Annals of Occupational Hygiene Advance Access originally published online on February 17, 2006
Annals of Occupational Hygiene 2006 50(4):359-370; doi:10.1093/annhyg/mel003
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Original Article |
From Expert-based to Quantitative Retrospective Exposure Assessment at a Söderberg Aluminum Smelter
1 Cancer Control Research, British Columbia Cancer Agency, 2-111, 675 West 10th, Vancouver, BC, Canada V5Z 1L3; 2 School of Occupational and Environmental Hygiene, University of British Columbia, 372-2206 East Mall, Vancouver, BC, Canada V6T 1Z3
* Author to whom correspondence should be addressed. Tel: +1-604-822-8960; fax: +1-604-822-9588; e-mail: melissaf{at}interchange.ubc.ca
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODOLOGYRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
|---|
Objectives: Expert judgement of exposure levels is often only poorly or moderately correlated with directly measured levels. For a follow-up of a historical cohort study at a Söderberg aluminum smelter we updated an expert-based semiquantitative job exposure matrix of coal tar pitch volatiles (CTPV) to quantitative estimates of CTPV and benzo(a)pyrene (BaP).
Methods: Mixed effects models to predict exposure for potroom operation and maintenance jobs were constructed from personal CTPV and BaP measurements. Mean exposures of jobs in non-potroom locations were directly calculated when measurements were available. Exposure estimates for jobs/time periods with no measurements were based on proportion of time spent in exposed areas compared to jobs where exposure was modeled or measured. For pre-1977, the original expert exposure assignments were calibrated using the updated 1977 estimates.
Results: The rate of change in exposure levels varied by time period and was accounted for in mixed models with a linear spline time trend. Other variables significant in the models were job, potroom group and season as fixed effects, and worker as a random effect. The models for potroom operations explained 45 and 27% of the variability in the CTPV and BaP measurements, respectively. The models for maintenance jobs explained 40 and 19% of the variability in the CTPV and BaP measurements, respectively. For 1977–2000 model estimates, direct calculation of means and extrapolation from modeled/measured exposures accounted for 57, 6 and 37% of the exposed person-years, respectively.
Conclusions: The above methodology maximized the use of exposure measurements and largely replaced the original expert-based estimates. Finer discrimination between exposure levels was possible with the updated exposure assessment. The new estimates are expected to reduce exposure misclassification and help better assess the exposure–response relationships.
Keywords: cohort studies • retrospective studies • statistical models • time trend
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODOLOGYRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
|---|
In the past 15 years significant advancements have been made in quantitative assessment methods for retrospective occupational studies. Many methods to estimate quantitative exposure levels have been used alone and in conjunction with other techniques, with the options dictated by the availability of exposure measurements (Stewart et al., 1996
). Calculation of means and the use of simple algorithms are limited to time periods with available exposure measurements. The use of statistical models, both deterministic and empirical, is increasing because of their ability to borrow information in a rigorous and repeatable manner to predict exposure levels for circumstances where no measurements were made (Dement et al., 1983; Eisen et al., 1984; Yu et al., 1990; Hornung et al., 1994; Kromhout et al., 1994; Burstyn et al., 2000). Experts have been frequently asked to estimate exposures for studies and time periods where measurements are limited or lacking (Spinelli et al., 1991; Astrakianakis et al., 1998; Kauppinen et al., 2002). However, numerous studies have found that the experts' exposure estimates are often only poorly to moderately correlated with directly measured exposure levels (Kromhout et al., 1987; Hertzman et al., 1988; Hawkins and Evans, 1989; Teschke et al., 1989; Post et al., 1991; de Cock et al., 1996; Teschke et al., 2002; Friesen et al., 2003). Their findings support the rigorous use of exposure measurements over expert opinion whenever possible.
A 15-year study update at a vertical stud Söderberg aluminum smelter together with 25 years of personal coal tar pitch volatiles (CTPV) exposure measurements collected at the smelter provided an opportunity to update the original expert-based semiquantitative job exposure matrix (JEM) (Spinelli et al., 1991). A comparison of the original JEM with available exposure measurements found that the expert committee's exposure estimates were only moderately correlated with a measurement-based exposure assessment strategy (Spearman's rho = 0.42) (Friesen et al., 2003). Additionally, several areas where improvements were possible were identified, including better characterization of transitions between exposure categories, accounting for exposure differences between pot lines, and providing finer discrimination between exposure categories.
