As management of hazardous wastes becomes an increasingly urgent social priority, we fiid ourselves confronted
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with a dilemma: There is mounting evidence that low-level exposures to hazardous substances can cause chronic human injuries, including cancer and birth defects. Yet we are poorly esuipped to find a reasonable means for compensating those who claim to have been harmed hy such exposures. As a result, the public has become htrated with the uncertainties of risk assessment, and emotional or political concern often preempt scientific debate. Historically, in the fields of environmental and public health, regulatory agencies and the courts have relied on scientific approaches for resolving questions of probable cause. Specifically, the sciences of hiology and epidemiology, as well as statistics and the physical sciences, have made great contributions to our understanding of hazardous phenomena. For example, we have developed methods for tracing the release, transport, and transformation of toxic substances through air, water, and soil and for quantifyii human exposures and potential health effects sociated with these substances (1). [pert scientific testimony concerning ese phenomena often is an important sis of argument for defendants and ,.mintifi alike in tort cases (2). There are, however, certain situations in which scientific knowledge is inadequate-where iimdamental uncertainties prevent firm conclusions. In particular, the problem of indeterminate causation has become increasingly troublesome. This problem arises when a plaintiffs injury may have been 5010
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caused by the joint action of several factors. For example, a uranium miner’s lung cancer may have been caused by radiation, by cigarette smoking, by both, or by some other combmtion of factors. In such a case, it is usually impossible to determine the actual cause of the disease through medical observation. Yet traditional tort law demands evidence of causation. Arguments are now being advanced for modifying the tort system to permit compensation of victims when the causes of their injuries are indeterminate,and a number of recent court decisions have relaxed the quimnents for proof of causation (3). An early and often-quoted example of indeterminatecausation is the case of Summers v. ?ice (1948), in which the plaintiff was injured by a shot fired by one of two hunters. The court held both hunters liable, shifting to them the burden of proving their innocence. This precedent was followed in the more recent case of Sindell v. Abbon Laboratories (1980). In this case the plaintiff was a cancer victim whose mother had taken the drug diethylstilbestrol (DES) but was unable to identify the manufacturer of the drug. The court held all nine major manufacturersjointly liable in proportion to their share of the market. Thus, LuKemln ’ ty about the exact cause of an injury was resolved by holding all the defendants collectively responsible. In these cases, however, the cause of the injury was known and the only uncertainty was about the identity of the responsible party.
Recently, there has been a great deal of interest in compensating those who have been exposed to a variety of carcinogenic agents. This situation is more difficult to resolve than the cases cited above because there offen is uncertainty about which carcinogenic agents may have been involved, as well as about the degree to which they may have contributed to the development of a cancer in an individual victim. One example is the case of Utah residents exposed to radioactive fallout during early atomic bomb tests. A recent court decision, Allen er al. v. United States (1984), upheld the claims of several victims on the grounds that the radiation had “contributed substantially” to their risk of cancer. In 1983, Sen. Orrin Hatch @-Utah) introduced S. 921, the Radiogenic Cancer Compensation Act, which was to provide ccmpensationto cancer victim5 in this group, even though only a small fraction of their cancers can allegedly be attributed to radiation. This can be characterid as a “reverse Sindell” situation in which there is only one defendant (the federal government) and many plaintiffs, with indeterminacy as to which, if any, have actually been injured by the cause in question (3). Figure 1 illustratesthe above situations and the more complex case in which there are both multiple plaintiffs and multiple defendants. Unfortunately, in most situations involving the effects of hazardous substanm there is uncertainty in the identification of causes and victims alike;
such indeterminate causation will continue to be a problem in the context of victim compensation. Vietnam veterans exposed to Agent Orange, workers exposed to chronic carcinogens, consumers seeking product liabiiity judgments, and residents living near hazardous waste sites are a few of the classes of victims for which remedies have been proposed through federal legislation. It is therefore important to examine from a scientific point of view the basis for attributing causation among multiple causes. In particular, the concept of probability of causation needs to be understood, because it is frequently advanced as a means of allmating compensation (4). There are certain situations in which probability of causation cannot be estimated based on available scientific data, making alternative approaches necessary for d d i n g with compensation claims. The principal focus of this paper is on multiple causation in the context of chronic diseases such as cancer. These diseases are characterized by long latency periods (including transgenerational reproductive effects) and our inability to isolate the origin of the disease. For example, cancer can be caused by a host of factors, including diet, smoking, ~ t u ord man-made radiation, environmental pollutants, and genetic factors (5). Moreover, as research progresses on carcinogenesis, there is increasing evidence that many cancers are caused by combinations of these factors, although the mechanisms of interaction are not yet M y understood (@. As a result, cancer victim compensation cannot yet be based on a reliile scientific approach for amibuting probabfities of causation (7).
