of downward curvature in the dose response, depending on the adjustment methods used. There were also borderline significant (p = 0.102) modifying effects of gender on the radiation dose trend, but no significant modifying effects of age at time of accident, or age at screening as modifiers of dose response (p>0.2).

Conclusions

In summary, the relatively small contribution of unshared classical dose error in the current study results in comparatively modest effects on the regression parameters.

Calculation of lifetime lung cancer risks associated with radon exposure, based on various models and exposure scenarios

The risk of lung cancer mortality up to 75 years of age due to radon exposure has been estimated for both male and female continuing, ex- and never-smokers, based on various radon risk models and exposure scenarios. We used risk models derived from (i) the BEIR VI analysis of cohorts of radon-exposed miners, (ii) cohort and nested case-control analyses of a European cohort of uranium miners and (iii) the joint analysis of European residential radon case-control studies. Estimates of the lifetime lung cancer risk due to radon varied between these models by just over a factor of 2 and risk estimates based on models from analyses of European uranium miners exposed at comparatively low rates and of people exposed to radon in homes were broadly compatible. For a given smoking category, there was not much difference in lifetime lung cancer risk between males and females. The estimated lifetime risk of radon-induced lung cancer for exposure to a concentration of 200 Bq m(-3) was in the range 2.98-6.55% for male continuing smokers and 0.19-0.42% for male never-smokers, depending on the model used and assuming a multiplicative relationship for the joint effect of radon and smoking. Stopping smoking at age 50 years decreases the lifetime risk due to radon by around a half relative to continuing smoking, but the risk for ex-smokers remains about a factor of 5-7 higher than that for never-smokers. Under a sub-multiplicative model for the joint effect of radon and smoking, the lifetime risk of radon-induced lung cancer was still estimated to be substantially higher for continuing smokers than for never smokers. Radon mitigation-used to reduce radon concentrations at homes-can also have a substantial impact on lung cancer risk, even for persons in their 50 s; for each of continuing smokers, ex-smokers and never-smokers, radon mitigation at age 50 would lower the lifetime risk of radon-induced lung cancer by about one-third. To maximise risk reductions, smokers in high-radon homes should both stop smoking and remediate their homes.

Simulation-extrapolation method to address errors in atomic bomb survivor dosimetry on solid cancer and leukaemia mortality risk estimates, 1950-2003

Analyses of the Life Span Study (LSS) of Japanese atomic bombing survivors have routinely incorporated corrections for additive classical measurement errors using regression calibration. Recently, several studies reported that the efficiency of the simulation-extrapolation method (SIMEX) is slightly more accurate than the simple regression calibration method (RCAL). In the present paper, the SIMEX and RCAL methods have been used to address errors in atomic bomb survivor dosimetry on solid cancer and leukaemia mortality risk estimates. For instance, it is shown that using the SIMEX method, the ERR/Gy is increased by an amount of about 29 % for all solid cancer deaths using a linear model compared to the RCAL method, and the corrected EAR 10(-4) person-years at 1 Gy (the linear terms) is decreased by about 8 %, while the corrected quadratic term (EAR 10(-4) person-years/Gy(2)) is increased by about 65 % for leukaemia deaths based on a linear-quadratic model. The results with SIMEX method are slightly higher than published values. The observed differences were probably due to the fact that with the RCAL method the dosimetric data were partially corrected, while all doses were considered with the SIMEX method. Therefore, one should be careful when comparing the estimated risks and it may be useful to use several correction techniques in order to obtain a range of corrected estimates, rather than to rely on a single technique. This work will enable to improve the risk estimates derived from LSS data, and help to make more reliable the development of radiation protection standards.

Radiation risks knowledge in resident and fellow in paediatrics: a questionnaire survey

BACKGROUND

Analyse through a multi-choice anonymous questionnaire the knowledge's level in paediatric residents and fellows in two different main Italian hospital, looking mainly to the information to patients and relatives related to risks of ionizing radiation used in common radiological investigations in children.

