1 ANNEX 2: Assessment of the 7 points agreed by WATCH as meriting attention (cover paper, paragraph 9, bullet points) by Andy Darnton, HSE The 7 issues to be addressed outlined in paragraph 9 of the cover paper have been grouped together and discussed under three headings as follows: 1. Low-dose extrapolations of the H&D model and associated uncertainties i. Determine extrapolated risks at 0.1, 0.01, and f/ml.years; ii. Determine confidence limits for the above extrapolations; and iii. Outline justification for limiting extrapolations to crocidolite & amosite. 2. Adjusting the H&D predictions to allow for life expectancy and age of first exposure iv. Determine age-related adjustment factors to age 0; v. Determine age-related adjustment factors for children with life expectancy to age 90; 3. Exposure ranges consistent with a given level of risk vi. Determine exposure levels for "acceptable" risk; vii. Determine exposure levels for risks that would be double those for spontaneous mesothelioma.
2 1. Low-dose extrapolations of the H&D model and associated uncertainties The summary statement agreed following the October 2008 WATCH meeting (Annex 1 here) included combined lifetime risk estimates for mesothelioma and lung-cancer for cumulative exposures to each of the three main asbestos types of 10, 1 and 0.1 f/ml.yr assumed to be accrued from age 30 over 5 years. These were included to illustrate the sorts of estimates that can be produced using the model. Consideration of the risk at these three particular exposure levels arose from the papers prepared for the October 2008 meeting. The intention of these papers was to illustrate the uncertainties in predictions at various points progressing down the exposure scale in order to appropriately inform the discussion about what could be reliably said about the risk at different points. Additional extrapolations have now been done, progressing further down the exposure scale beyond 0.1 f/ml.yr in orders of magnitude to f/ml.yr in order to illustrate the predictions of the model and how these differ from a simple linear extrapolation. These extrapolations are shown in Tables 1-3. For each of the three asbestos types extrapolations have been produced for mesothelioma and lung cancer separately and combined and using both the H&D models and a simple linear extrapolation in order to show the contribution of mesothelioma and lung cancer to the extrapolated total risk values at different exposures. Lifetime risks were calculated from the H&D models using the methods set out on p584 in the original paper.
3 Table 1. Lifetime risk extrapolations for crocidolite exposure for 5 years from age 30 Lifetime risk per million: Cumulative exposure (f/ml.yr) Crocidolite H&D best estimate (minimum, maximum) Linear Mesothelioma Lung cancer Total (24000, 58000) (8000, 26000) (32000, 84000) (2300, 13000) (200, 2600) (2500, 16000) * 1200 (230, 3300) (5, 260) (240, 3600) (23, 840) (0.13, 26) (23, 870) (2.3, 210) (0.0032, 2.6) (2.3, 210) (0.23, 53) ( , 0.26) (0.23, 53) (0.023, 13) ( , 0.026) (0.023, 13) (0.0023, 3.3) ( , ) (0.0023, 3.3) *Lower bound of exposure in WATCH October 2008 summary statement
4 Table 2. Lifetime risk extrapolations for amosite exposure for 5 years from age 30 Cumulative exposure (f/ml.yr) Amosite Lifetime risk per million: H&D best estimate (minimum, maximum) Linear Mesothelioma Lung cancer Total (1600, 14000) (8000, 26000) (9600, 40000) (150, 3100) (200, 2600) (350, 5700) * 160 (15, 770) (5, 260) (20, 1000) (1.5, 190) (0.13, 26) (1.7, 220) (0.15, 49) (0.0032, 2.6) (0.16, 51) (0.015, 12) ( , 0.26) (0.015, 13) (0.0015, 3.1) ( , 0.026) (0.0015, 3.1) ( , 0.77) ( , ) ( , 0.78) *Lower bound of exposure in WATCH October 2008 summary statement
5 Table 3. Lifetime risk extrapolations for chrysotile exposure for 5 years from age 30 Cumulative exposure (f/ml.yr) Chrysotile Lifetime risk per million: H&D best estimate (minimum, maximum) Linear Mesothelioma Lung cancer Total (63, 700) (170, 2700) (230, 3400) (6.3, 180) 7 15 (4.2, 270) (10, 450) * 8.2 (0.63, 44) (0.11, 27) (0.73, 71) (0.063, 11) (0.0027, 2.7) (0.065, 14) (0.0063, 2.8) ( , 0.27) (0.0063, 3.1) ( , 0.7) ( , 0.027) ( , 0.73) ( , 0.18) ( , ) ( , 0.18) ( , 0.044) ( , ) ( , 0.045) *Lower bound of exposure in WATCH October 2008 summary statement
6 Summary of main features of Tables 1-3 Crocidolite The mesothelioma risk dominates the total risk across the exposure range. At high exposures the mesothelioma risk predicted by H&D is similar to that based on the linear model but at low exposures the H&D predictions are much higher relative to the linear model. For lung cancer the opposite effect is seen: the H&D predictions are much lower than those of the linear model at low exposures. At the lowest exposures tabulated many orders of magnitude below the data range the mesothelioma risk based on H&D is over 50 times higher than that based on the linear extrapolation, whereas the lung cancer risk based on H&D is about 1/180th that based on the linear extrapolation. (Note, here the exposure has reduced a factor