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What follows is the continuation, in serial form, of a central chapter from my book A Primer in the Art of Deception: The Cult of Nuclearists, Uranium Weapons and Fraudulent Science.
SCAM NUMBER THIRTEEN: Promote simplistic, all-encompassing hypotheses of the relationship between dosage and physiological response to spellbind the public into believing that more is known about the effect of low doses of radiation than is actually the case.
Resolution of the debate over the hazards of low doses of radiation is hampered by the difficulty of designing and carrying out conclusive, indisputable, epidemiological studies. Dr. Alice Stewart succinctly diagnosed the problems confronting such research:
“Studies of the health effects of very small doses of radiation face three design problems: how to accurately measure the radiation doses large numbers of persons have received (the dosimetry problem), how to prevent comparisons between exposed and unexposed groups from being bedeviled by other differences (the selection problem), and how to cope with the varying lengths of cancer latency (the follow-up problem). These technical problems lie at the center of the current debate about the cancer effects of low levels of radiation, and the cancer issue is central to the controversy about nuclear technology.”
The absence of firm data on population effects from low-dose exposure has forced various interest groups to field their own mutually exclusive hypotheses of what the actual hazard might be. All such attempts to date have been overly reductionistic. All rely on an oversimplification of the complex processes that biological systems undergo in response to radiation exposure. All fail to account for all the available data. The dose-response models proposed have been derived by extrapolation from known high-dose effects down into a region of heightened uncertainty. Confirmation of the accuracy of such modeling has been frustrated by a paucity of incontrovertible evidence. As noted by the ECRR: “In the case of external irradiation studies, the small populations studied result in wide confidence intervals and a number of different curves can be drawn through the data.” This arcane subject of postulating the shape of the curve on a graph is the battlefield where pro- and anti-nuclear gladiators collide. “The disagreement concerns how to extrapolate from higher dose rates to the non-measurable range. The rancorous discord among scientists concerning the low-dose cancer danger is over hypotheses — not observable fact” [1].
The central question in the field of radiation safety is this: What is the relationship between a dose of radiation and the biological effect produced by that dose? At relatively high doses, answers to this question are straightforward. Some biological effects are nonstochastic, i.e., deterministic. A threshold dose must occur for these effects to be produced and the severity of the effects are directly proportional to the size of the dose. Examples of nonstochastic effects are acute radiation syndrome, cataract formation and skin burns. As the dose of radiation decreases, apparent deterministic effects disappear. In addition to these types of effects, radiation produces stochastic effects. These effects are produced by chance. Within current orthodox thinking, the stochastic effects of concern are cancer and genetic defects. At relatively high doses, as the dose increases the probability of stochastic effects likewise increases. Similarly, a decrease in dosage is accompanied by a reduced probability of a stochastic effect occurring.
Because of such problems as those mentioned by Alice Stewart for conducting epidemiological studies that will produce unequivocal results, various models have been proposed to capture the true relationship, as dosages decrease, between the dose received and the probability of a stochastic effect occurring. Since at relatively high dosages, the likelihood of stochastic effects is directly proportional to the dose, one model proposes that this proportional relationship exists down to the lowest possible dose. Another model postulates that at low doses more health detriment per unit dose is produced than that predicted by the linear relationship. A third prominent model proposes the opposite, that less health detriment per unit dose occurs. The Holy Grail in radiation research is to determine which model successfully captures the reality of what is actually going on.
Radiation protection agencies throughout the world have embraced the premise that biological damage from radiation exposure is directly proportional to the dose. This is called the Linear No-Threshold Hypothesis (LNTH). According to this model, the probability of an individual developing cancer is linearly related to the amount of radiation he/she receives. Applied to populations, the LNTH posits that as the radiation dose to members of the population increases, the number of cancers induced in that populations increases at the same rate. This hypothesis is supported by a great deal of empirical data based on relatively high levels of exposure to external irradiation. The problem is that sufficient data is not available to determine definitively that this relationship continues to hold as doses become smaller and smaller. For regulatory purposes, radiation protection agencies have adopted the LNTH “as if” it were true. They believe that this stance is overly conservative in estimating the hazard of low doses and offers the greatest measure of safety to radiation workers and the general public. Embedded in this model is the idea that the incidence of cancer is the same whether the radiation is delivered as a high dose in a short period of time or as a low dose over an extended period of time. Small doses are cumulative and each exposure increases the risk to the individual. According to the LNTH, there is no such thing as a threshold dose below which there are no adverse effects to health. Regardless of how small the dose, some probability always exists that a cancer will be induced.
The Supralinear Hypothesis represents a direct attack to the status quo of the nuclear establishment. According to a small number of dissenters, the LNTH dangerously underestimates the hazards to health of low doses of radiation. In their view, the existing data provides ample evidence that in the low dose range, 10 rem (0.1 Sievert) or less, more damage is created per unit dose than that predicted by a simple linear relationship. This counterintuitive conclusion is supported by a number of observations. For instance, the genetic instability and bystander effects induced in cell cultures by very low doses demonstrate a supralinear response. In vitro studies also provide evidence that free radicals, produced from the ionization of water molecules, have a tumor-promoting effect that enhances the hazard of low-dose radiation and alters the shape of the dose-response curve to one of supralinearity. Increased health detriment at low doses has also been attributed to the Petkau effect, described later in this chapter, whereby sparse free radical production can promote cell death through cell-membrane destruction. This phenomenon has been postulated as having a debilitating effect on the immune system, causing an elevated rate of cancer in the low-dose range. The hypothesis that the risk of cancer death per rad increases as the dose decreases was demonstrated by Gofman in his reanalysis of the Hiroshima Life Span Study. He showed that this data exhibited a supralinear relationship for breast cancer, leukemia, and overall deaths. The ECRR has concluded that sufficient evidence exists to reject the LNTH as inaccurate in the low dose range and favors relationships which show much greater effects per unit dose.
