A review of the Food and Drug Administration risk analysis for polychlorinated biphenyls in fish

I&XJLATORYTOXICOUX;Y ANDPHARMACYXDGY 4,192-219 (1984)

A Review of the Food and Drug Administration Risk Analysis for Polychlorinated Biphenyls in Fish

L. DANIEL MAXIM AND LEIGH HARRINGTON

Everest Consulting Associates, Inc., P.O. Box 786, Princeton Junction, New Jersey 08550

Received January 3, 1984

This paper reviews the Food and Drug Administration (FDA) analysis of the risk to humans Born consuming tish contaminated with polychlorinated biphenyls (PCBs). In brief, the FDA methodology employed “hii dose experiments on animals and extrapolated the observed rates of certain types of cancer at these elevated doses to the low doses found in human diets. These extrapolations were then used to define a recommended tolerance level of 5 ppm, and proposed reduction to 2 ppm, for 6sh sold in interstate commerce. Unfortunately, as is shown here, such a procedure is extremely sensitive to the basis for extrapolation. Important elements of the FDA analysis include the following: (i) FDA assumed a particular form of the dose-response model: the one-hit model. Many other models have been proposed and, on balance, appear equally plausible. These models estimate lower risks than does the one-hit model. (ii) FDA calculated 99% upper confidence bounds on these risks and, moreover, emphasized cases of fish eaters who consume greater amounts of PCB-contaminated fish than do 98.5% of the U. S. population. (iii) FDA based PCB ingestion computations on consumption of raw fish, whereas most fish are cooked before eating, and it is known that F’CB levels in cooked fish are lower than PCB levels in raw fish. (iv) FDA based estimates of cancer risk on the assumption that PCB levels in tish would be constant over the nominal ‘IO-year human life span used in the FDA “lifetime risk” computation. Recent data suggest that FCB levels have been declining in fish (particularly in sport fish) and humans as well. Such trends imply significantly lower cumulative lifetime PCB doses than were assumed in the FDA analysis. (v) FDA assumed that humans and test animals are equally sensitive to F’CB ingestion when measured on a parts per million in diet basis. Extrapolations on an equivalent consumption per unit of body weight, thought appropriate by most researchers, result in much lower health risks. In short, when conBonted with methodological choices, the FDA consistently selected “‘womt case” or conservative assumptions over other alternatives of at least equal plausibility. This philosophy of choice was explicitly acknowledged by the FDA. What was omitted from the FDA analysis, however, was the possible degree of overstatement of these risks. The results of replicate risk computations using alternative assumptions to examine the possible magnitude of overstatement of health risk are summarized in Table 12. As can be seen, this overstatement could easily account for a discrepancy of several orders of magnitude between actual and calculated risks. As well, the FDA analysis overstated the effectiveness of the imposition of tolerance limits, considering the sampling procedures currently in use for testing commercial fish. Taken together, these points challenge the analytical foundation underIying an FDA proposal to reduce the tolerance level from 5 to 2 ppm and, in addition, lend necessary perspective to the debate on the health risks of eating PCB-contaminated fish.

192

0273-2300184 83.00 cOp~~@t@ 1984by Academic FWs, Inc. All ri@s of npmduction in any form r*prvcd.

ANALYSIS FOR POLYCHLORINATED BIPHENYLS IN FISH 193

BACKGROUND AND INTRODUCTION

Concern over possible effects on human health from eating foods contaminated with polychlorinated biphenyls (PCBs) dates back to the early 1970s when several instances of accidental food contamination were discovered. Subsequently, a series of legislative and regulatory decisions by Congress and various agencies of the U. S. Government (including the Environmental Protection Agency (EPA) and Food and Drug Administration (FDA)), together with voluntary actions by manufacturers and users of these chemicals have led to a cessation of manufacture of PCBs and the initiation of numerous studies to investigate the costs and benefits of actions designed to reduce human exposure to PCBs.

Fish (both recreational and commercial) PCB levels have been of particular concern because ingestion of contaminated foods is thought to be a major pathway to human exposure and PCB levels in fish are often greater than these levels in other foods. The response by various federal and state agencies has been to implement a variety of alternatives, including warnings to the public to limit (or eliminate for certain elements of the population, e.g., pregnant and/or breast-feeding women) consumption of PCB-contaminated fish (e.g., in Michigan and Massachusetts); advisories on how to prepare fish to minimize human ingestion of PCBs; closure of selected commercial and recreational fisheries (e.g., closure of the commercial striped bass fishery on the Hudson River by New York State officials or institution of “catch and release” policies for trout on a stretch of the Housatonic River by Connecticut officials); and the imposition of “tolerance levels” for fish sold in interstate commerce by the FDA.’

The FDA action is of particular interest, for three reasons:

(i) there were significant economic and other impacts of this decision, (ii) the decision was made on the basis of a quantitative risk analysis that purportedly

provided a “scientific” basis for standard setting, and lastly (iii) because the FDA action has been cited as justification for numerous policies

and regulations at the state level.

FDA completed a preliminary assessment and imposed a temporary tolerance level of 5 ppm in the edible portion of fish, Fish above this limit could not enter interstate commerce. (Although regulation of sport fisheries is not within the authority of the FDA, as noted above, states have tended to issue similar directives, so that the FDA action has had much wider impact than is derived from their statutory authority.) By 1979, FDA had proposed to reduce this tolerance level to 2 ppm. As of this writing, no final decision has been made on the proposed 2 ppm final rule.

OVERVIEW OF THE FDA RISK ESTIMATION PROCESS USED TO JUSTIFY THE 2 ppm STANDARD

The evidence used to justify the 2 ppm tolerance or action level was based in part upon an FDA quantitative risk analysis’ that extrapolated the effects of high PCB doses administered to laboratory animals to the low dose rates associated with human consumption of contaminated fish. Given the correspondence between estimated health risk and content of PCBs in the diet of Americans, FDA was able to set a tolerance leveZ for PCBs in fish that entailed (at least implicitly) an “acceptable” risk,

194 MAXIM AND HARRINGTON

The steps required to make this extrapolation are outlined in Fig. 1. Letters shown above the boxes are used to refer to these computations later in this paper. Broadly, FDA first had to estimate the average (and 90th percentile) daily PCB intake for humans who eat fish species contaminated with PCBs. This was then adjusted to simulate the effect of imposing a specific tolerance level, such as 5 ppm. Next, human risks were estimated from high dose animal cancer studies using a dose-response model for extrapolation purposes. A 99% upper confidence limit to this risk was then calculated to provide a margin of safety. These computations were replicated for other assumed tolerance levels (i.e., 2 and 1 ppm) to determine approximately how the health risks (as measured by the number or probability of additional cancers) varied with the tolerance level. In parallel, other studies addressed the economic consequences of the imposition of various tolerance levels. Finally, a judgmental cost-benefit analysis was used to select a recommended tolerance level. (This paper examines only the quantitative risk analysis portion of the input to the FDA PCB standard. However, it should be made clear that if, upon examination, the risks of PCB exposure are revised downward, then (other things being equal) the appropriate tolerance level should be increased.)

The steps in the FDA analysis were as follows:

First, an analysis was performed of the commercial 6sh consumption patterns of the U. S. population (represented by boxes A and B of Fig. 1). These data, when combined with the distribution of FCB levels in each type of fish, enabled an estimate to be made of the average PCB content in the diet (expressed on a parts per million basis). Specifically, 50% of U. S. consumers of commercial fish known to be contaminated with PC% were estimated to consume less than 0.0056 ppm PCB in their diet, while 90% were estimated to consume 0.0147 ppm (or less) PCBs in their diet (box E, Fig. 1).

Second, the effects of imposing a given tolerance level (e.g., 5 ppm) were simulated. Specitically, it was assumed that all fish with PCB levels above the tolerance level could be removed from the market basket of these U. S. consumers. This truncation of the distribution of PCB levels in lish enabled recomputation of the ppm F’CB in the diet of consumers (as ilhtstmted schematically in Fig. 2). That is, it was assumed that the acceptance test procedure was perfect-no lish above the tolerance level would be accepted, whereas 100% of individual fish beneath the tolerance level would be accepted Using this assumption, for example, the imposition of a 5 ppm tolerance limit was estimated to reduce the average dietary intake of this group from 0.0056 ppm to 0.005 1 ppm.

Next, a dose-response model (box D, Fig. 1) assumed to be linear, was fitted to animal experiment

A

FIG. 1. Low dose extrapolation methodology.


5

PCB CONTENT PPm

ORIGINAL DISTRIBUTION OF PCB LEVELS IN GIVEN FISH SPECIES

PROBABILITY OF ACCEPTANCE

ASSUMED OPERATING CHARACTERISTlC CURVE OF INSPECTION PROCEDURE NEAVY SOLID LINE)

# OR % OF SAMPLES

” I 0

I 5

PCB CONTENT PPm

I

10

I I

I l- I - I I I I

I I I , I 5 10

PCB CONTEWl' pm

TRUNCATED DISTRIBUTION OF PCB LEVELS AFTER IMPLE- MENTATION OF PERFECT INSPECTION SCHBME

FIG. 2. Simulation of the imposition of a standard: perfkct inspection ihtrated.

data (box C) to extrapolate risks at lower doses. To convert from animal health risks to human health risk requires (shown in box F) an animal to human conversion factor. In the FDA analysis it was assumed that animals and humans would experience the same health risk ifthey consumed a diet containing the same concentration of PCBs.