hbabm of causation In cases where several factors are believed to be involved in causing an adverse health effect such as cancer, traditional tort law may not provide an adequate means for the victim to recover damages. Under tort law an injured party can be compensated only if a wrongdoer can be identified and only if it can be shown that the wrongdoer’s action was a cause-in-fact of the injury. The traditional test for cause-in-fact is whether the action in question was more likely than not the cause of the injury; this is also called the preponderance-ofevidence standard In mathematical terms, this test requires that the probabiity of the wrongdoer’s action having caused the injury exceed 50%. However, in the case of c h n i c health effects this condition can rarely be met. When multiple causes are present, the plaintiff cannot identify a wrongdoer, and even if one. cause
(e?
428 Envimn. Sci. Tkhnd.. Ibl.20. NO.5,1986
could be isolated, the associated prohbility of causation is seldom as much as 50% bxaw background incidence of the effect is usually higher than the incidence associated with that one cause. In its simplest form, the probabilityof-causation formula can be expressed as follows (9): Ihe probability of causntion is equal to the risk attributableto a pam'cuhr radiation aposure divided by the toral risk due to all causes. Here, the risk attributable to radiation is the expected increase over the normal cancer incidence rate when a population is exposed to the level of radiation in d o n . As an examvle, if the normal ihcidence rate for le'iemia in males aeed 30-34 is 28 x 1 W annuallv 1101. &d if the excess risk due to one tional rad of exposure were 2 x 1 W per year (11). then the probabiity of causation due to this exposure would be 2/(28 2) = 6.7%.Thii can be expressed symbolically as K=- RR (1)
a-
+
RR + RN
where PC is the probability of causation, RR is the risk attributable to the radiation exposure, RN is the risk attributable to normal incidence, and RR RN is the total annual risk of leukemia. The risk values in this formula (e.g., RR)are defined in applications of the simple probability-of-causation appmch as attributable risks based on epidemiological data (12). That is, RR purportedly represents the increased incidence rate of cancer due to exposure to a single factor, relative to a baseline of zero exposure. The above formula does not attempt to account for interactions among factors, although efforts have been made to g e n e r a k the formula to deal with such interactions (13). In theory, the formula can be extended to consider other agents, such as natural s o u ~ c e s , air pollution, dietary factors, cigarette smoking, and sources of radiation other than the one beiig singled out (14). Unfomnately, there are serious deficiencies in this approach when it is applied to chronic human health effects (15). It is important that these limitations be understood by policy makm who seek practical mechanisms for canpensating victims. For example, in the Superfimd reauthorization process, various administrative approaches were considered for compensating persons injured as a result of theii proximity to hazardous waste sites. The probabilityof-causation method would not provide a reliable tool for allocating compensation in such cases.