The study update was also an opportunity to quantitatively assess benzo(a)pyrene (BaP) exposure levels, one component of CTPV. BaP has been suggested to be a more specific marker of the carcinogenic potential of the potroom fumes (Theriault et al., 1984; Armstrong et al., 1986; Armstrong et al., 1994; Farant and Gariepy, 1998). The use of non-specific exposure metrics such as CTPV may introduce exposure misclassification, in particular when the causal component may be differentially related to the broader exposure mixture, i.e. by work area.
To move from an expert-based to a measurement-based exposure assessment for this study update required combining several approaches to maximize the information from exposure measurements, which we described in this article. The method used depended on the type of information and availability of exposure measurements for each job. Statistical models to predict annual exposure levels for each job within the potrooms were the foundation of the quantitative exposure assessment. Measurement-based approaches were used wherever possible; however, the use of expert opinion after calibration of their estimates with measurement-based approaches was necessary for estimating exposures for the earliest time periods. With a measurement-based approach, finer discrimination between exposures was possible compared to the original study. The move to a quantitative CTPV and BaP JEM is expected to reduce exposure misclassification so that we may better assess the exposure–response relationships between potroom exposures and cancer incidence and mortality in the 15-year aluminum smelter study update.
|
METHODOLOGY |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODOLOGYRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
|---|
Description of facility
The updated study cohort consists of 6395 men and 597 women who worked for three or more years at a vertical Söderberg aluminum reduction plant in British Columbia (BC), Canada, between 1954–2000. Aluminum is produced in carbon-lined steel shells (‘pots’) by electrolytic reduction at 1000°C of alumina (Al2O3) to aluminum in molten cryolite (Na3AlF6). In a Söderberg smelter the pots are supplied with an anode paste consisting of petroleum coke and coal tar pitch. In the potrooms the paste is baked in situ resulting in the continuous generation of CTPV, a complex mixture that includes known carcinogens such as polycyclic aromatic hydrocarbons. There are
900 operating pots at this smelter in seven pot lines. Each pot line represents a group of pots connected in series on the same electrical circuit. The pot lines were grouped (Lines 1–2, Lines 3–5 and Lines 7–8) based on construction dates to reflect similarities in technology and building ventilation and to reflect working units used administratively by the smelter. Exposure to CTPV occurs primarily to operators and maintenance workers in the potrooms, in potlining and potshell repair areas, and the carbon plant where anode paste is prepared. There is negligible exposure to CTPV in the casting, wharf/transportation, power operations and administration departments.
A modernization program was implemented at this smelter in 1975 to reduce the generation of potroom fumes and lower worker exposure. Over the next 10 years improvements were made in several areas as follows: building ventilation was improved, potroom vehicle and crane cabs were enclosed and cab air filters were improved, double burners and improved gas collection ducts were installed on the pots to capture the emissions, dry anode technology was implemented to reduce emissions at the top of the anode, automated studpulling was implemented to minimize the amount of time workers spent above the pots, crane maintenance bays were installed and improved to separate crane maintenance workers from the potrooms, and a new carbon plant was constructed. Many of these technological improvements took several years to fully implement and each pot line group was updated at different points in time. For instance, dry anode technology was implemented in Lines 1–2 in 1985–1988, in Lines 3–5 in 1979–1981 and 1984–1987, and in Lines 7–8 in 1995–1998. Additional details of these technological changes are included in Friesen et al. (2003).
Exposure measurements
Between 1975 and 2001, 2624 personal CTPV (measured as benzene soluble materials) and 1275 personal BaP exposure measurements were collected by the company and by a regulatory agency. Samples collected by the regulatory agency for compliance purposes accounted for 13% of CTPV measurements and 69% of BaP measurements. Personal exposure measurements were predominantly collected in the potrooms, accounting for 92% of the CTPV measurements and 86% of the BaP measurements. Approximately 180 and 100 CTPV area measurements were collected within the potrooms pre-1975 and 1976–1979, respectively.
Samples were collected using 37-mm sampling cassettes with fiberglass filters, desorbed with benzene. Company CTPV exposure measurements collected pre-1982 were analyzed by using a moving wire detector (Pye LCM-2) to carry the benzene extract to a flame ionization detector (Alcan Arvida Research Laboratory Method, unknown year). For company measurements after 1982 and all compliance measurements, the benzene was evaporated and the residue was weighed (Alcan Kitimat Laboratories Standard Method 2020, 1983; Workers' Compensation Board of BC Method 3350). BaP was analyzed by evaporating an aliquot of the benzene extract to dryness and redissolving the extract with acetonitrile. The acetonitrile aliquot was analyzed by liquid chromatography with a fluorescence detector (Workers' Compensation Board of BC Method 2102).