Finally, a 99% upper confidence limit to the expected risk was computed. For example, using National Cancer Institute (NCI) data (more on this later) for hematopoietic response in rats, this incremental lifetime risk of consuming PCB-contaminated fish was computed as 2.4 per 100,000 for 50th percentile consumers and 6.5 per 100,000 for 90th percentile consumers.

This paper documents each of these steps and identifies key assumptions. When reasonable alternative assumptions exist, these are examined to determine the sensitivity of the FDA risk estimates to these methodological choices. In this regard, it is correct to state that FDA attempted to be “conservative” in their selection. Such an approach owrstates the risk of consuming PCB-contaminated fish. At issue here is the possible magnitude of this overstatement.


LEVEL OF PCBs IN COMMERCIAL FISH (BOX A, FIG. 1)

As the first step in assessing the risk of PCBs in fish, information on PCB contamination is needed. An FDA survey in 197% 1979 identied 12 fish species with elevated levels of PCBs, as given in Table 1. Also shown are the estimated reduction in average PCB levels associated with the imposition of various tolerance levels. To calculate this effect, FDA simply deleted those fish having PCB levels in excess of the assumed tolerance level from the sample. As noted by Cordle et uI.,~

The mean PCB level estimated when a given tolerance is in effect is perhaps the most difficult part of the risk estimation. The effect of a tolerance on the distribution of PCB levels depends to a large degree on the actual distribution of PCBs before a tolerance is instituted. The most recent data available on PCB levels in fish were the 1978 and 1979 FDA survey data, consisting of 7 13 samples for 1978 and 179 samples for 1979 collected from all of the FDA districts. This sampling is not representative or extensive enough to permit estimation of an underlying nationwide distribution by species. As a rough approximation of the effect of a tolerance on the distribution, the values of PCB above the assumed tolerance were eliminated from the sample distribution and the mean was recalculated for each species. The resulting mean levels are shown in Table 1. It should be noted that assuming a zero tolerance is not equivalent to using all values, inasmuch as the 1975-1979 survey was carried out when a tolerance of 5 ppm was in effect. Thus, the e&t of going from zero tolerance to a tolerance of 5 ppm would be greater than shown here. Tuna and shellfish were assumed to have 0.0 mean levels of PCB. The limited data available on tuna show mean levels of less than 0.01 ppm. The values in Table 1 were then multiplied by consumption figures as described above to obtain intake of PCBs per day. (Emphasis added)

Thus, for example, the 54 carp in the sample (see Table 1 in the No Specified Tolerance column) had a mean PCB level of 1.1 ppm. Two of these carp exceeded

TABLE 1

MEAN PCB LEVELS IN F’DA 1978-1979 Domsnc SURVEY BY SPECIES OF INTERE.ST

No specified

tolerance

Assumed tolerance

5 mm 2 wm 1 mm

Species Mean PCB Mean PCB Mean PCB Mean PCB of interest (ppm) N (mm) N (mm) N (mm) N

Carp 1.10 54 0.90 52 0.68 46 0.54 38 Catfish 1.70 295 1.19 281 0.73 219 0.38 150 Buffalo 0.50 36 0.50 36 0.43 35 0.30 31 Fresh water trout 1.36 87 1.28 85 0.76 58 0.37 40 Sea trout 0.56 10 0.56 10 0.56 10 0.27 8 Bass 1.28 1.5 1.28 15 0.77 11 0.27 10 Chubs 1.14 19 1.14 19 0.96 17 0.58 9 Bluefish 0.53 23 0.53 23 0.44 22 0.37 20 8cuP (porpy) 0.72 10 0.72 10 0.72 10 0.53 8 Drum 0.49 12 0.49 12 0.49 12 0.32 10 Mackerel 0.53 21 0.53 21 0.53 21 0.28 17 All others 0.26 206 0.26 206 0.24 204 0.22 201

Source; See Note 3, pp. 171-182. ’ Tuna and shellfish were assumed to have 0.0 level of PCB. For assumed tolerance, PCB values above

the tolerance were eliminated in calculating the mean.


5 ppm, and when removed from the sample, the remaining (52 in this case) carp now had an average PCB level of 0.90 ppm. The difference was the assumed effect of instituting a 5 ppm tolerance level.

The amount of fish (of each species) consumed was estimated in a National Marine Fisheries Service-NOAA survey conducted in 1973. This study sampled 25,947 people who consumed fish. (About 93% of the U. S. population is estimated to include fish in their diet.) Of this total, 3939 (15.2%) individuals in the sample consumed at least 1 of the 12 species given in Table 1. Table 2 describes the daily intake of PCBs in those people who ate the 12 species of fish at the 50th and 90th percentiles as calculated by FDA. In particular, it was estimated that a “50th percentile eater” would be exposed to PCBs at a rate of 0.005 1 ppm of total diet, if a tolerance level of 5 ppm were instituted. This is 8.9% lower than the figure (0.0056 ppm PCB in total diet) if no tolerance level were in effect (i.e., if the threshold were infinite).

COMMENTS ON THIS PROCEDURE: THE EFFECTIVENESS OF CONTROLS

The extract from the FDA analysis quoted above asserts that the effect of instituting a 5 ppm tolerance “would be greater than that shown here.” This statement is tech- nically true4 but needs to be interpreted very carefully. It does not imply that the effects of imposition of any lower tolerance will be similarly understated. In fact, quite the reverse is true.

The truncation rule used to simulate the effects of imposition of a particular tolerance level is, in fact, more stringent than that used by FDA to enforce the current 5 ppm tolerance level. In particular, when an inspector tests commercial fish for PCBs, an “undirected” sample of several fish from the shipping lot is acquired. These fish are eviscerated, and heads, tails, fins, scales, inedible bones, and skin removed (if inedible) and then homogenized5 After extraction and cleanup, a portion of this composite is then tested for PCBs using a gas chromatograph. Using this procedure, it is clear

TABLE 2

INTAKE OF PCBs FROM FISH FOR EATERS OF SPECIES OF INTEREST (3939124,947)’

Assumed tolerance

(mm)

50th percentile eaters 90th percentile eaters

PI&i? Pa3 fig per &Y ppm of diet* hody wt= LG per day ppm of die? hody wtc

t-P 8.46 0.0056 0.12’ 22.1 0.0147 0.32 5 7.51 0.005 1 0.11 20.3 0.0135 0.29 2 5.59 0,0037 0.08 14.9 o,oOq9 0.21; 1 3.30 0.0022 0.05 9.22 0.0061 0.13

Source. See Note 3, pp. 171-182. a For assumed tolerance, PCB values above the tolerance were eliminated. b Assumed 1500 g daily intake. ‘Assuming body weight of 70 kg. d No tolerance. ‘Corrected from an apparent misprint in the original text.


that a shipping lot can pass inspection even though individual jish in the sample exceed the 5 ppm tolerance level. It is only the average value of the composite that is used in the accept/reject decision. The truncation rule used in the FDA analysis and shown in Tables 1 and 2 to calculate the efforts of a tolerance level, however, implies that every fish in the lot must satisfy the PCB tolerance. While (given available data) it is not possible to make a quantitative assessment of the magnitude of the error introduced by this truncation rule, its direction is to overstate the effectiveness of imposing a particular tolerance level (for tolerance levels beneath 5 ppm). In essence, the foregoing has simply reiterated the point well known in the quality control field, that it is impossible to have a cookie cutter-shaped operating characteristic curve (as is shown by the heavy line of the middle exhibit of Fig. 2) with any sampling inspection plan unless there is perfect 100% inspection.6 However, the cost of “perfect” sampling plans would be economically prohibitive, even if these were physically possible. Feasible sampling plans have an S-shaped operating characteristic (OC), as is illustrated by the dashed curve in the middle exhibit of Fig. 2. Using a plan with this OC curve will result in lots being rejected even if individual fish within the lot are beneath the threshold, while other lots will be accepted even though they contain fish above the threshold so long as the composite is beneath the threshold.’

The relevance of the above to the standard setting process is this: if the effectiveness of a particular control strategy is overstated, so too is its attractiveness in cost-benefit terms. A more realistic appraisal of the reduction in PCB levels associated with the imposition of lower tolerance levels based upon explicit computation of the operating characteristic curves of actual FDA inspection policies would certainly make such controls less attractive.

PCB EXPOSURE (BOX E, FIG. 1)

As a second point, it should be noted that the computed PCB intake values shown in Table 2 are likely to overstate actual PCBs in the diet of fish consumers. This is because these estimates are based upon PCB levels in raw fish, whereas these fish are normally consumed only after being cooked. Moreover, time trends indicate that PCB levels in fish are decreasing for many fish species in many locations, a factor omitted in the FDA analysis.