+
categories of causation At least four different categories of causation can be distinguished concep Emimn. Sci. TBchnol.. %I. 20, NO. 5,is86 427
tually: ordinary, sequential, simultaneous exclusive, and simultaneous joint causation. The first category involves a single causation factor; the other three involve multiple factors (Figure 2). ordinary camatinn. Ordinary causation describes a situation in which the presence of a single factor, such as asbestos insulation, is believed to cause an effect, such as mesothelioma. The incidence rate of the effect in the presence of factor A (measured by P I ) is often called the risk, or risk rate; for example, the value of Pl might be lW, or one case annually per 100,ooO persons exposed at a given level. If the effect can also occur when A is not present, then it is customary for epidemiologists to compute the anributabk risk, Pi PO.which represents the exc a s risk intrcduced by the presence of factor A, assuming there are no hidden factors (12). An important assumption in the calculation of attributable risk is that the effect induced hy A is independent and exclusive of any effects that result from background factors. This may not always be true; for example, cancer risks are known to be influenced by genetic factors and lifestyle (6). Thus, when Po > 0, the ordinary causation model may be somewhat implausible, and it may become necessary to consider other factors explicitly. Under ordinary causation, the legal preponderance-of-evidencetest can be interpreted to require that PI > Po, which asserts that A was more likely than not the cause of the effect (7).This can be seen by computing the retrw speaive probability of causation:
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sense to speak of a single cause (15). Thus, the above formula can be interpreted as a true probability only when the cause. A is completely separable from the background causes. In what follows, we will assume for simplicity that Po = 0, thus ignoring background effects. This will allow a clearer focus on the problem of multiple causation. Sequentid cansatinn. Sequential, or nonsimultaneous, causation describes a situation in which there is exposure to two factors at different times, followed by a single effect. Generally speaking, the effect may have been caused by either factor or by both acting together. (An example might be a tourist contracting dysentery after eating two meals in a foreign ccuntry.) Sequential causation can be modeled as shown in Figure 3. The probabdities PI and P2 can be drawn from the ordinary causation context, provided one important condition is met: Factors A and B must not interact. That is, the presence of A or the effects of A have not intluenced the effects of B. If this condition is violated, then new data are needed to evaluate possible effects of B in the presence of A. (For example, if a tumor is initiated by one.factor and then promoted by another factor, the attribution of cause tecomes much more difficult.) When the factors are indeed independent, the probability of causation can be computed as follows:
For small values of P I , these expressions reduce to:
.This expression has been used r e p t edly in expert testimony, frequently in c&ses where the above-mentioned as-
sumptions are violated. When there is interaction among causes, it makes no
488 Environ. Sci. Technol., Vol. 20, NO.5, 1986
These formulas resemble the probabdity-of-causation formula developed below for simultaneous exclusive causation. However, it must be noted that the above derivation makes a strong assumption: that A occu~s,and either does or does not produce an effect; that B then occurs and has an opportunity to produce an effect only if A did not already produce it. Simultaneous exclusive causation. Siultaneous exclusive causation describes a situationin which two or more causal factors are present, but the m sulting effect is caused by one and only one of these factors. Unfortunately, chronic diseases such as cancer usually do not fall into this category because.of synergism and other biological interaction among factors. Although the mathematical representation is simple, there is an important cautionary note to observe. The probabilities PI and P2,associated with the effects of A and B respectively, cannot be the same as in the case of ordinary causation. It has been shown that in the presence of competing risks these prob abilities are necessarily less than in the case of exposure to a single factor (16). Therefore, ordinary attributable risk estimates cannot be used when factors are mutually exclusive. Intuitively, this is because. of the limitation that A can have an effect only if B does not have that effect, and vice versa. In this case the probability of causation is easily found to be
Again, although the structure of the formula is similar to the previous case of sequential causation, the probability values will be different. In the sequential case,factors A and B were assumed to act independently, whereas here the exclusivity imposes a strong - depen. dence. Simultaneous joint causation. Simultaneous joint causation is the most difficult of the four cases to analyze and also the most realistic case in terms of representing chronic disease effects and other multifactorial liabiiliry questions. Joint causation describes a situation in which several factors can contribute in varying degrees lo the Occurrence of an effect. For example, a cigarette smoker who is exposed to radiation and chemical carcinogens in the workplace may develop a lung tumor. Whether the tumor was caused wholly by one factor or by a combination of factors is, at present, impossible to determine scientifically. However, there is increasing