Exposure assessment process
The exposure assessment strategy consisted of several stages to maximize the use of the exposure measurements (Fig. 1). While a large number of personal CTPV and BaP measurements had been collected by the company and a regulatory agency from the mid-1970s onwards, exposure measurements were not available for all exposed jobs. For 1977–2000 exposure estimates, these stages included modeling exposures for potroom operations and maintenance jobs, calculating mean exposures for non-potroom locations, and extrapolating exposures for jobs without measurements. Estimating pre-1977 exposure levels involved backwards extrapolation of 1977 exposure levels. These stages are described in detail below.
|
Develop statistical models
Personal full-shift measurements (minimum duration 6 h) were used to build statistical models to predict CTPV (1977–2001) and BaP (1975–2001) exposures. Samples less than the detection limit were assigned an exposure 1/
2 times the detection limit (CTPV: n = 51; BaP: n = 10) (Hornung and Reed, 1990). Separate mixed effects models were developed for potroom operations jobs (CTPV model: n = 1868; BaP model: n = 773) and for potroom maintenance jobs (CTPV model: n = 505; BaP model: n = 230). The potroom operations model for CTPV contained six jobs as follows: anode assistant, anode operator, controlman, equipment operator, studblast operator, foreman and cell operator (ref: cell operator and foreman). For the BaP potroom operations model, no exposure estimates for anode assistant could be determined as no measurements were collected on that job. The potroom maintenance model contained eight jobs as follows: crane maintenance (unspecified trade), gas collection (also known as exhaust maintenance), cell startup operator, stud repair, potroom repairman, electrician, welder and millwright (reference).
A key aspect of the statistical model was modeling the time trend as a linear spline. Using a linear spline allowed for different linear rates of change in each specified time interval while constraining the functions for each interval to meet at the interval boundaries (Harrell, 2001). A linear spline function can be modeled as fixed effects in any linear regression procedure by transforming the independent time variable. For instance, if exposure Y was to be explained by a linear spline function of a time variable X with four time periods with boundaries (knots) of a, b and c, then three additional variables (X – a), (X – b) and (X – c) would need to be created and offered in the model as fixed effects in addition to variable X (equation 1). Except for the main time effect X, the additional spline terms must be constrained to be positive values or else set to 0. Overall linearity in X can be tested by testing H0 : ß2 = ß3 = ß4 = 0.
 | (1) |
). These dates were used to determine time periods of similar technology and periods of transition, with each time period covering two or more years. The implementation dates of technological improvements varied by potroom group, so a separate time trend was modeled for each potroom group, with 5–6 time periods per potroom group. The time period cutpoints used in the potroom operations models were as follows: 1981, 1983, 1985, 1987 and 1989 for Lines 1–2; 1979, 1983, 1986 and 1988 for Lines 3–5; and 1980, 1983, 1995 and 1998 for Lines 7–8. In the maintenance models no potroom line specific time trend could be determined as maintenance services was centralized for many time periods. Instead, four time periods (cutpoints: 1979, 1981, 1983 and 1985) were specified dividing the major period of technological change into 2 year periods. Note that the variable ‘Year—1977’ is the main time effect in all models: we have subtracted 1977 to set the baseline to equal 0, but unlike the additional terms to allow a spline time trend, this main time effect is not constrained to be a positive value.
Other variables offered as fixed effects in the models were job, potroom group (Lines 1–2, Lines 3–5 and Lines 7–8), season (winter/summer), measurement source (company or regulatory agency) and a job–time period interaction term. A random worker term was included to account for correlation within repeated measurements on the same worker. The models were constructed using the PROC MIXED restricted likelihood method using the natural log of the exposure measurements as the dependent variable (SAS version 8.0; SAS Institute Inc., Cary, NC, USA). The model structure is given in equation 2, where Yij was the CTPV or BaP exposure level for the i-th individual on the j-th day; Xij was the log-transformed (base e) exposure level; ßo was the intercept; 
ßd x (determinant of exposure d) was the summation of all model coefficients for the fixed effects, including main effects, interaction terms and linear spline terms, multiplied by the value of the fixed effect (0/1 or continuous variable); bi is the random effect to account for between-worker differences not accounted for by the fixed effects; and 
ij is the within-worker differences not accounted for by the fixed effects. Both bi and ij are assumed to be statistically independent and approximately normally distributed with a mean value of 0 and with variances 
and 
, respectively. An example of how to apply each model is provided in the footnotes of the tables listing model parameters.
 | (2) |
0.10. For the BaP models, jobs that had been significant in the CTPV model were kept in the model regardless of P-value because measurements for many jobs were sparse and the aim was to predict exposures rather than determine statistical differences. Residual plots were examined for patterns in unexplained variance. Cook's D was used to identify influential values in the models.