Preparation and cooking of fish reduce the PCB concentration in the cooked fish. The reduction is oflen significant. For example, Humphrey et al.* reported that the mean PCB levels in whole raw lake trout from Lake Michigan were 18.9 and 22.9 ppm in 1973 and 1974, respectively. When filleted and cooked, these fish had PCB levels ranging from 1.03 to 4.67 ppm, a reduction of at least a factor of 4. Raw salmon had five times the amount of PCBs compared to cooked salmon. Since the FDA protocol for assaying the amount of PCBs in fish did not include cooking, it is appropriate to reduce the calculated human exposure to PCBs by a factor to adjust for this overstatement.

Such overstatement was explicitly acknowledged in FDA’s discussion of temporary tolerances for unavoidable contaminants in food9:

Actual PCB intake can be considered to be even lower in light of a study of FCB levels in cooked fish. Most of the PCB occurrence data are for raw fish and comparisons of PCB levels in raw versus cooked fish indicate that actual human exposure to PCBs from jish consumption is less


than might be expected from the raw fish data. This is not unexpected, because preparation (trimming away fatty tissue) and cooking have been shown to decrease the concentration of PCBs. (Emphasis added)

However, this realization did not prompt any attempt by FDA to correct for this phenomenon, an additional element of conservatism in the resulting analysis.

Turning now to the question of time trends in PCB levels in fish, it should be noted that the exposure levels given in Table 2 reflect PCB levels in fish appropriate for 1978-1979. FDA assumed, in their analysis, that these levels would not change over time. And, indeed, during the seventies, observed PCB levels in the environment showed only slight and uneven declines. But environmental controls for PCBs were not fully implemented until the mid-seventies. More recently, data have become available that indicate PCB levels for many fish species are now dropping by significant amounts. For example, a joint report prepared by the New York State Departments of Health and Environmental Conservation found that over the period from 1978 to 1982, the mean concentration of PCBs in striped bass found in the Hudson River had decreased from about 19 to 5 &g, a factor of nearly 4.” The PCB concentration in Hudson River largemouth bass had dropped by a factor of 6 or more, depending upon the location. Table 3 presents concentrations of PCBs in other species, as reported by New York State. Across all species, the concentration of PCBs has decreased at a rate of about 30% per year in the Hudson River. A similar decrease was observed for PCB levels in Hudson River water. A 30% decrease in PCB concentration per year represents an apparent “half-life”’ ’ (the time required for PCB concentrations to be reduced by one-half) of 1.9 years.

PCB levels have also decreased in humans. As reported to EPA,” of the study group in 1977,8% had 3 ppm PCBs or more in their fatty tissue. By 198 1, the fraction of people with 3 ppm PCBs had dropped to 1%. These data strongly suggest that PCBs in the environment have decreased substantially. Even if the PCB concentration in fish had dropped by only a factor of 2 since 1979, then this natural decline would have resulted in a lower level of PCB contamination in fish than that calculated by FDA as a result of dropping the tolerance level from 5 to 2 ppm.

Bopp et a1.13 report that PCB concentrations in surticial sediments in the lower Hudson River have been halved in periods from 1.3 to 3.8 years (depending upon location). This was cited by New York State personnel as being consistent with the observed reduction in PCB concentrations in the Hudson River fish noted above. Recognizing the influence of the remova! of the Fort Edward Dam, they suggested that as a more natural PCB sedimentary region evolved, a better estimate of the time required for a halving of PCB concentrations would be about 6 years. Such geometric reductions in annual PCB levels in fish (if continued into the indefinite future) will lead to markedly lower total PCB doses than are assumed in the FDA analysis (which assumes constant exposure at 1978-1979 levels). For -example, if PCB levels in fish were to be reduced by only 10% each year, then fish presently containing 100 ppm PCB would decrease to 0.0696 ppm (a nearly 1500-fold reduction) over the nominal 70-year human life span used in the FDA lifetime risk computation. The difference in cumulative lifetime exposure would, of course, be more modest, but is still a factor of 7! Table 4 shows relevant formulas for computation and the magnitude of the overestimate in dose as a function of the assumed time to 50% reduction in fish PCB levels. For half-fives between 5 and 10 years the cumulative reduction in average dose is roughly a factor of 5 to 10. Any reduction in PCB exposure levels would


TABLE 3

PCB CONCENTRATIONS FOR SEVERAL SPECIES AND JLXIATIONS IN THE HUDFWN RIVER

SpCieS/SOUrCe Location YC3f

Average Average yearly concentration reduction factor

btig) (%) Comments

Large mouth bass

stripedbass

Pumpkin seed

Brown bull head

Gold fish

caddis f ly larvae

Multiplate residue

River water

Average

Stillwater

Catskill

Not specified

Lower Hudson

Not specified

Not specified

Not specified

Not specified

Upper Hudson

Lower Hudson

Stillwater

Stillwater (summer)

1977 145.3 1981 10.2 1977 29.5 1981 1.0 1978 6010 1982 1000 1978 18.5 1982 5.0 1977 1019 1982 362 1977 1096 1982 368 1977 2510 1982 424 1977 6761 1982 310 1977 10.2 1981 5.3 1977 3.8 1981 2.0 1978 46 1981 30 1977 0.69 1982 0.11

48

57

41

28

19

20

30

46

15

14

13

30

30 29

Fillets

Fillets

Lipid based

Fillets

Lipid based

Lipid based

Lipid based

Lipid based

Dry weight

Dry weight

Dry weight

Source. Computed from data in Note 10.

reduce FDA% assessment of cancer risk (discussed later in the section titled Calculation of Risk) by a corresponding amount.

ANIMAL DATA ON THE CARCINOGENICITY OF PCBs (BOX C, FIG. 1)

To date there have been several studies to assess the carcinogenicity of PCBs in animals. Those explicitly considered by FDA for long-term risk assessment in humans were:

l a National Cancer Institute’4 bioassay of Aroclor 1254 using male and female Fischer 344 rats,

l a study by Kimbrough et al. l5 on the induction of liver tumors on Sherman strain female rats by Aroclor 1260,

l an 1 l-month study by Rimbrough and Linder16 on the toxic effects of Aroclor 1254 in male BALB/cJ mice,

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l a short-term study by Allen and Norbackr7 on the reproductive responses of rhesus monkeys.

In the NC1 study, groups of 24 male and 24 female Fischer rats were administered PCBs in a diet at 25, 50, and 100 ppm, for a period of 104-105 weeks. Matched controls were composed of 24 untreated rats of each sex. Although the number of discovered tumors increased with dose, this relationship was not statistically significant. The relevant data are given in Table 5, as well as the results from the other two cancer studies. The tumor incidence shown in this table was used to estimate human risk at low doses.

The practice of using data of undemonstrated statistical significance as a basis for fitting dose-response models-as was done with the NC1 data-is certainly subject to challenge. If a statistical test fails to demonstrate significance, it implies that the observed response pattern could have arisen by chance alone-an artifact of the data rather than a genuine correlation/causation. Surely a more rational policy would be to attempt to replicate the observed results with a larger number of test animals in a well-designed experiment. Of course, the costs and delays of further experiments need to be considered, but the costs of replication are small in comparison to the social and economic costs of imposing an arbitrary standard and the results would long since have been available if initiated when the lack of statistical significance was first noted.

COMMENTS

It is beyond the scope of this paper to provide a critique of the above studies from a biomedical perspective. l8 Rather, this analysis addresses only the quantitative and statistical elements of the FDA risk analysis.

First, it should be noted that the PCB doses employed by the various investigators were very high relative to doses that might be expected in practice. In a preliminary study, NC1 researchers fed rats a diet containing 400 ppm PCBs. After 8 weeks, a sixth of the population had died. Although death was not observed at lower doses, a diet of 100 ppm or even 25 ppm PCBs stresses the animal considerably. How this would affect low dose extrapolations is simply not known. What is clear, however, is that estimates of health effects at realistic doses of PCB involve extrapolations over many orders of magnitude with no supporting data to validate the model at lower doses. A dose of 100 ppm, for example, is higher by a factor of 17,857 (more than four orders of magnitude) than the 0.0056 ppm in the diet of the “50th percentile eater,” referred to earlier. Although this situation may be the rule rather than the exception in toxicology studies, it is important to realize the likely uncertainty of the resulting estimates.

As Table 5 indicates, the NC1 data were divided into three categories: total malignancies, liver carcinoma and adenomas, and the hematopoietic system (leukemia). Figure 3 shows that portion of total malignancies that were neither the hematopoietic nor liver carcinoma type and are denoted “other.” But for the category “other,” the observed number of malignancies actually decreased as the dose increased from 25 to 100 ppm. Since this category of malignancy is not dose related in any obvious way, the use of total malignancies seems inappropriate.

