Document g2bL22y4mdn1D56dY8aMY1Z7G

To assess the maximum possible impact of further government regulation of asbestos exposure, projections were made of the use of asbestos in nine product categories for the years 1985-2000. A life table risk assessment model was then developed to estimate the excess cases of cancer and lost person-years of life likely to occur among those occupationally and nonoccupationally exposed to the nine asbestos product categories manufactured in 1985-2000. These estimates were made under the assumption that government regulation remains at its 1985 level. Use of asbestos in the nine product categories was predicted to decline in all cases except for friction products. The risk assessment results show that, although the cancer risks from future exposure to asbestos are significantly less than those from past exposures, in the absence of more stringent regulations, a health risk remains. KEY WORDS: Asbestos; risk assessment; cancer; regulation.1* 1. INTRODUCTION Since the early part of this century, evidence has accumulated indicating that lung cancer, mesothe lioma, and asbestosis are causally related to exposure to asbestos fiber.(1) Over the past 12 years, the U.S. Government has been actively involved in regulating the asbestos-using industries to minimize the occur rence of such asbestos-induced disease. Several stud ies have estimated the number of cases of asbestosrelated diseases that we can expect to observe over the next few decades as a result of occupational exposure to asbestos in the past.(2~4) However, to assess the impact of further government regulations, it is necessary to predict what the health risks would be in their absence. This paper describes a health risk assessment model designed to estimate the excess lung cancer and mesothelioma cases that we can expect to observe over the next 100 years as a result `Center for Economics Research. Research Triangle Institute, Research Triangle Park. North Carolina 27709. of future occupational and nonoccupational ex posure to asbestos; specifically, exposure to asbestos products manufactured during the years 1985-2000. The estimates are derived assuming that government regulation remains at its 1985 level, andi, therefore, represent the maximum benefits achievable through more stringent regulations. The basic logic employed in the health risk assessment model is illustrated by the flow diagram in Fi|. 1. The exposure data for the level of exposure and number of people exposed for the year 1983 were obtained from government and private sources. Projections of the total amounts of asbestos used in each of nine asbestos product categories for the years 1985-2000 relative to the amount used in 1983 were combined with the data on persons exposed in 1983 to estimate the number of people exposed to asbestos attributable to products manufactured during the years 1985-2000. In most cases, current exposure levels were assumed to remain constant until the end of the century. Linear dose-response relationships were used to relate lung cancer and mesothelioma 477 0272-4332/S7/1200-0477$05.00/l1987 Society for Risk Analysis *4 HWBUI0001955 478 Mauskopf Fig. I. Flow diagram for cancer risk assessment for asbestos products manufactured 1985-2000. incidence rates to cumulative exposure. Using a life table model, these disease incidence rates were com bined with the estimated number of people exposed to give estimates of the expected number of cases of lung cancer and mesothelioma attributable to ex posure to asbestos products manufactured in 1985-2000 for each year after 1985. Estimates were also obtained of the mean age of the victims an^l the total person-years of life lost due to this asbestos exposure. 2. ASBESTOS EXPOSURE AND ILL HEALTH Numerous human and animal studies have documented the correlation of exposure to asbestos fibers with increased incidence of many diseases, including asbestosis, lung cancer, mesothelioma, gas trointestinal cancer, and other cancers. Although much of the research has focused on the effects of exposure to the high levels of asbestos typically asso ciated with occupational exposure before 1972, there is evidence that low exposures may be hazardous. At low concentration levels, lung cancer and mesothe lioma present the greatest threat to human health.15'61 Therefore, these are the only diseases analyzed in this paper. To the extent that asbestosis and other cancers are induced by low exposures, these estimates of the health effects from future exposure represent a lower bound. 