Excerpt Two - Chapter Four - "Risk of Cancer - All Sites," pp. 167-176. The Committee's Preferred Risk Models The committee's models for each site are discussed in the respective sections on site specific cancers in Chapter 5. Only a brief summary and the equations for dose response are presented here. Leukemia (ICD 204-207): The final model for leukemia is a relative risk model with terms for dose, dose squared, age at exposure, time after exposure, and interaction effects. A minimum latency of 2 years is assumed. There is a distinct difference between the risks exhibited by individuals exposed before age 20 and those exposed later in life. Within these two groups, there does not appear to be any effect of age at exposure but simply a different time pattern within each group. A simple step function with two steps fit both groups rather well. As indicated in Chapter 5, splines can be used to smooth these transitions when desired (e.g., in the calculation of probability of causation). The leukemia model mathematically is as follows (see the general equation 4.1): where the indicator function I(T < 15) is defined as 1 if T < 15 and 0 if T > 15, T is years after exposure, and E is age at exposure. The estimated parameter values and their standard errors, in parentheses, are: The standard errors for the dose effect coefficients were estimated by means of the likelihood method mentioned above and are both imprecise and highly skewed (see Annex 4F). The Monte Carlo analysis of the statistical uncertainty in the risk estimates for leukemia, described below in the section on uncertainty in point estimates, provides a better measure of the precision. Cancers other than leukemia: In fitting the data for cancers other than breast cancer and leukemia, a 10-year minimum latency was assumed; this was done simply by excluding all the observations (cases and person-years) less than 10 years after exposure. As for leukemia, similar fits could be obtained with either additive or relative risk models, but with different modifying effects (see Annex 4D). As was the case for leukemia, relative risk models were more parsimonious or required weaker modifiers. The committee subdivided solid tumors into cancers of the respiratory tract, breast, digestive tract, and other sites as described in the 8th revision of the International Classification of Diseases (ICD) (ICD67). Respiratory cancer (ICD 160-163): The committee's preferred model is as follows: where T = years after exposure and I(S) = 1 if female, 0 if male with a1 = 0.636(0.291), B1 = -1.437(0.910), B2 = 0.711(0.610). Under the committee's model, the relative risk for this site decreases with time after exposure. The coefficient for time after exposure, -1.437, means that the relative risk will decrease by a factor of about 5 over the period of 10 to 30 years post-exposure. The committee notes that few data are available, as yet, on respiratory cancer among those exposed as children. Finally, the relative risk is 2 times higher for females (owing to their much lower baseline rates) than for males, although the observed excess risks are similar. The fit of a constant relative risk model to the data on respiratory cancer is not statistically different from that for the committee's preferred model. When testing departures from a constant relative risk model, the addition of a parameter for time after exposure resulted in the greatest improvement in describing the data. This finding is consistent with the decreasing relative risk observed in the Ankylosing Spondylitis study (Da87) which influenced the committee's choice of parameters. While the inclusion of a parameter for sex did not improve the model's fit to the data significantly, there was some improvement, and the committee felt that it was appropriate to include a parameter for sex. Although it had been used in other risk models for respiratory cancer, there was no improvement whatever when a term for age-at-exposure was added to the regression model. When in fact such a term was estimated, its value was sufficiently close to zero as to have no influence on the estimated risk. Breast cancer (ICD 174): The breast cancer models are based on a parallel analysis of several cohorts. The important modifying factors found were age at exposure and time after exposure. The dependence of risk on age at exposure is complex, doubtless being heavily influenced by the woman's hormonal and reproductive status at that time. Lacking any data on these biological variables, the committee found that the best fit was obtained with the use of an indicator variable for age-at-exposure less than 16, together with additional indicator or trend variables depending on the data set. Both incidence and mortality models were developed. Although these differ, the highest risks are seen in women under 15-20 years of age at exposure. Risks are very low in women exposed at ages greater than 40. This suggests that risks decrease with age at exposure. Finally, risks decrease with time after exposure in all age groups. These issues are discussed in some detail in Annex 4E and the section on breast cancer, in Chapter 5. The model for breast cancer age specific mortality (female only) is where E is age at exposure and T is years after exposure with a1 = 1.220(0.610), B1 = 1.385(0.554), B2 = -0.104 (0.804), B3 = -2.212 (1.376), B4 = -0.0628 (0.0321). Digestive cancer (ICD 150-159): The most significant aspect of the LSS data is the greatly increased risk (factor of 7) for those exposed under the age of 30. Although the committee has no explanation for this observation, the LSS data strongly support this effect. There is no evidence of a significant