Exposure and risk scenarios

Different radiation risks can be directly compared to each other since there is a common link between exposure and risk. Such risk comparisons are based on the LNT model, combined with internal and external exposure models and estimates of the biological effectiveness of different radiation types. Table 1 summarises different exposures and risks from natural and medical radiation sources and compares these with illustrative exposures following the Chernobyl accident. Doses are expressed as risk for the total exposure over the specified time period. Note that exposures from Chernobyl do not here include dose to the thyroid or the 134 cases of ARS resulting from exposures during the accident (for a summary of the health effects of Chernobyl, including thyroid cancer, see [2, 10–12]).

Table 1 Illustrative radiation exposures from natural background, medical, routine nuclear operations and Chernobyl with hypothetical lifetime risks. Full size table

It is clear from Table 1 that current exposures from the Chernobyl accident are not greater (and are in some cases much smaller) than some exposures to natural background radiation (e.g. long-haul air crew or some residents of relatively high natural background areas). Doses to the population of approximately 200,000 emergency workers who worked in the Chernobyl 30-km exclusion zone in 1986–87 averaged approximately 100 mSv and doses to residents of the "strict control zones" were on average lower, but of the same order [37]. A group of people living unofficially in the 30-km exclusion zone around Chernobyl were found to receive annual doses of 1–6 mSv yr-1 in the late 1990s [2]. A lifetime's exposure to high natural background radiation in some parts of the world can result in an accumulated dose of 700 mSv or more (Table 1). More than 100,000 people in Finland, for example, receive natural radiation doses > 10 mSv yr-1 [10].

Mortality risks from example exposure scenarios for air pollution, passive smoking and radiation are shown in Table 2. Comparing risks between different risk factors is more uncertain than comparisons between different radiation sources. The time delay in the health impact following exposure to passive smoking or air pollution, for example, may be different to that following exposure to low-dose radiation.

Table 2 Approximate hypothetical lifetime increased mortality rate from illustrative scenarios of exposure to air pollution, passive smoking and radiationa. Full size table

There are significant uncertainties in risks in all the cases shown in Table 2, however, this comparison of time- and population-averaged risks can help to put radiation risks in context. The radiation exposures to emergency workers and to the most exposed populations following Chernobyl represented a potentially significant increase in fatal cancers in the exposed populations. But, the risk (from the evidence analysed here) appears to be no greater than potential mortality risks from air pollution, passive smoking, or high natural background radiation exposures.

Table 3 compares risks from acute, high dose radiation with active smoking and high BMI, in terms of expected average reduction in lifespan. Both of these latter risk factors are to a large extent determined by individual choice, though both are also influenced by cultural and socio-economic conditions. Active smoking and BMI therefore provide quantitative risk comparators for acute high dose radiation exposure. However, there is no intention here to make an ethical comparison between an imposed risk (radiation exposure in an extreme event) and a (to an extent) voluntary risk such as smoking or high BMI.

Table 3 Loss of life expectancy due to smoking, high body mass index and the long term effects of high acute radiation exposure. Full size table

The comparison for extreme radiation risks in Table 3 may be of limited value since such exposures are, fortunately, rare. In addition, the comparison does not account for the deterministic (i.e. ARS) effects of acute exposures in the range 1–5 Gy which (by definition) does not influence the YOLL of these A-bomb survivors . However, Table 3 does put the health risks of active smoking and obesity into a novel perspective.

Radiation risks

The risk estimates recommended by the International Commission on Radiological Protection [15] are for chronic exposures at relatively low dose rate rather than the high dose rate exposures to the atomic bomb survivors. In radiation risk assessments it is current practice to assume that even very low dose radiation carries with it an associated cancer risk (the linear, no-threshold or LNT model). This assumption is based on radiobiological evidence that DNA damage from a single radiation impact can potentially lead to cancer. Although often inconclusive at very low doses, epidemiological evidence also tends to support the LNT model. A recent study [38] has shown statistically significant excess cancer risk at acute doses down to 60 mSv in the Japanese bomb survivors. In a review [39] which included studies of medical and occupational radiation exposures, it was argued that "good evidence of an increase in risk for cancer is shown at acute doses > 50 mSv, and reasonable evidence for an increase in some cancer risks at doses above ≈ 5 mSv... good evidence of an increase in some cancer risks is shown for protracted ["chronic"] doses > 100 mSv, and reasonable evidence ... at protracted doses above ≈ 50 mSv".

Exposure to low level radiation can potentially result in hereditary effects on subsequent generations. Evidence of effects on offspring has been observed in studies on laboratory animals [40]. Studies on the children of the survivors of the Hiroshima and Nagasaki bombs have, however, found no evidence of hereditary effects of radiation [41].

Lung cancer from exposures to radon and its decay products forms the major excess risk at high radon concentrations in the home. The US National Academy of Sciences [17] and US Environmental Protection Agency [18] have recently re-assessed the lung cancer risk from exposures to radon in the home. The stochastic mortality risk of 3.7% at lifetime radon exposure of 750 mSv (Table 3, as calculated from [15]) will therefore be compared with these more recent radon risk estimates.