2.1. Time between Onset of Exposure and Diagnosis of an Asbestos-Related Disease This analysis is restricted to the health effects attributable to exposure to asbestos products manu factured between 1985 and 2000. These effects of future exposure would not be apparent until after 1995 because of the long time that usually elapses between onset of exposure and diagnosis of an asbestos-related disease. The time between onset of Risks of Future Exposure to Asbestos 479 exposure and diagnosis of disease for lung cancer 3. NUMBER OF PEOPLE EXPOSED TO usually ranges from 20 to 40 years with a minimum ASBESTOS AND LEVEL OF EXPOSURE 0f 10 years. This range can be partially explained by IN THE BASE YEAR 1983 the apparent action of asbestos to increase the gen eral population risk by a factor proportional to Individuals may be exposed to asbestos both in cumulative exposure. Seidman etal.0) reported a occupational and nonoccupational settings. Occupa shorter lag time when initial exposure occurred at the tional exposure may occur among individuals em cancer ages (over age 40), which would be consistent ployed in the manufacture, installation, repair, and with this explanation. Mesothelioma also has a long disposal of the asbestos product or among those s time to diagnosis (i.e., 20-50 years with a minimum using the product at work. Nonoccupational ex ; 0f 10 years) from onset of exposure, but this latency posure can be subdivided into ambient exposure and j appears to be independent of age at first exposure. consumption exposure. Ambient exposure may occur among persons living or working close to the asbes tos product manufacturing site or close to a site 2-2. Level and Duration of Exposure where the asbestos product is used or discarded. Consumption exposure may occur among the con Epidemiological studies of their dose-response sumers of asbestos products. relationships have been performed with heavily ex For each stage in the product life-cycle for each posed industrial cohorts. The data from these studies product, data on the mean level of exposure and the are consistent with the assumption that excess mortality from lung cancer and mesothelioma is pro portional to both the level and duration of exposure number of people exposed were developed, to the extent possible, from data collected by the Occupa tional Safety and Health Administration, the Re to asbestos fibers. The most direct evidence for a search Triangle Institute, Versar Incorporated, Con- linear dose-response relationship for lung cancer comes from two studies.10' However, the implied sad, and the Bureau of Labor Statistics. All data were assumed to correspond to the 1983 production dose-response constants for these two studies as well level of asbestos products. T#ble I presents the 1983 ' as those derived from other studies vary in magni- exposure estimltes for nine product types used in the ) tude, from 6.0X10-4 to 6.8X 10-2.(U) Dataware also analysis. available for mesothelioma that are consistent with a linear relationship to cumulative exposure/7,12,13' with implied dose-response constants varying from 0.7 X10~9 to 0.2 X 10~7/u> None of these studies showed any evidence of a threshold level of exposure below which exposure to asbestos is safe. 4. PROJECTED NUMBER OF PEOPLE EXPOSED TO ASBESTOS, LEVEL OF EXPOSURE, AND MEAN DURATION OF EXPOSURE DURING THE YEARS 1985-2000 2.3. Fiber Type Chrysotile is the main type of asbestos fiber used in the United States except for A/C pipe, which also contains amosite. Animal studies indicate that the physical condition of the fiber is the most im portant factor in inducing asbestos-related diseases. Differences in risk of lung cancer or mesothelioma, formerly attributed to fiber type, may reflect dif ferences in distribution of fibers of various sizes in the dust of each fiber type/14' In this paper, however, it is assumed that the dose-response constants do not vary according to the fiber type or size to which the person was exposed. To estimate the number of people exposed to asbestos during the years 1985-2000, projections of the^ total amounts of asbestos used in each of the nine asbestos product categories relative to the amount used in 1983 were made for these years. These projections of growth rates of asbestos use were made using the model described by Research Triangle Institute115' and are shown in Table II. From these growth rates, indexes were constructed, using 1983 as the base year, for the relative levels of asbestos use for the nine product categories for each year during 1985-2000. With the exception of fric tion materials, the growth rates for asbestos use were predicted to be negative. In order to use the linear no-threshold doseresponse relationships described below to estimate HWBUI0001957 480 Mauskopf l. Occupational Primary manufacturing0 10s fibers per year number of people Table I. Level of Exposure to Asbestos and Number of People Exposed in 1983 Friction products A/C pipe Coatings and sealants Paper products V/A floor