The models were used to predict an annual arithmetic mean 
for each job (m) in each year (k, 1977–2000) using equation 3, where 
is the predicted exposure (natural log-transformed) from the regression equation, 
is the estimated between-worker variance component and 
is the estimated within-worker variance component. Within each time period, the annual arithmetic means for each job were averaged to obtain a time-period specific exposure estimate.
 | (3) |
Calculation of means and extrapolation of exposures
The models did not predict exposures for jobs with no exposure measurements or jobs in work areas outside the potrooms. However, the models were used in conjunction with estimates of the proportion of time spent in exposed areas for each job to assign jobs in the models to one of seven exposure categories for CTPV and BaP for each time period. For CTPV, the four categories used in the original study (none, 0.01–0.2, 0.2–1, >1 mg m–3) were divided for finer discrimination of exposure levels. The seven CTPV categories were none, 0.01–0.09, 0.10–0.19, 0.20–0.39, 0.40–0.99, 1.0–1.9 and >2.0 mg m–3 (measured as benzene soluble materials). For BaP, the categories were determined from observing the range of predicted exposures over time from the BaP exposure models. The seven BaP categories were none, 0.05–0.49, 0.5–0.9, 1.0–2.9, 3.0–6.9, 7.0–13.9 and >14.0 µg m–3.
For some jobs in non-potroom plant areas (cathode lining, the anode paste plant and potshell repair) a limited number of exposure measurements were available. The arithmetic mean was calculated from the available personal full-shift measurements and used to determine the appropriate exposure category.
For jobs/time periods that had no exposure measurements, each job was compared to the time-specific mean for jobs with measurements with similar tasks but that may have differed in the amount of time spent in exposed areas. For instance, one maintenance job (Job 1) with no measurements that spent 25% of their work day in the potrooms was compared to another maintenance job (Job 2) that spent 75% of their work day in the potrooms with a predicted CTPV exposure of 0.7 mg m–3 for that time period from the CTPV maintenance model. Job 1 was then assigned to the CTPV exposure category ‘0.2–0.4 mg/m3’ (0.7 x 0.25/0.75 = 0.23 mg m–3). The type of tasks and the proportion of time spent in exposed areas for each job and time period were determined from interviews with senior employees. Time period differences occurred primarily in maintenance and service departments as departments were reorganized, centralized and decentralized at various times, which were represented in the work histories as changes in department names. As in this example, the exposure category assigned to the job was often in a lower exposure category than its comparison job as these jobs spent less time in exposed areas than the jobs with measurements. Jobs with no exposure measurements were primarily maintenance, technician and engineering jobs that spent only a portion of their time in exposed areas and the remainder of their time in trade shops, offices or other non-exposed plant areas (i.e. casting, wharf and power operations).
Backwards extrapolation of 1977 exposure estimates
The time period 1954–1976 was a relatively stable technological period at the smelter with few major changes. The original exposure assessment identified two major improvements that would be expected to decrease exposure levels as follows: (i) improvements to the pot gas collection system (gas skirts and gas burners) in 1960 and (ii) an expansion of the carbon plant in 1966, which included ventilation and exhaust improvements. There were no measurements available for this time period aside from a limited number of area measurements in the early 1970s. The updated measurement-based 1977 exposure levels were used to calibrate the original expert-based exposure estimates. We then applied the relative changes in exposure levels from the original assessment to assign exposure categories for each job and time period pre-1977.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODOLOGYRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
|---|
The fixed effects from the potroom operations and potroom maintenance models explained approximately 45 and 40% of the variability in the data for CTPV and 27 and 19% for BaP, respectively (Tables 1 and 2). The source of measurement (company or regulatory agency) was not significant in the models. The season was only significant for the BaP potroom operations model. For CTPV four to five distinct time periods with different rates of change in exposure were identified for each potroom group for potroom operations jobs whereas only two distinct time periods were identified for BaP exposures. A short-term peak in BaP exposures in 1981 is accounted for by the use of a specific pitch type in the smelter that was only in use for 1 month; this pitch had a substantially higher level of BaP than other pitch types. This peak was not observed in the maintenance model as no measurements of maintenance workers were collected in during the 1 month this pitch was used. No statistical differences between electricians, welders and millwrights could be determined due to the large variability in exposures. Similarly, maintenance job differences pre-1981 were not observed, although job differences in later time periods were observed with the inclusion of job–time period interaction terms. Other pitch differences could not be detected in the BaP models due to the use of year in the models and the lack of overlap between pitch types.
