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9
Applying Population-Based Results to Individuals: From Observational Studies to Personal Compensation

The previous chapters address problems of inferring causation from observational studies in the context set by other types of evidence. We also provide a classification scheme for the level of evidence in support of causation in general. Having determined that the level of evidence is sufficient to infer causation, two additional matters are of interest: (1) What is the burden of disease among those exposed that is caused by the exposure? and (2) What is the likelihood that the disease was caused by exposure in a particular individual? Information pertinent to the first question is relevant to forecasting the administrative and financial implications of the causal determination. Information relevant to the second question may be of value in handling individual cases.

In the present chapter we address these topics, beginning with the simplifying assumption that a causal association has been established in the population, and that we have an accurate estimate of relative risk (RR) among exposed people. We turn to an epidemiologic measure, the attributable fraction (AF), to answer these questions. This chapter also addresses the complexities of using the AF in devising compensation approaches. It provides a conceptual foundation that should prove useful in the further elaboration of the Committee’s framework and its implementation.



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9 Applying Population-Based Results to Individuals: From Observational Studies to Personal Compensation The previous chapters address problems of inferring causation from observational studies in the context set by other types of evidence. We also provide a classification scheme for the level of evidence in support of cau- sation in general. Having determined that the level of evidence is sufficient to infer causation, two additional matters are of interest: (1) What is the burden of disease among those exposed that is caused by the exposure? and (2) What is the likelihood that the disease was caused by exposure in a particular individual? Information pertinent to the first question is relevant to forecasting the administrative and financial implications of the causal determination. Information relevant to the second question may be of value in handling individual cases. In the present chapter we address these topics, beginning with the simplifying assumption that a causal association has been established in the population, and that we have an accurate estimate of relative risk (RR) among exposed people. We turn to an epidemiologic measure, the attribut- able fraction (AF), to answer these questions. This chapter also addresses the complexities of using the AF in devising compensation approaches. It provides a conceptual foundation that should prove useful in the further elaboration of the Committee’s framework and its implementation. 1

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1 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS ATTRIBUTABLE FRACTION Definitions and Assumptions The attributable fraction (AF) is used several ways in the literature (Rothman and Greenland, 1998). We use the term in a way most relevant to compensation by the Department of Veterans Affairs (VA), namely as the proportion of disease in an exposed group that can be attributed to the exposure. This report uses the terminology service-attributable fraction (SAF) when the exposed group is a military population. We begin with two simplifying assumptions: (1) exposure produces new cases of the disease that would not have occurred otherwise, and (2) the additional RR from exposure is stable over age and across subgroups within the population of exposed veterans. Later we discuss complications that might occur when these assumptions do not hold. Under these two assumptions, the AF is interpreted as the probability that among the exposed people with the dis- ease, their disease has actually been caused by the exposure. Crucial Properties of the AF In applying the AF, there are two key properties. First, it is not a state- ment about whether the exposure is able to cause the disease. In calculating the AF, we take as given that the exposure does, in fact, cause the disease. However, even among exposed persons, the exposure does not necessarily cause all cases of the disease—most diseases have many possible causes. When an exposed person gets the disease, the chance that the disease is caused by the exposure is almost certainly less than one. The AF represents this probability. The second important aspect of the AF is that it cannot specifically tell us which exposed people have their disease because of the exposure. All the AF can provide is an estimate of the average probability for all exposed persons. We can refine this estimate in various ways (e.g., by age or levels of exposure), but even with perfect information it is seldom, if ever, possible with current methods to identify which particular cases of a disease with multiple causes were caused by the exposure and which were not. Estimating the AF An AF is based on an estimation of RR, which is the ratio of disease risk among exposed persons compared to the risk among otherwise similar, but unexposed persons. RR is the most common expression of disease risk in epidemiologic studies. As discussed in Chapter 7, odds ratios from case- control studies approximate the RR.

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00 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS The AF is calculated by the following formula: [ Risk (exposed) − Risk (unexposed)] AF = Risk (exposed) This may also be expressed as: AF = (RR – 1)/RR. Consider the example of smoking and lung cancer. The RR for lung cancer among smokers is around 20. Applying the formula above, a RR of 20 yields an attributable risk of 95 percent, AR = (20 – 1)/20. In other words, 95 percent of all lung cancers among smokers can be attributed to their smoking, possibly acting in combination with other factors, while the remaining 5 percent of the lung cancers of smokers come from other causes. For another example, smoking and cardiovascular disease, the RR is close to 2. Thus, only half of the cases of cardiovascular disease among smokers, 50 percent = (2 – 1)/2, can be attributed to smoking. The lower the RR, the less likely it is that the disease in an exposed person is caused by the exposure, and the more likely that other factors are the cause. Factors That Can Distort the AF Broadly speaking, there are two problems that can bias an estimate of disease burden due to an exposure when using the AF. One is portability— that is, the degree to which the RR estimated from one population can be properly applied to another population. The second problem is error in the measurement of exposure. We consider each of these limitations below. Problems in Portability Chapter 7 discusses the biases and confounding that can distort esti- mates of RR. Even if these problems have been carefully handled in the original estimation of RR, the application of a valid RR to a new group introduces a fresh set of opportunities for distortions. This problem goes under the general rubric of portability, that is, the ability of a valid RR in one population to be applied to a new population. The portability of RRs and hence of AFs is often uncertain because of differing characteristics of the population in which the AF was estimated compared to those in which it is to be applied. A simple example of a problem with portability is effect modification by gender. Suppose that, at a given level of exposure, the RR of disease differs by gender of the exposed person (a common observation). If the distribution of men and women in the study group is different from the exposure group to which the RR is being applied, and gender is ignored,