tile Gaskets and packing Textiles A/C sheet Plastics 1,360 240 620 260 120 5,104 512 1,327 387 276 740 740 1,380 315 413 203 560 324 Secondary manufacturing0 10s fibers per year number of people 540 1,504 -- -- ---- ---- 160 1,180 900 200 -- 9,972 164 345 2,450 Installation'' 10s fibers per year number of people -- 200 330 100 49 652 520 260 -- -- 1,675 100,000 10,168 6,800 7,500 1,500 1,265 -- Use" 10s fibers per year number of people -- -- -- __ -- -- 1,560 -- -- -- -- -- -- 2,507 '-- -- Repair/disposal'' 106 fibers per year number of people 30 551,207 -- _ 520 _ _ -- -- -- 1,500 -- -- 2. Nonoccupational Primary manufacturing** 10* fibers per year number of persons 0.0063 2.79 0.00003 0.00153 1.65 X106 1.7 X106 6.35 X106 2.25 X104 0.045 0.666 0.0504 2.79 0.00003 1.66 X106 0.78 XlO6 0.27 X 10s 1.17 XlO6 1.32 XlO6 Secondary manufacturing 106 fibers per year number of persons -- ~% - -. -- -- _ -- _ -- _ -- Installation' 10s fibers per year number of persons 0.13 _ __ -- -- -- -- 300,000 -- -- -- -- Use^ 10s fibers per year 0.008 -- -- -- 0.797 ---- -- -- number of persons 139.2 X106 -- ~ . -- 75X106 ---- -- Repair/disposalf 106 fibers per year number of persons 0.16 -- -- -- 0.6 -- -- ---^ -- 1.5 X 10s -- -- -- 64 X 10s -- -- -- -- "Derived from OSHA data (1979-1985) and RTI survey d|ita (1984) presented in Source 1. ''Derived from Consad data, Versar data, and BLS data presented in Sources 2, 3, 4, and 5. "Derived from EPA data and Lumley (1971) presented in Sources 6 and 7. aDerived from Versar data presented in Sources 3 and 5, "Derived from Versar data and BLS data presented in Sources 3jA_and.5, - Derived from Versar data and RTI data presented in Sources 3 and 5. The persons exposed to V/A tile use and repair/disposal are included in the 139.2 X10s persons living in urban regions who are exposed to use of friction products. Sources: (1) Research Triangle Institute, Regulatory Impact Analysis of The Proposed OSHA Asbestos Standard. Prepared for U.S.D.O.L., Contract No. J-9-F-4-0027, November 1985. (2) Consad Research Corporation, Asbestos Task Orderfor Construction Alternatives. Prepared for U.S.D.O.L., Contract No. J-9-F-4-0Q24, May 1984. (3) Versar Incorporated, Exposure Assessment for Asbestos. Prepared for U.S. E.P.A., Contract No. 69-01-6271, 1983. (4) U.S. Department of Labor, Employment and Earnings 30, #6, BLS, June 1983. (5) Research Triangle Institute, Regulatory Impact Analysis of Controls on Asbestos and Asbestos Products. Prepared for U.S. E.P.A., August 1985, Volume III, Appendix N. (6) K. P. S. Lumley, "Asbestos Dust Levels Inside Firefighting Helmets with Chrysotile Asbestos Covers." Annals of Occupational Hygiene 14, 285-286. (7) Memo from Amy Moll, U.S. Environmental Protection Agency, October 1, 1985. HWBUI0001958 Risks of Future Exposure to Asbestos 481 Table II. Projected Annual Growth Rates for Nine Asbestos Product Categories" Asbestos products Net growth rate {%) Friction products A/C pipe Coatings and sealants Paper products V/A floor tile Gaskets and packing Textiles A/C sheet Plastics 3.78 -6.11 -10.42 -6.90 -6.41 -2.22 -15.60 -3.18 -53.6 "Source: Research Triangle Institute, "Asbestos Products Baseline Projections," Regulatory Impact Analysis of Controls on Asbestos and Asbestos Products: Appendix C. Prepared for U.S. Environ mental Protection Agency under Contract No. 68-02-4055 (1985). the health effects of asbestos exposure, it was neces sary to have estimates of the number of persons exposed, the mean duration of exposure to asbestos for those exposed, and the level of that exposure. The total number of person-years of exposure for each exposure category was estimated using the popula tion exposure data for 1983, the production indexes described above (except for the consumer use cate gory), and the mean duration of exposure for each year of production. The person-years of exposure were then divided into estimates of number of per sons exposed and their mean duration of exposure.(15) The total number of people estimated to be occupa tionally exposed to asbestos products manufactured from 1985 to 2000 was approximately 0.9 million, and nonoccupationally exposed, 150 million. Those nonoccupationally exposed included many persons who might be exposed to more than one source of asbestos. Mean levels of exposure were assumed to remain constant at the 1983 level during the years 1985 to 2000 except for the consumer use category where they were assumed to vary with projected production levels. 