Document jBqJvJjRYvVpZy00YDvr4BG45

-. d.o IIt, ELSEVIER Available online at www.sciencedirect.com SI;:IENI;:E DIREI;:T8 (if) Chemico-Biological Interactions 153-154 (2005) 23- Chemico-Biological Interactionr www.elsevier.comllocate/chembioint Health Watch exposure estimates: Do they underestimate benzene exposure? C. Glass , C.N. Gray , D.J. Jolley , C. Gibbons , M.R. Sim Monash University, Department of Epidemiology and Preventive Medicine, Central and Eastern Clinical School Alfred Hospital, Commercial Road, Melbourne, Vie. 3004, Australia Deakin University, Geelong Australia Deakin University, Burwood, Australia Available online 19 April 2005 Abstract A nested case--control study found that the excess of leukemia, identified among the male members ofthe Health Watch cohort, was associated with benzene exposure. Exposure had been retrospectively estimated for each individual occupational history using an algorithm in a relational database. Benzene exposure measurements, supplied by Australian petroleum companies, were used to estimate exposure for specific tasks. The tasks carried out within each job, the products handled, and the technology used, were identified from structured interviews with contemporary colleagues. More than half of the subjects started work after 1965 and had an average exposure period of 20 years. Exposure was low; nearly 85% of the cumulative exposure estimates were at or below 10 ppm-years. Matched analyses showed that leukemia risk increased with increasing cumulative benzene exposures and with increasing exposure intensity of the highest-exposed job. Non-Hodgkin lymphoma and multiple myeloma were not associated with benzene exposure. A reanalysis reported here, showed that for the 7 leukemia case-sets with greater than 16 ppm-years cumulative exposure, the odds ratio was 51.9 (5. 6-477) when compared to the 2 lowest exposed categories combined to form a new reference category. The addition of occasional high exposures, e.g. as a result of spillages, increased exposure for 25% of subjects but for most, the increase was less than 5% of total exposure. The addition of these exposures reduced the odds ratios. Cumulative exposures did not range as high as those in comparable studies; however, the recent nature of the cohort and local handling practices can explain these differences. ~ 2005 Elsevier Ireland Ltd. All rights reserved. Keywords: Benzene; Leukemia; Exposure; Petroleum industry . Corresponding author. Tel.: +61 39903 0554; fax: +61 3 9903 0556. E-mail address: deborah. glassiBImed. monash. edu. (D. c. Glass). 1. Introduction Benzene is present in crude oil, gasoline and at many stages in the refining process (1). It has been designated a Group 1 carcinogen by rARC because of its 0009-2797/$ - see front matter I!:i 2005 Elsevier Ireland Ltd. All rights reserved. doi: 10. 1016/j. cbi.2005. 03. 006 GARABRANT/BERRYMAN 00203 D. C. Glass et al. Chemico-Biological Interactions 153-154 (2005) 23- leukemogenic properties (2). The extent of the risk as- sociated with benzene exposure below 10 ppm is a mat- ter of debate however (3- 10). Health Watch is a prospective cohort study of about 000 employees who have worked for more than 5 years in the Australian petroleum industry. About 1300 of the cohort are women. The cohort commenced in 1981 with a face-to-face survey which was repeated in 1986, 1991 and 1996. Subjects provided demographic details, health status information and details of their work history. All Australian petroleum company employees were invited to participate except those employed at head offices and at sites with fewer than 10 employees. About 95% of eligible employees have participated (11). The cohort thus consists of employees from offices, upstream extraction and processing sites, ,refineries terminals and airports all over Australia. Subjects were followed by matching with the Australian national death and cancer registries. An excess incidence of Iympho-hematopoietic cancer was identified among the male members of the cohort (12). A nested case-control study was carried out in which exposure to benzene was quantitatively, estimated for individual cases and their matched controls (13). Cases were defined as male members of the Health Watch cohort, who had: . first diagnosis ofLH cancer after entering the Health Watch cohort; . and diagnosis confirmed by pathology report, cancer ,registration letter from medical practitioner, or death certificate; . and had reported LH cancer to Health Watch either by self or by family, unless they were lost to contact by Health Watch, or were deceased. There were 31 non-Hodgkin lymphoma, 15 multiple myeloma and 33 leukemia cases meeting these criteria identified in the cohort between 1981 and 1999. The leukemia subtypes were: 11 chronic lymphocytic, 6 chronic myeloid, 2 acute lymphocytic 11 acute non-lymphocytic and 3 other leukemias. Leukemia, but neither