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01 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS the resulting AF will be incorrect. This particular problem can be handled by applying gender-specific RRs to gender strata of the exposed population. However, more complicated scenarios of confounding or effect modification can be readily anticipated; methods to handle these have been proposed by Bruzzi et al. (1985) and Benichou (2001). Although such methods work in principle, they may require detailed data for subgroup-specific RRs that are difficult to obtain. A similar problem can arise when the RR is estimated in a population with high exposure (as in an occupational setting) and then applied to a population with lower levels of exposure. The RR will tend to overestimate the AF for the lower-exposed population. With enough information from epidemiologic studies on exposure-specific risk and information on levels of exposure in the population of interest, the appropriate adjustments of the AF can be made to account for exposure. In summary, problems of portability do not call into question the valid- ity of the original RR, but rather its generalizability—that is, the validity of its application to a new population and the estimate of the AF. Even when the causal association is certain (as we assume here), the problems of portability raise questions about the strength of the disease risk in the population of interest—and thus the size of the AF. Problems in Exposure Classification The second distortion that can occur with the AF is when the RR is applied to a population in which some persons classified as exposed were not in fact exposed. (This problem is especially relevant in the context of the charge of this Committee, in that VA is frequently unable to establish actual exposure during military service, and therefore must infer it—with inevitable error.) Usually the decision is to err in the direction of assuming people are exposed when they may not be exposed. When an “exposed” group includes some people who are not actually exposed, the AF for the whole “exposed” group would be ( P ) × ( RR − 1) , exp exp “exp” = AF  P × ( RR − 1)  + 1   exp exp where AF“exp” = the AF for an “exposed” group containing some unexposed people, Pexp = the proportion of the supposedly “exposed” group who are truly exposed, and RRexp = the RR for the truly exposed group. Typically, we do not know what proportion of those reported as exposed were actually exposed. However, we can make reasonable assumptions and

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0 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS see how much difference such errors might make. In our previous smoking example, if only half of the “exposed” group had truly been exposed, then the true AF for the “exposed” group would be 90 percent instead of 95 percent. Thus, exposure misclassification introduces a trivial difference between the true AF and the calculated AF when the RR is high. This is not true when the RR is lower (as is often the case). Taking the example of RR = 2 (for smoking and cardiovascular disease), the true AF is 50 percent when every person in the “exposed” group was truly exposed, but only 33 percent, if half of the “exposed” group had actually been unexposed. Thus, when the RR is low, misclassification of exposure results in an estimated AF that is an overestimate of the true AF. Reconsidering the Assumptions of the AF and Alternatives A basic assumption in calculating the AF is that the RR is known. In the real world, RRs are only estimates and typically include some measure of the statistical imprecision of that estimate. The implications of such uncertainty can be explored by calculating a range of AFs under different assumptions for the RR. What if other assumptions about the AF are untrue? For example, what if the exposure accelerates disease among persons who were going to get the disease anyway, instead of causing disease in persons who would not otherwise have gotten the disease? In this more complex scenario, the AF may not adequately capture the total burden of disease caused by the exposure. Alternatives to the AF that attempt to capture the total impact of exposure on disease include “years of life lost” (YLL) (Robins and Greenland, 1989a; Steenland and Armstrong, 2006) and “years of life lived with disability” (YLD). Methods for the calculation of YLL, YLD, and disability-adjusted life years (DALYs) have been recently reviewed (Steenland and Armstrong, 2006). YLL is the total number of deaths caused by the disease of interest multiplied by the average number of years of survival expected beyond the age of death from that disease. YLD is the product of the number of incident cases, a disability weight, and mean duration of disease; in the absence of incidence data an estimate can be based on mortality data and case-fatality rates. The total burden of disease can be estimated as the sum of YLL and YLD and is known as disability-adjusted life years, or DALYs. YLL, YLD, and DALYs caused specifically by military service exposure can be calculated by multiplying them by the AF (Steenland and Armstrong, 2006). In the case of military service, we refer to this as the service-attributable fraction (SAF). Because the AF is a fundamental element of these alternatives to the AF, they are subject to the same caveats about portability and misclassification of exposure as the AF itself. In the section

