Document 6OLkjkGka5RZwwMqmQr8Z7zE

The Utility of Mortality Surveillance: The Solutia Mortality Experience 1980-1994 6/7/99 James J. Collins, PhD Solutia 10300 Olive Blvd. St. Louis, Missouri 63166 Telephone: 341-674-8811 Telefax: 314-674-6858 email: james.j.collins@solutia.com Susan G. Riordan, BS Monsanto 700 Chesterfield Parkway North Chesterfield, Missouri 63017 Address all correspondence to first author. Short title: The Utility of Mortality Surveillance OSW 146832 STLCOPCB4038679 Abstract Background: Several investigators argue that company wide mortality rates for recent workers allow early identification of potential health problems. We compare our mortality rates for recent workers with published studies of several plants in our company to determine the efficacy of mortality surveillance for identifying previously unknown health effects. Methods: We examine the percentage of workers in the surveillance reports which appear in the published studies and compare relative risk estimates for relevant causes of death in the studies with the relative risk in the mortality surveillance. Results: As reported by other companies, we find low mortality rates among workers in surveillance. Life expectancy at age 20 for White hourly males were 1.7 years greater and for non-White hourly males 4.6 years greater than the comparable U.S. population life expectancies. We did not find disease specific relative risk for our workers in surveillance predictive of relative risk in the studies. Conclusion: Mortality surveillance provides an indication of the health status of current workers. However, mortality surveillance is of limited use for identifying health effects from past workplace exposures to specific materials. The healthy worker and survivor effects, the failure to identify DSW 146833 2 STLCOPCB4038680 subsets of workers exposed to potentially toxic substances, and the inability of recent mortality levels to reflect historical conditions all may make it difficult to use mortality surveillance to identify workplace hazards. We recommend combining mortality surveillance with studies of workers with suspected toxic exposures to help identify previously unknown occupational hazards. Keywords: mortality surveillance, occupational cancer, chemical industry, life expectancy, bladder cancer, leukemia OSW 146834 3 STLCOPCB4038681 Introduction Several investigators report mortality rates for recent workers for an entire company (Pell et al., 1978; Divine et al., 1985; Hanis et al. 1985; Pifer et al., 1986; Teta et al., 1987; Bond et al., 1987; Teta et al. , 1990; Shallenberger et al., 1992; Olsen et al., 1994; Tsai et al., 1996; Divine et al., 1999a; Divine et al., 1999b). We refer to these reports as mortality surveillance. Mortality surveillance provides an evaluation of recent mortality levels without examining specific exposures as is done in a formal study. These reports uniformly show low rates of total mortality and total cancer mortality for the workers studied, although occasionally rates of some causes of death are greater than expected. Among other benefits, mortality surveillance may facilitate early identification of potential health problems (Pell et al., 1978; Teta et al., 1987; Teta et al., 1990; Tsai et al.,1991; Olsen et al., 1994; Tsai et al., 1996; Devine et al., 1999a). However, the utility of routine mortality surveillance for identifying disease occurrences which are the result of workplace exposure has not been demonstrated. The healthy worker and survivor effects, the failure to identify subsets of workers exposed to potentially toxic substances, the failure to trace workers who leave employment for reasons other than retirement, and the inability of recent mortality levels to reflect DSW 146835 4 STLCOPCB4038682 historical conditions all may make it difficult to use mortality surveillance to identify workplace hazards. We present the results of mortality surveillance for a company and compare results to past published studies of several plants in this company to determine the efficacy of mortality surveillance for identifying previously unknown health effects from workplace exposure. Methods and Materials The population for our mortality surveillance is the 43,339 workers who worked