of 100,000 below the lower level considered by WATCH in October 2008 (ie 0.1 f/ml.yr), whereas the risk estimated by the H&D best model has reduced by a factor of about 5500.) For the combined risk, the predictions of the linear model are close to the bottom end of the H&D uncertainty range. Amosite Lung cancer risks are the same as those for crocidolite but mesothelioma risks are lower. Therefore, lung cancer dominates the total risk at high exposures, though mesothelioma still dominates at low exposures. At the lowest exposures the mesothelioma risk based on H&D is about 40 times higher than that based on the linear extrapolation, whereas the lung cancer risk based on H&D is again about 1/180th that based on the linear extrapolation. For the combined risk at lower exposures, the H&D predictions are still higher than the linear model but the predictions are closer than for crocidolite. Chrysotile Risks are generally much lower than for the amphiboles and soon become insignificant progressing below 0.1 f/ml.yr, whether based on the H&D model or the linear extrapolation. At high exposures H&D predicts similar risks for mesothelioma and lung cancer whereas the linear model predicts fewer mesotheliomas and more lung cancers. However, the combined risk at high exposures is similar for both the H&D and linear models. At lower exposures mesothelioma dominates based on H&D whereas lung cancer dominates based on the linear model. Basis for uncertainty ranges The uncertainty ranges of the H&D predictions encompass variation because of the different possible shapes of the non-linear dose-response curves. Thus any discussion about the width of the uncertainty intervals inevitably involves consideration of these non-linear curves and the evidence underpinning them. In order to facilitate discussion, these issues are summarised below.
7 It is important to note that the analysis in the H&D paper took in to account the statistical uncertainty in the mortality outcomes and not that of the exposure estimates. WATCH has previously considered whether any evidence based adjustment to cohort level average exposures should be made to improve the model, but concluded that there was no convincing empirical basis for doing this. In general, ignoring uncertainty in the independent variable in regression analyses may tend to lead to an underestimation of the magnitude of the slope of any fitted line. WATCH may wish to keep these issues in mind when considering the following section. Mathematical form of the H&D model mesothelioma A natural approach for exploring dose-response relationships is to fit statistical models with particular mathematical forms, use evidence to select the best model, and then produce predictions with associated confidence intervals based on that particular model. Another approach is to simply assume a given mathematical form for the model (perhaps because there is evidence from other studies), and fit this to the available data, again producing predictions and confidence intervals. Indeed, this was the starting point for the H&D analysis: a linear relationship between risk and cumulative exposure underpins the summaries produced in Tables 1 and 2 of H&D 2000 giving the mesothelioma and excess lung cancer risk per unit of cumulative exposure. Here, the resulting uncertainty ranges are derived from the statistical variation in the observed mortality outcomes from each of the studies. In the H&D analysis, further consideration of the epidemiological evidence led to a different approach in which a range of plausible models with different mathematical forms were considered. All these models can be described with the same basic equation, in which risk is proportional to cumulative exposure raised to some power which is estimated from the data. But the value of the power parameter (or slope) determines the mathematical form: the extent to which the models are non-linear (a power of 1 representing the linear case). In fact, the final mesothelioma model has two terms, the first representing pleural mesothelioma, and the second, peritoneal mesothelioma as follows: Total mesothelioma risk, P M = A pl.x r + A pt.x t Here, A pl and A pt are constants and r and t are the slope parameters for pleural and peritoneal mesothelioma respectively. In the best fitting model both terms are non-linear with r=0.75 and t=2.1, with values of A pl and A pt dependent on fibre type. (Note, A pt =0 for chrysotile which implies that there is no associated peritoneal risk for this fibre type). It is unclear why this should be the case. However, the limitations in the underlying data to which the models were fitted meant that the basis for preferring a model with particular values of the slope parameters r and t was not clear-cut and relied upon a degree of judgment. Confidence intervals for predictions based on our preferred ( best ) model are