A third competing model for low-dose effects is the Linear Quadratic Hypothesis. According to this viewpoint, hazard at low doses is even less than that postulated by the LNTH, the risk of cancer death per rad decreases as the dose decreases. This conclusion is supported by data that shows that the body has numerous mechanisms for mitigating the effects of radiation damage. These mechanisms kick in at different dosages, and thus, simple extrapolation from high doses down to low doses cannot be made accurately.
The ECRR have postulated an explanation for effects manifesting a linear quadratic dose response:
"There are sound theoretical reasons for interpreting this [a Linear Quadratic response] as due to independent track action in the linear range with a much increased effect when the dose is so great that two tracks impinge on a cell at the same time. These two tracks (or correlated tracks) are thought by most to have a high probability of inducing a mutation because they can cause damage to both DNA strands in such a way that there is a ‘double-strand break,’ an event which is difficult for the cell to repair. This may not be the true reason for the increased mutation efficiency but the observation that two hits have a very much larger chance of causing mutation is now well accepted. Recent work with alpha particles and cell cultures has confirmed this empirically" [2].
What this means is that at the lowest possible dosages, not all cells within a population are hit, and those that are hit are extremely unlikely to be sufficiently damaged by a single hit to cause irreparable mutation or to be hit twice to produce a double-strand break. As the dose increases, the percentage of cells in the population that are hit increases as well as those receiving two or more hits. Finally, when a sufficient percentage of the cells receives two or more hits, the likelihood of irreparable mutation becomes directly related to the dosage. The dose-response curve then becomes linear.
The ECRR has drawn attention to research which displays a fourth dose-response curve. This curve displays a biphasic relationship. In some cell culture experiments, as the dose increases from zero, the effect increases to a maximum point. With further increase in dose, the effect falls back to a minimum. As the dose increases still further, a second rise in effect is witnessed. Busby has proposed an interesting explanation for this phenomenon. Within the body, different types of cells have different sensitivity to radiation. Further, at any one moment in time, a percentage of cells throughout the organism is undergoing replication. During this time, they are in a heightened state of sensitivity to radiation damage. As the dose increases from zero, the first cells to sustain damage are among those that are the most sensitive. They will be exposed to a greater likelihood of irreparable mutation and cancer induction. As the dose increases still further, these most sensitive cells will be killed rather than survive in a mutated form. This cell death cancels out the potential deleterious effects to the organism from the oncogenic events at lower doses. Thus, potential detriment to the organism decreases. At still greater doses, the less sensitive cells will begin to respond and the effect will once again increase and continue to do so with increasing doses until expression of a cancer or death to the organism from radiation illness.
The quest for a universal hypothesis to encompass and explain all responses of the organism from all doses harkens back to the time when physicists dominated the discussion on the health effects of radiation. What was sought was a tidy, all-inclusive model of how radiation affects the human organism, from the tiniest of exposures up through to a lethal dose. However, no evidence has ever been presented that biology is amenable to such oversimplification. As far as we know, it is nothing more than wishful thinking to hope that a single, universal dose-response relationship can be found to account for all types of exposure to all types of cells in all individuals and for all possible effects. It is conceivable that each of the dose-response models that have been proposed hold true under various circumstances or for different medical endpoints under consideration.
The health of humanity is being jeopardized by the reductionistic thinking embodied in the dose-response models embraced by the radiation protection agencies. Often, epidemiological evidence is rejected as being invalid because it fails to conform to what the models predict should be observable. A case in point is the cancer clusters found in proximity to nuclear installations. Another is the debilitated health of those exposed to uranium weaponry. Rather than calling the models into question, such evidence is rejected by those in authority as being invalid and unscientific because it is at odds with the accepted models. Once again this is a distortion of the scientific method. As noted by the ECRR:
“There is not sufficient evidence to show that there is a universal dose-response relation for all types of exposure and all endpoints, and to assume such a function is an example of a fatal reductionism. However, there are good reasons for assuming that effects in the low dose range from zero dose to about 10 mSv are likely to follow some kind of supralinear or fractional exponent function. Since there is good theoretical and empirical evidence for the existence of biphasic dose response relationships, the committee strongly recommends that no epidemiological findings should be dismissed on the basis that it does not conform to a continuously increasing dose response relation of any form.”
The reader is cautioned not to get sucked into the vortex of confusion created by the academic debate over how to extrapolate known high-dose effects to low levels of exposure. The Cult of Nuclearists profits from the irresolution of this controversy and has a vested interest in keeping it alive. While academicians argue over the shape of the dose-response curve, the United States military is saturating other peoples’ homelands with just those levels of radiation that are being argued over. As long as consensus among the experts remains unachieved, the United States can disguise itself in pristine innocence and escape accountability for its crimes.
Bibliography
[1] Ball H. Justice Downwind: America’s Atomic Testing Program in the 1950s. New York: Oxford University Press; 1986. Citing R.S. Yalow: Reappraisal of Potential Risks Associated with Low-Level Radiation. Annals of the New York Academy of Science. November 1981; 49.
[2] European Committee on Radiation Risk (ECRR). Recommendations of the European Committee on Radiation Risk: the Health Effects of Ionising Radiation Exposure at Low Doses for Radiation Protection Purposes. Regulators' Edition. Brussels; 2003. www.euradcom.org.