METHODS

65 multi choice questionnaires were distributed to paediatric residents and fellows of two different hospitals, an University Hospital (A.O.U.P. "P. Giaccone"- University of Palermo) and a national reference centre for paediatrics (Ospedale Pediatrico Bambino Gesù - Rome). The questionnaire included twelve multiple-choice questions with the aim of analyzing the knowledge about ionizing radiation related risks in infants and children who undergo common diagnostic radiology investigations. The data obtained were processed using software Stata/MP version 11.2. In order to measure the level of expertise of each interviewee a binary indicator was built. The value 1 was assigned if the percentage of correct answers exceeds the median of the distribution and 0 for values not exceeding the median. The association between the level of competence and demographic characteristics (gender, age) and training experience was measured by means of α(2) test.

RESULTS

51/65 questionnaires were completed, returned and analysed (87.7%). Only 18 surveyed (35%), (95% IC = [22%-48%]) can be defined as competent in radiation risk knowledge for common radiological investigations, considering the percentage of correct answers at least of 50% (sufficient knowledge was given with a minimum score of 8 correct answers out of 12).

CONCLUSIONS

The study demonstrates an urgent need to implement the radiation protection knowledge in the training programme of paediatricians, that improve if just a short targeted training is performed.

Comments on the DDREF estimate of the BEIR VII Committee

Radiation cancer risk estimates have been based primarily on the atomic bomb survivor cohorts, which involve an acute exposure. To adjust for lower dose and continuous exposures, a dose and dose rate effectiveness factor (DDREF) is applied to the acute risk estimates. The commonly accepted value for the DDREF at 1 Gy by the ICRP and the NCRP has been 2. BEIR VII changed this by estimating the value to be 1.5 including an uncertainty distribution. The BEIR VII committee used the atomic bomb solid cancer incidence data to make this estimation, and they chose to truncate the data at 1.5 Gy. The Committee also used the Oak Ridge mouse studies and estimated a DDREF based on these data. Finally they used the animal data-derived DDREF distribution as a Bayesian prior distribution for the atomic bomb survivor DDREF distribution and used the posterior distribution as their DDREF result. The resulting distribution had a maximum likelihood estimate of 1.4, and the Committee chose 1.5 as their best estimate. The purpose of this paper is to reexamine the BEIR VII analysis of both the atomic bomb survivor data and the mouse data. Based upon this analysis, the author concludes that changing the DDREF from 2 to 1.5 is not justified.

Task-based measures of image quality and their relation to radiation dose and patient risk

The theory of task-based assessment of image quality is reviewed in the context of imaging with ionizing radiation, and objective figures of merit (FOMs) for image quality are summarized. The variation of the FOMs with the task, the observer and especially with the mean number of photons recorded in the image is discussed. Then various standard methods for specifying radiation dose are reviewed and related to the mean number of photons in the image and hence to image quality. Current knowledge of the relation between local radiation dose and the risk of various adverse effects is summarized, and some graphical depictions of the tradeoffs between image quality and risk are introduced. Then various dose-reduction strategies are discussed in terms of their effect on task-based measures of image quality.

Association of chromosome translocation rate with low dose occupational radiation exposures in U.S. radiologic technologists

Chromosome translocations are a well-recognized biological marker of radiation exposure and cancer risk. However, there is uncertainty about the lowest dose at which excess translocations can be detected, and whether there is temporal decay of induced translocations in radiation-exposed populations. Dosimetric uncertainties can substantially alter the shape of dose-response relationships; although regression-calibration methods have been used in some datasets, these have not been applied in radio-occupational studies, where there are also complex patterns of shared and unshared errors that these methods do not account for. In this article we evaluated the relationship between estimated occupational ionizing radiation doses and chromosome translocation rates using fluorescent in situ hybridization in 238 U.S. radiologic technologists selected from a large cohort. Estimated cumulative red bone marrow doses (mean 29.3 mGy, range 0-135.7 mGy) were based on available badge-dose measurement data and on questionnaire-reported work history factors. Dosimetric assessment uncertainties were evaluated using regression calibration, Bayesian and Monte Carlo maximum likelihood methods, taking account of shared and unshared error and adjusted for overdispersion. There was a significant dose response for estimated occupational radiation exposure, adjusted for questionnaire-based personal diagnostic radiation, age, sex and study group (5.7 translocations per 100 whole genome cell equivalents per Gy, 95% CI 0.2, 11.3, P = 0.0440). A significant increasing trend with dose continued to be observed for individuals with estimated doses <100 mGy. For combined estimated occupational and personal-diagnostic-medical radiation exposures, there was a borderline-significant modifying effect of age (P = 0.0704), but little evidence (P > 0.5) of temporal decay of induced translocations. The three methods of analysis to adjust for dose uncertainty gave similar results. In summary, chromosome translocation dose-response slopes were detectable down to <100 mGy and were compatible with those observed in other radiation-exposed populations. However, there are substantial uncertainties in both occupational and other (personal-diagnostic-medical) doses that may be imperfectly taken into account in our analysis.