8 therefore unlikely to capture the full extent of the uncertainty. The uncertainty ranges for the H&D predictions (eg as presented in Table 11 of H&D) therefore take into account the range of predictions from all the plausible models that we argued could be consistent with the observed data. This includes models with values of r and t both lower and higher than 0.75 and 2.1 respectively ie models with different degrees of non-linearity. Thus any consideration of the extent of the uncertainty of the predictions necessarily involves a consideration of the extent of the non-linearity. The evidence led to the fitting of 3 sets of models with slope parameters as follows: Best slope model: P M =A pl.x A pt.x 2.1 High slope model: P M =A pl.x + A pt.x 2.5 Low slope model: P M =A pl.x A pt.x 1.7 The shape of each of these models for either crocidolite or amosite exposure (with confidence intervals) is illustrated in the following graph (not drawn to scale). Because the value of A pl is much bigger than A pt, the pleural term accounts for most of the combined risk unless the exposure X is large. For low-dose extrapolations, the low slope model (with r=0.6) predicts the highest risks, and the high slope model (r=1 ie linear in this region) predicts the lowest risks. Note, since for chrysotile there is no peritoneal term the rapid increase at high exposures does not occur, though the shape for low dose extrapolations is as illustrated. 5 Comparison of dose response shapes, H&D high-slope, best, and lowslope models (illustrative purposes only) Combined pleural and peritoneal mesothelioma risk Low slope: P M =A pl X 0.6 +A pt X 1.7 Best slope: P M =A pl X A pt X 2.1 High slope: P M =A pl X+A pt X Cumulative exposure, f/ml.yr As illustrated in the following chart, the H&D maximum and minimum models are then defined by the maximum and minimum predictions from any of these models over the whole exposure range, taking account of the confidence
9 intervals of the parameters A pl and A pt. The H&D best model is given by the best slope model throughout the exposure range. Combined pleural and peritoneal mesothelioma risk Comparison of dose response shapes, H&D max, best, and min models (illustrative purposes only) H&D max model H&D best model Cumulative exposure, f/ml.yr H&D min model
10 Mathematical form of the H&D model lung cancer The H&D model for lung cancer is somewhat simpler than for mesothelioma: Excess lung cancer risk, P L = A L.X r The constant A L is common for both crocidolite and amosite, but is lower for chrysotile for each of three models with slope parameters as follows: Linear (low slope): P L = A L.X Best slope: P L = A L.X 1.3 High slope: P L = A L.X 1.6 The same approach used for mesothelioma of considering the highest and lowest predictions across the whole exposure range was used to define the H&D maximum and minimum models. Again these extremes set the upper and lower uncertainty limits of the predictions. Since the slope, r, is greater than or equal to 1 in all of these models, in contrast to mesothelioma, the upper bound of the uncertainty range in the lowest dose extrapolations is defined by the linear model, and the lower bound by the non-linear high-slope model as illustrated in the following graph. 5 Comparison of dose response shapes, H&D high-slope, best, and lowslope lung cancer models (illustrative purposes only) 4 Excess lung cancer 3 2 Best-slope: X^1.3 Low-slope (linear) High-slope: X^ Cumulative exposure, f/ml.yr
11 Summary of the evidence for non-linear risk models for mesothelioma The evidence is set out in the H&D 2000 but set out again here in summary form: Separate regressions for pleural and peritoneal mesothelioma suggest risk is proportional to a power of cumulative exposure (ie pleural risk, P r X r ; peritoneal risk, P t X t ). Best fitting values of these powers were r=0.75 and t=2.1, but the limited range of data points led to wide confidence intervals such that linear models (r or t=1) are also consistent with the data. However, at least one of the pleural or peritoneal outcomes must be non-linear since regressions of peritoneal risk (P t ) vs pleural risk (P r ) from a wider group of amphibole exposed cohorts (included those that lack the quantitative exposure data required for the dose-response modelling) suggest the peritoneal risk is proportional to at least the square or even the cube of the pleural risk (P t P r k, with k>2). The power, k, above equates to the ratio t/r in the initial regressions, so the range of plausible values of k can be fed into regressions which include terms for both pleural and peritoneal mesothelioma to further confirm that the slopes r and t are likely to be in the region of 0.75 and 2.1 respectively. Nevertheless there is considerable uncertainty associated with the slope parameters. A lower bound for the uncertainty range of r=0.6 was chosen on the grounds that an analysis by Berry etal. gave a value of 0.5 but with a likelihood of this being biased downwards. An upper bound of r=1 was chosen on the grounds that a linear relationship represents a natural assumption (effect is proportional to cause) in the absence of evidence to the contrary. Regressions were then carried out with r constrained to 0.6, 0.75 