5. PROJECTION OF HEALTH EFFECTS 5.1. Life Table Model Overview The health effects of exposure to asbestos prod ucts manufactured between 1985 and 2000 were estimated on a product-by-product basis. For each product, the population at risk was subdivided into the exposure categories according to when in the product life-cycle exposure takes place and whether they were exposed occupationally, in the ambient air, or in use of the product. Each exposure category was further subdivided into 10-year age groups. All mem bers of each group were assumed to have the age in 1985 equal to the midpoint of the group range. For each member of an exposure category, future ex posure was defined as the duration and intensity of exposure to asbestos that results from products manufactured during the years between 1985 and 2000. For one member of each age subgroup, the health effects of this future exposure were then estimated using an adaptation of the life table model described in Eddy.(16) To make this estimate, a nonstationary Markov process was constructed containing 11 states that an individual might be in during a specific 1-year time period: 1. 2. 3. 4. 5. 6. 4 7. 8. 9. 10. 11. Alive, with no known lung cancer or mesothelioma. Alive, but cured of lung cancer or mesothelioma. Dying of lung cancer duetto base rate or exposure to asbestos products manufactured^rior to 1985. Dying of lung cancer due to exposure to asbestos products manufactured between 1985 and 2000. Dying of mesothelioma due to exposure to asbestos products manufactured prior to 1985. Dying of mesothelioma due to exposure to asbestos products manufactured between 1985 and 2000. Dead of lung cancer--base rate or previ ous exposure. Dead of lung cancer--future exposure. Dead., of mesothelioma--previous ex posure. Dead of mesothelioma--future exposure. Dead of other causes. It was assumed that in 1985, an individual has a probability 1 of being in state 1 and at age 90 a probability 1 of being dead; i.e., in states 7-11. It was also assumed that an individual can only con tract either lung cancer or mesothelioma once and that he or she cannot contract both diseases. The s 1 HWBUI0001959 482 Mauskopf appropriate transition matrix for each year between 1985 and age 90 was estimated and used to calculate the change in probability of an individual being in states 8 and 10 each year. The probability of con tracting lung- cancer or mesothelioma each year was also calculated using the transition probabilities. Estimates of excess cancers from previous exposures are not presented in this paper. These probabilities, generated for each remaining year of the person's life, were multiplied by the number of people in the subgroup to obtain estimates of the expected cancer deaths/cases for the whole population subgroup. The outputs just described were for a single age subgroup of an exposure category for a single prod uct category. To compute the total health effects of future exposure to asbestos, these outputs were ob tained for all the age subgroups of all exposure categories for all fhe product categories and com bined. Timing of exposure was important here. For the occupational exposure categories, it was assumed that manufacture and installation take place in the same year. Disposal or repair of products manufac tured between 1985 and 2000 takes place after life time use is completed. Thus, the deaths/cases avoided for this occupational exposure category were dis placed by a number of years equal to average lifetime use before being added to tho^e related to manufac ture and installation. Those exposed nonoccupationally to the manufacture, installation, and disposal of products were treated the same way as the corre sponding occupationally exposed groups. Those ex posed during the use of the asbestos product were assumed to start exposure at the time of manufacture and continue to be exposed for the average life of the product. Results of such calculations for the nine asbes tos product categories were summed to determine expected total cancer deaths/cases. The lifetime probability of contracting lung cancer or mesothe lioma due to exposure to asbestos products manufac tured between 1985 and 2000 was also estimated using the life table model, as were the person-years of life lost due to the asbestos exposure. 5.2. Life Table Model Inputs A different transition matrix for each year start ing from 1985 for each population subgroup for each product was calculated to determine health effects using the data described in this section. Table III. Sex. Race, and Age Distribution of Exposed ' Populations" Characteristic Sex Male Female Proportion of population (decimal share) Occupational Konoccupational 0.79 0.49 0.21 0.51 Race White Nomvhite 0.91 0.09 0.88 0.12 Age 0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 0 0.1 0.205 0.210 0.193 0.175 0.117 0 0 0.146 0.174 0.176 0.139 0.108 0.099 0.083 0.055 0.020 "Sources: U.S. Department of the Census, Statistical Abstract of the United States (Bureau of the Census, Washington, D.C., 1980). USDOL. Employment and Earning 30, No. 6 (Bureau of Labor Statistics, Washington, D.C., June 1983). % It was assumed that the nonoccupationally ex posed population was identical" to the U.S. popula tion in terms of sex, race, smoking habits, and age distribution (see Table III). All occupational cate gories were assumed to have the same demographic characteristics, and these were estimated from in dustry data (see Table III). Smoking habits were assumed to be the same as those in the general population. Duration of previous exposure by age for the occupationally exposed was assumld to vary between 0 years for 10-19 year olds and 11 years for those over 50 years.