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0 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS “Promoting the Use of AFs in Determining Compensation for Veterans” we return to this discussion with regard to the AF and the SAF. Further Refinements of the AF Once we have estimated the total disease burden caused by the expo- sure using the AF, it may be possible to refine this estimate further by con- sidering characteristics of persons, including features of their exposure, that make their individual risk of disease higher or lower than in the exposed population as a whole. In other words, we can move from an estimate of overall AF, applied equally to all, to estimates for subgroups or even indi- viduals, based on additional information regarding their risk factors. This refined expression of the AF as estimated for individuals has usually been designated as the probability of causation, or PC, although in actuality it is simply the AF estimated for subgroups of those exposed. PROBABILITY OF CAUSATION FOR AN INDIVIDUAL The AF attempts to apportion the disease burden in a population between the exposure and other factors causing the disease. In contrast, the PC attempts to address the question of whether the exposure could have caused a particular disease in an individual, given that individual’s particu- lar characteristics of genetic makeup, lifestyle, and personal history—that is, the characteristics that determine individual susceptibility. For the past two decades, estimates of the PC have been available to guide the compen- sation of atomic veterans and others exposed to ionizing radiation (DHHS, 2002; NIH, 1985; Thomas, 2000). These estimates have been possible because of the extensive information on cancer risk in relation to radiation exposure. PC Definition A precise definition of PC will help illustrate the challenges in estimat- ing it. Following the National Institutes of Health (NIH) Working Group (1985), the PCi is the probability that individual i’s disease was caused by the exposure, given i’s unique set of characteristics (call them Xi). Symbolically, PCi, is defined using two terms—the probability that the individual would () ( ) have developed the disease given no exposure, E ,  P D, E Xi  , and the   probability that the individual developed the disease given the exposure, ( )  P D, E Xi  :  

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0 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS )( ) (  P D, E Xi − P D, E Xi  PCi =   ( ) P D, E Xi PCi = probability of causation for an individual P = probability D = disease E = exposure E = no exposure Xi = an individual’s unique set of characteristics Notice the equation represents an allocation of the probability of dis- ease between the exposed and unexposed. The probability that the back- ground caused the disease in individual i is simply 1 – PCi. If everyone in the population is identical in susceptibility to the disease and in the extent of exposure, and if the exposure-induced cases are completely indepen- dent of other cases (i.e., cases that were caused by something else), then the PC for every individual in the population is given by the population AF. Implications of not meeting these assumptions are discussed below in “Promoting the Use of AFs in Determining Compensation for Veterans.” Still, additional information on the levels of exposure, age at exposure, time since exposure, mechanism of disease causation, and other factors can be used to define subgroups within populations, and then to estimate AFs for these subgroups. As described below, these subgroup measures provide the basis for better estimates of PCs for individuals within these classes. Because individuals may vary in susceptibility to a particular exposure because of genetics, lifestyle, and other factors, estimates of PCs are not necessarily expected to resemble the true probability that an individual’s disease was caused by the exposure. The term assigned share (AS) has been used in place of PC, especially as it pertains to issues of compensa- tion (Lagakos and Mosteller, 1986). The NIH Ad Hoc Working Group to Develop Radioepidemiological Tables (NIH, 1985) (for use in compensat- ing cancer victims exposed to ionizing radiation) found itself constrained by legislative mandate to use the term probability of causation rather than assigned share. Reflecting on the history of the use of the terms, a more recent National Research Council (NRC) committee decided to use the terms synonymously (NRC, 2000).

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0 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS Refining Estimates by Differences in Exposure Levels Exposures to any given chemical or other disease-causing factor would be expected to vary substantially among veterans in any theater of war or duty, with corresponding large differences in individual risk and hence PCs. Also, the pattern of exposure can be important. For the same cumulative dose, the dose rate, timing, and duration may play a significant role in determining those who get the disease and those who do not. Route and exposure pathways are also to be considered. Although obtaining accurate estimates of exposure among veterans will typically be challenging, in some cases exposures may differ by orders of magnitude, and it may be possible to develop at least crude exposure estimates. If exposure can be quantified and the relationship between risk and exposure is understood, the AF can be expressed as a function of exposure. For example, dose-response relationships can be derived from occupational epidemiologic data for various carcinogens. As the example below shows, it can be important to stratify by the degree of exposure if this information is available, because the AF can vary substantially with exposure level. Consider the following simple relationship between the lifetime prob- ability of cancer P, or risk, and the air concentration, D, and the duration of exposure t, P(D) = 1 – exp(– a – bDt), which at P less than 10 percent is closely approximated by a simple linear relationship P(D) = a + bDt, where a, the intercept, is the risk in the absence of exposure, and b, the slope, describes the incremental increase in risk with increasing air concentration and time. Here RR is given by ( a + bDt ) = 1 + bD , RR = a a and the AF is given by ( RR − 1) , AF = RR which, substituting in the above, reduces to  bD  a   bDt AF = = . bDt  ( a + bDt )  1+ a   