one or more days at any Solutia U.S. location between January 1, 1980 and December 31, 1994. Solutia was formally the chemical businesses of the Monsanto Company. An extensive number and variety of chemicals have been produced at these locations. Studies have been done examining some of these chemicals (Melick et al., 1955; Melick et al., 1971; Zack and Suskind, 1980; Marsh, 1983; Zack and Gaffey, 1983; Suskind and Hertzberg, 1984; Blair et al., 1986; Wong, 1987; Fingerhut et al., 1991a; Wong et al., 1991; Collins et al., 1993; Strauss et al., 1993; Ireland et al. , 1997; Blair et al., 1998; Collins et al., 1999). Company personnel and payroll records provided demographic and work history data. For all eligible workers sex, race, birth date, hire date, employment termination date, and wage status were recorded. The completeness of study population ascertainment was evaluated by comparison DSW 146836 5 STLCOPCB4038683 with the employer's quarterly report on earnings. Internal Revenue Service Form 941-Schedule A (Marsh and Enterline, 1979). A sample of the study population was verified as being 99% complete. We followed guidelines for study conduct given by the Chemical Manufacturers Association (Chemical Manufacturers' Association, 1991). Vital Status Follow-up Vital status was determined through company records and the National Death Index. Employees receiving wages or retirees receiving benefits on the last day of 1994 were assumed alive through the end of 1994. Vital status was complete for 98% (42,459 of 43,339) of the study population. 2,406 deaths were recorded. Death certificates were requested from the respective states when not available in company records. All but two of the death certificates were obtained. Death certificates were coded independently by two nosologists according to the International Classification of Diseases in use at the time of death. Differences were resolved through discussion between the two nosologists. Statistical Methods Person-years for study subjects were accumulated across 5-year age- and calendar year-specific intervals beginning with the date of hire or January 1, 1980, whichever was later. Subjects continued to contribute person-years at risk OSW 146837 6 STLCOPCB4038684 until the earliest of the date lost to follow-up, the date of death, or December 31, 1994. We limited the racial breakdown to White and non-White workers because of the small number of workers in some racial groups. We calculated standardized mortality ratios (SMRs) to compare mortality rates with those of the U.S. population (Marsh and Preininger, 1980). We present common causes of death and causes of death which have been associated with occupational exposures (Doll and Peto, 1981) . We constructed lifetables using the Greville method on the age, race, and salary specific rates to estimate life expectancy (U.S. Bureau of the Census, 1975). Life expectancy provide an overall summary of total mortality levels and, unlike the SMR, allows direct comparison among all sub-groups in the study and U.S. population. The life expectancy also provides an easily understandable summary of death rates expressed in individual terms, or average years of life remaining for an individual at a specific age. Life eiqpectancy at ages 20, 40, 60 and working life (age 20 to 65) are presented as summaries. We selected all published studies which examined the mortality rates of workers at any of our plants to compare to the findings of the mortality surveillance. When multiple studies reported on the same study population, we used only the most recent study (Melick et al., 1971; Marsh, 1983; OSW 146838 7 STLCOPCB4038685 Blair et al., 1986; Wong et al., 1991; Fingerhut et al., 1991a; Collins et al., 1993; Ireland et al., 1997; Blair et al., 1998; Collins et al., 1999). We used weighted and nonweighted linear regression to compare the relative risks of hypothesized diseases among workers in the study plant with relative risks of workers in the mortality surveillance at that plant(SAS, 1995). We limited the comparison in the surveillance data to workers with 20 or more years since first hire to exclude recently hired workers. The weights used in the regression are the percentage of workers in the study which are included in the surveillance. Results Table I presents the distribution of the 43,389 workers by selected characteristics. The study population is mostly White (85.6%) and male (72.7%). The Whites and Hispanics from Table I are combined to form the "White" percentage noted. Most workers are salaried (64.5%) which included administrative, technical and professional staff, clerks and secretaries, all workers at the General Offices, and all workers at two plants after 1993 which reclassified all hourly workers to salaried. 