Origins of the Relative Risk of Two Argument for Specific Causation

In an unpublished paper, which Professor Susan Haack has presented several times over the last few years, she has criticized the relative risk [RR] >2 argument.  In these presentations, Haack has argued that the use of RR to infer specific causation is an example of flawed “probabilism” in the law.  Susan Haack, “Risky Business:  Statistical Proof of Individual Causation,” in Jordi Ferrer Beltrán, ed., Casuación y atribución de responsibilidad (Madrid: Marcial Pons, forthcoming)[hereafter Risky Business]; Presentation at the Hastings Law School (Jan. 20, 2012);  Presentation at University of Girona (May 24, 2011).  Elsewhere, Haack has criticized the use of relative risks for inferring specific causation on logical grounds.  See, e.g., Susan Haack, “Warrant, Causation, and the Atomism of Evidence Law,” 5 Episteme 253, 261 (2008)[hereafter “Warrant“];  “Proving Causation: The Holism of Warrant and the Atomism of Daubert,” 4 J. Health & Biomedical Law 273, 304 (2008)[hereafter “Proving Causation“].  (See Schachtman, “On the Importance of Showing Relative Risks Greater Than Two – Haack’s Arguments” (May 23, 2012) (addressing errors in Haack’s analysis).

In “Risky Business,” Haack describes the RR > 2 argument as the creation of government lawyers from the litigation over claims of Guillain-Barré syndrome (GBS), by patients who had received swine flu vaccine.  Like her logical analyses, Haack’s historical description is erroneous.  The swine flu outbreak of 1976, indeed, had led to a federal governmental immunization program, which in turn generated claims that the flu vaccine caused GBS.  Litigation, of course, ensued.  The origins of the RR > 2 argument, however, predate this litigation.

GBS is an auto-immune disease of the nervous system.  The cause or causes of GBS are largely unknown. In the GBS vaccine cases, the government took the reasonable position that treating physicians or clinicians have little or nothing to contribute to understanding whether the swine-flu vaccine can cause GBS or whether the vaccine caused a particular patient’s case.  Cook v. United States, 545 F. Supp. 306 (N.D. Cal. 1982); Iglarsh v. United States, No. 79 C 2148, 1983 U.S. Dist. Lexis 10950 (N.D. Ill. Dec. 9, 1983).  The government did, however, concede that cases that arose within 10 weeks of vaccination were more likely than not related on the basis of surveillance data from the Centers for Disease Control.  After 10 weeks, the relative risk dropped to two or less, and thus the plaintiffs who developed GBS 10 weeks, or more, after immunization were more likely than not idiopathic cases (or at least non-vaccine cases).  See Michael D. Green, “The Impact of Daubert on Statistically Based Evidence in the United States,” Am. Stat. Ass’n, Proc. Comm. Stat. Epidem. 35, 37-38 (1998) (describing use of probabilistic evidence in the GBS cases).

Haack’s narrative of the evolution of the RR > 2 argument creates the impression that the government lawyers developed their defense out of thin air.  This impression is false.  By the time, the Cook and Iglarsh cases were litigated, the doubling of risk notion had been around for decades in the medical literature on radiation risks and effects.  Ionizing radiation had been shown to have genetic effects, including cancer risk, in the 1920’s.  By the time of the Manhattan project, radiation was a known cause of certain types of cancer. Although there was an obvious dose-response relationship between radiation and cancer, the nature of the relationship and the existence of thresholds were not well understood.  Medical scientists, aware that there were background mutations and genetic mistakes, thus resorted to a concept of a “doubling dose” to help isolate exposures that would likely be of concern.  See, e.g., Laurence L. Robbins, “Radiation Hazards:  III. Radiation Protection in Diagnostic Procedures,” 257 New Engl. J. Med. 922, 923 (1957) (discussing doubling dose in context of the medical use of radiation).

