Uncertainty, low-dose extrapolation and the threshold hypothesis

Correlation between infectious disease and soil radiation in Japan: an exploratory study using national sentinel surveillance data

We investigated the relationship between epidemics and soil radiation through an exploratory study using sentinel surveillance data (individuals aged <20 years) during the last three epidemic seasons of influenza and norovirus in Japan. We used a spatial analysis method of a geographical information system (GIS). We mapped the epidemic spreading patterns from sentinel incidence rates. We calculated the average soil radiation [dm (μGy/h)] for each sentinel site using data on uranium, thorium, and potassium oxide in the soil and examined the incidence rate in units of 0·01 μGy/h. The correlations between the incidence rate and the average soil radiation were assessed. Epidemic clusters of influenza and norovirus infections were observed in areas with relatively high radiation exposure. A positive correlation was detected between the average incidence rate and radiation dose, at r = 0·61-0·84 (P < 0·01) for influenza infections and r = 0·61-0·72 (P < 0·01) for norovirus infections. An increase in the incidence rate was found between areas with radiation exposure of 0 < dm < 0·01 and 0·15 ⩽ dm < 0·16, at 1·80 [95% confidence interval (CI) 1·47-2·12] times higher for influenza infection and 2·07 (95% CI 1·53-2·61) times higher for norovirus infection. Our results suggest a potential association between decreased immunity and irradiation because of soil radiation. Further studies on immunity in these epidemic-prone areas are desirable.

Dose-responses from multi-model inference for the non-cancer disease mortality of atomic bomb survivors

The non-cancer mortality data for cerebrovascular disease (CVD) and cardiovascular diseases from Report 13 on the atomic bomb survivors published by the Radiation Effects Research Foundation were analysed to investigate the dose-response for the influence of radiation on these detrimental health effects. Various parametric and categorical models (such as linear-no-threshold (LNT) and a number of threshold and step models) were analysed with a statistical selection protocol that rated the model description of the data. Instead of applying the usual approach of identifying one preferred model for each data set, a set of plausible models was applied, and a sub-set of non-nested models was identified that all fitted the data about equally well. Subsequently, this sub-set of non-nested models was used to perform multi-model inference (MMI), an innovative method of mathematically combining different models to allow risk estimates to be based on several plausible dose-response models rather than just relying on a single model of choice. This procedure thereby produces more reliable risk estimates based on a more comprehensive appraisal of model uncertainties. For CVD, MMI yielded a weak dose-response (with a risk estimate of about one-third of the LNT model) below a step at 0.6 Gy and a stronger dose-response at higher doses. The calculated risk estimates are consistent with zero risk below this threshold-dose. For mortalities related to cardiovascular diseases, an LNT-type dose-response was found with risk estimates consistent with zero risk below 2.2 Gy based on 90% confidence intervals. The MMI approach described here resolves a dilemma in practical radiation protection when one is forced to select between models with profoundly different dose-responses for risk estimates.

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.

Low-dose extrapolation of radiation health risks: some implications of uncertainty for radiation protection at low doses

Ionizing radiation is a known and well-quantified human cancer risk factor, based on a remarkably consistent body of information from epidemiological studies of exposed populations. Typical examples of risk estimation include use of Japanese atomic bomb survivor data to estimate future risk from radiation-related cancer among American patients receiving multiple computed tomography scans, persons affected by radioactive fallout, or persons whose livelihoods involve some radiation exposure, such as x-ray technicians, interventional radiologists, or shipyard workers. Our estimates of radiation-related risk are uncertain, reflecting statistical variation and our imperfect understanding of crucial assumptions that must be made if we are to apply existing epidemiological data to particular situations. Fortunately, that uncertainty is also highly quantifiable, and can be presented concisely and transparently. Radiation protection is ultimately a political process that involves consent by stakeholders, a diverse group that includes people who might be expected to be risk-averse and concerned with plausible upper limits on risk (how bad could it be?), cost-averse and concerned with lower limits on risk (can you prove there is a nontrivial risk at current dose levels?), or combining both points of view. How radiation-related risk is viewed by individuals and population subgroups also depends very much on perception of related benefit, which might be (for example) medical, economic, altruistic, or nonexistent. The following presentation follows the lead of National Council on Radiation Protection and Measurements (NCRP) Commentary 14, NCRP Report 126, and later documents in treating radiation protection from the viewpoint of quantitative uncertainty analysis.

Interactive RadioEpidemiological Program (IREP): a web-based tool for estimating probability of causation/assigned share of radiogenic cancers

The Interactive RadioEpidemiological Program (IREP) is a Web-based, interactive computer code that is used to estimate the probability that a given cancer in an individual was induced by given exposures to ionizing radiation. IREP was developed by a Working Group of the National Cancer Institute and Centers for Disease Control and Prevention, and was adopted and modified by the National Institute for Occupational Safety and Health (NIOSH) for use in adjudicating claims for compensation for cancer under the Energy Employees Occupational Illness Compensation Program Act of 2000. In this paper, the quantity calculated in IREP is referred to as "probability of causation/assigned share" (PC/AS). PC/AS for a given cancer in an individual is calculated on the basis of an estimate of the excess relative risk (ERR) associated with given radiation exposures and the relationship PC/AS = ERR/ERR+1. IREP accounts for uncertainties in calculating probability distributions of ERR and PC/AS. An accounting of uncertainty is necessary when decisions about granting claims for compensation for cancer are made on the basis of an estimate of the upper 99% credibility limit of PC/AS to give claimants the "benefit of the doubt." This paper discusses models and methods incorporated in IREP to estimate ERR and PC/AS. Approaches to accounting for uncertainty are emphasized, and limitations of IREP are discussed. Although IREP is intended to provide unbiased estimates of ERR and PC/AS and their uncertainties to represent the current state of knowledge, there are situations described in this paper in which NIOSH, as a matter of policy, makes assumptions that give a higher estimate of the upper 99% credibility limit of PC/AS than other plausible alternatives and, thus, are more favorable to claimants.

Thyroid disorders in employees of a nuclear power plant

BACKGROUND

The thyroid gland is a potential target organ for radiation-related damage. The aim of this analysis was to investigate the association between occupational exposure to ionizing radiation and the risk of autoimmune thyroid disease as well as thyroid nodules and dysfunction in workers of a former nuclear power station.

METHODS

Seventy-one male power station workers 38 to 57 years of age who had been exposed to a lifetime dose in the upper allowed range (accumulated lifetime dose 70 to 400 mSv) were compared to a population-based sample of 670 males who were not exposed to occupational radiation. Thyroid ultrasound was performed by the same observers. Laboratory parameters were analyzed in a central laboratory.

RESULTS

After controlling analyses for age and further relevant confounders no significant differences with respect to thyroid nodules and markers of autoimmune thyroid disease were detected between exposed and nonexposed individuals. However, nuclear power plant employees had higher odds for elevated serum thyrotropin (TSH) levels than the reference group (odds ratio 4.54; 95% confidence interval 1.43; 13.91).

CONCLUSIONS

Workers of a nuclear power plant with occupational exposure to ionizing radiation within the upper allowed dose range have an increased risk of elevated serum TSH levels. Further studies are required to confirm possible effects of occupational exposure to radiation on thyroid function.