half the blame is arbitrarily assigned to each cause, the probability of causation is as follows:
When PI and Pz are low probabilities, the situation is greatly simpWied, and the above expressions can be appmximated as
evidence that tumor development is a complex, multistage process involving interactions between a number of endogenous and exogenous factors. The most general model of joint causation in the twc-factor case has four possible results, three of which end in an effect. (That is, caused by A alone, B alone, or by A and B together. If n factors were present, there would be 2" 1 different combinations of factors that could cause an effect.) If an effect is actually observed in a given individual, the only way to retrospectively attribute that effectto a particular factor combination is through knowledge of the probabilities of causation. However, once again, these probabilities cannot be determined from ordinary attributable risks. Knowing the effects of each factor in isolation tells us nothing a b u t thew joint effects. Moreover, there is no scientific basis for assessing the contribution of different factors that act jointly to produce an effect. Such allocation of blame is really not a scientific exercise but rather involves legal and ethical issues. For example, if smoking and radiation combine to cause a cancer, does the voluntary nature of smokng increase or demase. its culpabiiity? From a statistical point of view, joint probability is indivisible, yet legal compensation mhanisms demand that it be apportioned. There are many possible methods of apportionment; none of them is intrinsically c o w (16). In particular, Formula 1 is theoretically inapplicable to joint causation, even though it may have a superficial mechanistic appeal (1.5). There is a special cax of joint causation in which the calculation of probability of causation may be possible.
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Al(eraPtivecompensation Victim compensation in the presence of multiple risk factors will continue to This is the situation in which the effects be a controversial topic in Congreas of A and B are independent; that is, the and the courts. Although it is socially probability of A causing an effect is in- desirable to have some remedy availadependent of the presence or absence of ble to injured victims, the traditional B. A tivial example of a situation that method of compensation-the tort syssatisfies the assumption of indepen- tem-will be hard pressed to deal with dence would be if two mcks were si- issues of indeterminate causation. multaneously hurled at a window and Recent efforts to retrospectively calculate probability of causation for an the window broke. Firmre 4 shows how the mobabilities individual victim based on a-m m-a t e for i;;dependentcausation b y be com- epidemiological data are inapproPriate puted from the attributable risks PI and for dealiig with chmnic disease riska, P2. The only obstacle to risk attribution as shown above. Nevertheless, it may is the problem of allocating the proba- prove desirable to have some kind of bility of a joint effect (PIP2)between liability index as a basis for allocating the two causes; this is a trans-scientific compensation. The index could be used issue, as mentioned above. However, if to meen claimants for eligibility and to
Envimn. Sci. Technol.. Vol. 20, No. 5, I9W 42¶
set proportional compensation levels. One possible approach to developing a risk attribution index has been proposed by Cox (17). Rather than pursuing a probabiity derivation, he develops an “attributable proportion of risk,” which satisfies certain consistency axioms and has a structure similar to the PC formula. But use of Cox’s formula requires empirical data about the risk rates for combinations of factors; such data are often unavailable. In fact, any mathematical approach that seeks to accurately portray the risk contrihutions of different factors will encounter enormous data gaps. On the other hand, the use of a simplistic formula cannot provide a reliable tool for distinguishing among the claims of individual victims. This dilemma is illustrated in Figure 5 , which shows the inevitable obstacles created by uncertainty. The burden of creating an acceptable method for compensating victims a p pears too great for our current scientific knowledge to support.Instead, it seems that administrative and judicial intervention will be needed to ensure fairness and consistency. The most extreme mechanism would be some sort of government-administered no-fault compensation program that would award automatic compensation to claimants who meet eligibility requirements. The sources of funds for such a mechanism would need to be established through legislation, as in the case of Superfund. This method does, however, have several drawbacks. Because of the ubiquitous M~URof chronic diseases and the difficulty of tracing all possible risk factors, such a program might be obliged to compensate large numbers of victims, only a small fraction of whom had truly been injured by man-made
hazards.