|
|
For both CTPV and BaP the total variance was smaller for the operations models than the maintenance models. For CTPV, the between-worker and within-worker variance components were greater for the maintenance model (0.328 and 0.489, respectively) than for the operations model (0.087 and 0.371). For BaP, the between-worker variance was not estimatable (
0) in the potroom operations model due to very few identifiable repeated measures on the same worker, but the within-worker variance was large (1.27). The BaP maintenance model had much larger between-worker (1.67) and within-worker (0.624) variance components than the other models. Total variance was much greater in the BaP models than the CTPV models. The between-worker variance component contributed more to the total variance in the maintenance models than in the operations models. The models' predicted CTPV and BaP exposure levels for pot operator and potroom maintenance workers are displayed in Figs 2 and 3, respectively. Since the time trend was modeled on a log-transformed scale, the linear trend for each time period in the model translates to an exponential change once retransformed to the original exposure units. To account for the seasonal differences in BaP exposure levels for potroom operations jobs, the annual exposure level was weighted by the proportion of months in each season (5/12 * summer exposure levels + 7/12 * winter exposure levels).
|
|
The updated CTPV and BaP JEMs for all jobs are presented in Table 3. For the epidemiological analyses, cumulative exposure was calculated. For jobs not in the models the midpoints of the exposure categories were used. The highest exposure categories were assigned a value of 2.5 mg m–3 for CTPV exposure and 18 µg m–3 for BaP exposure, respectively. For the time period 1977–2000, the models were used to estimate exposure for
57% of the exposed person-years and accounted for nearly all of the high and moderately exposed jobs at the smelter. Direct calculation of exposure was used for 6% of the exposed-person-years. The remaining 37% of the exposed person-years required adjusting the model-based exposure estimates based on proportion of time spent in the potrooms and represented jobs with low exposures.
|
Substantial changes occurred in the 1977–2000 CTPV estimates compared to the original estimates. These differences occurred because we better characterized when exposure changes occurred through the use of the linear spline time trends and because we used more exposure categories. Discrepancies between a measurement-based and the original CTPV exposure estimates for this time period were previously reported in a validation study of the original exposure estimates (Friesen et al., 2003
). For the most part, the updated CTPV exposure estimates for 1954–1976 did not differ substantially from the original exposure estimates. Small differences occurred due to the finer discrimination in exposure categories and the resulting change in category midpoints (i.e. 0.6 mg m–3 became 0.7 mg m–3; 0.1 became 0.15 or 0.05 mg m–3 in updated study). Whereas the maximum exposure assigned in the original study was 1.5 mg m–3, 2.5 mg m–3 was now assigned to two jobs as follows: anode assistant, 1954–1976; gas collection, 1954–1960. A few jobs originally assigned to the medium exposure category (0.2–1.0 mg m–3) were now assigned to the 0.2–0.4 mg m–3 CTPV category, reducing their assigned exposure from 0.6 to 0.3 mg m–3; these jobs included mtce/tech-moderate, cathode lining and carbon plant maintenance. CTPV exposure estimates for 1977 for anode operators (0.3 mg m–3) and studblast operators (0.7 mg m–3) suggested that their original exposure estimates (0.1 and 0.6 mg m–3, respectively) for 1954–1960 were too low, thus their updated exposure estimates were increased to 0.3 and 1.0 mg m–3 CTPV, respectively.
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODOLOGYRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
|---|
The advantage of a statistical model over direct calculation of means is its ability to borrow information across jobs and time periods to predict exposures where direct measurements were not available (Stewart et al., 1996
). However, owing to limitations in the type of determinants offered in models, exposure levels for some jobs or time periods may not be directly obtained from predictive models. In this study, exposure estimates from statistical models served as the foundation of the exposure assessment methodology and were used to directly estimate exposures for the moderate and highly exposed potroom jobs. The models were also used indirectly to extrapolate exposure levels for the lower exposed jobs that spend only a fraction of their time in exposed areas and to calibrate expert exposure ratings for the earliest time periods.