By 1960, the connection between “doubling dose” and a legal “more likely than not” evidentiary standard was discussed in the law review literature.  See, e.g., Samuel D. Estep, “Radiation Injuries and Statistics: The Need for a New Approach to Injury Litigation, 59 Mich. L. Rev. 259 (1960).  If the doubling dose concept was not obviously important for specific causation previously, Professor Estep made it so in his lengthy law review article.  By 1960, the prospect of litigation over radiation-induced cancers, which had a baseline prevalence in the population, was a real threat.  Estep described the implications of the doubling dose:

“This number is known technically as the doubling dose and has great legal significance under existing proof rules.”

Id. at 271.

* * *

“The more-probable-than-not test surely means simply that the trier of fact must find that the chances that defendant’s force caused the plaintiff’s injuries are at least slightly better than 50 percent; or, to put it the other way, that the chances that all other forces or causes together could have caused the injury are at least no greater than just short of 50 percent. Even if such an analysis is inapplicable to other types of cases, in those cases in which the only proof of causal connection is a statistical correlation between radiation dose and injury, the only just approach is to use a percentage formula. This is the case with all nonspecific injuries, including leukemia. Under existing rules the only fair place to draw the line is at 50 percent. These rules apply when the injury is already manifested as of the time of trial.”

Id. at 274.

The RR >2 argument was also percolating through the biostatistical and epidemiologic communities before the Cook and Iglarsh cases.  For instance, Philip Enterline,  a biostatistician at the University of Pittsburgh, specifically addressed the RR > 2 argument in a 1980 paper:

“The purpose of this paper is to illustrate how epidemiologic data can be used to make statements about causality in a particular case.” 

* * *

“In summary, while in a given instance we cannot attribute an individual case of disease to a particular occupational exposure, we can, based on epidemiologic observation, make a statement as to the probability that a particular occupational exposure was the cause.  Moreover, we can modify this probability by taking into consideration various aspects of a particular case.” 

Philip Enterline, “Attributability in the Face of Uncertainty,” 78 (Supp.) Chest 377, 377, 378 (1980).

About the time of the Cook case, the scientific media discussed Enterline’s suggestion for using epidemiologic data to infer specific causation.  See, e.g., Janet Raloff, “Compensating radiation victims,” 124 Science News 330 (1983).  Dr. David Lilienfeld, son of the well-known epidemiologist Abraham Lilienfeld, along with a lawyer, further popularized the use of attributable risk, derived from a relevant RR to quantify the probability that an individual case is causally related to an exposure of interest.  See David Lilienfeld & Bert Black, “The Epidemiologist in Court,” 123 Am. J. Epidem. 961, 963 (1986) (describing how a relative risk of 1.5 allows an inference of attributable risk of 33%, which means any individual case is less likely than not to causally related to the exposure).

In the meanwhile, the RR argument picked up support from other professional epidemiologists.  In 1986, Dr. Otto Wong explained that for many common cancers, tied to multiple non-specific risk factors, probabilistic reasoning was the only way to make a specific attribution:

“In fact, all cancers have multiple causes. Furthermore, clinical features of cancer cases, caused by different risk factors, are seldom distinguishable from one another. Therefore, the only valid scientific way to address causation in a specific individual is through use of probability.”

Otto Wong, “Using Epidemiology to Determine Causation in Disease,” 3 Natural Resources & Env’t 20, 23 (1988).  The attributable risk [AR], derived from the RR, was the only rational link that could support attribution in many cases:

“For AR [attributable risk] to be greater than 50% (more likely than not), RR has to be greater than 2.  Thus, for any exposure with a RR of less than 2, the cancer cannot be attributed to that exposure according to the ‘more likely than not’ criterion.  That is, that cancer is ‘more likely than not’ a background case.”

***

“The epidemiologic measure for probability of causation is attributable risk, which can be used to determine whether a particular cause in an individual case meets the ‘more likely than not’ criterion.”

Id. at 24.

In 1988, three Canadian professional epidemiologists described the acceptance of the use of epidemiologic data to attribute bladder cancer cases in the aluminum industry. Ben Armstrong, Claude Tremblay, and Gilles Theriault, “Compensating Bladder Cancer Victims Employed in Aluminum Reduction Plants,” 30 J. Occup. Med. 771 (1988).