43.5% of the workers left the company before retirement and 24.2% are retired. More than 40% of the workers are hired before 1970. Almost 70% of the workers are in production facilities. DSW 146839 8 STLCOPCB4038686 The 43,339 workers contributed a total of 525,764 person years of observation for a mean followup of 12.1 years. Table II presents the observed and expected deaths by cause for hourly and salaried workers by sex for the entire follow-up period and 20 years since first hired. Male hourly workers have an SMR of 0.8 (95%CI 0.8-0.9) for all causes of death. Death rates for all cancer (SMR 0.9, 95%CI 0.8-1.0) and heart disease (SMR 0.9 95%CI 0.8-1.0) are also below expected levels. Most individual cancer sites for hourly males are at or below expected levels with the exception of leukemia (SMR 1.8, 95%CI 1.1-2.6), large intestine (SMR 1.2, 95%CI 0.9-1.6), bone (SMR 1.2, 95%CI 0.0-6.6), and prostate (SMR 1.1, 95%CI 0.7-1.6). SMRs for non-malignant respiratory disease (SMR 0.5, 95%CI 0.3-0.6) and accidents are also at or below expected levels. The workers who were hired 20 or more years before the end of the study period have SMRs similar to all workers. Male salaried workers have an SMR for all causes of death of 0.5 (95%CI 0.5-0.5). Observed deaths for all cancer (SMR 0.6 95%CI 0.6-0.7) and all heart disease (SMR 0.6 95%CI 0.5-0.6) are also less than expected. All individual sites of cancer and other causes of death are at or below expected levels. The SMRs for workers who were hired 20 or more years before the end of the study period are similar to all workers. ,, DSw 146840 STLCOPCB4038687 Female hourly workers have death rates (SMR=0.7, 95%CI 0.6-0.8), cancer rates (SMR=0.7 95%CI 0.5-0.9), and heart disease rates (SMR=0.5 95%CI 0.3-0.5) which are less than the U.S. population. Death rates for motor vehicle accidents are greater than the U.S. population, although there was a deficit among women 20 or more years after hire. SMRs for individual cancer sites for hourly females are at or below expected levels with the exception of leukemia (SMR 1.7, 95%CI 0.3-4.9), and large intestine (SMR 1.1, 95%CI 0.3-2.5). The SMRs for female hourly workers with 20 or more years since being hired are similar to the SMRs for all female hourly workers with the exception of stomach cancer (SMR = 1.3, 95%CI0.0-7.5). The SMRs for female salary workers are very similar to female hourly workers with low death rates (SMR=0.6 95%CI 0.5-0.7), cancer rates (SMR=0.9 95%CI 0.7-1.0), and heart disease rates (SMR=0.7 95%CI 0.4-1.0) relative to the U.S. population. Cancer SMRs are at or below expected levels with the exception of leukemia (SMR=1.5 95%CI 0.5-3.5), esophagus (SMR 1.1, 95%CI 0.0-6.2), and stomach (SMR 1.1, 95%CI 0.1 3.9). The SMRs for female salary workers 20 or more years since hire are generally similar to the SMRs for all female salary workers with the exceptions of stomach cancer (SMR=2.0, 95%CI 0.2-7.1) and malignant melanoma of the skin (SMR=1.4, 95%CI 0.0-7.6). DSN 146841 10 STLCOPCB4038688 We compare the life expectancies for Solutia workers for the year 1980-94 with the U.S. population for the years 1985-89 in Table III. Solutia males and females of both racial groupings have life expectancies at all ages which exceed life expectancies for U.S. population. At age 20, Solutia White male salary workers are estimated to live 5.3 years longer (58.9 years for Solutia White salary males minus 53.6 years for U.S. White males) and White male hourly workers 1.7 years longer than White males in the U.S. population. Solutia non-White males enjoy a larger advantage over their counterparts in the U.S. population. Salary non White males live 8.3 years longer and hourly non-white males live 