Impact on the Japanese atomic bomb survivors of radiation received from the bombs

The Radiation Effects Research Foundation (RERF) studies various cohorts of Japanese atomic bomb survivors, the largest being the Life Span Study (LSS), which includes 93,741 persons who were in Hiroshima or Nagasaki at the times of the bombings; there are also cohorts of persons who were exposed in utero and survivors' children. This presentation attempts to summarize the total impact of the radiation from the bombs on the survivors from both an individual perspective (both age-specific and integrated lifetime risk, along with a measure of life expectancy that describes how the risk affects the individual given age at exposure) and a group perspective (estimated numbers of excess occurrences in the cohort), including both early and late effects. As survivors' doses ranged well into the acutely lethal range at closer distances, some of them experienced acute signs and symptoms of radiation exposure in addition to being at risk of late effects. Although cancer has always been a primary concern among late effects, estimated numbers of excess cancers and hematopoietic malignancies in the LSS are a small fraction of the total due to the highly skewed dose distribution, with most survivors receiving small doses. For example, in the latest report on cancer incidence, 853 of 17,448 incident solid cancers were estimated to be attributable to radiation from the bombs. RERF research indicates that risk of radiation-associated cancer varies among sites and that some benign tumors such as uterine myoma are also associated with radiation. Noncancer late effects appear to be in excess in proportion to radiation dose but with an excess relative risk about one-third that of solid cancer and a correspondingly small overall fraction of cases attributable to radiation. Specific risks were found for some subcategories, particularly circulatory disease, including stroke and precedent conditions such as hypertension. Radiation-related cataract in the atomic bomb survivors is well known, with evidence in recent years of risk at lower dose levels than previously appreciated. In addition to somatic effects, survivors experienced psychosocial effects such as uncertainty, social stigma, or rejection, and other social pressures. Developmental deficits associated with in utero exposure, notably cognitive impairment, have also been described. Interaction of radiation with other risk factors has been demonstrated in relation to both cancer and noncancer diseases. Current research interests include whether radiation increases risk of diabetes or conditions of the eye apart from cataract, and there continues to be keen interest as to whether there are heritable effects in survivors' children, despite negative findings to date. Introduction of Impact on the Japanese Atomic- Bomb Survivors (Video 1:52, http://links.lww.com/HP/A29).

Impact of uncertainties in exposure assessment on estimates of thyroid cancer risk among Ukrainian children and adolescents exposed from the Chernobyl accident

The 1986 accident at the Chernobyl nuclear power plant remains the most serious nuclear accident in history, and excess thyroid cancers, particularly among those exposed to releases of iodine-131 remain the best-documented sequelae. Failure to take dose-measurement error into account can lead to bias in assessments of dose-response slope. Although risks in the Ukrainian-US thyroid screening study have been previously evaluated, errors in dose assessments have not been addressed hitherto. Dose-response patterns were examined in a thyroid screening prevalence cohort of 13,127 persons aged <18 at the time of the accident who were resident in the most radioactively contaminated regions of Ukraine. We extended earlier analyses in this cohort by adjusting for dose error in the recently developed TD-10 dosimetry. Three methods of statistical correction, via two types of regression calibration, and Monte Carlo maximum-likelihood, were applied to the doses that can be derived from the ratio of thyroid activity to thyroid mass. The two components that make up this ratio have different types of error, Berkson error for thyroid mass and classical error for thyroid activity. The first regression-calibration method yielded estimates of excess odds ratio of 5.78 Gy⁻¹ (95% CI 1.92, 27.04), about 7% higher than estimates unadjusted for dose error. The second regression-calibration method gave an excess odds ratio of 4.78 Gy⁻¹ (95% CI 1.64, 19.69), about 11% lower than unadjusted analysis. The Monte Carlo maximum-likelihood method produced an excess odds ratio of 4.93 Gy⁻¹ (95% CI 1.67, 19.90), about 8% lower than unadjusted analysis. There are borderline-significant (p = 0.101–0.112) indications of downward curvature in the dose response, allowing for which nearly doubled the low-dose linear coefficient. In conclusion, dose-error adjustment has comparatively modest effects on regression parameters, a consequence of the relatively small errors, of a mixture of Berkson and classical form, associated with thyroid dose assessment.