(17) For nonoccupationally ex posed categories, previous exposure dating back to birth was assumed. Excess lung cancer incidence rates were assumed to be age dependent and also to vary according to smoking habits, sex, race, and exposure duration and intensity, while excess mesothelioma incidence rates were assumed to be independent of age, sex, race, and smoking habits, to depend on exposure duration and intensity, and to depend on age only through age at onset of exposure. Age-specific mortality rates from other causes were assumed to be constant over time, and the age distribution of the population was assumed to be stable. 8 Risks of Future Exposure to Asbestos 483 Base-line incidence and death rates for lung cancer by sex, race, and age used in the computation were taken from the Surveillance Epidemiology and End Results (SEER) study for 1973 through 1978.(18) However, these values were-adjusted upward for the older age cohorts because of past smoking rate in creases as suggested in Doll and Peto.'19' Initial annual increase rates of 2% for men over 50 and 4% for women over 40 were assumed; these rates of increase were allowed to decline over time. Death rates for mesothelioma were taken from the SEER study and were assumed to remain con stant. Death rates from all other causes were esti mated as the difference between death rates from all causes and death rates from lung cancer and mesothelioma. Death rates from all causes by sex, race, and age were estimated based on the 1978 life tables and were assumed to remain constant in the future.'20' All persons still alive at age 89 were as sumed to die during their 90th year. In this paper, the linear, no-threshold doseresponse relationships proposed by Nicholson'21' were used to convert information on asbestos exposure into excess lung cancer and mesothelioma incidence rates for each time period. For lung cancer, Nicholson postulated a relative risk model that includes a minimum 10-year latency period between onset of exposure and increased risk of cancer to give the annual excess risk of lung cancer: Il'K-l' io)> t>l0 = 0, r <10 where IL is the age-specific lung cancer incidence rate without exposure to asbestos, t is the time from onset of exposure until current age (years), d(t_lQ) is the duration of exposure from onset until 10 years (latency period) before current age (years), / is the level of exposure (fibers per milliliter, f/ml), and KL is the dose-response constant. For mesothelioma, Nicholson postulated an ab solute risk model to give annual excess risk of mesothelioma: Im= KM-f-[(t~l0f-{t-l0~df\, t>\Q+d = (t -- io)3, = 0, 10 + d > t > 10 , 10 > f where t is the time since first exposure (years), d is the total duratioh of exposure (years), / is the level of exposure (f/ml), and KM is the dose-response constant. Values used for KL and KM were those esti mated in the dose-response study of asbestos workers by Selikoff era/.,'22' KL = 1.0xl(T2, KM= 1.5X 10 "8. A sensitivity analysis was then performed based on the alternative estimates of these dose-response constants by Seidman et al.,(1) where KL = 6.8 X10"2, Km -- 5.7 X10~8. Lower bound values of KL = 0.31 X10-2'23' and Km = 0.07x10 "8 (24) were also used for a sensitivity estimate. KL and KM were assumed to be independent of fiber size or type and, thus, are the same for all exposure categories of all products. The unit measure for exposure level (/) in these equations is fibers per milliliter. These equations were developed from studies that used occupationally exposed workers with a typical exposure to 40 h/week and 50 weeks/yr and a breathing rate of 1 m3/h. Thus, 1 f/ml is equivalent to 2,000 xlO6 f/yr breathed. A normalizing factor of 2,000XlO6 was therefore used to convert exposure data given in fibers breathed per year for both occupationally and nonoccupationally exposed populations into Nicholson's measure of exposrtfe level, /, to use the Nicholson dose-response relationships for all, ex posed population categories. Cancer cure rates and annual death rates for the terminal patients were assumed to be constant across demographic groups, exposure categories, and prod ucts. These constants were estimated by fitting the equation (relative survival rate), = c 4- (l -- c)(l -- b)` for both lung cancer and mesothelioma, where c is the cancer cure rate, b is the annual mortality rate for dying patients, and t is the time since diagnosis (years). Data for these estimations were obtained from Axtell era/.'25' and Chahinian.'26' Values esti mated and used in the analysis were cure rates of 8% and 2%, and annual death rates of 81% and 71% for lung cancer and mesothelioma, respectively. 