4.6 years longer than their counterparts in the U.S. population. There are also large differences between salaried and hourly Solutia workers. Non-White male hourly workers have a life expectancy at age 20 of 53.6 compared to 57.3 years for non-White salary workers, or a 3.7 year difference. There are smaller differences between the racial groupings among Solutia workers. For example. White male hourly workers have a life expectancy at age 20 of 55.3 years compared to 53.6 for non-White hourly workers. Life expectancies for Solutia women are much higher than Solutia men but show less variance across groups. Life expectancy at age 20 is 60.1 years for the U.S. White female. Solutia hourly women have a life expectancy at age 20 of QSW 146842 11 STLCOPCB4038689 61.6, and Solutia salary women a life expectancy at age 20 of 62.8. We identified nine studies at six different plants included in the mortality surveillance (Melick et al., 1971; Marsh, 1983; Blair et al., 1986; Fingerhut et al., 1991b; Wong et al., 1991; Collins et al., 1993; Ireland et al., 1997; Blair et al., 1998; Collins et al., 1999). Figure 1 shows the years of employment inclusion criteria for workers in these nine studies and our mortality surveillance. Only a recent study by Blair et al. (1998) which includes workers employed between 1952 and 1983 overlaps with the employment inclusion criterion of surveillance, or employed between 1980 and 1994. The remaining eight studies have study employment inclusion criterion which are several years earlier than the employment criterion for surveillance. The percentage of workers in surveillance which appear in the studies ranges from 72% for the Blair et al.(1998) study to 7% for the study of Fingerhut et al.(1991b). We compare the study relative risks for causes of death which are causally evaluated in the studies with the relative risk in the surveillance population in Figure 2 (Marsh, 1983; Fingerhut et al. 1991b, Collins et al., 1993; Ireland et al., 1997; Blair et al., 1998; Collins et al., 1999). Three studies could not be included in this analysis because either there were no relative risks presented (Melick et al., 1971), OSW 146843 12 STLCOPCB4038690 or the relative risks were not presented by plant (Blair et al., 1986; Wong et al., 1991). There were 21 causes of death evaluated in the 6 studies included. There is little correlation when either the unweighted (R2=0.01) or the weighted (R2=0.01) regression is used to evaluate the relationship between the relative risks in the studies and the mortality surveillance. Discussion Mortality surveillance by design focuses on the mortality rates of recent workers. Thus, mortality surveillance provides a better overall assessment of mortality risk for current workers than does a formal study which typically focuses on workers who have long since left employment. We found low rates of total mortality, heart disease, accidents, and cancer among our workers in surveillance. These low mortality rates are probably the result of relatively short follow-up since workers were hired, a low prevalence of smoking and alcohol abuse, and no major impact of recent plant operations on workers' mortality rates. However, mortality rates of recent workers may not be useful for evaluating safety of the workplace in the past. The mortality rates of long term hourly workers are the most useful in mortality surveillance for detecting possible work related effects in the past. The rates observed in our surveillance report do not indicate widespread cancer DSW 146844 13 STLCOPCB4038691 increases among workers in these production facilities. With the exception of increased rates of leukemia, mortality from cancer, non-malignant respiratory disease, heart disease, and accidents were comparable to or lower than mortality rates for the U.S. population at each plant examined. This is similar to findings in other industrial groups with the exception of high rates of leukemia in our surveillance. The high rates of leukemia in our surveillance occur at a single plant. The workers at this plant have been subject of two benzene studies (Wong, 1987; Ireland et al., 1997). The low mortality rates in our surveillance particularly for non-cancer causes of death probably reflect