Frequency and collective dose of medical procedures in Kenya

The first comprehensive national survey on frequency and radiation dose imparted to the population from radiological procedures was carried out in Kenya and reported here. This survey involved assessment of frequency, typical patient radiation exposure, and collective effective dose from general radiography, fluoroscopy, interventional procedures (IPs), mammography, and computed tomography. About 300 x-ray facilities across the country were invited to participate in the survey, and a 31% response was recorded. The individual and collective radiation burdens of more than 62 types of pediatric and adult radiological examinations were quantified using effective and collective dose. The average effective dose for each radiological examination was assessed from the x-ray efficiency performance tests and patient data from over 30 representative radiological facilities. The results found indicated that over 3 million x-ray procedures were performed in 2011, resulting in an annual collective effective dose of 2,157 person-Sv and an annual effective dose per capita of 0.05 mSv. The most frequent examinations were general radiography (94%), computed tomography (3.3%), and fluoroscopy (2.5%). Although the contribution of computed tomography was small in terms of frequency, this procedure accounted for 36% of the effective dose per capita. General radiography was the most frequent type of examination with a contribution of 55% of the effective dose per capita.

A formulation of cell surviving fraction after radiation exposure

Local energy transfer from electrons generated in biotissues that are exposed to ionizing radiation is fundamental to cell damage. Our aim in this investigation was to quantify the probability of cell mortality associated with the damage by electrons and the repair processes in the cell nucleus, envisaging a new interpretation of the cell surviving fraction (SF). We introduced a SF formula for cells exposed to X-rays, which is given as a linear combination of the Poisson distributions about the number of long-lived lesions per nucleus and their "non-lethal probabilities", to show the non-linearity of log SF as a function of dose. The model selection was rated by a statistical index, Akaike's information criterion (AIC). It was shown that the new formula is suitable for describing cell survival and explicitly takes account of the non-lethality in damage-processing pathways of the cells.

Second solid cancers after radiation therapy: a systematic review of the epidemiologic studies of the radiation dose-response relationship

Rapid innovations in radiation therapy techniques have resulted in an urgent need for risk projection models for second cancer risks from high-dose radiation exposure, because direct observation of the late effects of newer treatments will require patient follow-up for a decade or more. However, the patterns of cancer risk after fractionated high-dose radiation are much less well understood than those after lower-dose exposures (0.1-5 Gy). In particular, there is uncertainty about the shape of the dose-response curve at high doses and about the magnitude of the second cancer risk per unit dose. We reviewed the available evidence from epidemiologic studies of second solid cancers in organs that received high-dose exposure (>5 Gy) from radiation therapy where dose-response curves were estimated from individual organ-specific doses. We included 28 eligible studies with 3434 second cancer patients across 11 second solid cancers. Overall, there was little evidence that the dose-response curve was nonlinear in the direction of a downturn in risk, even at organ doses of ≥60 Gy. Thyroid cancer was the only exception, with evidence of a downturn after 20 Gy. Generally the excess relative risk per Gray, taking account of age and sex, was 5 to 10 times lower than the risk from acute exposures of <2 Gy among the Japanese atomic bomb survivors. However, the magnitude of the reduction in risk varied according to the second cancer. The results of our review provide insights into radiation carcinogenesis from fractionated high-dose exposures and are generally consistent with current theoretical models. The results can be used to refine the development of second solid cancer risk projection models for novel radiation therapy techniques.

Potential impacts of radon, terrestrial gamma and cosmic rays on childhood leukemia in France: a quantitative risk assessment