Others also have raised objections to the use of a “total malignancy” category, or


TABLE 5

ANIMAL DATA USED FOR RISK EXTRAPOLATION TO HUMANS

Dose of Aroclor fed (ppm)

Study Parameter 0 25 50 100 300

Fischer rats fed Aroclor 1254 Total malignancies

Males 5124 2124 9124 12124 Females 4124 13124 8124 9124 Combined 9148 15148 17148 21148

Liver carcinoma and adenomas Males O/24 O/24 l/24 2124 Females O/24 O/24 l/24 2124 Combined O/48 O/48 2148 4148

Hematopoietic system Males 3124 2124 5124 9124 Females 4124 6124 6124 6124 Combined 7148 8148 1 l/48 15148

Female Sherman rats fed Aroclor 1260 Hepatocellular carcinomas l/173 261184

BALB/cJ male mice fed Aroclor 1254 Hepatomas, neoplastic nodules 015 9122

Source. See Note 3, pp. 171-182.

even to the practice pooling &ta from both sexes. Dr. Joseph Rodricks, the Chairman of the PCB Assessment Work Force which prepared the original analysis, has apparently changed his views on this matter. Writing a critique of the EPA Office of Toxic Substances’ (OTS) risk assessments of PCBs-an analysis that, in this respect, was identical to the FDA analysis-he stated,”

Hematopoietic System

Total Mal ignanc

AROCLOR IN FEED (PPM)

-

FIG. 3. National Cancer Institute data relating various types of carcinoma to dose of Aroclor 1254 in Fischer rats. See text for comments re other category. Total rats in population = 48. Source. NCI.


I know of no scientific justification for an anaIysis of the type presented for the NC1 bioassay data, especially for the grouping of animals bearing %ny malignancy.” I have consulted with pathologists and statisticians at the National Toxicology Program (NTP) and learned that they too do not consider the type of analysis performed by OTS appropriate. In fact, they stated that such a practice, if consistently applied, would obscure carcinogenic effects in most cases, simply because, under current protocols, most control animals develop some type of tumor by the end of their lifetimes.

The NTP, the International Agency for Research on Cancer, and, as far as I know, CAG and the Office of Pesticide Programs conduct analyses of tumor data by specific site and sex. None of these organizations has ever treated tumor categories in the way they have been treated by OTS. There is no basis for combining either sexes or, especially, histologically unrelated tumor sites fir analysis (OTS combined both to achieve a statistically significant increase). OTS has given no adequate justification for the type of analysis it performed. (Emphasis added)

The Allen study employed a very small sample size and needs to be replicated with a larger number of animals before useful conclusions can be drawn. Additionally, it is thought that rhesus monkeys may be unusually sensitive to PCBs. While sensitive species are an appropriate object of scientific inquiry, it is necessary to examine several species-not just those most sensitive-to develop a balanced appraisal of effects,

LOW DOSE EXTBAPOLATION MODELS

The low dose levels of PCBs predicted by FDA in human diets (0.0056 ppm of total diet for 50th percentile eaters) implied a similar low risk of cancer in individuals: a risk so low as to have little impact on the total number of deaths due to cancer.20 Nevertheless, because there is such a large population exposed to PCBs (15.2%) of the total U. S. population (or roughly 37 million people) the cumulative effect of PCBs is a potential health concern. To avoid (or minimize) extrapolation errors, animal experiments should be performed using dose levels at or near or at the actual human exposure level to PCBs in the environment. However, this would require extremely large sample sizes, tens of thousands of animals, to observe even one PCB- associated cancer (assuming the rates calculated by FDA). Thus, as a practical matter, animal experiments are conducted at high dose levels (where response rates are expected to be high) and the results are extrapolated to lower doses. This technique relies heavily on the extrapolation methodology. Depending upon the model used, it is possible to obtain estimates of risk at low doses that can vary by several orders of magnitude. Since many of the models will fit the high dose data equally well, it is not easy to select, on an empirical basis, the most appropriate of these models. This point has been made by numerous researchers.

Munro and Krewsldz’

. . . Because all of these models fit the data more or less equally well in the observable range, it is difficult to select an appropriate model or range of risks using statistical goodness-of-fit criteria alone. Recent theoretical results by Crump . . . in fact indicam that statistical discrimination between two plausible models is difficult even with an experiment designed specifically for this purpose.

Hoe1 et al.22

Basically the problem is that in the experimental region of the curve, several competing models will fit the data well, while the tails of these response distribution curves often d&r by many orders of magnitude.


Crump and Mastermanz3

It might be supposed that it should be possible to discriminate among the various potential dose response functions on the basis of experimental data but, unfortunately, two different dose response functions can often fit experimental data equally well but still differ by several orders of magnitude at very low doses. Moreover, even if a particular dose response tinction were to give a significantly better fit to data than several others this would still not furnish assurance that this function would necessarily correlate in any way with the true dose response at very low doses where it is not feasible to measure the true extra risk directly.

Faced with this dilemma, many researchers have sought methodologies that simply try to place an upper bound on the actual risk. FDA has explicitly acknowledged this choice noting24: “Of the available methods that appear to be consistent with what is known about the biological mechanism of carcinogenesis, the linear method is the least likely to underestimate risk” (emphasis added).

ALTERNATIVE MODELS OF RISK (BOX D, FIG. 1)

This section presents several alternative dose-response models and offers some general comments on their properties.

One collection of approaches involves a “safe dose” concept. The safe dose concept appears to have been first codified in what is now known as the Mantel-Bryan procedure.25 It is based upon the normal cumulative distribution function +(x), but modified so as to generally overstate risk. In particular, the function 4 is defined as

r#(d) = -& -“, e-x212&. s (1)

The function 4(d) is zero for d = -00 and increases to 1.0 as d goesto +co, and the risk of a dose d > 0 (i.e., the probability of cancer) is modeled by the equation

P(d) = 4(a + b log(d)). (2)

The parameters a and b > 0 are empirical constants determined by use of statistical fitting procedures. Note that when the dose is zero, log(O) = -a and so p(O) = 0. If the dose, d, is infinity then so is log(d), and p(d) is 1.0. For doses between these two values, the value will be greater than 0 but less than 1.0.

The Mantel-Bryan procedure, in addition to using model (I), the so-called probit model, assumes that the value of b in Eq. (2) is equal to 1.0. The authors claimed this choice was conservative since b is usually greater than 1.0 when estimated from the data. To provide an additional margin of safety, the procedure also required the use of the 99% upper confidence bound to the estimate of a. Together, these two requirements were thought to be sufficient to upper bound the true but un- known risk.

Although initially perceived as a conservative procedure, the Mantel-Bryan methodology actually estimated lower risks than most other models that have been proposed subsequently. This is because the probit function (1) approaches zero very quickly as the dose goes to zero. An alternative model, and one utilized by FDA, is the so- called one-hit model. It assumes that risk can be modeled as

P(d) = 1 - eVM (3)

206 MAXIM AND HABBINGTON

where X > 0. It is based on the concept that a response will be induced a&z the target site has been hit by a unit dose within a specific time interval. For small d

P(d) r Ad,

and so the estimated risk of cancer under this model is linear at low dose levels.26 As with the probit model, an added safety factor is often included by calculating the 99% upper confidence bound for X, as was done by FDA, noting that the “use of such upper bounds adds an additional degree of conservatism to the estimate.“*

Table 6 presents a sampling of other models of risk that have appeared in the literature. Of those shown, all but the logistic and probit models include the one-hit model as a special case. Most can be either convex or concave depending upon the value of the parameters and so are more flexible than the one-hit model. Although concave dose-response models are theoretically possible, current thinking is that these do not accurately reflect physical knowledge. Munro and Krewski:’ for example, make this point: “The dose-response curves for the logit, Weibull and multi-hit models can approach zero at a faster than linear, or supralinear, rate, although the biological plausibility of this behaviour seems questionable.” Models that are linear at low doses generally (but not always) estimate higher risks than those models that are nonlinear, and are therefore viewed as more conservative.

The one-hit model, in particular, has been viewed as a highly conservative approach to risk estimation. As Park and See” point out,

TABLE 6

Model Formula Linear at low doses Comments

One-hit FDA

Probit

Mantel- BV=

LQgiStiC

Extreme value

Multistage

Gamma

1 - QM , - (A99d

f# 6% + wo

l/( 1 + e@ + * wn)) Onlyifb= 1

1 - exp(-exp(a + b log(d))) Onlyifb= 1

1 - exp(- 5 b&‘) i-l

Onlyifb,>O

Gbla,b) Onlyifa= 1 (e.g., the one-hit model)

Yes YeS

No

No

This is the one-hit model using an upper 99% confidence hound on X

4 (x) is the normal distribution: x

s (1/2x) e-X212 atx

Thicis the probit model using an upper 99% confidence hound on aandsettingb= 1

For small d, it is convex if b > 1, and concave if b <: 1

For small Uoses, it is convex if b> 1,andconcaveifbcl

For small d, it is convex if a > 1, and concave if a -ZI 1. G(x) is the Gamma distribution

(l/b’I’u) j xa-le-xlb &

0


The one-hit model and variations on it utihzing upper statistical limits (Gaylor and Kodell, 1980) represent a highly conservative approach to the extrapolation problem (Hoel, 1981). For example, a linear extrapolation of the Chemical Industries Institute for Toxicology formaldehyde study pmdicted that an average lifetime dose of less than .66 X 10m3 ppm was needed to keep the lifetime potential risk of tumor less than 10e6 (Gibson, 1982). Such an estimate has little credibility as an estimate of the risk to humans when viewed in light of about 100 years of experience with human exposures to formaldehyde that generally are less than . 1 ppm but have oftenbeeninthe.1 to5ppmrange. . . with no apparent increased carcinogenic risk.