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06 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS P = lifetime probability (or risk) of cancer D = air concentration t = duration of exposure a = risk in the absence of exposure b = incremental increase in risk with increasing air concentration and time RR = relative risk AF = attributable fraction Benzene provides an example of how very different AFs can result from differently exposed subgroups. We show how when exposure is taken into account PC estimates can be made for individuals with different levels of exposure. Benzene is an established cause of acute myelogenous leukemia (AML) in humans (Austin et al., 1988). Benzene exposures are ubiquitous; for example, benzene is a constituent of gasoline and also cigarette smoke. The general population in the United States is exposed to levels averaging from 1 to 5 ppb in indoor and outdoor air, and it would be anticipated that Service members would be similarly exposed. In addition, some in the service may have duties that result in exposures to considerably higher levels of benzene (e.g., those servicing airplanes or working in garage facili- ties). We might assume that some service jobs result in exposures as high as 10 ppm. AML is relatively uncommon, with a lifetime risk of diagnosis (an estimate for parameter a in the equation above) of about 0.3 percent. A lifetime exposure to 1 ppm causes AML in roughly 5 percent of those so exposed, corresponding to an estimate of b in the above equation of 0.00062/ppm-years (= 0.05/ppm/78 years). Applying these parameter esti- mates, AFs can be derived, while acknowledging the caveats and limitations noted above, including the issue of portability since the b was estimated from occupational cohorts in China and the United States (OEHHA, 2001). Service members who contract AML after being exposed during a tour of duty of 3 years to general background levels (e.g., 5 ppb) have a 0.2 percent chance that their leukemia arose because of their military exposure (AF = [0.00062 × 0.005 × 3]/[0.003 + 0.00062 × 0.005 × 3]). Thus, any case of AML among the exposed Service members would be highly unlikely to have resulted from military exposure. The AF associated with military service lasting 30 years is higher, at 3 percent. In contrast, Service members with a 3-year tour of duty who were exposed to 10 ppm during work hours, or a daily average of approximately 5 ppm, have an

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0 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS AF of 76 percent: AF = (0.00062 × 5 × 3)/(0.003 + 0.00062 × 5 × 3). This simple example illustrates the importance of segregating groups in terms of the magnitude and duration of exposure when those data are available. It also illustrates the importance of identifying those most heavily exposed in determining whether disease caused by an exposure to a given factor deserves compensation. As illustrated below, it is occasionally possible to classify or otherwise identify individuals or groups of individuals in terms of their exposure duration, age at exposure, exposure intensity, as well as other factors to derive exposure group-specific AFs and hence more refined estimates of PCs for individuals. Refining Estimates in the Presence of Multiple Known Causes of Disease Many illnesses considered for compensation have multiple causes. For example, lung cancer has many known causes in addition to smoking, including asbestos, arsenic, radiation, and environmental tobacco smoke (NCI, 2006). Some lung cancers occur in individuals without significant exposures to any known cause. The proportion of disease that is attribut- able to military service exposure can be estimated when there are valid RR estimates for (1) the military service-related exposure in the absence of the other known causes (RRE), (2) other known causes that individuals in the group were subjected to (such as smoking—RRS), and (3) the joint exposure of both military service and other specific known causes (RRboth). As an illustration, the proportion of disease attributable to military exposure and smoking, separately and jointly, is estimated for the hypo- thetical case described in Chapter 7. This is derived for two cases below, with smoking and military exposure interacting either additively or multi- plicatively. In the additive model, smoking adds a constant RR over back- ground (RRS – 1) regardless of whether there is military exposure. Thus, in the presence of both exposures, the RR is RRboth = RRE + RRS – 1. In the multiplicative model, RRboth = RRE × RRS. We introduce new terminology to aid in the derivation of AFs for the joint and separate effects of these multiple known causal exposures. The population attributable fraction (PAF) is the proportion of disease in the entire population attributable to an exposure or any other factor. (The rela- tion of the PAF to the AF will be discussed below.) Assume that all disease in the population is attributed either to the background or to the two identified causes. PAF0 is the proportion of dis- ease attributable to background factors unrelated to smoking or military exposure. PAFboth is the proportion of disease attributable to smoking and military exposures. The two sum to unity 1 = PAF0 + PAFboth. Analogous to the AF, the PAFboth is given by

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0 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS ( RRboth − 1) . PAFboth = RRboth Thus PAF0 is given by ( RRboth − 1) , PAF0 = 1 − PAFboth = 1 − RRboth which reduces to 1 PAF0 = . RRboth PAFE, the proportion attributable to military exposure alone, is simply the proportion of disease attributable to background factors multiplied by the excess RR above the background (RRE − 1): PAFE = ( RRE − 1) × PAF0 , which reduces to ( RRE − 1) . PAFE = RRboth Similarly PAFS, the proportion attributable to smoking acting alone, is ( RRS − 1) . PAFS = RRboth The additional proportion attributable to the two factors acting in combi- nation (i.e., interacting), PAFInt is ( RRboth − RRE − RRS + 1) . PAFInt = RRboth

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6 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS extremely high sensitivity, but at high cost. This plan is not economically rational, in that it provides more compensation to the exposed population (in some cases, far more compensation) than the total disease burden would justify. It is worth noting that other criteria may legitimately be regarded by policy makers as more important than economic rationality. The two plans described in Figures 9-2 and 9-3 define the boundaries of practical plans for compensation. The economically rational model provides the minimum rational compensation (Figure 9-2) and the compensation-to- all model provides the maximum possible compensation (Figure 9-3). All remaining plans discussed here fall somewhere between. One in-between approach would be to establish a level of AF necessary for 100 percent compensation. The usual criterion is 50 percent—that is, the chance that the disease in an exposed person was caused by their expo- sure is at least 50:50. This approach is shown in Figure 9-4. A criticism of the “50 percent criterion” is its all-or-none property. Because of uncertainty in the estimate of RR, there is little real difference between an RR of 2.1 and 1.8 (producing AFs of 52 percent and 44 per- cent), and yet a disease with the first AF might get 100 percent compen- sation, and a disease with the other might get none. By the criterion of economic rationality, the plan in Figure 9-4 undercompensates persons when the AF is less than 50 percent, and overcompensates them when it is 50 percent or greater. 100 Compensation for Exposed Persons 80 with Disease (%) 60 40 20 0 10 20 30 40 50 60 70 80 90 100 Attributable Fraction (%) FIGURE 9-4 Complete compensation for all exposed persons only when attribut- able fraction is 50 percent or more. FIGURE 9-4