non-Hodgkin lymphoma nor multiple myeloma was strongly associated with benzene exposure. Matched analyses showed that leukemia risk increased with increasing cumulative benzene exposures expressed in ppm-years and with increasing exposure intensity of the highest-exposed job in ppm. (12). Cumulative exposure estimates were low in this study compared to the Pliofilm study (6) and the Chinese cohort (10). It has been suggested that there may be no increased risk of . leukemia at cumulative benzene exposures below 200ppm-years (8) or intensity ofless than 20-60 ppm (9). In our study the odds ratios for leukaemia were significantly raised unlike those reported from similar petroleum industry studies (14 15). This paper re-evaluates the exposure-response re- lationship and investigates the factors behind the low exposure estimates. 2. Materials and method The exposure assessment was described in a previous paper (13) but is briefly reviewed here. A job history was prepared for each subject, based on data which had been collected largely prospectively from the four cohort interviews that took place between 1980 and 1999 (12). The job histories which were complete for 98% of subjects (n = 494) were then checked by the employing company against their records. We used each subject's job history to identify the range of products handled and tasks comprising each job by interviewing contempo- rary colleagues. This was carried out by interviewers blind as to the case status of each subject. The interviews were structured and used standard job-specific questionnaires. We collated Australian personal benzene exposure monitoring data from participating companies and calculated the arithmetic mean exposure for each task represented. These were taken as base estimates (BE) of the exposure for the task. If more than one technology could have been used, e. g. top and bottom loading for road tankers, separate BEs were calculated for each technology. The BE was then allocated to individuals carrying out that task over the relevant period, in a relational database. No local data were available for some short-term tasks and data for these were sought from the literature. The Australian REs were derived from data largely collected since 1975 , so there is uncertainty about how applicable they are to earlier time periods (Fig. I). There was more available data associated with lower exposed jobs in refineries, e.g. reformer, crude distillation and catalytic cracker unit operators than with the more highly exposed jobs in terminals. Few data were available for short-term tasks , e. g. dipping and gauging. GARABRANT/BERRYMAN 00204 GARABRANT/BERRYMAN 00205 23-D. C. Glass et al. Chemica-Biological Interactions 153-154 (2005) I:: I:: Q) .J:) en Q) 0; 1; ~ II! 0 "S E E Q. =' ..e- Q) .... E~ "" 8Q) )( Geometric Exposure Group Fig. 2. Benzene exposure categories (lifetime cumulative exposure to benzene in ppm-years). (Vertical bars show range of exposures in each category except for outlier). for top loading was attributed to a driver who used top splash loading and a factor of three was applied to increase the exposure estimate. Most of the multipliers were derived from exposure data; some were derived from the literature and a few were agreed by a panel oflocal Occupational Hygienists. These expert-derived multipliers were used for fewer than 160 tasks from a total of3457 tasks assessed. The algorithm was used to estimate for each subject, the cumulative exposure to benzene in ppm-years and to identify the intensity of the most highly exposed job in ppm (cumulative exposure divided by years spent on that job). The subjects were categorized by cumulative exposure. Because the absolute difference between exposure for the two lowest exposure ~ategories was small (Fig. 2), we decided for this reanalysis to combine them. This provided a larger reference category. We also explored exposure that might not be represented in the BEs. Subjects may have experienced infrequent but potentially high exposures (high exposure events or HEEs) for example from spills or from tasks no longer performed, e.g. washing overalls in gasoline. This was considered important because these infrequent or historic exposures were unlikely to be represented in the BEs. The HEEs that were considered were identified from the reports collected during the site visits. These were collected and allocated to a job category, e.g. a double fill for drum fillers with metered drum filling. The col- lab orating industry occupational hygienists were each sent the list of possible HEEs and their opinion of the likelihood of such exposures was individually canvassed in a telephone survey. The collated replies were examined by a panel of three industry hygienists and a consensus reached on: . the probability ofthe exposure from each BEE, e. product spill during tanker loading; . the group(s) of workers that might have been