Previous epidemiological studies and quantitative risk assessments (QRA) have suggested that natural background radiation may be a cause of childhood leukemia. The present work uses a QRA approach to predict the excess risk of childhood leukemia in France related to three components of natural radiation: radon, cosmic rays and terrestrial gamma rays, using excess relative and absolute risk models proposed by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Both models were developed from the Life Span Study (LSS) of Japanese A-bomb survivors. Previous risk assessments were extended by considering uncertainties in radiation-related leukemia risk model parameters as part of this process, within a Bayesian framework. Estimated red bone marrow doses cumulated during childhood by the average French child due to radon, terrestrial gamma and cosmic rays are 4.4, 7.5 and 4.3 mSv, respectively. The excess fractions of cases (expressed as percentages) associated with these sources of natural radiation are 20 % [95 % credible interval (CI) 0-68 %] and 4 % (95 % CI 0-11 %) under the excess relative and excess absolute risk models, respectively. The large CIs, as well as the different point estimates obtained under these two models, highlight the uncertainties in predictions of radiation-related childhood leukemia risks. These results are only valid provided that models developed from the LSS can be transferred to the population of French children and to chronic natural radiation exposures, and must be considered in view of the currently limited knowledge concerning other potential risk factors for childhood leukemia. Last, they emphasize the need for further epidemiological investigations of the effects of natural radiation on childhood leukemia to reduce uncertainties and help refine radiation protection standards.

Independent analysis of the radiation risk for leukaemia in children and adults with mortality data (1950-2003) of Japanese A-bomb survivors

A recent analysis of leukaemia mortality in Japanese A-bomb survivors has applied descriptive models, collected together from previous studies, to derive a joint excess relative risk estimate (ERR) by multi-model inference (MMI) (Walsh and Kaiser in Radiat Environ Biophys 50:21-35, 2011). The models use a linear-quadratic dose response with differing dose effect modifiers. In the present study, a set of more than 40 models has been submitted to a rigorous statistical selection procedure which fosters the parsimonious deployment of model parameters based on pairwise likelihood ratio tests. Nested models were consequently excluded from risk assessment. The set comprises models of the excess absolute risk (EAR) and two types of non-standard ERR models with sigmoidal responses or two line spline functions with a changing slope at a break point. Due to clearly higher values of the Akaike Information Criterion, none of the EAR models has been selected, but two non-standard ERR models qualified for MMI. The preferred ERR model applies a purely quadratic dose response which is slightly damped by an exponential factor at high doses and modified by a power function for attained age. Compared to the previous analysis, the present study reports similar point estimates and confidence intervals (CI) of the ERR from MMI for doses between 0.5 and 2.5 Sv. However, at lower doses, the point estimates are markedly reduced by factors between two and five, although the reduction was not statistically significant. The 2.5 % percentiles of the ERR from the preferred quadratic-exponential model did not fall below zero risk in exposure scenarios for children, adolescents and adults at very low doses down to 10 mSv. Yet, MMI produced risk estimates with a positive 2.5 % percentile only above doses of some 300 mSv. Compared to CI from a single model of choice, CI from MMI are broadened in cohort strata with low statistical power by a combination of risk extrapolations from several models. Reverting to MMI can relieve the dilemma of needing to choose between models with largely different consequences for risk assessment in public health.

A method for determining weights for excess relative risk and excess absolute risk when applied in the calculation of lifetime risk of cancer from radiation exposure

Radiation-related risks of cancer can be transported from one population to another population at risk, for the purpose of calculating lifetime risks from radiation exposure. Transfer via excess relative risks (ERR) or excess absolute risks (EAR) or a mixture of both (i.e., from the life span study (LSS) of Japanese atomic bomb survivors) has been done in the past based on qualitative weighting. Consequently, the values of the weights applied and the method of application of the weights (i.e., as additive or geometric weighted means) have varied both between reports produced at different times by the same regulatory body and also between reports produced at similar times by different regulatory bodies. Since the gender and age patterns are often markedly different between EAR and ERR models, it is useful to have an evidence-based method for determining the relative goodness of fit of such models to the data. This paper identifies a method, using Akaike model weights, which could aid expert judgment and be applied to help to achieve consistency of approach and quantitative evidence-based results in future health risk assessments. The results of applying this method to recent LSS cancer incidence models are that the relative EAR weighting by cancer solid cancer site, on a scale of 0-1, is zero for breast and colon, 0.02 for all solid, 0.03 for lung, 0.08 for liver, 0.15 for thyroid, 0.18 for bladder and 0.93 for stomach. The EAR weighting for female breast cancer increases from 0 to 0.3, if a generally observed change in the trend between female age-specific breast cancer incidence rates and attained age, associated with menopause, is accounted for in the EAR model. Application of this method to preferred models from a study of multi-model inference from many models fitted to the LSS leukemia mortality data, results in an EAR weighting of 0. From these results it can be seen that lifetime risk transfer is most highly weighted by EAR only for stomach cancer. However, the generalization and interpretation of radiation effect estimates based on the LSS cancer data, when projected to other populations, are particularly uncertain if considerable differences exist between site-specific baseline rates in the LSS and the other populations of interest. Definitive conclusions, regarding the appropriate method for transporting cancer risks, are limited by a lack of knowledge in several areas including unknown factors and uncertainties in biological mechanisms and genetic and environmental risk factors for carcinogenesis; uncertainties in radiation dosimetry; and insufficient statistical power and/or incomplete follow-up in data from radio-epidemiological studies.