6. RESULTS Estimates of asbestos-related cancer cases attributable to future exposure to all asbestos products manufactured during the 16 years 1985-2000 are HWBUI0001961 484 Mauskopf Table IV. Estimates of Asbestos-Related Cancers by Type of Exposure: Occupational or Nonoccupational Lung cancer Mesothelioma Decade Occupational Nonoccupational Occupational Nonoccupational 1985T-1994 0 .0 0 0 1995-2004 27 2 1 0 2005-2014 75 17 12 3 2015-2024 90 37 35 17 2025-2034 76 61 55 48 2035-2044 49 77 54 94 2045-2054 19 80 32 131 2055-2064 3 69 8 138 2065-2074 0 42 0 101 2075-2084 0 17 0 51 2085-2094 0 4 0 14 2095-2104 0 0 0 2 Total: 339 406 197 599 presented in Table IV separately by type of exposure, occupational and nonoccupational. The projected total number of cancer cases is 1,539 occurring over the next 100 years. These 1,539 cases of cancer occur among the 0.9 million people projected to be occupa tionally exposed and the approximately 150 million nonoccupationally exposed to the asbestos products, manufactured in 1985-2000. Of the people getting lung cancer, approximately 91% die from the disease. For mesothelioma, the death rate is 95%. Of the total number of projected cancer cases, 536 (35%) are predicted to occur in those occupationally exposed and 1,005 (65%) in those nonoccupationally exposed. These results demonstrate that, as occupational ex posure levels fall, an increasing proportion of asbes tos-related cancers will occur in those exposed to asbestos in the ambient air. The results presented in Table IV were obtained using the dose-response constants for lung cancer and mesothelioma estimated by Selikoff(22> from his study of insulation workers. In Table V, the implications of using different dose-response constants are seen. Kt = 6.8x10"2 and KM = 5.7x10"8 are the dose-response constants estimated by Seidman et al.{1) from a study of workers exposed to asbestos for only short time periods (most less than 1 year). Projected cases of cancer were 8,090 as opposed to 1,539 with the Selikoff constants. KL = 3.1x10"3 (23) and Km = 0.7x10~9<24) were used to give lower bound estimates, 268 cases projected. An important feature of the risk assessment method described in this paper is that projections of the calendar time when the excess cancer cases are Table V. Sensitivity of the Estimates of Asbestos-induced Cancers to Choice of KL and Ku, the Dose-Response Constants Dose-response constants Total lung cancer cases Total mesothelioma cases Total cancer cases o1 O r*4 743 796 1,539 N 90 XX ^11 *II o1 rH H Kl~ 6.8X10-2 Km= 5.7 X*0-8 Kl = 3.1 XlO-3 Kl = 0.7X10-9 5,092 w 231 2,998 3f & 8,090 268 expected to occur are also obtained as well as the age of the person getting the cancer. Table IV illustrates the timing of the projected excess cancer cases. For the exposed population as a whole, 50% of the excess cancer cases are projected to occur during the ftars 2025 and 2054. However, when comparing the timeing of onset of disease between those occupationally exposed and those nonoccupationally exposed, it can be seen that the bulk of the health effects are pro jected to occur 10-20 years later among those nonoc cupationally exposed. This is due both to delays in onset of exposure and to the younger ages of some of the nonoccupational group. Age difference at onset of exposure affects the time lag from onset of ex posure until onset of lung cancer or mesothelioma. For the occupationally exposed population, the mean time lag from onset of exposure to lung cancer is 34 years, and to mesothelioma, 46 years. For the nonoc cupationally exposed population, the mean time lag from onset of exposure to lung cancer is 44 years, i HWBUI0001962 Risks of Future Exposure to Asbestos 485 Table VI. The Effects on Person-Years and Life Expectancy of Exposure to Asbestos Products Manufactured 1985-2000 Occupational Nonoccupational exposure exposure Person-years of life lost due to asbestos exposure - -5,407 12,038 Changes in individual life expectancy due to asbestos exposure 1. All persons 2. Those contracting an asbestos-related disease 2.12 days 10.09 years 0.029 days" 11.98 years Probability of getting asbestos-induced cancer 1. Lung cancer 2. Mesothelioma 3.1 X10'4 2.1 X10'4 2.7X10'6" 4.0 X IQ" 6 " "Calculated