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 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS 100 Compensation for Exposed Persons 80 with Disease (%) 60 40 20 0 10 20 30 40 50 60 70 80 90 100 Attributable Fraction (%) FIGURE 9-5 Complete compensation for an AF of 50 percent or more, plus gradu- ated compensation below 50 percent. This problem of all or none has been addressed in an alternative com- pensation scheme. The 1984 NRC Oversight Committee (NRC, 1984) sug- gested that when the AF was 50 percent or greater, compensation should be 100 percent, and when it is less than 50 percent, it should be linearly scaled FIGURE 9-5 down to 10 percent. This is shown in Figure 9-5. A graded strategy of compensation (such as Figure 9-5), is more fair than an arbitrary cutoff of 50 percent and comes closer to achieving eco- nomic rationality—although at any given AF, it still provides total payouts much larger than the total burden of disease. In the past, there has been a policy not to compensate individuals at levels less than 10 percent. Such a threshold would be relevant if any these models generated estimated compensations under 10 percent. INTEGRATIVE SYNTHESIS: ROLES FOR PRESUMPTIONS In this chapter the factors that can serve as information inputs to the decision-making process underlying the determination of veterans’ compen- sation have been identified and discussed. These factors and the point(s) in the process at which they come into play are displayed in Figure 9-6. Additional inputs to the compensation process that are not strictly informa- tional in nature are also displayed. These consist of ethical, political, and economic considerations that in practice can be critical determinants of the

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 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS Biomedical knowledge (observational and toxicological studies) Exposure effect Other estimates risk factors (and uncertainties) Unified exposure- risk factor disease model Service-attributable Individual fraction(s) (SAFs) disease (strata specific) (diagnosis, timing, age) Values Individual (financial, exposure moral, SAF for the (amount, political) individual timing) Compensation Individual criteria disease risk profile Resources Compensation (and/or medical care) FIGURE 9-6 A rational process for determining veterans’ compensation. FIGURE 9-6

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 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS outcome of the process, but do not lend themselves to the type of explora- tion that has been undertaken in this chapter. A Rational Compensation Determination Process The compensation determination process for veterans displayed in Figure 9-6 is, we argue, a scientifically based and rational scheme, albeit necessarily somewhat idealized for most situations. Scientific findings here form the foundation for compensation. These scientific findings are more typically observational in nature, although toxicological or other experi- mental findings, depending on the specific exposure or disease, can either complement the observational findings or occasionally comprise the bulk of the findings. However, to be useful in this scheme, science must provide estimates of exposure effect that can be used to derive estimated AFs, and such effect estimates are best derived from observational data. Measures of uncertainty in the effect estimates could potentially play a role in some compensation schemes, although we have not here presented applications of how this might work. Toxicological findings, or the lack of them, could be reflected in the measures of uncertainty. The size of effect estimates are affected by the extent of exposure and, by definition, effect modifiers. These, then, also affect the SAFs. More- over, as we have seen, the SAF can be affected by how other risk factors interact with the exposure in causing disease, being affected not at all if they interact multiplicatively, but potentially dramatically if they interact additively (see Table 9-2 and corresponding discussion). To make use of these influences on the AF in the compensation process, science must not only provide information on how exposure and effect modifiers influence effect estimates, but also provide information on the form of interactive effects of other disease risk factors. Armed with a host of exposure SAFs that correspond to levels (strata) of exposure, to states (strata) of the modifying factors, and to states of other risk factors if their interactions with the exposure are other than additive, individual SAFs can be estimated. Assuming that this information on exposure and these factors are also available for a given veteran seeking compensation, a corresponding SAF can be assigned to the veteran in lieu of our ability to know any individual’s PC. A compensation scheme for a specific exposure-disease combina- tion that is economically rational (i.e., one that provides a total award reflecting the total disease burden of the exposed group) is formulated. A compensation scheme can potentially incorporate features relating to the certainty of the scientific information that is used in estimating the SAF, although we have not provided examples as to how this might be done. Criteria that define who is eligible for some form of compensation