Indeed, these authors are quite specific about the limitations of one-hit models. Later, in this same paper, they state,

With appropriate species conversion, the one-hit model does, however, estimate an upper limit on the potential risk and may be useful in situations where an upper bound is of interest. For example, if the potential risk calculated by the one-hit model is not unacceptable, then there would be less need to consider other models. On the other hand, ifpermissible exposures predicted by the one-hit model are unrealistically low, which is ojten the case, then further risk arudyses would have to be made to con&n or refute the one-hit model results. In all cases we must keep in mind that potential risks predicted by the one-hit model may be several orders of magnitude more than that of the true potential risk (factor of 10 = one order of magnitude). (Emphasis added)

To our knowledge, FDA has not conducted other experiments to confirm or refute the adequacy of the one-hit model, as is suggested above.

Returning to the general topic of alternative dose-response models, it is important to note that

l the collection of models shown in Table 6 is only a sample of the possible models that might be used to approximate cancer risk, and

l these models are not arbitrary mathematical forms, rather, each of these has some justification (either normative or descriptive).

MORE RECENT INVESTIGATIONS: IMPLICATIONS FOR THE FDA MODEL

Hoe1 et aL2’ have recently developed a hybrid mechanistic/kinetic approach for estimating carcinogenic risk for chemicals with metabolites that interact with DNA. Of interest in this context are a series of their findings, based on both analytical results and numerical examples, which indicate that “the mathematical models typically used for low-dose extrapolation are shown potentially to overestimate risk by several orders of magnitude when nonlinear kinetics are present.” In the majority of the cases investigated by Hoe1 et al. the computed dose-response curve was convex in the low dose region and exhibited a characteristic “hockey stick” appearance. A linear or one-hit model fitted to these data overestimates the actual risk. The authors summarized their results as follows:

Both the analytical results and the numerical examples provide some general conclusions:

(1) The dose-response curve at low doses may be convex because of saturation of the enzyme systems, although this may be undetected experimentally.

(2) The errors in fitting simple dose-response functions tend to overestimate the low-dose e@ects because of the possible convexity.

(3) In no situation did the kinetic model suggest the possibility of undetected concave dose- response behavior leading to an underestimate of lowdose effects. (Emphasis added)


The obvious implication of the above is that there is excessive conservatism in models of the type used by the FDA, and therefore the models merit an additional examination of the issue. In an earlier paper Hoe1 et a1.22 defended the use of linear models in preliminary research:

For the purpose of estimating an upper limit of risk associated with a given dose or estimating the lower limit of dose associated with a given risk, assuming no other complicating factors, the linear model for low dose extrapolation should be used for incidence data. This should be the case until improved mathematical models based on biological considerations are developed, or in specific instances where sulhcient biological information is available to indicate the appropriateness of an alternative model. The development of such improved models should be encouraged.

The closing sentences of the above quotation are quite prescient in the light of the principal author’s later work.

BACKGROUND RESPONSE

To model background response, let 0 < y < 1 denote the spontaneous response rate (i.e., the response probability, due to other sources, if the PCB dose is zero). Then, assuming the spontaneous and induced responses are independent, the probability of observing a response of either type at a dose d is given by

P*(d) = y + (1 - y)P(d).

Alternatively, the background response can be modeled by assuming it is an additive component to the dose,

P*(d) = Z-‘(d + y).

The parameter y can be estimated along with those parameters that define P(d), assuming, of course, the experiment includes observations at enough different dose levels. For example, if P*(d) is defined by a total of three parameters, then at least three dose levels am required to estimate these parameters. In particular, the Kimbrough studies utilized ony two dose levels, and so some of the model’s choices cannot be fitted.

OTHER CHOICES IN ANALYSIS (BOX G, FIG. 1)

The selection of an appropriate model and background correction is only the hrst step in the process of extrapolating to low doses. Figure 4 presents a taxonomy of the required decisions. The FDA choices are emphasized in boldface. These choices are summarized below.

l Class of Prediction. The FDA analysis attempted to estimate lifetime risk as opposed to average time to response. This required experimental data where PCBs were fed to animals over their entire life span. For rats,, this means at least 104 weeks.

l Sole Cause (Background Adjustment). A spontaneous’response rate independent of the induced response can be modeled using an additional parameter as discussed earlier.

l Fitting Procedure. There are several methods used to estimate parameters of a model, e.g., maximum likelihood, least squares, x2, and others. Sometimes these techniques converge and praduee the sameestimate; but not always.

r-l

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poss

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FDA

choi

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are

emph

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d in

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ldfa

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l Quantity Estimated. Models will usually estimate the expected value of the low dose risk. As an extra margin of safety, some procedures entail estimation of the 99% upper confidence bound to this risk. While the differences between these two approaches become small for large sample sizes, it is appreciable for the sample sizes in the animal experiments reported here.

. Dose Assumption. FDA examined both 50th and 90th percentile eaters of contaminated fish.

l Species to Human Extrapolation. PCBs may affect an animal species differently from humans. Without specific data, however, researchers are forced to assume that risks are identical when exposed to “similar amounts” of PCBs. But, what measure of similarity to use is subject to debate. FDA equated exposure levels for rats and humans on a parts per million basis of total diet. Alternative measures include milligrams PCB per kilogram body weight per day and milligrams PCB per surface area (weighted to the two-thirds power) per day. The parts per million measure will, of course, predict the same risk for animals eating the same diet even if one consumes twice as much food (and, therefore, twice as much PCBs) as another. This is not true of the weight of PCB per kilogram body weight per day measure.29

l Response Variable. Animal studies have identified the liver and hematopoietic system as the principal sites for PCB carcinogenic activity. Each can be analyzed separately, or together. However, when combining categories, care should be taken to include only those that are clearly dose related. In particular, use of “total malignancies” appears inappropriate in view of the results shown in Fig. 3, and for this reason is omitted in what follows.

l Study Species. To date, PCB carcinogenic studies have used rats, mice, monkeys, dogs, and others. The choice should make no difference if the correct species to human conversion factor is known. Absent this information, studies using animals that are either highly sensitive to be gained or highly insensitive to PCBs should be viewed with caution.

As indicated in Fig. 4, there are over 60,000 ways to combine these necessary elements of a methodology to give an estimate of risk. The next section selects some reasonable alternatives from this taxonomy to compute possible alternatives to the estimates obtained by the FDA methodology. Comparisons among these enable a better understanding of how these choices might alter the assessment of risk at low doses.

CALCULATION OF RISK (BOX H, FIG. 1)

Table 7 reproduces FDA estimates of the upper 99% confidence limits on lifetime risk of cancer using the data given in Table 5 for various assumed tolerance levels for 50th and 90th percentile eaters. The inclusion of 90th percentile eaters reflected a concern that, even if the health risks of consuming contaminated tish were small for the majority of consumers, it was possible that a subgroup of the population could face significant risks. To place these figures in perspective, however, note that as only 15.2% of the population consumed these fish species to begin with, only about 1.5% of the population were exposed to the levels of 90th percentile eaters.

Before presenting alternative estimates of risk it is worthwhile to note that the estimated differences in health risks arising from the imposition of various tolerance

ANALYSIS FOR PDLYCHLORINATED BIPHENYLS IN FISH 211

TABLE 7

UPPER CONFIDENCE LIMITS (99%) ON LIFETME RISKS OF CANCER AND PROBLEMS OF REPRODLJC~ION IN EATERSOFFISH SPEIXSOF~NTEREST, ASCALCULATEDBYFDA

Lifetime risks per 100,W

50th percentile eatets 90th percentile eaters

Assumed tolerance Assumed tolerance No No

Basis assumed 5 2 1 assumed 5 2 1 Study parameter/species tolerance ppmb ppm ppm tolerance ppm ppm ppm

NCl Total malignancies 4.1 3.7 2.1 1.6 10.6 9.8 7.2 4.4 (male and female rats)

NC1 Liver carcinoma and 0.9 0.9 0.6 0.4 2.5 2.3 1.7 1.0 adenomas (male and female rats)

NC1 Hematopoietic (male 2.7 2.4 1.8 1.1 7.0 6.5 4.7 2.9 and female rata)

Rimbrough Liver carcinoma 1.3 1.2 0.8 0.5 3.4 3.1 2.3 1.4 Rimbrough Liver hepatomas 2.0 1.8 1.2 0.8 5.2 4.8 3.5 2.2

(mice)

Source. See Note 3, p. 179. ’ All risks are lifetime risks computed as rates per 100,000 of the population at risk. b For each assumed tolerance. PCB values above the tolerance were eliminated.

levels are not large, particularly considering the large uncertainties underlying these estimates. The authors of the FDA risk analysis observed this, yet concluded inex- plicably,30

In light of the uncertainties upon which these risk estimates have been made, perhaps an equally compelling argument could be made for the establishment of either a 2 ppm or a 1 ppm tolerance. As suggested previously, the difference in risk between the two levels decreases only slightly even in the species of interest.