in part the "healthy worker" and the "healthy survivor" effects. The healthy worker effect refers in part to the initial selection of relatively healthy persons at the time of hire (Checkoway et al., 1989). The healthy survivor effect is attributed to the tendency of the least healthy workers to leave the active workforce (Arrighi and Hertz-Picciotto, 1994). Both of these effects may impact the mortality rates of the surveillance populations. In our study, 15,367 workers were hired in the last fifteen years of follow-up and the remaining 27,972 workers are a censured population since they had to be in the active Solutia workforce in 1980 to be included. The potential biases associated with the healthy worker effect diminish with time since employment and are DSW 146845 14 STLCOPCB4038692 less important for interpreting cancer mortality rates (Checkoway et al., 1989). In our surveillance report, long term and recently hired workers had similar mortality patterns indicating the healthy worker effect has not had a major impact on our results. The potential impact of the healthy survivor effect is impossible to evaluate since our surveillance report has no information on workers who left employment before 1980. We determined that our surveillance results are not predictive of the causal findings from our studies. This is not surprising since the studies mostly examined workers not included in the mortality surveillance. The workers in the studies were employed during periods of generally higher exposures to different substances and processes than the population in the mortality surveillance. We find it difficult to justify using surveillance results to do further study unless the relative risk values are large and very precise. Teta et al. (1990) mention that large increases in mortality, especially for rare diseases in plant sub-populations can often be observed in surveillance data. Thus Teta et al. are able to detect a liver cancer excess in their surveillance data at a plant with vinyl chloride exposures. However, while we were successful in identifying from our plant specific surveillance high rates of leukemia at our plant in a benzene study (Ireland et al., DSW 146846 15 STLCOPCB4038693 1997), we were unable to detect high rates of bladder cancer in a plant with past exposure to 4-aminobiphenyl a potent bladder carcinogen (Collins et al., 1993). The difference in these cases appears to be in the timing of the exposure, the induction-latency period of the cancers, and the percentage of workers exposed to toxic agent in the surveillance report. In the case of 4-aminobiphenyl, exposure ceased in 1955 and only 17% of the workers employed in 1980 worked at the time when 4-aminobiphenyl exposure occurred. With the possible exception of the leukemia findings, it is unlikely that any of the findings in our surveillance reports would of themselves had caused additional study. Conclusion Mortality surveillance reports which'focus on recent workers provide important information on mortality risk to current workers. However, our plant surveillance reports are of limited value in discovering occupational hazards in the past. While there have been examples where these reports have been useful in identifying hazards, we did not find our surveillance reports to be predictive of substance specific studies which identified potential occupational hazards. Mortality surveillance reports as they become longer running and cover more of the worker population with each year of update could become more useful in identifying unknown hazards. Mortality surveillance also has other uses including DSW 146847 16 STLCOPCB4038694 giving a quick response to health related inquires, providing data for other studies, and setting priorities for programs and studies (Tsai et al., 1991). At present, however, it seems prudent to combine mortality surveillance with long running substance studies on suspect toxic exposures to help identify previously unknown occupational hazards. DSW 146848 17 STLCOPCB4038695 References Arrighi HM, Hertz-Picciotto I (1994): The evolving concept of the healthy worker survivor effect. Epid 2:189-196. Blair A, Stewart P, O'Berg M, Gaffey W, Walrath J, Ward J, Bales R, Kaplan S, Cubit D (1986): Mortality