Clinical, aesthetic, and patient-related outcome of immediately loaded single implants in the anterior maxilla: a prospective study in extraction sockets, healed ridges, and grafted sites

PURPOSE

The objective of this prospective clinical study was to document the overall treatment outcome of immediately loaded single Astra Tech Osseospeed™ (Astra Tech AB, Mölndal, Sweden) implants placed in extraction sockets, healed ridges, and grafted sites.

MATERIALS AND METHODS

Forty-eight patients in need of a single implant in the anterior maxilla (15-25) were recruited. Patients were allocated to a conventional implant treatment (CIT) or immediate implant treatment (IIT) group on the basis of specific criteria. If the buccal bone plate was damaged or missing upon tooth removal, patients were allocated to a grafted implant treatment (GIT) group. Irrespective of the treatment concept, implants were immediately provisionalized. Hard and soft tissue alterations, aesthetic parameters (pink and white esthetic scores, [PES and WES]) and patient's opinion (Oral Health Impact Profile [OHIP-14] questionnaires) were registered at different time points.

RESULTS

After 1 year of function, the overall implant survival rate was 98% with one failure following IIT. The mean bone level to the implant-abutment interface was 0.65 (SD 0.79), 0.85 (SD 0.64), and 0.56 mm (SD 0.44) for CIT, IIT, and GIT. Complete papilla loss was rare following either strategy. Mean midfacial recession amounted to 1.00 (SD 1.15), 0.12 (SD 0.78), and 0.49 mm (SD 0.82) for CIT, IIT, and GIT, respectively. The aesthetic outcome showed a mean PES of 10.30 (SD 1.89) and mean WES of 7.11 (SD 2.14), all patients considered. Patient's satisfaction showed a significant improvement after 1 year of function on all seven domains (p < .001).

CONCLUSIONS

This prospective study showed that single implants clinically and aesthetically perform well under immediate non-occlusal loading conditions in the premaxilla. In this context, it is of pivotal importance to stress that patients were carefully selected for IIT and GIT.

Modeling the risk of secondary malignancies after radiotherapy

In developed countries, more than half of all cancer patients receive radiotherapy at some stage in the management of their disease. However, a radiation-induced secondary malignancy can be the price of success if the primary cancer is cured or at least controlled. Therefore, there is increasing concern regarding radiation-related second cancer risks in long-term radiotherapy survivors and a corresponding need to be able to predict cancer risks at high radiation doses. Of particular interest are second cancer risk estimates for new radiation treatment modalities such as intensity modulated radiotherapy, intensity modulated arc-therapy, proton and heavy ion radiotherapy. The long term risks from such modern radiotherapy treatment techniques have not yet been determined and are unlikely to become apparent for many years, due to the long latency time for solid tumor induction. Most information on the dose-response of radiation-induced cancer is derived from data on the A-bomb survivors who were exposed to γ-rays and neutrons. Since, for radiation protection purposes, the dose span of main interest is between zero and one Gy, the analysis of the A-bomb survivors is usually focused on this range. With increasing cure rates, estimates of cancer risk for doses larger than one Gy are becoming more important for radiotherapy patients. Therefore in this review, emphasis was placed on doses relevant for radiotherapy with respect to radiation induced solid cancer. Simple radiation protection models should be used only with extreme care for risk estimates in radiotherapy, since they are developed exclusively for low dose. When applied to scatter radiation, such models can predict only a fraction of observed second malignancies. Better semi-empirical models include the effect of dose fractionation and represent the dose-response relationships more accurately. The involved uncertainties are still huge for most of the organs and tissues. A major reason for this is that the underlying processes of the induction of carcinoma and sarcoma are not well known. Most uncertainties are related to the time patterns of cancer induction, the population specific dependencies and to the organ specific cancer induction rates. For radiotherapy treatment plan optimization these factors are irrelevant, as a treatment plan comparison is performed for a patient of specific age, sex, etc. If a treatment plan is compared relative to another one only the shape of the dose-response curve (the so called risk-equivalent dose) is of importance and errors can be minimized.