and 1.0 in order to obtain the corresponding values of t (1.7, 2.1 and 2.5 respectively) and the estimates and confidence intervals for the constants A pl and A pt within the model: P M =A pl.x r +A pt.x t. Possible biological basis for a convex (r<1) dose-response for pleural mesothelioma Low-dose extrapolations of the mesothelioma model are dominated by the pleural mesothelioma term (A pl.x 0.75 ). The shape of the resulting doseresponse curve is unusual: it is the opposite of an S-shaped dose-response curve which would tend to be consistent with threshold-like behaviour. This raises the question of whether the H&D model is biologically plausible. It is possible to envisage a non-linear relationship between cumulative exposure and pleural mesothelioma risk if the risk is dependent on some function of the concentration of fibres within the target tissue. For example, one possible explanation risk being proportion to a power of cumulative exposure less than 1 is as follows:
12 Asbestos fibres produce foci of inflammation in or near the pleura (perhaps at lymph nodes which get clogged up with fibres). These foci become the centres of approximate spheres of "inflammatory product". The volume of the inflammatory product is proportional to number of fibres in the area. Pleural cells are more susceptible to the carcinogenic influence of the inflammatory product than other cells in the area. The diameter of the sphere of inflammatory product is large in relation to the thickness of the pleura. The susceptible pleural cells thus effectively form a 2-D slice through the spherical volume of inflammatory product. Thus, cumulative exposure should be proportional to the total number of cells within the volume of inflammatory product produced, but risk will be proportional to the number of susceptible pleural cells in the slice through these spheres. If the radius of a sphere of inflammatory product is r, then the total volume of cells will be proportional to r cubed. But the volume of susceptible cells will be proportional r squared (the slice of pleural cells through the sphere). If so, then pleural mesothelioma risk should be approximately proportional to cumulative exposure to the power of 2/3. Presumably this relationship would cease to hold at some point down the exposure scale since the number of fibres would be insufficient to initiate the inflammatory process in the first place. However, this point could presumably still be at very low exposures such that in practical terms no threshold would apply. There are a number of assumptions within this argument, including the notion of spheres of inflammatory product. It also begs the questions why the power function for peritoneal mesothelioma should be very different, i.e. 2.1 rather than 0.75.
13 2. Adjusting the H&D mesothelioma predictions to allow for life expectancy and age of first exposure The key question at the root of this is how to get from the risk metric in the H&D analysis to an estimate of lifetime risk LR that is relevant to today s population. The metric for mesothelioma is just the observed number of mesotheliomas as a percentage of expected all cause mortality (adjusted to age 30): P M = 100.O m /E ac. If mesothelioma and all cause mortality follow the same pattern indefinitely within cohorts, then P M is effectively equal to the LR. After an initial follow-up period O m /E ac will be a good estimate of the LR regardless of the amount of subsequent follow-up. In the H&D 2000 paper we argued that this is not likely to be the case. If the mesothelioma risk eventually levels off and then decreases at very long times since exposure, then this means that at some point all cause mortality will accumulate more quickly than mesothelioma. If true, O m /E ac will tend to overestimate the lifetime risk. We argued that for exposure starting at age 30, the mesothelioma risk is unlikely to continue beyond age 80, so that all of the mesotheliomas resulting from such exposure will have occurred by this point. For an exposed population of 30 year old men the mesothelioma risk P M can be estimated from the model. The predicted number of mesotheliomas can then be calculated from the expected number of all cause deaths under the assumption that the ratio O m /E ac is constant up to age 80 after which O m is level (since all the mesotheliomas have occurred). The average lifetable for the 1970s predicts that 70% of men aged 30 will die before age 80. The estimated LR is therefore 0.7 x P M. With life expectancy as it was in 1970, if the mesothelioma risk does in fact continue to increase beyond age 80 in line with the all cause mortality, the predicted LR would need to be increased by about 40% (= 1 / 0.7). In reality, P M is only approximately constant up to age 80. Even if mesothelioma rates continue beyond age 80 according to the pattern of the HEI model (where the rate increases in proportion to the cube of time since exposure), the ratio of mesothelioma to all cause mortality gradually reduces at long follow up times, as shown in the following graph.