among industrial workers exposed to formaldehyde. J Nat Cancer Inst 76:1071 1084. Blair A, Stewart PA, Zaebst DD, Pottern L, Zey JN, Bloom TF, Miller B, Ward E, Lubin J (1998): Mortality of industrial workers exposed to acrylonitrile. Scand J Work Environ Health 2(suppl):25-41. Bond GG, McLaren EA, Cartmill JB, Wymer KT, Lipps TE, Cook RR (1987) : Mortality among female employees of a chemical company. Amer J Indust Med 12:563-578. Checkoway H, Pearce N, Crawford-Brown DJ (1989): Research methods in occupational epidemiology. Oxford University Press, New York. Chemical Manufacturers Association's Epidemiology Task Group (1991): Guidelines for good epidemiology practices for DSW 146849 18 STLCOPCB4038696 occupational and environmental epidemiologic research. J Occup Med 33:1221-1229. Collins JJ, Strauss ME, Levinskas GJ, Conner PR (1993): The mortality experience of workers exposed to 2,3,7,8tetrachlorodibenzo-p-dioxin in a trichlorophenol process accident. Epid 4:7-13. Collins JJ, Strauss ME, Riordan SG (1999) : The mortality rates of workers at the Nitro plant with exposure to 2mercaptobenzothialzole Occup Environ Med (in press). Divine BJ, Barron V, Kaplan SD (1985): Texaco mortality study: I. mortality among refinery, petrochemical, and research workers. J Occup Med 27:445-447. Divine BJ, Hartman CM, Wendt JK (1999a): Update of the Texaco mortality study 1947-93: part I. Analysis of overall patterns of mortality among refining, research, and petrochemical workers. Occup Environ Med 56:167-173. Divine BJ, Hartman CM, Wendt JK (1999b): Update of the Texaco mortality study 1947-93: part II. Analysis of specific causes of death for white men employed in refining, research, and petrochemicals. Occup Environ Med 56:174-180. DSW 146850 19 STLCOPCB4038697 Doll R, Peto R (1981): The causes of cancer: quantitative estimates of avoidable risks of cancer in the United States today. Oxford University Press, New York. Fingerhut MA, Halperin WE, Marlow DA, Piacitelli LA, Honchar PA, Sweeney MH, Greife AL, Dill PA, Steenland K, Suruda AJ (1991): Mortality among U.S. workers employed in the production of chemicals contaminated with 2,3,7,8tetrachlorodibenzo-p-dioxin (TCDD) NTIS # PB 91-125971, NIOSH Cincinnati, Ohio. Fingerhut MA, Halperin WE, Marlow DA, Piacitelli LA, Honchar PA, Sweeney MH, Greife AL, Dill PA, Steenland K, Suruda AJ (1991): Cancer mortality in workers exposed to 2,3,7,8tetrachlorodibenzo-p-dioxin. New Engl Med 324:212-218. Hanis NM, Shallenberger LG, Donaleski DL, Sales EA (1985) : A retrospective mortality study of workers in three major U.S. refineries and chemical plants. J Occup Med 27:283-292. Ireland B, Collins JJ, Buckley CF, Riordan SG (1997): Cancer mortality among workers with benzene exposure. Epid 8:318- 320. OSW 146851 20 STLCOPCB4038698 Marsh G (1983): Mortality among workers from a plastics producing plant: a matched case-control study nested in a retrospective cohort study. J Occup Med 25:219-230. Marsh GM, Enterline PE (1979): A method for verifying the completeness of cohorts used in occupational mortality studies J Occup Med 21:665-670. Marsh GM, Preininger M (1980): OCMAP. A user oriented occupational cohort mortality analysis program. The American Statistician 34:245. Melick WF, Escue HM, Naryka JJ, Mezera RA, Wheeler EP (1955): The first reported cases of human bladder tumors due to new carcinogen--xenylamine. J Urol 74:760-766. Melick WF, Naryka JJ, Kelly RE (1971): Bladder cancer due to exposure to para-aminobiphenyl: a 17-year followup. J Urol 106:220-226. Olsen GW, Lacy SE, Cartmill JB, Kravat BA, Chamberlin SR, Spadacene NW, Lipps TE (1994): Half-century of cause-specific mortality experience of chemical manufacturing employees. Amer J Indust Med 26:203-219. QSW 146852 21 STLCOPCB4038699 Pell S, O'Berg MT, Karrh BW (1978): Cancer Epidemiologic Surveillance in the DuPont Company. J Occup Med 20:725-740. Pifer JW, Hearne FT, Friedlander BR, McDonough JR (1986): Mortality study of men employed at a large chemical plant, 1972 to 1982. J Occup Med 28:438-444. SAS Institute Inc (1990): SAS procedures guide. Version 6, Third Edition, Cary, NC: SAS Institute Inc., 750 pp. Shallenberger LG, Acquavella JF, Donaleski D (1992): An updated mortality study of workers in