A key aspect of the models developed here for predicting exposures for a retrospective health study was the use of a linear spline time trend. Calendar year is often used as a surrogate for technological and administrative changes not captured by available exposure determinants. Most commonly year is offered as a continuous variable which assumes a linear change in the log-transformed exposure with time (Symanski et al., 1998a,b; Burstyn et al., 2000). Technological changes entered as dichotomous variables (yes/no) are often treated as having an ‘instantaneous’ impact on exposure levels. More realistically, technological improvements are incorporated over time and there may be periods of stability between periods of technological change. Production and economic factors may also cause non-linear exposure changes with time (Symanski et al., 1998a). Non-linear time trends have previously been accounted for by the use of quadratic terms (Hornung et al., 1994) or by offering categorical time periods in the model that provides stepwise changes in exposure but do not allow information to be borrowed from adjoining time periods (Dement et al., 1983). One previous study has used a linear spline term to describe the time trend in exposures for retrospective exposure assessment (Raaschou-Nielsen et al., 2002).
In the case of technological improvements in this aluminum smelter, technological improvements occurred over a period of years. To account for varying rates of exposure changes over time, we instead used information available on technological changes to define time periods where technology was similar or under transition and then modeled the exposure time trend for each time period while constraining the exposures to be equal at the interval boundaries. This has the added advantage of borrowing information from adjoining time periods, and while more flexible than incorporating a linear, quadratic, or categorical time trend it also required several assumptions. Our approach assumed that all jobs in the model have the same proportional decrease in exposure within each time period (and by pot line group for the potroom operations model). In the potroom operations model the time trend was driven by the exposure levels of pot operators, a job that accounts for one-third of all exposed person-years. Pot operators were sampled annually and accounted for approximately half of all exposure measurements. We were unable to estimate a job-specific time trend and thus we may have missed some job-specific impacts of technological changes, but because of the open and large environment of the potrooms this assumption is reasonable and was necessary given insufficient samples and significant gaps between sampling campaigns for most jobs.
The models presented here assumed constant variance over the time periods, pot lines and jobs; however, ignoring changes in the variance components would impact the arithmetic means. We tested this assumption by allowing heterogeneity in the variance components by time period in the models and also by visually inspecting plots of the geometric standard deviations for jobs over time, but we observed no substantive differences between pot lines within the same job or over time (not shown). However, we did observe that maintenance jobs had much greater variability than operations jobs as has previously been reported by Kromhout et al. (1993), which is unsurprising due to the non-routine nature and varying locations of their tasks. As such, we chose to develop the maintenance jobs and operations jobs models separately. The time trends and variance components were substantially different for the maintenance jobs models compared to the operations jobs models, confirming that it is important to explore possible heterogeneity of variance in models developed for retrospective exposure assessment.
The proportion of variability in the data explained by the models described here was typical of models developed for retrospective exposure assessment (Burstyn and Teschke, 1999). The BaP measurements were much more variable than the CTPV (dataset GSD 5.1 versus 2.8, respectively). The resulting BaP models explained a much smaller proportion of the variability in the data than the CTPV models. Fewer distinctions between rates of exposure change by time periods were observed and the predicted changes in BaP exposure were generally less steep than for CTPV. The cause of the increased variability in BaP exposure levels is not certain; however, some potential but untested explanations include differences in analytical techniques between company and compliance samples (though source of measurement was not significant in the models) and BaP is known to be less stable than some other polycyclic aromatic hydrocarbons (Aubin and Farant, 2000).
The drawback of using jobs as predictors in the models is that they cannot directly predict exposure levels for jobs where no measurements were present. An alternative option would have been to identify process conditions, control measures and specific job tasks that influence exposure, such as was done by Romundstad et al. (1999). Using more specific determinants of exposure could increase the proportion of variability explained by the fixed effects in the model. We were limited in our ability to do so as operating condition and job task information were not collected alongside the exposure measurements, though some parameters could be extracted from plant records. Additionally, to be useful potential exposure determinants need to be available for the entire length of the retrospective study and exposure measurements must exist for every combination of process parameters. Rather, our goal was to predict average annual personal exposures for each job. Thus, using dates of technologically similar time periods and surrogates such as job was sufficient to borrow information across jobs and time periods to estimate average annual exposures for the health study, but would be insufficient for making decisions about control measures.