If indeed the differences in estimated risks between the 2 and 1 ppm standard are small in comparison to the uncertainty in analysis, then so too are the differences between the 5 and 2 ppm cases (see Table 7). Moreover, if these differences are so small as to render the alternative tolerance levels virtually indistinguishable, it is difficult to understand the basis for a choice of any but the largest, since imposition of these lower tolerance levels is not without other social and economic costs.

Table 8 presents alternative estimates of risk using seven other low dose extrapolation models for 50th percentile eaters and assuming no tolerance level is in effect. The first line is the original FDA estimate of risk. The second line uses the FDA one-hit model but deletes the 99% upper confidence bound requirement on the X parameter. This results in an estimate of risk that is smaller by a factor of 2.1 on average. The Mantel-Bryan procedure, for example, estimates a risk smaller by a factor of about 8, on average. If the two cases previously identilied as questionable, total malignancies and monkey reproduction, are excluded (as is done in Table 8), all the models examined estimate a substantially smaller risk than that obtained by FDA. Among all models


TABLE 8

ALTERNATIVE CALCULATIONS OF RISK (PER lOO,OOO) (JXSE = 0.0096 ppm IN DIET-Z&~ PERCENTILE, No TOLERANCE)

Case”

Model 1 2 3 4

FDA (one-hit 99%) 0.90 One-hit 0.42 Mantel-Bryan (probit 99%, b = 1) 0.005 Probit Less than 10-‘ob LQgiStiC 0.002 Extreme value 0.004 Multistage 0.18 Gamma (multihit) 0.160

2.1 1.3 2.0 1.06 0.82 0.98 0.09 0.02 0.73 Less than 10-‘w 0.04 -L‘ - 0.08 - - 0.71 - - 0.048 - -

‘See Notes 15 and 16. Case 1, liver carcinoma and adenomas (NCl); 2, hematopoietic system (NCl); 3, liver carcinoma (Kimbrough); 4, liver hepatomas (Kimbrough).

* Accurate values of these quantities are beyond the numerical limits of the computational algorithm. c Insufficient data.

(except FDA’s) and among all cases, the median lifetime risk per 100,000 is about 0.085 (=(0.08 + 0.09)/2). The median among FDA’s calculated risks is 1.65 (=( 1.3 + 2)/2), greater by a factor of 19, than the median risk among the other models. Thus, the FDA methodology appears to be conservative when compared broadly with other equally plausible models of risk by a median factor of 9 (excluding the factor of 2.1 due to the use of the 99% upper confidence bound). Specific comparisons vary, sometimes by several orders of magnitude so the possible overstatement could be much larger. Table 9 presents the estimated parameters for these models.

ANIMAL TO HUMAN EXTRAPOLATION (BOX F, FIG. 1)

The calculation of risk given in Table 8 assumes that animals and humans face identical risk when fed a diet containing equal amounts of PCBs on a parts per million basis. Many investigators challenge this conversion and hold that the proper basis for comparison is on a dosage basis, i.e., a micrograms of PCBs per kilogram of body weight per day basis. In particular, the chairman of the very FDA task force that prepared the PCB risk analysis has apparently rejected the “parts per million- in-diet” approach employed in the original analysis in favor of a weight per kilogram of body weight per day basis! Again,i8 commenting on a similar EPA analysis, Rodricks stated, “Thus, in the absence of good evidence for the use of a more complex procedure, and because the available evidence appears to support it, EPA should use, mg/kg/ day as the basis for interspecies doseage comparison.” The consequences of this change in species conversion factor are described below.

Table 10 shows the necessary steps required to compare species on a microgram per kilogram basis. It indicates that with this measure, a diet of 0.0056 ppm PCBs in humans (50th percentile eaters, no tolerance) is equivalent to 0.00 164 ppm PCBs for rats, 0.00071 ppm for mice, and 0.00235 ppm for monkeys.

On a weight basis, the concentration of PCBs in feed for rats should be reduced

TABL

E 9

PARA

MET

ER E

STIM

ATES

FOR

VAR

IOU

S M

ODE

LS'

case

Mod

el Pa

ram

eter

Fo

rmul

a 1

2 3

4

FDA

One

-hit

Man

tel-B

ryan

Prob

it

x (X

10

0)

Y

x (X

10

0)

Y

a 7 : Y

; Y

z Y a(X

100)

b

(X

1000

0)

Y

; Y

r+(l

-y)(l

-fY

) 0.

160

0.56

6 0.

229

0.35

3 0.

0 0.

140

0.00

58

0.0

0.07

46

0.21

9 0.

0 0.

137

-3.0

79

-2.4

94

0.0

0.13

5 -4

.729

-4

.105

1.

695

1.63

2 0.

0 0.

1440

-9

.545

-7

.582

3.

611

3.10

0 0.

0 0.

1433

-9

.329

-7

.315

3.

476

2.91

1 0.

0 0.

1433

0.

033

0.14

9 0.

055

0.07

6 0.

0 0.

1426

1.

1 1.

33

1000

.0

245.

85

0.0

0.14

4

0.14

7 0.

175

0.00

58

0.0

-2.8

30

-2.0

84

0.00

58

0.0

- - -

-

-

-

- -

- -

- -

- -

- -

- -

- -

- -

- -

- -

- - -

y +

(1 -

y)

(1

-e-M

)

r+(l

-Y)#

(a-lo

g(d)

)

r+(l

-Y)b

(a+b

log(

d))

LOgi

StiC

1

’ +

(’ -

‘) 1

+ ex

p(-a

-b

log(

d))

y +

(1 -

y)

(1

- ex

p(-e

xp(u

+ b

log(

d))))

Ex

trem

e va

lue

Mul

tista

geb

y +

(1 -

y)

(l-e

xp(-a

d -

bd*))

Gam

ma’

y

+ (1

-

Y) G

(d/

d9

’ Par

amet

ers

were

est

imat

es u

sing

the

Sta

tistic

al Ana

lysi

s Sys

tem

(SA

S pr

oced

ures

m

odifi

ed

to p

erfo

rm

max

imum

lik

elih

ood

estim

atio

n sim

ilar

to t

hat

show

n in

the

S

AS

Use

rs Gui

de: S

tatis

tics,

(p.

36)

1982

ed.

). Fo

r so

me

case

s th

e ob

ject

ive

func

tion

was

quite

fla

t in

the

vic

inity

of

the

optim

um

and

para

met

er

estim

ates

are

hig

hly

corre

late

d an

d no

t pr

ecis

ely

dete

rmin

ed.

For

thos

e m

odel

s in

volv

ing

the

func

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log

(dos

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umer

ical

prob

lem

s wi

ll re

sult

from

zer

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se d

ata

poin

ts

unle

ss th

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odel

is

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ompo

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into

tw

o su

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els

for

likel

ihoo

d m

axim

izatio

n.

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proc

edur

e wa

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llow

ed

in t

he S

AS “

Mod

el St

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feat

ure.

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aram

eter

s u

and

b ar

e co

nstra

ined

to

be

posi

tive.

’ G(d

/u,b

) = 1

(x

/b /(

x/b)

/I’u.


TABLE IO

CALCULATION OF RISK WHEN COMPUTED ON A MICROGRAM PER KILOGRAM BASS

Assumptions Diet of 1 ppm = 0.021 mgjkg/day humans”

= 0.072 mg/kg/day rat-8 = 0.166 mg&,/day r&4? = 0.05 mg/kg/day monkeys’

Thus 1 ppm for rats = 0.021/0.072 (= 0.292) ppm humans 1 ppm for mice = 0.02 l/O. 166 (= 0.127) ppm humans 1 ppm for monkeys = 0.021/0.05 (= 0.42) ppm humans

In particular, 50th percentile eaters of contaminated fish consume 0.12 &kg/day of PCBs, assuming no tolerance. Therefore,

0.12 day = 0.0056 ppm humans = 0.00164 ppm rats = 0.00071 ppm mice = 0.00235 ppm monkeys

“See Note 3, pp. 180-181. b See Note 16, p. 547.

by a factor of 3.42 and by a factor of 7.9 for mice. For linear models, this would reduce the estimated risk by a similar amount. The nonlinear models generally have even larger reductions. Table 11 summarizes these results. When computed on a weight basis, the median risk (0.0075 = (0.009 + 0.006)/2) is reduced by a factor of 11 compared to the median risk from these same models (0.085) computed on a parts per million in diet basis. The median risk of the FDA model recomputed on this basis (0.33) is smaller by a factor of 5 than that computed on a parts per million in diet basis.