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0 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS implicitly assume some sensitivity and specificity, and for the population of concern, corresponding false positive and false negative rates. The AF, as shown above (Tables 9-5 to 9-7), has a great impact on both of these rates for a fixed sensitivity and specificity, and therefore could influence the selection of compensation criteria apart from its role in determining the individual SAF. Because of the trade-offs involved in choosing one set of criteria over another, the choice will always entail invoking values of some sort, for example, ethical, political, or financial (i.e., resource) values. For many cases, including those involving veterans, this version of a compensation process may seem farfetched. It is unlikely that any single instance involving determination of compensation for veterans has fea- tures that look exactly like this. Nevertheless, it is instructive to consider how individual processes differ from this version. First, by identifying how actual cases differ from this framework, insight can be gained into some not so obvious assumptions that have been made in these cases. Second, this framework might help pinpoint or clarify the critical influences on any given compensation process. Finally, this framework can serve to identify information needs that, if met, would improve the specific presumptive dis- ability decision-making process. One way of viewing presumptions is as compromises necessitated by either poor or absent information on one or several of the inputs to the veterans’ compensation determination process. For example, absent infor- mation on exposures of Service members in a specific theater, a presumption could be used to make the determination that any Service member who served in that theater was exposed. As another example, epidemiologic and other evidence about the association between a specific exposure and disease may be inadequate for estimating the AF. A presumption could be used to determine that any veteran with the disease who had been exposed most likely had the disease caused by the exposure. Or, there may be no information on how risk factors other than the exposure of interest influ- ence the risk of disease, or how timing of disease occurrence influences the likelihood that the exposure was relevant. Even if this information existed, there may be no reliable way of obtaining this information for a veteran. A presumption might be used to determine that all exposed veterans, regard- less of gender, age, the time since exposure, or exposure to other suspected disease risk factors, have the same AF. Some Case Studies Revisited It is instructive to again examine some of the case studies in light of this view of presumptions, and to assess characteristics of presumptions pertaining to sensitivity and specificity, and positive and negative predictive

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1 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS value. The contrasting cases of Agent Orange and type 2 diabetes and of radiation and lung cancer will be used for this purpose. With Agent Orange and type 2 diabetes, much of the information needed to apply our proposed process is unavailable or of poor quality (see Agent Orange and type 2 diabetes case study in Appendix I). First, scientific information that can be used to estimate an SAF indicates that the SAF is small, at best, and uncertain. Consequently, it is difficult based on this information to provide meaningful SAFs that correspond to different levels of exposure, or SAFs that correspond to different strata of possible modifying factors. There is information on the effect of several diabetes risk factors, but none is available on how any of these interact with Agent Orange exposure to influence diabetes risk. Assuming that meaningful SAFs could be estimated, information about the veteran to make use of the SAFs is needed. First, information on exposure to Agent Orange is needed, including first and foremost whether exposure occurred, and the intensity and duration of exposure. Although there has been little attempt to make use of possible sources of exposure information for Agent Orange in Vietnam veterans for the purpose of compensation, attempting to characterize exposure based on current infor- mation will nevertheless be difficult. Information on the time of onset of diabetes, the certainty of diagnosis, and diabetes risk factors, while not perfect, is comparatively good. Compensation schemes for Agent Orange and type 2 diabetes could take several forms, any of which might require some type of presumptions. We assume that the scientific findings indicate that exposure to constituents of Agent Orange causes type 2 diabetes, with some degree of certainty. With the SAF being small, even among those with well-documented exposure, the total disease burden is relatively small. This is true whether total dis- ease burden is estimated directly from the SAF or using the SAF to modify a YLD or DALY. For compensation to be economically rational, the total compensation award to the veterans affected would then also be relatively small. Alternatively, if the overall cost of compensation for Agent Orange and type 2 diabetes is not relevant, then economic rationality is also not pertinent. With a small SAF, it is also likely that the proportion of those who would receive compensation under any scheme whose diabetes was actually caused by Agent Orange exposure would also be small (i.e., the false positive rate would be high). With crude measures of exposure that would include large numbers of veterans without the degree of exposure needed to cause disease, the SAF would be smaller still, with a correspond- ingly high proportion mistakenly compensated. Any available information on the above inputs to the compensation process could restrict the scope of presumptions needed for Agent Orange and type 2 diabetes. Clearly the use of some information on exposure to

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 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS Agent Orange would reduce the effect on reduction of the SAF caused by including unexposed veterans. In the absence of better scientific information on Agent Orange exposure and type 2 diabetes, it will not be possible to utilize veteran information on potential modifying factors such as age of exposure, age of disease onset, or latency period to provide strata of SAF, although assumptions might have a role. For example, a hypothetical case could be made to limit the number of years allowable between exposure to Agent Orange and onset of diabetes. Regarding the presence of other diabetes risk factors that modify baseline risk, in the absence of good infor- mation on the form of interactions with Agent Orange, an assumption of additivity of risks might be made that could make it possible to provide stratified SAFs. A later age of diabetes onset in this case, for example, would increase baseline risk and reduce the SAF. The current presumption for Agent Orange and type 2 diabetes has implicit assumptions as to sensitivity and specificity. With a presumption that requires service in Vietnam and diagnosed type 2 diabetes, sensitivity, TP/(TP + FN), is nearly 1.0 as there are essentially no FNs. That is, any Vietnam veteran who got diabetes from Agent Orange exposure will receive compensation. On the other hand, specificity, TN/(TN + FP), is 0 since there are no TNs. That is, no Vietnam veterans whose diabetes was caused by something other than Agent Orange will not be compensated. The positive predictive value of compensation, TP/(TP + FP) is very small because FP is exceedingly large due to the small SAF. That is, only a small fraction of those who receive compensation will actually have had their diabetes caused by exposure. The negative predictive value, TN/(TN + FN), is almost meaningless because both TN and FN are essentially nonexistent. These characteristics of this particular presumption are therefore quite extreme. The case of radiation and lung cancer presents distinct contrasts to that of Agent Orange and type 2 diabetes. As detailed earlier (see radiation case study in Appendix I), here the case for causation is strong, uncer- tainty is relatively well estimated, and there is extensive information on dose-response relationships. Information on the effects of other factors, principally smoking and age, is also relatively extensive. However, even in this case there are deficiencies in the state of the information that invite presumptions. These deficiencies include (1) poor individual person-dose estimation; and (2) inadequate information on the form of the radiation interaction model for other risk factors, again principally smoking. Based on the above information it is clear that the estimated SAF is relatively large for radiation and lung cancer and has less associated uncer- tainty than most exposures. As noted before, the radioepidemiological tables (NIH, 1985) provide estimated PCs for radiation in lung cancer by exposure dose, age at exposure and at cancer diagnosis, latency, and ciga- rette smoking history. However, there is little agreement on the form of the