A multicellular basis for the origination of blast crisis in chronic myeloid leukemia

Chronic myeloid leukemia (CML) is characterized by a specific chromosome translocation, and its pathobiology is considered comparatively well understood. Thus, quantitative analysis of CML and its progression to blast crisis may help elucidate general mechanisms of carcinogenesis and cancer progression. Hitherto, it has been widely postulated that CML blast crisis originates mainly via cell-autonomous mechanisms such as secondary mutations or genomic instability. However, recent results suggest that carcinogenic transformation may be an inherently multicellular event, in departure from the classic unicellular paradigm. We investigate this possibility in the case of blast crisis origination in CML. A quantitative, mechanistic cell population dynamics model was employed. This model used recent data on imatinib-treated CML; it also used earlier clinical data, not previously incorporated into current mathematical CML/imatinib models. With the pre-imatinib data, which include results on many more blast crises, we obtained evidence that the driving mechanism for blast crisis origination is a cooperation between specific cell types. Assuming leukemic-normal interactions resulted in a statistically significant improvement over assuming either cell-autonomous mechanisms or interactions between leukemic cells. This conclusion was robust with regard to changes in the model's adjustable parameters. Application of the results to patients treated with imatinib suggests that imatinib may act not only on malignant blast precursors, but also, to a limited degree, on the malignant blasts themselves.

Multi-model inference of adult and childhood leukaemia excess relative risks based on the Japanese A-bomb survivors mortality data (1950-2000)

Some relatively new issues that augment the usual practice of ignoring model uncertainty, when making inference about parameters of a specific model, are brought to the attention of the radiation protection community here. Nine recently published leukaemia risk models, developed with the Japanese A-bomb epidemiological mortality data, have been included in a model-averaging procedure so that the main conclusions do not depend on just one type of model or statistical test. The models have been centred here at various adult and young ages at exposure, for some short times since exposure, in order to obtain specially computed childhood Excess Relative Risks (ERR) with uncertainties that account for correlations in the fitted parameters associated with the ERR dose-response. The model-averaged ERR at 1 Sv was not found to be statistically significant for attained ages of 7 and 12 years but was statistically significant for attained ages of 17, 22 and 55 years. Consequently, such risks when applied to other situations, such as children in the vicinity of nuclear installations or in estimates of the proportion of childhood leukaemia incidence attributable to background radiation (i.e. low doses for young ages and short times since exposure), are only of very limited value, with uncertainty ranges that include zero risk. For example, assuming a total radiation dose to a 5-year-old child of 10 mSv and applying the model-averaged risk at 10 mSv for a 7-year-old exposed at 2 years of age would result in an ERR=0.33, 95% CI: -0.51 to 1.22. One model (United Nations scientific committee on the effects of atomic radiation report. Volume 1. Annex A: epidemiological studies of radiation and cancer, United Nations, New York, 2006) weighted model-averaged risks of leukaemia most strongly by half of the total unity weighting and is recommended for application in future leukaemia risk assessments that continue to ignore model uncertainty. However, on the basis of the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

Population-based mammography screening below age 50: balancing radiation-induced vs prevented breast cancer deaths

INTRODUCTION

Exposure to ionizing radiation at mammography screening may cause breast cancer. Because the radiation risk increases with lower exposure age, advancing the lower age limit may affect the balance between screening benefits and risks. The present study explores the benefit-risk ratio of screening before age 50.

METHODS

The benefits of biennial mammography screening, starting at various ages between 40 and 50, and continuing up to age 74 were examined using micro-simulation. In contrast with previous studies that commonly used excess relative risk models, we assessed the radiation risks using the latest BEIR-VII excess absolute rate exposure-risk model.

RESULTS

The estimated radiation risk is lower than previously assessed. At a mean glandular dose of 1.3 mGy per view that was recently measured in the Netherlands, biennial mammography screening between age 50 and 74 was predicted to induce 1.6 breast cancer deaths per 100,000 women aged 0-100 (range 1.3-6.3 extra deaths at a glandular dose of 1-5 mGy per view), against 1121 avoided deaths in this population. Advancing the lower age limit for screening to include women aged 40-74 was predicted to induce 3.7 breast cancer deaths per 100,000 women aged 0-100 (range 2.9-14.4) at biennial screening, but would also prevent 1302 deaths.