three major United States refineries and chemical plants. Brit J Indust Med 49:345-354. Strauss ME, Barrick ED, Bannister RM (1993): Mortality experience of employees exposed to 2-mercaptobenzothiazole at a chemical plant in Nitro, West Virginia. Brit J Indust Med 50:888-893. Suskind RR, Hertzberg VS (1984): Human health effects of 2,4,5-T and its toxic contaminants. J Amer Medical Assn 251:2372-2380. DSW 146853 22 STLCOPCB4038700 Teta MJ, Schnatter AR, Ott MG, Pell S (1990): Mortality surveillance in a large chemical company: the Union Carbide Corporation experience, 1974-1983. Amer J Indust Med 17:435 447 . Teta MJ, Ott MG, Schnatter AR (1987): Population based mortality surveillance in carbon products manufacturing plants. Brit J Indust Med 44:344-350. Tsai SP, Dowd CM, Cowles SR, Ross CE (1991): Prospective morbidity surveillance of Shell refinery and petrochemical employees. Brit J Indust Med 48:155-163. Tsai SP, Gilstrap EL, Cowles SR, Snyder PJ, Ross CE (1996): Long-term follow-up mortality study of petroleum refinery and chemical plant employees, Amer J Indust Med 29:75-87. U.S. Bureau of the Census (1975): The methods and materials of demography, by HS Shyrock and JS Siegel, and associates. Third printing (rev.) U.S. Government Printing Office, Washington, D.C. Wong 0 (1987): An industry wide mortality study of chemical workers occupationally exposed to benzene. II Dose response analyses. Brit J Indust Med 44:382-395. DSW 146854 23 STLCOPCB4038701 Wong 0, Whorton MD, Foliart DE, Ragland D (1991): An industry-wide epidemiologic study of vinyl chloride workers, 1942-1982. Amer J Indust Med 20:317-334. Zack JA, Suskind RR. 1980. The mortality experience of workers exposed to tetrachlorodibenzodioxin in a trichlorophenol process accident. J Occup Med 22:11-14. Zack JA, Gaffey WR. 1983. A mortality study of workers employed at the Monsanto company plant in Nitro, West Virginia. In: Tucker RE, Young AL, Grey AP (eds) Human and environmental risks of chlorinated dioxins and related compounds. Plenum Press, New York, p 575-591. DSW 146855 24 STLCOPCB4038702 Table I. Distribution of Solutia workers 1980-1994 by- selected characteristics. Characteristic C 'vby. % of Total Race White 36,415 Black 5,299 Hispanic 687 Asian 739 American Indian 164 Other and Unknown 35 Sex 84.0 12.2 1.6 1.7 0.4 0.1 Male Female Pay Status 31,494 11,845 72.7 27.3 Hourly Salary Hourly to salary 15,026 27,964 349 34.7 64.5 0.8 Working Status Currently employed 14020 Left company (not 18834 retired) Retired 10485 32.3 43.5 24.2 DSto 146856 25 STLCOPCB4038703 Characteristic Year of Hire <1970 1970 + Location General Offices Carondolet Chocolate Bayou Columbia Decatur Fovil Greenwood Indian Orchard Nitro Pensacola Port Plastics Queeny Soda Springs Texas City Trenton All other ,V . N 17,955 25,384 13,386 541 1730 714 2986 590 2650 3013 1017 5566 1316 1657 751 2378 1194 3850 % of Total 41.4 58.6 30.1 1.3 4.0 1.6 6.9 1.4 6.1 7.0 2.3 12.8 3.0 3.8 1.7 5.5 2.8 8.9 Csw 146857 26 STLCOPCB4038704 Table II. Solutia mortality surveillance standardized mortality ratios (SMR) and 95% confidence intervals (Cl) for hourly and salary workers for entire population and 20+ years induction-latency period. Cause of Death (ICD9) Hourly Workers Salary Workers Entire 20+ Entire 20+ Population Induction- Population Induction- SMR(95%CI) Latency SMR(95%CI) Latency SMR(95%CI) SMR(95%CI) I. Males All Causes (001-999) 0.8(0.8-0.9) 0.8 (0.8-0.9) 0.5(0.5-0.5) 0.5(0.5-0.6) All Cancers (140-208) 0.9(0.8-1.0) 0.9(0.8-1.0) 0.6(0.6-0.7) 0.6 (0.6-0.7) Esophagus (150) 0.8(0.4-1.5) 1.0(0.5-1.7) 0.3(0.1-0.8) 0.3(0.0-0.8) Stomach (151) 1.0(0.5-1.6) 1.0(0.5-1.7) 0.4(0.1-0.9) 0.3(0.0-0.8) Large intestine(153) 1.2 (0.9-1.6) 1.1(0.8-1.6) 0.8 (0.6-1.2) 0.9(0.6-1.2) Liver & biliary passages (155 0.6(0.2-1.2) 0.7(0.2-1.5) 0.2(0.0-0.6) 0.1(0.0-0.6) 156) Larynx (161) Bronchus, trachea, lung (162) All other respiratory 0.0 (0.0-0.6) 0.9(0.8-1.1) 0.0(0.0-0.5) 0.0(0.0-0.7) 0.9(0.8-1.1) 0.0(0.0-5.8) 0.3(0.0-1.1) 0.6(0.5-0.7) 0.2 (0.0-0.8) 0.2 (0.0-1.0) 0.6 (0.5-0.7) 0.1(0.0-0.8) (160,163,164,165) Prostate (185) Bladder (188,189.3,189.4,189.8, 1.1(0.7-1.6) 0.8(0.3-1.6) 1.0 (0.7-1.5) 0.6 (0.2-1.4) 0.6(0.4-1.0) 0.5 (0.2-1.3) 0.6(0.3-0.9) 0.6(0.2-1.4) 189.9) Malignant melanoma