change in the relative risk with time after exposure. The committee's preferred model is: where I(S) equals 1 for females and 0 for males and with E = age at exposure. The estimated parameter values are a1 = 0.809(9.327), B1 = 0.553(0.462), B2 = -0.198(0.0628). Other cancers (ICD 140-209 less those listed above): This group of miscellaneous cancers contributes significantly to the total radiation-induced cancer burden. Finer subdivision of the group did not, however, provide sufficient cases for modeling individual substituent sites. When attempted, the models were quite unstable, resulting in risk estimates for which there was little confidence. The general group of "other cancers" was reasonably fit by a simple model with only a negative linear effect by age-at-exposure at ages greater than 10. There was no evidence of either an effect by sex or by time after exposure. The preferred model is where E = age at exposure and a1 = 1.220(0.519), B1 = -0.0464(0.0234). Nonleukemia: For risk estimation, the committee simply chose to sum the risks of the components of the nonleukemia cancer group (i.e. respiratory cancer, digestive cancer, etc.). Alternatively, modeling the risk for all nonleukemia cancers directly yielded models which are linear in dose with additional variables for sex and time. These models provided a significantly poorer fit than other reasonable models and also project greater estimated risks (see Annex 4D). Analysis of the ankylosing spondylitis study (ASS) data for all cancers other than leukemia and colon gave a somewhat different picture. Here the fit was significantly improved by the addition of linear and quadratic terms for time after exposure, so that the risk essentially decreases to zero after about 20 years post-exposure. Part of the difference between the LSS and ASS data may be due to differences in the proportions of cancers of different sites. The most common cancers in the ASS series are lung cancer and breast cancer, the frequency of which declined with time after exposure in both data sets. On the other hand, cancers of the digestive system were very common in the LSS and showed no variation with time after exposure. RISK ASSESSMENT Point Estimates of Lifetime Risk Methods: The committee used standard lifetable methods as outlined in Chapter 1. Vital Statistics of the United States 1980 was used as the source of baseline data on cancer mortality (PHS84). The fitted risk models described above were applied to a stationary population having United States death rates for 1979-81 (NCHS85) and lifetime risks calculated for the following patterns of exposure. * Instantaneous exposure causing a dose equivalent to all body organs of 0.1Sv (10 rad of low-LET radiation), varying the age at exposure by 10-year intervals and taking the population-weighted average of the resulting estimates, weighted by the probability of surviving to a specified age in an exposed stationary population. * Continuous lifetime exposure causing a dose equivalent in all body organs of 1 mSv (0.1 rad of low-LET radiation) per year. * Continuous exposure from age 18 to age 65 causing a dose equivalent to all body organs of 10 mSv (1 rad of low-LET radiation) per year. Application to low dose rates: Since the risk models were derived primarily from data on acute exposures (a single instantaneous exposure in the case of the LSS data, or fractionated but still high dose rate exposures in the case of most of the medical exposures), the application of these models to continuous low dose-rate exposures requires consideration of the dose rate effectiveness factor (DREF), as discussed in Chapter 1. For linear-quadratic models, there is an implicit dose-rate effect, since the quadratic contribution vanishes at low doses and, presumably, low dose-rates leaving only the linear term which is generally taken to reflect one-hit kinetics. The magnitude of this reduction is expressed by the DREF values. For the leukemia data, a linear extrapolation indicates that the lifetime risks per unit bone marrow dose may be half as large for continuous low dose rate as for instantaneous high dose rate exposures. For most other cancers in the TABLE 4-2 Excess Cancer Mortality Estimates and Their Statistical Uncertainty-Lifetime Risks per 100,000 Exposed Personsa Male Female Total Nonleukemiab Leukemiac Total Nonleukemia Leukemia Single exposure to 0.1 Sv (10 rem) 770 660 110 810 730 80 90% confidence limitsd 540-1,240 420-1,040 50-280 630-1,160 550-1,020 30-190 Normal expectation 20,510 19,750 760 16,150 15,540 610 % of normal 3.7 3.3 15 5 4.7 14 Total years of life lost 12,000 14,500 Average years of life lost per excess death 16 18 Continuous lifetime exposuree to 1 mSv/y (0.1 rem/y) 520 450 70 600 540 60 90% confidence limitsd 410-980 320-830 20-260 500-930 430-800 20-200 Normal expectation 20,560 19,760 790 17,520 16,850 660 % of normal 2.5 2.3 8.9 3.4 3.2 8.6 Total years of life lost 8,100 10,500 Average years of life lost per excess death 16 18 Continuous exposuree to 0.01 Sv/y (1 rem/y) from age 18 until age 65 2,880 2,480 400 3,070 2,760 310 90% confidence limitsd 2,150-5,460 1,670-4,560 130-1,160 2,510-4,580 2,120-4,190 110-910 Normal expectation 20,910 20,140 780 17,710 17,050 650 % of normal 14 12 52 17 16 48 Total years of life lost 42,200 51,600 Average years of life lost per excess death 15 17 aBased on an equal dose to all organs and the committee's preferred risk models--estimates rounded to nearest 10. bSum of respiratory, breast, digestive, and other cancers. cEstimates for leukemia contain an implicit dose rate reduction factor. dAdditional sources of uncertainty are discussed in Annex 4F. eA dose