14 Cumulative expected all cause and cumulative predicted mesothelioma mortality by time since first exposure Number of deaths Time since first exposure (yrs) (1) Cumulative expected all cause mortality (2) Cumulative predicted mesothelioma mortality Ratio (2)/(1) *Cumulative all cause mortality for a cohort of 100,000 men based on the 1970s lifetable. Pattern of predicted mesothelioma following a 5 year exposure based on the HEI model in which rate (t-10) 3 (t-10-d) 3, where t is time since start of exposure and D is the duration of exposure in years. Summing the total predicted mesothelioma deaths to age 90 and the total all cause deaths to age 90 based on the 1970s average lifetable suggest that if the mesothelioma risk continues to increase indefinitely according to the HEI model, we would be need to increased the H&D LR estimate by about 20% (not 40%). We can also use this approach based on the HEI model to look at the effects of life expectancy by comparing the results when applying a more up-to-date lifetable for all cause mortality. Here the number of observed mesotheliomas is increased because of increased survival to ages when the mesothelioma risk is expressed. The approach can also be extended to derive adjustment factors for exposure starting at ages other than 30 years, adopting different assmptions for how long the mesothelioma risk continues following exposure. The following table summarises various adjustment factors under different assumptions about life expectancy and the length of time the mesothelioma risk continues following the start of exposure
15 Table 4. Adjustment factors for the H&D mesothelioma predicted LR Life Age at first exposure expectancy Number of years mesothelioma risk continues following start of exposure Up to 60* Up to 90** Today Up to 60* Today Up to 90** * Risk truncated at age 80 or age+60 if age<20 years ** Risk truncated at age 90 This analysis suggests that some adjustment may be warranted when applying the H&D model to predict risks resulting from exposure at younger ages. However, it should be noted that assumption that mesothelioma risk continues for 90 years from exposure (rows 2 and 4 in Table 4) is likely to represent an extreme worst-case scenario. In fact the limited evidence available about the risk at very long follow up times suggests that the risk may eventually reduce substantially. If so this would imply that the adjustment factors in rows 1 and 3 are more appropriate. In considering whether adjustment factors should be used, WATCH may also wish to consider that these factors may be counterbalanced by the fact that airborne concentrations are likely to be estimated as being substantially higher based on modern measurement methods than those used in the original epidemiological studies. Thus, whereas ignoring the age at first exposure will tend to lead to an underestimation of the risk, ignoring the fibre measurement differences will tend to lead to an overestimation.
16 3. Determine exposure ranges consistent with a given levels of risk Acceptable risk is subjective notion. Nevertheless, in the H&D paper we referred to LRs below 1 in 100,000 as insignificant on the grounds that they would be equivalent to annual risk well below 1 per million and are also well below the risk of mesothelioma in the absence of asbestos exposure (ie so called spontaneous or idiopathic cases). The H&D models have therefore been used to derive the range of exposures to each of the three kinds of asbestos that are consistent with LR of 1 in 100,000 (Table 5). There is considerable uncertainty in the evidence about the extent of the lifetime risk of spontaneous mesothelioma. However, the evidence points to annual incidence rate of 1-2 per million, or between about 60 and 120 cases per year (half in men and half in women). The equivalent lifetime risk is therefore in the range 1 in 5000 to 1 in 10,000. In order to illustrate the range of exposures consistent with levels of risk of this order, the H&D model has been used to estimate exposures for the mid-point of this range, ie 1 in Table 5: Cumulative exposures accrued over 5 years from age 30 that are consistent with insignificant LR or of a similar order as the plausible spontaneous risk level. Lifetime risk Cumulative exposure (f/ml.yr): H&D best estimate (maximum, minimum) Linear Crocidolite Amosite Chrysotile 1 per 100,000 (insignificant) ( , ) ( , 0.064) (0.0084, 1.6) per 7500 (spontaneous) ( , 0.056) (0.0051, 0.84) (0.60, 21) 19 Table 5 shows that a wide range of cumulative exposures could be consistent with both these levels of LR. However, in each case the linear extrapolation suggests that the exposure would have to be considerably higher than suggested by the H&D model in order to achieve LRs of these levels.