TABLE 11

ALTERNATIVE CALCULATIONSOF RISK (PER 100,000)0~ A MICROGRAM PER KILOGRAM BASIS, 50th PERCENTILE EATERY

Model 1 2 3 4

FDA (one-hit 99%) 0.27 Gnehit 0.12 Mantel-Bryan (probit 9996, b = 1) 0.0002 Probit Less than lo-‘Ob LOgiStiC 0.00032 Extreme value 0.00057 Multistage 0.055 Gamma (multihit) 0.04

0.81 0.38 .25 0.32 0.24 .12 0.006 0.001 2 x lo-I0 Less than 10-‘Ob -’ - 0.008 - - 0.017 - - 0.2 - - 0.0094 - -

’ gee Notes 15 and 16. Case 1, liver carcinoma and adenomas (NCl); 2, hematopoietic system (NCl); 3, liver carcinoma (Kimbrough); 4, liver hepatomas (Kimbrough).

b Accurate values of these quantities are beyond the numerical limits of the computational algorithm. c Insuthcient data.


SUMMARY AND PERSPECTIVE

Table 12 provides a convenient summary of this analysis. Specifically, it provides a brief statement of the various FDA assumptions and the possible degree of overstatement compared to methodological alternatives. For some assumptions, such as the use of upper confidence bounds in preference to best estimates, the FDA procedure clearly overstates the expected risk. For other assumptions, such as the choice of dose-response model, similar conservatism cannot be proven in a rigorous sense, save to note that the alternative models are often equally plausible, a priori, and generally lead to substantially lower risk estimates.

Taken together, the consequences of these assumptions combine in a multiplicative rather than additive manner. As shown here, the degree of overstatement of the risks associated with consumption of PCB-contaminated fish could easily be several orders of magnitude; hardly a firm analytical foundation for standard setting.

To some, such overstatement is a regrettable if necessary consequence of the need to make important policy decisions in the face of uncertainty. Resulting environmental standards are perforce based on both “objective science” and “value judgments.”

TABLE 12

SOURCES OF POSSIBLE OVERESTIMATES OF RUSK IN FDA ANALYSIS

source Possible risk inflation factor’

Remarks

Use of upper confidence bound to risk rather than expected value

Use of 90th percentile eaters rather than median

Assumption of constant levels of PCBs in fish when evidence suggests declines for many species in many locations

2.1

2.65

? (5-10)

No real justification for this conservatism

No real just&cation for this conservatism

For Hudson River tish plausible vahtes of the risk intlation factor range from 5 to 10, but separate analyses need to be conducted for commercial fish

Choice of particular dose-response model

9 FDA choice arbitrary, other models “equally plausible” on both normative and statistical grounds. Figure at left is tbe ratio of median risks among ah models and data sets

Using ppm PCB on a raw fish rather than a cooked fish basis

Species-to-human extrapolation factor

1-6

(S-11)

Cooking known to reduce PCB levels in fish

Baseduponmedianrisksacrossall models and cases with no tolerance level in e&ct

Other sources not included here ? Methodological choices are shown inFig.

E Figures shown in this column are summarized from several sets of computations, see main text for more comprehensive comparisons. Factors shown are approximately multiplicative rather than additive.


Such was true, for example, in a widely discussed decision of the U. S. Court of Appeals for the Eighth Circuit Court in Reserve Mining Company v. EPA (1975). According to one observer (Yellin, 1983”j) the outcome “effectively allowed the expert to set a value-laden, legal standard.” This case is particularly interesting because of the eloquence of the defense for the “conservative” point of view. Martin3* notes that “the Court quoted extensively and with obvious approval the testimony of an expert witness, a Dr. Brown, concerning Brown’s belief that when questions of human health are involved, ‘I have to err, if err I do, on the side of what is best for the greatest number.’ ”

Modern thinking on the conduct of risk analyses has shifted away from the conservative “better safe than sorry” approach to one that provides the most accurate risk estimates possible and reflects the ambiguity of the analytical results. Raiffa,33 Chairman of the Committee on Risk and Decision Making, National Research Council, offered the following guidance:

Probabilistic reports should not prejudice policy issues and purposely report with a prudent bias. Cascading prudent reports could result in imprudent actions, and there is a danger of double- counting competing risks. Such reporting should be honest, and not attempt to second-guess policy choices. Probabilistic reports about diverse consequences to health, for example, are very often slanted to be conservative. I believe that it is better to report honestly, and that prudence should, more appropriately, be accountedfor in the evaluation process, rather than in the assessment process. (Emphasis added)

UNCERTAINTY IN RISK ANALYSIS: A PERSPECTIVE LOST IN THE APPLICATION

Uncertainty and sensitivity to unverified, and perhaps unverifiable, assumptions are all too common problems with quantitative risk analyses. As Munro and Kmwski2’ note in connection with the saccharin analysis,

This inability to assess, with a reasonable degree of certainty, the risk to man at low levels of exposure continues to present serious problems in regulatory applications. For example, the National Academy of Sciences (1978), in its report on saccharin, estimated that the expected number of cases of bladder cancer in the United States due to exposure to 120 mg saccharin/day may range from as low as 0.22 over the next 70 yr to as high as 1,144,OOO. These estimates of risk span a range of eight orders of magnitude and are of rather limited assistance to regulators charged with the responsibility of making critical public health decisions.

Likewise, a special study by an eight-member committee of the American Industrial Health Council which examined 21 EPA and IDA decisions covering 14 substances over the period 1974 to 198 1 concluded: “We concluded that agency estimates of risk overstate the risk probably by one to two orders of magnitude.“34

But to admit to the prevalence of this situation is not to admit to its acceptability. To be sure, conservative choices lower the likelihood that unacceptable health risks will be borne by the population. But, when this conservatism is compounded at all stages in the analysis it lends an air of unreality to the resulting estimates which is ill-suited for making such important decisions.35 When a particular standard, such as 5 ppm, is finally established, the very real uncertainties behind the supporting analysis are lost and the resulting standard is treated as if it were based upon a solid foundation of fact. These uncertainties were not lost on the authors of the FDA analysis when they remarked,3


. . . Quantitative assessments of this sort, however, are no stronger than the structure of assumptions on which they rest. Because many of the assumptions are merely pragmatic and are derived from limited information, risk estimates of this type cannot be accepted as conclusive results. They should instead be viewed as initial attempts that are subject to revision as better information becomes available.

Notwithstanding such a measured appraisal, standards, once set, are treated as though they were based upon incontrovertible evidence and unambiguous analysis. For example, a recent decision by New York State officials not to reopen the Hudson River striped bass commercial fishery reflected a concern that, even though the measured average PCB concentration was 4.8 ppm, this was too close to the FDA threshold (considering possible sampling errors) and, moreover, some individuaf fish exceeded the 5 ppm standard. In our view, this imparts a specious precision to the debate over reopening the fishery that is completely inappropriate in view of the factual basis for the FDA standard.

NOTES

’ Such policies are described in the following references: DPH releases data on PCBs in bluefish (Nov. 3, 1983), Mass. Dept. of Public Health News; B. D. Stutz (Oct. 23, 1983), Fishermen seek clearer PCB rules, The New York Times; PCB levels in striped bass in the Hudson decline (Nov. 20, 1983), The New York Times; Fishermen fear law will ruin livelihood (Nov. 14, 1983), The New York Times; 6NYCRR 12.19 and 6NYCRR 11.4 banning commercial fishing for striped bass in the Hudson River; Abstract of trapping and sport fishing laws and regulations (1982), state of Connecticut, Dept. of Environmental Protection, Hartford.

2 Food and Drug Administration (1979), An Assessment of Risk Associated with Human Consumption of Some Species of Fish Contaminated with Polychlorinated Biphenyls PCBs, Food and Drug Administration, Exhibit 45. Prepared by PCB Risk Assessment Work Force, Joseph Rodricks, Chairman.

3 F. Cordle, R. Locke, and J. Springer (1982), Risk assessment in a federal regulatory agency: An assessment of risk associated with the human consumption of some species of fish contaminated with PCBs. Environ. Health Perspect. 45, 177-182.

’ The data given in Table 1 were acquired from samples of fish being shipped interstate. Since such fish were required to satisfy the temporary tolerances of 5 ppm, as specified in the 1973 ruling the actual distribution of PCBs in fish under the no-tolerance scenario is most likely understated in Table 2. Note that of the 788 fish in the sample given in Table 1, only 18 exceeded the 5 ppm limit. However, the temporary tolerance already in effect is not relevant to the other estimated PCB levels under the 2 or 1 ppm tolerance level cases given in Table 2.

*Fed. Reg., June 2, 1978; and personal communications, FDA. 6 A. J. Duncan (1965), Quality Control and Industrial Statistics, 3rd ed., Irwin, Homewood, Ill. ’ Of course, the accept/reject level can be altered to shift the operating characteristic curve to the right

or left of that shown in Fig. 2. By lowering the “accept level,” for example, the curve can be shifted to the left and the ‘probability that fish above the tolerance level are accepted can be made arbitrarily small. But this would come at the expense of rejecting many more “acceptable” fish-hence, increasing the costs and economic impacts of the imposition of the standard-and the argument in the text remains valid. In short, the FDA analysis neglected both the o! and @ errors of the sampling plans.

r H. E. B. Humphrey, H. Price, and M. Budd (1976), Evaluation of Changes ofthe Level ofPolychlorinated Biphenyls (PCB) in Human Tissue, Final Report on FDA Contract 233-73-2209.