of skin (172) 0.7(0.2-1.7) 0.6(0.1-1.7) 0.9 (0.4-1.6) 0.8(0.3-1.7) DSW 1 4 6 8 5 8 27 Cause o Death (ICD9) Bone (170) Leukemia (204-208) All heart disease (390 398,402,404,410-429) Non-malignant respiratorydisease (460-519) Motor vehicle accidents (E810825) All other accidents (E800807,E826-949) All Causes (001-999) All Cancers (140-208) Esophagus (150) Stomach (151) Large intestine(153) Hourly Workers Entire 20+ Population Induction- SMR(95%CI) Latency SMR(95%CI) 1.2(0.0-6.6) 0.0(0.0-6.4) 1.8(1.1-2.6) 1.5(0.8-2.4) 0.9(0.8-1.0) 0.9(0.8-1.0) Salary Workers Entire 20+ Population Induction- SMR(95%CI) Latency SMR(95%CI) 0.0(0.0-3.2) 0.0(0.0-5.0) 0.7 (0.4-1.3) 0.7(0.4-1.4) 0.6(0.5-0.6) 0.6(0.5-0.6) 0.5(0.3-0.6) 0.5 (0.4-0.7) 0.3 (0.2-0.5) 0.3(0.2-0.5) 1.0(0.7-1.4) 1.1(0.6-1.7) 0.3 (0.2-0.5) 0.6(0.3-1.0) 0.6(0.4-0.9) 0.7(0.4-1.1) 0.3(0.2-0.5) 0.4(0.2-0.7) II. Fema]Les 0.7(0.6-0.8) 0.6(0.5-0.8) 0.7 (0.5-0.9) 0.6(0.4-0.9) 0.0(0.0-5.8) 0.0(0.0-8.3) 0.9(0.0-5.0) 1.1 (0.3-2.5) 1.3(0.0-7.5) 0.9(0.2-2.5) 0.6(0.5-0.7) 0.9(0.7-1.0) 1.1(0.0-6.2) 1.1(0.1-3.9) 0.9(0.4-1.8) 0.7(0.5-0.8) 0.8(0.6-1.1) 1.8(0.010.2) 2.0(0.2-7.1) 0.9(0.2-2.1) OSW 1 4 6 8 5 9 28 Cause of Death (ICD9) Liver & biliary passages (155-- 156) Larynx (161) Bronchus, trachea, lung (162) All other respiratory (160,163,164,165) Breast (174,175) Bladder (188,189.3,189.4,189.8, 189.9) Malignant melanoma of skin (172) Bone (170) Leukemia (204-208) All heart disease (390 398,402,404,410-429) Non-malignant respiratory disease (460-519) Hourly Workers Entire 20+ Population Induction- SMR(95%CI) Latency SMR(95SsCI) 0.0(0.0-3.0) 0.0 (0.0-4.2) Salary Workers Entire 20+ Population Induction- SMR(95%CI) Latency SMR(95SsCI) 0.0(0.0-1.8) 0.0(0.0-3.1) 0.0(0.013.9) 0.9(0.5-1.5) 0.0(0.0-9.2) 0.5(0.2-1.0) 0.0(0.0-8.0) 0.0(0:0-4.7) 0.0(0.031.3) 1.7(0.3-4.9) 0.5(0.3-0.5) 0.0(0.019.2) 0.8(0.4-1.5) 0.0(0.013.2) 0.5(0.2-1.3) 0.0(0.010.5) 0.0(0.0-7.8) 0.0(0.058.6) 1.8(0.2-6.5) 0.5(0.3-0.9) 0.0(0.0-9.2) 0.9 (0.5-1.3) 0.0 (0.0-5.8) 0.7(0.4-1.1) 0.0 (0.0-4.9) 0.6(0.0-3.4) 0.0 (0.0 16.5) 1.5(0.5-3.5) 0.7(0.4-1.0) 0.0(0.015.1) 1.0(0.6-1.6) 0.0(0.010.3) 0.4(0.1-0.8) 0.0(0.0-7.6) 1.4(0.0-7.6) 0.0(0.042.2) 1.9 (0.4-5.5) 0.7 (0.4-1.0) 0.8(0.3-1.5) 0.8(0.3-1.7) 0.7 (0.3-1.2) 0.8(0.4-1.6) STLCOPCB4038707 29 Cause of Death (ICD9) Motor vehicle accidents (E810- Hourly Workers Entire 20+ Population Induction- SMR(95"oCI) Latency SMR(95SSCI) 1.7(0.7-3.5) 0.8 (0.0-4.2) Salary Workers Entire 20+ Population Induction- SMR(95%CI) Latency SMR(95%CI) 0.5(0.2-1.2) 0.5(0.0-2.6) 825) All other accidents (E800- 0.7 (0.1-2.4) 0.0(0.0-2.8) 0.6 (0.2-1.7) 0.5(0.0-2.8) 807,E826-949) III. Persons, Person Years, and Deafhs Persons at risk 15,026 6,853 27,964 13,354 Person years 189,658 70,377 336,106 130,526 Total deaths 1,325 1,063 1,079 845 STLCOPCB4038708 to sc *o- CD O' 30 Table III. Life expectancies for the U.S. Population and Solutia workers by race, sex, and wage status. Group Life Life Life Working Life Expectancy Age Expectancy Age Expectancy Age Expectancy 20 40 60 ages 20-65 White Males Solutia Hourly 55.3 36.6 19.6 42.6 Solutia Salary 58.9 39.2 20.7 44.0 U.S. White Males" 53.6 35.1 18.3 42.1 Non-White Males* Solutia Hourly Solutia Salary U.S. non-White Males* 53.6 57.3 49.0 35.3 38.2 31.7 19.4 20.6 16.9 41.7 43.2 40.0 White Females* Solutia Hourly Solutia Salary U.S. White Females* 61.6 62.8 60.1 42.5 43.4 40.8 23.9 24.7 22.9 43.8 44.0 43.6 Non-White Females* Solutia Hourly Solutia Salary U.S. non-White Females* 59.7 58.0 57.0 41.1 38.7 38.2 23.5 + 21.6 43.1 + 42.6 * Rates for 1985-89 # Rates for 1980-94. The rates of the U.S. population 1985-89 are used for ages 80-84 and 85+. For non-White females age 20-24, 25-29, U.S. rates 1985-89 are used because of few or zero deaths in these categories. + Not calculated because of zero deaths in several age groups. DSW 1 4 6 8 6 2 31 Figure 1. Employment criteria for workers in the study and the percentage of workers in the study who are in Solutia Surveillance % in Surveillance CM m 0> O O O O 0> Employed During DSW 146863 STLCOPCB4038710 Figure 2. Scatterplot of the relative risks of causes of death featured in the studies with the relative risks in the surveillance report for workers 20+ years after hire. o (s> sc O' OS O' 33 STLCOPCB4038711