The lifetime fatal lung cancer risk to an average member of the US population at an average radon air concentration of 37 Bq m-3 is 0.58% assuming 70% of time is spent at home [18]. At the UK action level for radon in the home (200 Bq m-3), assuming LNT, this corresponds to a lifetime fatal lung cancer risk of 3.1%. This compares well with the mortality risk estimate of 3.7% presented in Table 1 for lifetime radon exposure at the UK action level, though this does not necessarily imply that the ICRP and EPA risk coefficients are the same: the former risk is calculated on the basis of an estimated effective radiation dose whilst the latter relates risk directly to radon concentration in air from epidemiological studies of miners. In addition, it should be noted that the more recent radon risk estimates [17, 18] show a much higher excess absolute risk in smokers than in non-smokers due to the synergistic effects of smoking and radon. The risk estimate presented here is for an average population of smokers and non-smokers (as is the case in the ICRP approach).

Air pollution risks – time series vs. cohort studies

It is well known that air pollution in cities can lead to significant health problems. The London smog of 1952 was reported to have caused an extra 4000 deaths in the capital and a huge increase in hospital admissions for respiratory and cardiovascular diseases. A pollution episode in December 1991 was associated with an additional 101 to 178 deaths in London [42]. The impacts of air pollution on health may be estimated by studies of short-term relationships between incidents and immediate health effects ("time-series studies") or by "cohort" studies relating long-term air pollution to average morbidity (illness) and mortality rates.

Time-series studies have identified clear relationships between pollution episodes and mortality as exemplified by the London incidents. There is uncertainty, however, concerning assessment of the impact of such short-term incidents, particularly in assessing the years of life lost (YOLL) of the victims. Analyses of such incidents have shown that they tend to bring forward the deaths of elderly or seriously ill people (by a relatively small time period) rather than immediately affecting generally healthy people. A report of the Committee on the Medical Effects of Air Pollutants [23] assumed that the loss of life expectancy following short-term pollution episodes is on average in the range 2–6 months, though it is possible that deaths are brought forward by just a few days in many cases.

Longer-term cohort studies, on the other hand, tend to emphasise the long-term effects of chronic exposures. For example, the U.S. "Six Cities Study" [43] followed the health of a group of 8111 adults from 1974–1991. The mortality rate in the most polluted of the six cities was 1.26 times higher than in the least polluted city (95% CI: 1.08–1.47). Deaths from lung cancer and cardiopulmonary disease were correlated with levels of fine particulate air pollution.

A discussion of the differences between cohort and time-series studies of air pollution can be found in [44]. It has been suggested [23] that reductions in air pollution would lead to a "gain in life years from the cohort studies [which] is at least 10-fold greater than estimates from the time-series studies alone". Thus, cohort studies show a much greater influence of air pollution on YOLL than time-series studies. It has been noted [44] that "the total impact (YOLL) of air pollution advancing deaths by a long time ... is estimable from cohort studies results". The meaning of "a long time" in this context is not precisely defined, but is likely to be greater than several months [44].

Whilst noting the many uncertainties and potential confounding factors in cohort studies, these can be used to make tentative estimates of deaths brought forward by a "long time" as a result of exposure to air pollution.

Uncertainties

All of the risk estimates discussed above are based on epidemiological studies and are therefore subject to statistical uncertainties and potential confounding factors. Quoted confidence intervals are limited in that they do not necessarily encapsulate all possible sources of error in relative risk estimates: it is rarely (if ever) possible to account for all confounding factors. The limitations of epidemiological studies are well known and results need to be treated with great caution, particularly when observed relative risks are low (less than, say, 2–3; [45]). Some of the risk factors discussed here ( acute exposure to > ~100 mSv radiation, active smoking, very high BMI) are based on strong epidemiological evidence and show clear dose-response relationships, as illustrated in Figures 1, 2, 3. The other risk factors (chronic low-dose radiation, passive smoking, air pollution) are all subject to much greater uncertainty and potential bias.

For statistical analyses of the various epidemiological studies used, the reader is referred to the original references on which the excess relative risks are based. It is not always possible to present accurate objective confidence intervals for these risk estimates. Where possible, confidence intervals of relative risks are presented here, though accurate confidence intervals were not always available (for example, ref. [14] cites only a subjective CI). It is also noted that quoted confidence intervals are limited in that they do not necessarily encapsulate all possible sources of error in relative risk estimates. Uncertainties in the various risk factors are summarised in Table 4.

Table 4 Summary of available uncertainties in various risk factors. Full size table

The risks arising from chronic, low-dose radiation are determined to a large extent by linear extrapolation (LNT model) from the data on Japanese atomic bomb survivors, with a reduction due to predicted lower effectiveness of low dose rate radiation in cancer induction (the DDREF). There are ongoing arguments concerning the shape of the dose-response curve at low doses and dose rates with some arguing that risks may be significantly higher or lower than predicted by the standard extrapolation from high dose data.

There is also uncertainty in the risks of passive smoking and air pollution. Both air pollution and passive smoking studies may be compromised by socio-economic, environmental or lifestyle factors which could not be accounted for, even in large scale studies or meta-analyses [46–48]. In addition, cohort studies of air pollution are necessarily based on health risks from past (generally higher) exposures which may not apply today [46].