9 Federal Register Dept. of Health, Education, and Welfare, FDA. (Apr. 1, 1977). Fed. Reg. 42(63), 17493.

lo M P Brown, M. B. Werner, R. J. Sloan, and K. W. Simpson (Apr. 1983), Recent Trends in the . . Distribution of PoryChIorinated Biphenyts in the Hudson River System. draft manuscript. Inclusion of Hudson River fish data here is appropriate on two grounds. First, there was a commercial lishcry for striped bass on the Hudson that was closed. A decision to reopen the fishery rests, in part, on compliance with the FDA standard. Second, the Hudson River also has a recreational fishery aad these fish are monitored for compliance with the 5 ppm standard.


” Use of the term half-life is common in this context (see R. W. Armstrong and R. J. Sloq Pf.3 pam.rns

in Hudson R~VCT Fish 1 Resident/Freshwater Species, New York State Department of Environmental Conservation, 1982), but not entirely accurate. It does not, for example, imply that the pCB levels in a& original fish will have declined by this amount. Rather, it means that the average PCB concentration of the extant fish will be 50% of the original value and is a complex function of rates of PCB. accumulation and elimination as well as birth and death rates of fish.

” R. M. Lucas et al. (Nov. 12, 1982), PCBs in Human Adipose Tissue and Mothers Milk, final report, Research Triangle Institute RTI/l864/50-03F to USEPA Contract EPA 68-O l-5848.

I3 R F. Bopp H. J. Simpson, C. R. Olsen, R. M. Trier, and N. Kostyk (1982), Environ. Sci. Technol. 16(10x 666-678.

I4 National Cancer Institute (1978) Bioassay of Aroclor 1254 for Possible Carcinogenicity, DHEW Pub. No. NIH 78-838. NCI, Washington, D. C.

“R D Kimbrough R. A. Squire, R. E. Linder, J. D. Strandbert, R. J. Mondali, and V. W. Burse ( 1975): Induction of livkr tumors in Sherman strain female rats by polycblorinated biphenyl Aroclor 1260, J. Nat. Cancer Inst. 55, 1453-1459.

I6 R D Kimbrougb (1974), Toxicity of polychlorinated polycyclic compounds and related chemicals, . . Crit. Rev. Toxicol.. 2(4), 445448. R. D. Kimbrough and R. E. Linder (1974), Induction of adenofibrosis and hematomas of the liver in BALB c/J mice by chlorinated biphenyls (Aroclor 1254) J. Nat. Cancer. Inst. 53, 547-552.

” J. R. Allen and D. H. Norback (1976), Pathobiological responses of primates to polychlorinated biphenyl exposure, in National Conference on Polychlorinated Biphenyls, Chicago, EPA Publication No. 560 6-75-004, EPA, Washington, D. C.

‘s For example, the reader is directed to Drill, Friess, Hays, Loomis, and Schafer, Inc. (Feb. 1982), Potential Health Effects in the Human from Exposure to Polychlorinated Biphenyls and Related Impurities, Arlington, Va.

I9 J. Rodricks (Feb. 1984). A Review of EPA’s Carcinogenic and Reproductive Assessments. In a report to CMA PCB Panel, Environ Corp., Washington, D. C.

za According to the Statistical Abstract of the United States an individual has between a l/4 and l/5 chance of death from malignant neoplasms. As of 1978, men aged between 55 and 64 years of age, for example, have a cancer death rate of 522 per 100,000 population. Rates for other groups or ages differ, but these numbers serve to place a figure such as 2.4 per 100,000 in perspective. Advances in diagnostic skills and medical science generally, coupled with environmental changes, have brought about significant changes in apparent cancer risks. As Virginia Ernster noted, “We have come a long way since John Graunt, in his Bills of Mortality, published in the 17th century, tabulated the causes of death for the combined parishes of London and reported, among a total of 9,535 deceased, only 10 whose deaths fit the rubric ‘cancer and wolf.’ ” See V. L. Emster (Nov. 1983), The measurement of associations between environmental exposures and cancer, The Amer. Stat. 37(4), 420, et seq.

” I C Munro and D. R. Krewski (198 1), Risk assessment and regulatory decision making, Food Cosmet. . . Toxicol. 19, 552, 556.

‘* D. G. Hoe1 et al. (1975). Estimation of risks of irreversible delayed toxicity, J. Toxicol. Environ. Health 1,33-151.

23 K. S. Grump and M. D. Masterman (Apr. 1979). Assessment of Carcinogenic Risks from PCBs in Food, p. 48, prepared for the U. S. Congress, Office of Technology Assessment, under Contract 933.1350.0, Ruston, La.

24 See Note 2, p. 25. 25 N. Mantel and W. R. Bryan (1961), Safety testing of carcinogenic agents, J. Natl. Cancer Inst. 27,

455-470. 26 Given FDA’s linearity assumption, it is d&cult to rationalixe some of the current interpretations of

the tolerance level standard. The imposition of, say, a 5 ppm standard for 6r.h suggests the absurd conclusion that hsh containing 4 ppm are “safe” to eat in unlimited quantities, whereas consumption of only one fish containing 6 ppm would be “unsafe.” This would imply a much different dose-response curve, one that would resemble a “right-angled hockey stick.” In fact, in the above example, consumption of six tlsh, each containing 4 ppm, would be equivalent to the consumption of four fish, each containing 6 ppm, given the FDA model. Clearly, what is relevant here is the average PCB level of contaminated fish. Whether or not a fishery is “safe to open” (in the case of lisheries closed by FDA or other regulation) should be judged by this average level rather than by whether any fish in a population exceeds the tolerance. And indeed, such is the rationale underlying the compositing procedure currently employed by FDA. If it is argued that the


true dose-response relationship is nonlinear, thus justifying a more literal interpretation of the standard, the issue becomes moot because, as is shown later, the resulting health risks are much lower than those calculated by the FDA analysis. Nonlinear dose-response models yield tolerance levels so high (considering reported PCB levels in fish) that virtually all 6sh would satisfy the standard.

*’ C. N. Park and R. D. Snee (1983), Quantitative risk assessment: State-of-the-art for carcinogenesis, Amer. Statistician 37(4), 427 et seq.

28 D. G. Hoel, N. L. Kaplan, and M. W. Anderson (1983), Implication of nonlinear kinetics on risk estimation in carcinogenesis, Science 219,

29 Some authors, e.g., Crump (op. cit., p. 36) have used the milligram per kilogram per day approach to approximate species equivalence. Others, such as Hoe1 et al. (op. cit., p. 135), have argued for parts per million in diet or two-thirds power of weight approaches:

. . . The total species conversion factor should be arrived at by giving separate, caretul consideration to each of the following aspecta genetic species susceptibility, tissue storage, retention and distribution of material, metabolic pathways for activation and/or detoxification, interaction and synergism with other exposures, solvent effects, physiologic states (e.g., age, pregnancy, hormonal state), nutritional conditions, pathologic states (e.g., chronic inflammatory diseases, endocrine diseases), and conditions of exposure in man. In calculating the conversion factor for dose exposure, it is recommended that the dose unit should be a “surface” unit, which is approximately the two- thirds power of the weight of the two species. There is no evidence as to what an appropriate total species conversion factor should be.

As is shown, important differences in risk estimates emerge from this factor. )o See Note 3, p. 181. ” J. Yellin (1983), Who shall make environmental standards, Amer. Stat. 37(4), 365. 32 J. A. Martin (1983), Science and democracy in the age of technology: Separating fact from value,

Amer. Stat. 37(4), 367. 33 H Raiffa (1982), Science and policy: Their separation and integration in risk analysis, in The Risk

Arudy& Controversy: An Institutional Perspective, (H. C. Kunreuther and E. V. Ley, eds.), pp. 32-33, Springer-Verlag, Berlin/Heidelberg/New York.

34 Statement of F. Hoerger (August 8, 1983), Director of Regulatory and Legislative Issues, Health and Environmental Sciences, Dow Chemical, as quoted in Occup. Saf: Health Rep. See also J. G. Cobler and F. D. Hoerger (1983), Analysis of Agency Estimates of Risk for Carcinogenic Risk, presented at a meeting of the Society of Risk Analysis, New York.

35 Indeed, the very meaning of words become distorted as if in a tale written by Lewis Carroll: “ ‘When I use a word,’ Humpty Dumpty said, in rather a scornful tone, ‘it means just what I choose it to mean- neither more nor less’ ” (Through the Looking Glass), to which the decision maker is likely to feel like the Duchess, “ ‘Oh, don’t bother me,’ said the Duchess. ‘I never could abide figures’ ” (Alice in Won&r/and).

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A review of the Food and Drug Administration risk analysis for polychlorinated biphenyls in fish

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