ex- posed, e. g. mechanics; . the frequency for a particular exposure, e. g. once a year; . the era over which the high exposures might have occurred, e.g. pre- 1960; . how long the exposure would have lasted on each occasion, e.g. 10 min washing overalls; . the extent of skin exposure if any, e.g. hands wetted with product. The HEEs were then allocated to groups of workers based on job activities and era considerations. After due consideration some possible HEEs were not included for the following reasons: . Some activities were thought to be so unlikely or infrequent that they could not be allocated to any individual, e.g. siphoning gasoline by mouth. . Some activities occurred too infrequently to be included in the model, e.g. major spillages during GARABRANT/BERRYMAN 00206 23-D. C. Glass et al. Chemica-Biological Interactions 153-154 (2005) Table I Incidental high exposure events included in daily exposure assessment model Job Drum fillers Fitters Fitters and mechanics Mechanics High exposure event (HEE) Extensive splashes of overalls Frequency per person I per year Extensive splashes of overalls Washing hands in gasoline I per year 2 per day I per day Washing tools in gasoline 4 min per day l5min Friday Washing components and equipment 15 min per day Washing overalls in gasoline I per week I 0 min exposure Lab technicians and samplers (not chemists) Rail car loader and road tanker filler Washing hands in LVN Washing fuel oil sample bottles in gasoline Washing hands in benzene Tanker overflow 2 min twice daily 15 min per day 2 min once per week I per year Changes over time? Stopped early 1990s Site Drum sheds Pre 1970 Pre 1950 1950-1980 Terminals and refineries Terminals and refineries Pre 1975 Terminals and refineries Stops 1975 Terminals and refineries Stops 1955 1 company Pre 1965 Stops 1990 Terminals and refineries Refinery only Pre 1980 2 refineries Comments From occasional drum overfills during metered delivery Arms and top of legs Practice widespread few individuals continue Used gasoline until about 1975 , kerosene thereafter Practice widespread. After 1975 used proprietary cleaners or kerosene Friday left to soak for half an hour, pick but drain, wear next Monday. After 1955 Clean overalls provided weekly LVN (1% benzene) at this time After 1990, only mineral turps used Top dipped a This includes exposure by inhalation added to exposure through the skin calculated as an 8h equivalent exposure. Exposure (ppm)a 3.43 3.43 1.34 1.34 1.88 gasoline delivery that probably occurred less than once every 20 years for anyone driver. . Activities which were already represented in the BE data, e.g. fitter breaking lines containing gasoline. . Activities that were thought to result in low expo- cupboard.sures, e.g. handling benzene as a reagent in a fume The likely exposure (including via skin and inhalation) resulting from the BEE was then estimated as an equivalent 8-h TWA (inhalation only exposure) from new monitoring data for road tanker and rail car spills and laboratory simulations for the remainder of the BEEs (Table 1). The total exposure was added to the BE for the appropriate number of days. We calculated odds ratios (ORs) for leukemia, using conditional logistic regression using Stata TM , for subjects categorized by cumulative exposure and by the exposure intensity of their highest exposed job. We calculated the odds ratio associated with benzene in ppm-years when treated as a continuous variable. We then examined the effect of adding HEEs to the cumulative exposure and again calculating odds ratios. We compared the Health Watch exposure assess- ment outcomes, cumulative exposure, intensity and duration of exposure to those reported for the two GARABRANT/BERRYMAN 00207 23-53-c. Glass et al. Chemica-Biological Interactions J 154 (2005) 40% 30% 20% 10% f-f-- ,,10 10-20 20-30 30-40 :.40 Years of employment Fig. 3. Distribution of the years of employment of cases and controls. comparable studies, the Canadian IOL study (14) and the UK IF study (15). 3. Results Subjects had an average exposure period of20 years (range 4-42) (Fig. 3). Exposure was relatively recent in this study, 63% of controls and 47% of cases started employment after 1965. Estimated lifetime cumulative benzene exposures were low for the majority of the subjects, ranging from 005 to 57.3 ppm-years with a mean of 4. 9 ppm-years. Nearly 85% of subj ects had estimated cumulative exposures less than or equal to 10ppm-years and only 3. had greater than or equal to 40 ppm-years. The addition of HEEs increased exposure for 25% of subjects notably fitters and vehicle mechanics, but for most of these subjects the increase was modest (less than 5% 50% of total exposure) (Fig. 4). The allocation of an HEE was not related to case-status (Pearson s chi2 0.5). The odds ratio for leukemia increased with cumulative exposure when exposure was treated as a continuous variable, OR 1.10 (1.04-1.16) per ppm-year. When the HEEs were added to the cumulative exposure the odds ratio was lower, OR 1.03 (1.01- 1.05) per ppm-year. Leukemia was strongly associated with cumulative benzene exposure of greater than l6ppm-years with and without the addition ofHEEs (Table 2) and was also associated with exposure of highest intensity job held greater than 0. 