The models were limited to predicting the time trend for years where measurements were available. Back extrapolation to earlier periods requires some knowledge of the shape of the time trend. Limiting the use of predictive models to specific time ranges is not uncommon. For example, Hornung et al. (1994) limited the predictive model for ethylene oxide exposures to 1978 forward and for earlier time periods assumed a steady exposure level equivalent to 1978 levels. The exposure measurements covered the majority of the technological improvements at this smelter. The smelter modernization program began in 1975, so some continuation of the exposure decrease was expected between 1975 and 1977 and is supported by area measurements collected in the early 1970s. Prior to 1975 few major changes took place. The impact of these changes on exposure is impossible to calculate given the lack of exposure measurements; thus, decisions made by the original study's exposure assessment committee were used after calibration of their exposure estimates to the measurement-based 1977 exposure levels. With this calibration, and the finer discrimination provided with seven compared to the original four categories, we expect these exposure estimates to be improved over the original estimates.
Even when exposure measurements are available many assumptions are inherent in quantitative exposure assessment for retrospective studies. Yet quantitative exposure estimates have been found to more often yield larger risk estimates and sharpened exposure–response gradients suggesting that exposure misclassification is being reduced (Blair and Stewart, 1992). Changes in the exposure–response relationship when moving from an expert-based semiquantitative exposure assessment to quantitative exposure estimates using statistical models as its foundation will be examined in the aluminum smelter study update. In addition, the use of BaP as a more specific marker of exposure will be assessed.
|
ACKNOWLEDGEMENTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODOLOGYRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
|---|
The authors thank the joint ALCAN/Canadian Auto Workers advisory committee for their assistance. We also thank Barry Boudreault, Jim Thorne, Nela Walter and Mary Lo for their assistance in abstracting historical information and Kay Teschke for her comments on an earlier draft of this article. This research was partially supported by grants from Alcan and the WCB. Trainee support for M.F. was provided by the Michael Smith Foundation for Health Research and the Canadian Institutes for Health Research.
Received September 8, 2005; in final form January 4, 2006
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODOLOGYRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
|---|
Armstrong B, Tremblay C, Baris D et al. (1994) Lung cancer mortality and polynuclear aromatic hydrocarbons: a case-cohort study of aluminum production workers in Arvida, Quebec, Canada. Am J Epidemiol; 139: 250–62.
Armstrong BG, Tremblay CG, Cyr D et al. (1986) Estimating the relationship between exposure to tar volatiles and the incidence of bladder cancer in aluminum smelter workers. Scand J Work Environ Health; 12: 486–93.[ISI][Medline]
Astrakianakis G, Anderson J, Band PR et al. (1998) Job-exposure matrices and retrospective exposure assessment in the pulp and paper industry. Appl Occup Environ Hyg; 13: 663–70.
Aubin S, Farant JP. (2000) Benzo[b]fluoranthene, a potential alternative to benzo[a]pyrene as an indicator of exposure to airborne pahs in the vicinity of soderberg aluminum smelters. J Air Waste Manag Assoc; 50: 2093–101.[Medline]
Blair A, Stewart PA. (1992) Do quantitative exposure assessments improve risk estimates in occupational studies of cancer? Am J Ind Med; 21: 53–63.[ISI][Medline]
Burstyn I, Teschke K. (1999) Studying the determinants of exposure: a review of methods. Am Ind Hyg Assoc J; 60: 57–72.[ISI][Medline]
Burstyn I, Kromhout H, Kauppinen T et al. (2000) Statistical modelling of the determinants of historical exposure to bitumen and polycyclic aromatic hydrocarbons among paving workers. Ann Occup Hyg; 44: 43–56.
de Cock J, Kromhout H, Heederik D et al. (1996) Experts' subjective assessment of pesticide exposure in fruit growing. Scand J Work Environ Health; 22: 425–32.[ISI][Medline]
Dement JM, Harris RL Jr, Symons MJ et al. (1983) Exposures and mortality among chrysotile asbestos workers. Part I: Exposure estimates. Am J Ind Med; 4: 399–419.[ISI][Medline]
Eisen EA, Smith TJ, Wegman DH et al. (1984) Estimation of long term dust exposures in the Vermont granite sheds. Am Ind Hyg Assoc J; 45: 89–94.[Medline]
Farant JP, Gariepy M. (1998) Relationship between benzo(a)pyrene and individual polycyclic aromatic hydrocarbons in a Söderberg primary aluminum smelter. Am Ind Hyg Assoc J; 59: 758–65.