assuming 150 million people nonoccupationally ex posed to asbestos. and to mesothelioma, 55 years. The mean age of the victims is approximately 71 years for those occupa tionally exposed and 69 years for those nonoccupa tionally exposed. Table VI presents estimates of the person-years of life that would be gained if no asbestos products were manufactured during the period 1985-2000, subdivided according to occupational or nonoccupational exposure. This table also includes estimates of the increase in life expectancy per person exposed if no asbestos exposure occurred, and of the lifetime probability of getting lung cancer or mesothelioma attributable to products manufactured in 1985-2000. These estimates indicate that those relatively few who were occupationally exposed are much more likely to suffer adverse effects at the individual level than those who were nonoccupationally exposed. However, in the aggregate, more person life-years lost attributable to asbestos-induced cancers were estimated for those who were nonoccupationally ex posed. 7. DISCUSSION Projections of asbestos-related cancers over the next few decades attributable to occupational ex posure during the time period 1940-1979 have been made by Nicholson eia/.,(3) Walker et al.,{V> and Hogan and HoeI.(2) Their projections, adjusted to refer to a 16-year exposure period, range from ap proximately 145,000 to 233,000. The large difference between their estimates for the effects of past ex posures and the projections presented here for future occupational exposures, 536 cancer cases, can be explained both by the lower levels of asbestos ex posure now experienced and the smaller number of people occupationally exposed because of the sharp decline in the use of asbestos products. Estimates of cancer cases attributable to ex posure to asbestos use dose-response constants that have been estimated from epidemiological studies. However, the range of estimated dose-response con stants between the studies is large. Table V illustrates that projections of future cancer cases are sensitive to the dose-response constants assumed. Thus, a single numerical estimate of future cancer cases should be interpreted with caution. Alternatively, a range of dose-response constants can be used to derive a range of excess cancers likely to be observed. The estimates of excess cancers presented are also sensitive to other assumptions made during the estimation procedure. For example, at these low ex posure levels, excess cancer estimates are directly proportional to the level of exposures assumed, as well as to the number of people exposed. The excess lung cancer estimates are also directly proportional to the base-line incidence rates assumed for thp period 1995-2095. In this paper, 1977 incidence rates were adjusted upwards for the older age groups to reflect past increases in smoking rates in these cohorts. Since smoking rates now appear to be declining in the younger age cohorts, a decline in lung cancer incidence could be projected for the twenty-first cen tury. Such a decline would result in a proportionate decline in predicted excess lung cancer cases for the same time period. An important property of the risk assessment model described in this paper is that as well as giving estimates of cancer cases attributable to future ex posure to asbestos, information is obtained as to when in the future those cases would occur and what the mean ages are of those persons contracting these diseases. This enables us to discount excess cancer cases back to the present at a social rate of discount, if desired, as well as estimate the person-years of life lost as a result of exposure to asbestos. In comparing the effects of different government policies or regulations, it is apparent that "lives" saved are not a uniform commodity. The following 486 Mauskopf three questions illustrate important questions that should be asked when using the results of a quantita tive risk assessment as an input into regulatory deci sion making: 1. Should saving the life of a 40 year old be valued the same as saving the life of a 50 year old or a 20 year old? 2. Should saving the life of a 40 year old today be valued the same as saving the life of a 40 year old in 20 years time? 3. Is it of greater value to society to increase the life expectancy of a few people by many days each or to increase the life expectancy of many people by only a few days each? Only when such questions can be answered, can the estimated risks for different hazards truly be com pared. Risk estimates derived using the methodology proposed in this paper allow for a complete specifica tion of the lives saved in terms of age, timing, and size of population at risk. REFERENCES 1. I. J. 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