CONCLUSION

The benefits of mammography screening between age 40 and 74 were predicted to outweigh the radiation risks.

Review and evaluation of updated research on the health effects associated with low-dose ionising radiation

While radiation health risks at low doses have traditionally been estimated from high-dose studies, we have reviewed recent literature and concluded that the mechanisms of action for many biological endpoints may be different at low doses from those observed at high doses; that acute doses <100 mSv may be too small to allow epidemiological detection of excess cancers given the background of naturally occurring cancers; that low-dose radiation research should use holistic approaches such as systems-based methods to develop models that define the shape of the dose-response relationship; and that these results should be combined with the latest epidemiology to produce a comprehensive understanding of radiation effects that addresses both damage, likely with a linear effect, and response, possibly with non-linear consequences. Continued research is needed to understand how radiobiology and epidemiology advances should be used to effectively model radiation worker risks.

Do non-targeted effects increase or decrease low dose risk in relation to the linear-non-threshold (LNT) model?

In this paper we review the evidence for departure from linearity for malignant and non-malignant disease and in the light of this assess likely mechanisms, and in particular the potential role for non-targeted effects. Excess cancer risks observed in the Japanese atomic bomb survivors and in many medically and occupationally exposed groups exposed at low or moderate doses are generally statistically compatible. For most cancer sites the dose-response in these groups is compatible with linearity over the range observed. The available data on biological mechanisms do not provide general support for the idea of a low dose threshold or hormesis. This large body of evidence does not suggest, indeed is not statistically compatible with, any very large threshold in dose for cancer, or with possible hormetic effects, and there is little evidence of the sorts of non-linearity in response implied by non-DNA-targeted effects. There are also excess risks of various types of non-malignant disease in the Japanese atomic bomb survivors and in other groups. In particular, elevated risks of cardiovascular disease, respiratory disease and digestive disease are observed in the A-bomb data. In contrast with cancer, there is much less consistency in the patterns of risk between the various exposed groups; for example, radiation-associated respiratory and digestive diseases have not been seen in these other (non-A-bomb) groups. Cardiovascular risks have been seen in many exposed populations, particularly in medically exposed groups, but in contrast with cancer there is much less consistency in risk between studies: risks per unit dose in epidemiological studies vary over at least two orders of magnitude, possibly a result of confounding and effect modification by well known (but unobserved) risk factors. In the absence of a convincing mechanistic explanation of epidemiological evidence that is, at present, less than persuasive, a cause-and-effect interpretation of the reported statistical associations for cardiovascular disease is unreliable but cannot be excluded. Inflammatory processes are the most likely mechanism by which radiation could modify the atherosclerotic disease process. If there is to be modification by low doses of ionizing radiation of cardiovascular disease through this mechanism, a role for non-DNA-targeted effects cannot be excluded.

Relationship between occupational exposure to ionizing radiation and mortality at the French electricity company, period 1961-2003

PURPOSE

Epidemiological studies in nuclear industry workers can produce relevant information to better appreciate the health risks related to chronic external exposure to low doses of ionizing radiation (IR). This work examined the relations between exposure to IR and mortality in workers at the French Electricity Company (EDF), followed up to year 2003.

METHODS

Permanent staff who had worked for at least 1 year at EDF during period 1961-1994 and who had been monitored for exposure to IR were included (n = 22,393). One-sided trend tests for mortality according to cumulative dose and relative risks at 100 mSv were estimated using Poisson regression. Main analyses were stratified on age, sex, calendar time and education.

RESULTS

A total of 874 deaths occurred, and 66 workers were lost to follow-up. Median age at end of follow-up was 48. None of the causes of death investigated increased significantly according to dose, except cerebrovascular diseases (p = 0.01), but this last observation was based on only 22 cases.

CONCLUSIONS

These results do not allow dismissing a possible influence of IR on cancer risk in this population. The cohort is still relatively young and therefore confidence intervals for estimated relative risks remain wide, although they have considerably narrowed since a previous analysis. Chance is a possible explanation for the association between IR and cerebrovascular mortality, due to the low number of cases on which it is based. These results thus need to be stabilized by conducting joint analyses with similar cohorts.