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 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS radiation-smoking interaction in lung cancer, with initial evidence favoring additive effects and more recent interpretations favoring multiplicative (or submultiplicative) effects (see radiation case study in Appendix I). Because the form of the interaction has a large impact on estimation of the SAF, uncertainty about the form complicates use of an SAF even for lung cancer (see discussion on effects on the positive predictive value below). The current presumption for radiation and lung cancer also has implicit assumptions as to sensitivity and specificity. Sensitivity, TP/(TP + FN), is good as there will only be a few FNs. That is, most radiation-exposed veterans who get lung cancer from radiation exposure will receive com- pensation, at least when based on a presumption of exposure. Specificity, TN/(TN + FP) is also good because those with low radiation risk, such as those with low estimated exposure, are less likely to receive compensation, although because of inaccuracies in the estimated exposure, some will (these will be FPs). The positive predictive value of compensation, TP/(TP + FP), is very good because FPs are relatively uncommon because of the large AF. That is, a large fraction of those who receive compensation will actually have had their lung cancer caused by exposure. The negative predictive value, TN/(TN + FN), is also very good because FNs are relatively uncom- mon. These characteristics of this particular presumption therefore seem to be reasonable. However, even for the case of radiation and lung cancer, the above assessment of these characteristics is dependent on the way other disease risk factors, for example smoking, are determined to interact with the effect of radiation. For nonsmokers the above assessment is reasonable and does not depend on the form of the interaction. For smokers the assessment varies depending on whether smoking is determined to interact additively or multiplicatively with radiation exposure. With a multiplicative interaction, exposure RR and therefore AF is the same regardless of smoking status. In the additive case, the RR and the AF can vary dramatically by smoking status (see Tables 9-1 and 9-2, above). This will have no effect on sensitivity or specificity, as these are unaffected by the AF. However, positive and nega- tive predictive values will be affected as both change as a function of the AF. Because the AF is substantially smaller in smokers than nonsmokers under an additive model, the positive predictive value is likewise smaller, indicating that for smokers a substantial percentage of those receiving compensation will not have gotten lung cancer as a result of exposure to radiation. The AF (and the PC derived from it) in the radioepidemiological tables is therefore critically dependent on the form of the interaction that is determined to be the case. Therefore, even with better scientific informa- tion than is available for any other military exposure, there is still room for presumptions. However, the better the information, the more focused and limited in scope the presumptions can be.

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 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS Science and Compensation Presumptions The inability to identify the disease role of specific military exposures in any individual veteran partly motivates the need for compensation pre- sumptions. The scope of a presumption is, in turn, partly determined by the available scientific information and corresponding information on any given veteran. The more scientific information available on the causal rela- tionship between a service exposure and disease, and the more information available on an individual veteran’s exposure and disease risk, the more narrow in scope the presumption needs be. The contrasting cases of Agent Orange and type 2 diabetes, and radiation and lung cancer, discussed above serve to make these points. It is likely that there will be substantial advances in our ability to quantify exposure and other disease risks in individual veterans, perhaps through the use of specific biomarkers of exposure or disease for example, and by exploiting findings from toxicogenetics and toxicogenomics. These advances could possibly refine the scope of presumptions or conceivably do away with the need for some presumptions. Earlier chapters laid out a framework for determining when a com- pensation presumption may be considered based on the state of scientific information. The intent in this chapter has been to identify concepts and tools that can be utilized in making decisions as to the form and content of a compensation presumption. These include (1) a set of SAFs that is as refined as the science permits; (2) features of alternative presumptions, such as sensitivity, specificity, and positive predictive value, that characterize and quantify the relative strengths and unavoidable mistakes (and therefore costs) implicit in each presumption; and (3) an appreciation of the relation- ship between the SAFs and some of these features, namely the positive and negative predictive values. It has not been the intent here to determine whether a specific presump- tion and its form are justified or sensible or to identify better alternatives. These are issues on which VA and Congress deliberate. It is hoped, however, that the framework outlined can be used in these deliberations to allow for more rational, science-based decisions about the content of specific presumptions than may have been the case in the past. REFERENCES Austin, H., Delzell, E., and Cole, P. 1988. Benzene and leukemia: A review of the literature and a risk assessment. American Journal of Epidemiology 127:419-439. Benichou, J. 2001. A review of adjusted estimators of attributable risk. Statistical Methods in Medical Research 10:195-216.