The use of the RR > 2 argument was not a phenomenon limited to defense counsel or defense-friendly expert witnesses.  In 1994, a significant textbook, edited by two occupational physicians who were then and now associated with plaintiffs’ causes, explicitly embraced the RR argument. Mark R. Cullen & Linda Rosenstock, “Principles and Practice of Occupational and Environmental Medicine,” chap. 1, in Linda Rosenstock & Marc Cullen, eds., Textbook of Clinical Occupational and Environmental Medicine 1 (Phila. 1994) [Cullen & Rosenstock].

The editors of this textbook were also the authors of the introductory chapter, which discussed the RR > 2 argument.  The first editor-author, Mark R. Cullen,  is now a Professor of Medicine in Stanford University’s School of Medicine.  He is a member of the Institute of Medicine (IOM). Professor Cullen has been involved in several litigations, almost always on the plaintiffs’ side.  In the welding fume litigation, Cullen worked on a plaintiff-sponsored study of Mississippi welders.  Linda Rosenstock was the director for the National Institute for Occupational Safety and Health (NIOSH) from 1994 through 2000. Dr. Rosenstock left NIOSH to become the dean of the University of California, Los Angeles School of Public Health.  She too is a member of the IOM.  Here is how Cullen and Rosenstock treat the RR > 2 argument in their textbook:

“In most workers’ compensation and legal settings, one of the physician’s roles in OEM [occupational and environmental medicine] practice is to establish whether or not it is probable (greater, than 50% likelihood) that the patient’s injury or disease is occupationally or environmentally related. Physicians, whose standards of scientific certainty are usually considerably higher than those of the legal field (for example, often at the 95% level that an observed association did not occur by chance), need to appreciate that a disease may be deemed work related (i.e., in legal jargon, with medical certainty or more probable than not) even when there remains significant uncertainty (up to 50%) about this judgment.

Epidemiologic or population-based data may be used to provide evidence of both the causal relationship between an exposure and an outcome and the likelihood that the exposure is related to the outcome in an individual case. *** Although they are not fully conclusive, well-performed and interpreted epidemiologic studies can play an important role in determining the work-relatedness of disease in a person, using some of the additional guidelines below.”

***

“The concept of attributable fraction, known by many names, including attributable risk and etiologic fraction, has particular utility in determining the likelihood of importance of a hazardous exposure. Although these numbers refer to risks in groups, as shown in the following section, reasonable extrapolations from these numbers can often be made about risks in individuals.”

Cullen & Rosenstock at 13. Cullen & Rosenstock work through an easy example and discuss its implications:

“For example, if all the members of a population are exposed to a factor, and there is a RR of 5 of disease in relation to the factor, then the PAR = 80% (= (5 – 1)/5 X 100). If exposures and other population characteristics are similar in a second population, then it also can be assumed that this factor will account for 80% of cases of the disease. A short conceptual leap can be made to individual attribution:  if an affected individual is similar (e.g., in age and gender) to those in the population and is similarly exposed (e.g., similar duration, intensity, and latency), then there is an 80% likelihood that the factor caused the disease in that individual.”

***

“By this reasoning of assuming that all in a population are exposed and the relative risk is greater that [sic] 2, then the PAR [population attributable risk] is greater than 50% (where PAR = (2 – 1)/2 X 100%).  Accordingly, if an affected individual is similar to the population in a study that has demonstrated a RR ≥  2, then the legal test (that there is a greater than 50% likelihood that the factor caused disease) can be met.”

***

“In cases in which the relative risks are stable (i.e., very narrow confidence intervals) and the patient is typical of the population studied, one can state these individual attributable risks with some assurance that they are valid estimates. When the studies are of limited power or give varying results, or if the patient’s exposure cannot be easily related to the study population., caution in using this method is appropriate.”

Cullen & Rosenstock at 13-14. Cullen and Rosenstock embraced probabilistic evidence because they understood that antipathy to probabilistic inference meant that there could be no rational basis for supporting recoveries in the face of known hazards that carried low relative risks (greater than 2).  The “conceptual leap” these authors described is small compared to the unbridgeable analytical gaps that result from trying to infer specific causation from clinicians’ hunches.

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