8 ppm, controlling for exposure duration (Table 3). The cumulative exposures were similar for the majority of the subjects in the three comparable petroleum industry case-control studies. Fig. 5 shows that there were only a few subjects in the IOL and IP studies with a much higher exposure than was estimated for any of the Australian subjects. The range of career intensities of exposure to benzene and the job durations were also similar in the three studies (Fig. 4; Table 4). 4. Discussion These data provide evidence of an association between modest exposure to benzene (greater than 16 ppm-years cumulative exposure and greater than 0. 8 ppm intensity of exposure) and increased risk of leukemia and provide some evidence of a dose-response relationship. E 40% :s: 30% Iii . Dally exposure e 20% II! '55 10% IZI Daily Exposure + HEEs c... :50.01 :50. 05 :50. :5 0. :51 :5 2 Daily Exposure (ppm) :.2 Fig. 4. Daily exposures by percentage of work time showing the effect of including the contribution of high exposure events (HEEs). GARABRANT/BERRYMAN 00208 23-c. Glass et al. Chemica-Biological Interactions 153-154 (2005) Table 2 Conditional (fixed-effects) logistic regression, leukemia by cumulative exposure to benzene (ppm-years) Benzene exposure (ppm-years) Number of controls Number of cases Cumulative exposure OR (95% CI) ::::2 :0-2-4 :0-4-8 :0-8:0-16 1.00 89 (0.97- 52) 1.17 (0.27-4.98) 11 (0.91- 10. 56) 51.88 (5. 64-477) Cumulative exposure including HEEs OR (95% CI) 1.00 07 (1.02- 28) 1.22 (0. 28- 22) 68 (0.71-\0.08) 79 (2.34-25.89) Table 3 Conditional (fixed-effects) logistic regression, leukemia by intensitY of highest exposed job (ppm) ever held controlling for career duration Benzene exposure intensitY (ppm) Number of controls Number of cases OR (95% CI)a ::::0. :0-0.1-0. :0-0. 2-0.4 :0-0. 4-0. :0-0. 1.6 :0-1.6-3. :0-3. 1.00 84 (1.16-12. 69) 2.16 (0.49-9. 35) 1.52 (0.34-6. 70) 6.34 (1.54-26.18) 5.58 (1.00-31.04) 19. 56 (1.41-270. a Controlled for career duration. Table 4 Comparison of AlP, IP and IOL benzene exposure intensities and duration of employment Range of mean career intensitY (ppm) Range highest job intensitY (ppm) AlP (23) IP (15 19) IOL (14)a 001 to 2. .;0. 02 to ;::0.4 0 to 6. 001 to 4. .;0. 02 to 14.4 .;0. 5 to ;::6. a Data for leukemia cases and controls only. Mean job duration (years) 20.4 21.4 30.3 cases , 28 controls The combination of the two lowest cumulative exposure categories reduced the odds ratios compared to those reported previously (12). The association between increased risk of leukemia and benzene expo- 100 sure for the most highly exposed category remained strongly and significantly raised however. The study is a small one so that modest changes to the original reference category (which contained only three cases "#. 20 --- AlP -t.-- I P -+-.. IOL Log ppm-years benzene Fig. 5. Inter-study comparison of cumulative exposure estimates. GARABRANT/BERRYMAN 00209 GARABRANT/BERRYMAN 00210 GARABRANT/BERRYMAN 00211 C Glass et al. Chemico-Biological Interactions 153- 154 (2005) 23- (16) D. C. Glass, C.N. Gray, G. Adams, R.W. Manuell A. Bisby, Validation of exposure estimation for benzene in the Australian petroleum industry, Toxico!. Ind. Health 17 (200 I) 113- 127. (17) Radian Corporation, Study of vapor control methods for gaso- line marketing operations, vol I , Industry Survey and Control Techniques, 1975 187. (18) T.w. Armstrong, E. D. Pearlman, R.A. Schnatter, S. M. Bowes, N. Murray, MJ. Nicolich, Retrospective benzene and total hydrocarbon exposure assessment for a petroleum marketing and distribution worker epidemiology study, Am. Ind. Hyg. Assoc. J. 57 (1996) 333-343. (19) S.J. Lewis, G. M. Bell , N. Cordingley, E.D. Pearlman, L. Rushton, Retrospective estimation of exposure to benzene in a leukaemia case-control study of petroleum marketing and distribution workers in the United Kingdom, Occur. Environ. Med. 54(1997) 167- 175. (20) K. T. Copeland, H. Checkoway, A.J. McMichael , R.H. Holbrook, Bias due to misclassification in the estimation of relative risk, Am. J. Epidemio!. 105 (1977) 488-495. (21) ED. Groves, M. S. Linet, S.S. Devesa, Patterns of occurrence of the leukaemias, Eur. J. Cancer 31A (1995) 941-949. (22) H. Zeeb, M. Blettner, Adult leukaemia: what is the role of currently known risk factors? Rad. Environ. Biophys. 36 (1998) 217-228. (23) D. Glass, C. Gray, D. Jolley, M. Sim, C. Gibbons, L. Fritschi Lympho-haematopoietic cancer and exposure to benzene in the Australian petroleum industry, Report to AlP, Melbourne, 200 I. GARABRANT/BERRYMAN 00212