490 Envimn. Sci. Technol.. Vol. 20, No. 5, l9aS
It appears that for now the most reasonable means of handling victim compensation is through evolution of common-law procedures. Courts in some states have relaxed the proof-of-causation requirements by shifting the burden of p m f to the defendant or by imposing joint or several liability, thus easing the difficulty of victim compensation under the tort system (3). This trend, however, threatens to unfairly bias the presumption of causation in favor of the plaintiff, potentially imposing large economic penalties on private industry. Before sweeping reforms are attempted, further dialogue is needed among officials of federal and state agencies, health scientists, lawyers, and workers in the insurance industry and other industries that might be strongly affected by various methods of Compensation. The evidentiary rquirements and economic implications of such methods need to be examined fully in advance of legal decisions, lest an undesirable precedent be set for blanket compensation for any and all diseases.
Acknowledgment The contributions o f Louis Anthony Cox and Andrew Sivak of Arthur D. Little and Joseph Marrone of American Nuclear Insurers are gratefully acknowledged. Before publication this article was reviewed for suitability as an ES&T feature
by Julian Andelman, University of Pittsburgh, Pittsburgh, Pa. 15261; and Thomas
Moss, Case Western Reserve University, Cleveland, Ohio 44106. References
( I ) Fiksel, 1.; Scow, K. In Fate of Chemicals in the Environment; Swann, R. L.; Es-
chenraeder, A,, Eds.; American Chemical Society: Washington, D.C., 1984; pp. 287307. (2) “TOrl Actions far Cancer: Deterrence, Compensation, and Environmental Carcino-
(4) U S . Senate Committee on Labor and Human Resources, Hearings an the Probability of Causation Method, September 1984 and June 1985. (5) Doll, R.; Pew, R. The Causes of Concec Oxford University Press: Oxford, U.K., 1981; I). 1246. (6) Onenbaum, E.; Weinstein, I. E. In Progress in Surgical Pathology; Fenoglio, C. M.; Wolff, M.,Fds.; Massonhblishing: New York. 1981; p. 39. (7) Cox, L. A,; Fiksel, 1. Evaluation of Uncertainties in Probability of Causation Estimotes; American Nuclear lnsuren and Mutual Atomic Energy Liability Underwriters: Farmington, Conn.. July 1985. (8) Kaye, D. Am. Bar Found. Res. J . 1982, p. 487. (9) Bond, V. P. Health Phys. 1981, 40, 10811. (IO) “Surveillance, Epidemiology, and End Results: Incidence and Mortality Data, 1973-77,” National Cancer Institute Monograph 57, NIH Publication No. 81-2330; National Institutes of Health: Washington, D.C., 1979. (11) Advisory Committee on the Biological Effects of Ionizing Radiation. The Effectson Populations of Exposure to Low Levels of Ionizing Rodidon; National Academy of Sciences, National Research Council: Washington, D.C., 1980. (12) Cole, P.; MacMahon, B . BE J . Prev. Soc. Med. 1971,ZS. 242. (13) Catlin, R. 1.; Parmentier, N. “Implications of Radiation Compensation Criteria for the Nuclear Industry”: Nuclear Safety Analysis Center, Electric Power Research Institute: Pal0 Alto, Calif., 1982. (14) Bond, V. R; lablon, S. Testimony before the Senate Committee on Labor and Human Resources Relative to the Radiation Ex o. sure Compensation Act of 1981, Oct. 47, 1981. (15) Cox, L. A.; Fiksel, I. R. “A Critical Rev i e w of the Probability of Causation Method.” Arthur D. Link report; American Nuclear Insurers and Mutual Atomic Encre, Liability Underwriters, Farmington, Coni,., 1984. (16) Chiang. C. L. In Fourth Berkeley Symposium on Mathemetical Statistics and Probability; Neyman, I., Ed.; University of CaliforniaPress: Berkeley, Calif., 1961; Vol. N, pp. 169-80. (17) Cox, L. A. Risk Anal. lW, 4(3), 22130.