rate reduction factor has not been applied to the risk estimates for solid cancers. LSS, the quadratic contribution is nearly zero, and the estimated DREFs are near unity. Nevertheless, the committee judged that some account should be taken of dose rate effects and in Chapter 1 suggests a range of dose rate reduction factors that may be applicable. It must be emphasized, however, that such reductions should be applied only to the non-leukemia risks, as the leukemia risks already contain an implicit DREF owing to the use of the linear-quadratic model. For this reason, the tables which follow report excess risks for leukemia and all other cancers separately even though the quadratic term for leukemia is numerically negligible at 0.1 Sv. Faced with a similar situation, the BEIR III Committee chose to estimate a DREF from the leukemia data and apply it to the nonleukemia data as a fixed constant. After considerable discussion, this committee concluded that it could not justify assuming the same dose-response model for all cancer sites and, therefore, fitted separate dose-response models, with no DREF. The method of lifetime excess risk estimation used in this report differs slightly from that used in BEIR III (NRC80) and UNSCEAR (UN77,UN88) reports. In this report, separate lifetime risks are estimated for exposed and unexposed populations, and the excess risk is simply the difference between the two lifetime risk estimates. Competing risks due to other radiogenic cancers are included in the population decrement. In the other reports, the differences in age-specific rates between exposed and unexposed populations were multiplied by the survival probabilities for an unexposed population and summed. Because an exposed population will have smaller survival probabilities, the method used here produces lower excess risk estimates, which more correctly reflect the difference in the lifetime risk of cancer mortality. Vaeth and Pierce (Va89) have shown that the ratio of the two estimates is approximately the lifetime probability of not dying of cancer, or in this case, about 0.8. Results: Table 4-2 summarizes the estimates of lifetime risks for leukemia and all other cancers resulting from two continuous exposure situations (lifetime and ages 18-65) and a population-weighted instantaneous exposure to persons of all ages. These results were obtained using the committee's preferred relative risk models for each site and a lifetable analysis that accounts for all competing risks including those due to radiation-induced cancer. Stratification of these results by age at exposure and by cancer site, for the case of instantaneous exposure, is provided in Table 4-3. Results from alternative risk models are considered in Annex 4D to this chapter. Table 4-4 provides a comparison of the risk projections under the preferred relative risk models from this report and the relative and absolute risk models in the BEIR III report. Overall, the risk estimates in this report are consistently higher than in the BEIR III report. This is due, in part, TABLE 4-3 Cancer Excess Mortality by Age at Exposure and Site for 100,000 Persons of Each Age Exposed to 0.1 Sv (10 rem) MALES Age at Exposure Total Leukemia Nonleukemia Respiratory Digestive Other 5 1,276 111 1,165 17 361 787 15 1,144 109 1,035 54 369 612 25 921 36 885 124 389 372 35 566 62 504 243 28 233 45 600 108 492 353 22 117 55 616 166 450 393 15 42 65 481 191 290 272 11 7 75 258 165 93 90 5 - 85 110 96 14 17 1 1 Averagea 770 110 660 190 170 300 FEMALES Age at Exposure Total Leukemia Nonleukemia Breast Respiratory Digestive Other 5 1,532 75 1,457 129 48 655 625 15 1,566 72 1,494 295 70 653 476 25 1,178 29 1,149 52 125 679 293 35 557 46 511 43 208 73 187 45 541 73 468 20 277 71 100 55 505 117 388 6 273 64 45 65 386 146 240 - 172 52 16 75 227 127 100 - 72 26 3 85 90 73 17 - 15 4 - Average 810 80 730 70 150 290 220 aAverages are weighted for the age distribution in a stationary population having U.S. mortality rates and have been rounded to the nearest 10. See also footnotes to Table 4-2. 90% confidence interval for these risk estimates are listed in Annex 4D, Table 4D-4. to this Committee's use of a linear dose response model for cancers other than leukemia rather than a linear quadratic one with an implicit DREF of nearly 2.5, as was the case in the BEIR III Committee's report. However, there are several other reasons for the differences between the two sets of results. These include the new dosimetry for the LSS data (Annex 4B), the additional years of follow-up, and the changes in the structure of the fitted models. In their work on the comparison of T65D and DS86 risk estimates using linear dose response models, Preston and Pierce (Pr88) concluded that while the changes in leukemia risk estimates were largely attributable to changes in dose estimates, the other two factors were more important for solid cancers; so that only 35-40% of the increase in their risk estimates was due to the use of the DS86 dose estimates. TABLE 4-4 Comparison of Lifetime Excess Cancer Risk Estimates from the BEIR III and BEIR V Reports FOR REFERENCE SEE (5bb016) FIGURE 4-1 Excess mortality due to solid cancers per 104 person Sv (million person rem). Results of 1,000 Monte Carlo simulations and lifetable analyses of the excess mortality from all solid cancers following an acute total body dose of 0.1 Sv. The populations at risk are 100,000 males and 100,000 females. The Committee's preferred models yield a point estimate for males of 660 excess deaths; for females, 730. In 50% of the trails, the excess mortality for males was between 590 and 820 deaths; for females, between 670 and 860 deaths.