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 APPLYING POPULATION-BASED RESULTS TO INDIVIDUALS Bruzzi, P., S. B. Green, D. P. Byar, L. A. Brinton, and C. Schairer. 1985. Estimating the popula- tion attributable risk for multiple risk factors using case-control data. American Journal of Epidemiology 122(5):904-914. Cox, A. T. 1984. Probability of causation and the attributable proportion of risk. Risk Analysis 4(3):221-230. Cox, A. T. 1987. Statistical issues in the estimation of assigned shares for carcinogenesis liability. Risk Analysis 7(1):71-80. DHHS (Department of Health and Human Services). 2002. 42 CFR Parts 81 and 82. Guide- lines for determining the probability of causation and methods for radiation dose recon- struction under the Employees Occupational Illness Compensation Program Act of 2000; Final Rules. Federal Register 67(85):22296-22330. Greenland, S. 1999. Relation of probability of causation to relative risk and doubling dose: A methodologic error that has become a social problem. American Journal of Public Health 89:1166-1169. Greenland, S., and J. M. Robins. 1988. Conceptual problems in the definition and interpreta- tion of attributable fractions. American Journal of Epidemiology 128:1185-1197. Greenland, S., and J. M. Robins. 2000. Epidemiology, justice and the probability of causation. Jurimetrics 40:321-340. Lagakos, S. W., and F. Mosteller. 1986. Assigned shares in compensation for radiation-related cancers. Risk Analysis 6(3):345-357. NCI (National Cancer Institute). 2006. Lung cancer prevention. http://www.cancer.gov/ cancertopics/pdq/prevention/lung/Patient/page2 (accessed May 23, 2007). NIH (National Institutes of Health). 1985. Report of the National Institutes of Health Ad Hoc Working Group to Develop Radioepidemiological Tables. Responding to the con- gressional mandate under Section (b) of the Orphan Drug Act of January , 1 (PL- -1). NIH Pub. No. 85-2748. Washington, DC: U.S. Government Printing Office. NIOSH (National Institute of Occupational Safety and Health). 2007. Users guide for the Interactive RadioEpidemiological Program (NIOSH-IREP). Version 5.5.2. http://0-www. cdc.gov.mill1.sjlibrary.org/niosh/ocas/pdfs/irep/irepug0607.pdf (accessed July 28, 2007). NRC (National Research Council). 1984. Assigned share for radiation as a cause of cancer, review of radioepidemiologic tables assigning probabilities of causation. Washington, DC: National Academy Press. NRC. 2000. A review of the draft report of the NCI-CDC Working Group to Revise the 1 Radioepidemiological Tables. Washington, DC: National Academy Press. Obuchowski, N. A. 2003. Receiver operating characteristic curves and their use in radiology. Radiology 229:3-8. OEHHA (Office of Environmental Health Hazard Assessment). 2001. Public health goal for benzene in drinking water. California Environmental Protection Agency. http://www. oehha.ca.gov/water/phg/pdf/BenzeneFinPHG.pdf (accessed February 7, 2007). Oh, W. K., Hurwitz, M., D’Amico, A. V., Richie, J. P., Kantoff, P. W. 2003. Neoplasms of the prostate (Ch. 111). In: Holland-Frei Cancer Medicine 6, 6th ed. Hamilton, ON: BC Decker. OSTP (Office of Science Technology and Policy). 1988. Use of probability of causation by the Veterans Administration in the adjudication of claims of injury due to exposure to ionizing radiation. OSTP Committee on Interagency Radiation Research and Policy Coordination. Executive Office of the President, Washington, DC. Presidential Commission. 1990. Ch. 4. Standards and procedures for latent illnesses. Presiden- tial Commission on Catastrophic Nuclear Accidents: Report to Congress. Washington, DC. Robins, J. 2004. Should compensation schemes be based on the probability of causation or expected years of life lost? Journal of Law and Policy 12:537-548.

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6 IMPROVING THE PRESUMPTIVE DISABILITY DECISION-MAKING PROCESS Robins, J. M., and S. Greenland. 1989a. Estimability and estimation of excess and etiologic fractions. Statistics and Medicine 8:845-859. Robins, J., and S. Greenland. 1989b. The probability of causation under a stochastic model for individual risk. Biometrics 45:1125-1138. Robins, J., and S. Greenland. 1991. Estimability and estimation of expected years of life lost due to a hazardous exposure. Statistics and Medicine 10:79-93. Rothman, K. J., and S. Greenland. 1998. Modern Epidemiology, 2nd ed. Philadelphia, PA: Lippincott-Raven. Pp. 54-55. Steenland, K., and B. Armstrong. 2006. An overview of methods for calculating the burden of disease due to specific risk factors. Epidemiology 17(5):512-519. Thomas, D. C. 2000. The probability of causation can be used in an equitable manner to resolve radiation tort claims and design compensation claims. Radiation Research 154(6):718-719.