Friesen MC, Demers PA, Spinelli JJ et al. (2003) Validation of a semi-quantitative job exposure matrix at a Söderberg aluminum smelter. Ann Occup Hyg; 47: 477–84.
Harrell FEJ. (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis: Springer-Verlag New York, Inc.
Hawkins NC, Evans JS. (1989) Subjective estimation of toluene exposures: a calibration study of industrial hygienists. Appl Ind Hyg; 4: 61–8.
Hertzman C, Teschke K, Dimich-Ward H et al. (1988) Validity and reliability of a method for retrospective evaluation of chlorophenate exposure in the lumber industry. Am J Ind Med; 14: 703–13.[ISI][Medline]
Hornung RW, Reed LD. (1990) Estimation of average concentration in the presence of non-detectable values. Appl Occup Environ Hyg; 5: 46–51.
Hornung RW, Greife AL, Stayner LT et al. (1994) Statistical model for prediction of retrospective exposure to ethylene oxide in an occupational mortality study. Am J Ind Med; 25: 825–36.[Medline]
Kauppinen T, Teschke K, Astrakianakis G et al. (2002) Assessment of exposure in an international study on cancer risks among pulp, paper, and paper product workers. Am Ind Hyg Assoc J; 63: 254–61.
Kromhout H, Oostendorp Y, Heederik D et al. (1987) Agreement between qualitative exposure estimates and quantitative exposure measurements. Am J Ind Med; 12: 551–62.[ISI][Medline]
Kromhout H, Symanski E, Rappaport SM. (1993) A comprehensive evaluation of within- and between-worker components of occupational exposure to chemical agents. Ann Occup Hyg; 37: 253–70.
Kromhout H, Swuste P, Boleij JS. (1994) Empirical modelling of chemical exposure in the rubber-manufacturing industry. Ann Occup Hyg; 38: 3–22.
Post W, Kromhout H, Heederik D et al. (1991) Semiquantitative estimates of exposure to methylene chloride and styrene: the influence of quantitative exposure data. Appl Occup Environ Hyg; 6: 197–204.
Raaschou-Nielsen O, Hansen J, Thomsen BL et al. (2002) Exposure of danish workers to trichloroethylene, 1947–1989. Appl Occup Environ Hyg; 17: 693–703.[CrossRef][Medline]
Romundstad P, Haldorsen T, Ronneberg A. (1999) Exposure to PAH and fluoride in aluminum reduction plants in norway: historical estimation of exposure using process parameters and industrial hygiene measurements. Am J Ind Med; 35: 164–74.[CrossRef][Medline]
Spinelli JJ, Band PR, Svirchev LM et al. (1991) Mortality and cancer incidence in aluminum reduction plant workers. J Occup Med; 33: 1150–5.[CrossRef][ISI][Medline]
Stewart PA, Lees PS, Francis M. (1996) Quantification of historical exposures in occupational cohort studies. Scand J Work Environ Health; 22: 405–14.[ISI][Medline]
Symanski E, Kupper LL, Rappaport SM. (1998a) Comprehensive evaluation of long-term trends in occupational exposure: Part 1. Description of the database. Occup Environ Med; 55: 300–9.[Abstract]
Symanski E, Kupper LL, Hertz-Picciotto I et al. (1998b) Comprehensive evaluation of long-term trends in occupational exposure: Part 2. Predictive models for declining exposures. Occup Environ Med; 55: 310–6.[Abstract]
Teschke K, Hertzman C, Dimich-Ward H et al. (1989) A comparison of exposure estimates by worker raters and industrial hygienists. Scand J Work Environ Health; 15: 424–9.[ISI][Medline]
Teschke K, Olshan AF, Daniels JL et al. (2002) Occupational exposure assessment in case-control studies: opportunities for improvement. Occup Environ Med; 59: 575–93.
Theriault G, Tremblay C, Cordier S et al. (1984) Bladder cancer in the aluminium industry. Lancet; 1: 947–50.[CrossRef][ISI][Medline]
Yu RC, Tan W-Y, Mathew RM. (1990) A deterministic mathematical model for quantitative estimation of historical exposure. Am Ind Hyg Assoc J; 51: 194–201.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  M. C Friesen, P. A Demers, J. J Spinelli, M. F Lorenzi, and N. D Le Comparison of two indices of exposure to polycyclic aromatic hydrocarbons in a retrospective aluminium smelter cohort Occup. Environ. Med., April 1, 2007; 64(4): 273 - 278. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||