Environmental Health Perspectives • volume 120 | number 9 | September 2012 1301

Research

Concern has been raised for > 40 years that 

exposure to diesel exhaust results in lung cancer in humans (National Research Council 

1981). Diesel exhaust has included a mixture of elemental carbon (EC) particles with 

adsorbed mutagenic and carcinogenic polycyclic aromatic hydrocarbons (PAH), condensed engine oil, other organic compounds, 

and exhaust gases. Previous epidemiologic 

studies have reported a positive association 

between lung cancer risk and work in occupations with some degree of diesel exhaust exposure [e.g., Bhatia et al. 1998; Diesel Working 

Group 1995; Garshick et al. 2004; Lipsett 

and Campleman 1999; Steenland et al. 1990; 

U.S. Environmental Protection Agency 

(EPA) 2002].

Previous evaluations by administrative 

and regulatory authorities have judged diesel 

exhaust to be a probable lung carcinogen 

(International Agency for Research on Cancer 

1989) likely capable of causing lung cancer 

at ambient levels [California Environmental 

Protection Agency 1998; U.S. EPA 2002]. 

The main criticisms have been that few 

studies have included exposure measurements, 

and those studies that have included such 

measures have been limited in their ability 

to credibly extrapolate changes in exposure 

over time, to link exposure to personal 

work records, or to provide quantitative risk 

estimates (Health Effects Institute Air Toxics 

Review Panel 2007). In the United States 

increasingly stringent emission standards have 

been implemented for new on-road and offroad diesel engines (U.S. EPA 2001, 2007). 

However, older diesel engines have remained 

in service for several decades in the United 

States and for even longer in other parts of 

the world. Therefore, understanding the 

quantitative assessment of lung cancer risk 

remains important.

Accurate linkage between work history 

and quantitative or semiquantitative estimates 

or exposure categories requires knowledge of 

current and historical exposure scenarios and 

estimates of intensity. To address this issue, 

we designed a retrospective cohort study and 

a current and historical exposure assessment 

of workers employed by four large unionized (Teamsters) carriers in the United States. 

Our approach takes advantage of three key 

characteristics: a) the large number of detailed 

work records available, b) stable relationships between union job titles and duties over 

time, and c) known dates of diesel equipment 

use (Garshick et al. 2002). We conducted a 

national exposure assessment in this industry 

between 2001 and 2006 to characterize exposures to EC as a marker of exposure to diesel and other traffic-related particulate matter 

(PM) (Davis et al. 2006, 2007, 2009; Smith 

et al. 2006) including a source apportionment assessment for components of exposure 

(Sheesley et al. 2008a, 2008b). EC was chosen as our exposure marker because it is an 

exhaust constituent that may serve as a tracer 

for diesel emissions when the source is close 

to the subject, as it is in trucking operations. 

Exposure models were used to estimate joband location-specific exposures to EC in the 

United States between 1971 and 2000, taking 

into account spatial and temporal determinants 

(Davis et al. 2006).

We have previously reported elevated standardized mortality ratios (Laden et al. 2007) 

and an elevated risk of lung cancer mortality with increasing years of work as a driver 

or dockworker in this cohort (Garshick et al. 

2008). For the present analysis we linked a historical EC exposure model to work records to 

estimate personal EC exposures and examine 

their association with lung cancer mortality.

Methods

Population and work history. Computerized 

work records were obtained for 58,326 unionized trucking industry employees (54,319 

men and 4,007 women) employed in 1985 

in four large national trucking companies. 

Job titles and duties were common among all 

companies, and records included dates of hire 

Address correspondence to E. Garshick, Pulmonary 

and Critical Care Medicine Section, VA Boston 

Healthcare System, 1400 VFW Parkway, West 

Roxbury, MA 02132 USA. Telephone: (617) 323-

7700, ext 35536. Fax: (857) 203-5670. E-mail: eric.

garshick@va.gov

We thank M.J. Canner for statistical programming 

assistance; F.E. Speizer for suggestions on the manuscript and the study design; L. Byrd, International 

Brotherhood of Teamsters Safety and Health 

Department; B. Schaefer, Central States Pension Fund; 

and the participating companies and the workers who 

participated in this study.

The study was supported by grants from the 

National Institute of Health, National Cancer 

Institute (R01 CA90792), the National Institute of 

Environmental Health Sciences (ES 00002), and the 

Health Effects Institute (4705-RFA03-1/04-1).

The authors declare they have no actual or potential 

competing financial interests.

Received 19 January 2012; accepted 31 May 2012.

Lung Cancer and Elemental Carbon Exposure in Trucking Industry Workers

Eric Garshick,1,2 Francine Laden,2,3,4 Jaime E. Hart,2 Mary E. Davis,3,5 Ellen A. Eisen,6,7 and Thomas J. Smith3

1Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, Massachusetts, USA; 2Channing Division of 

Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA; 3Exposure, Epidemiology 

and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 4Department 

of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA; 5 Department of Urban and Environmental Policy and 

Planning, Tufts University, Medford, Massachusetts, USA; 6Environmental and Occupational Medicine and Epidemiology Program, 

Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 7Environmental Health Sciences 

Division, School of Public Health, University of California, Berkeley, California, USA

Background: Diesel exhaust has been considered to be a probable lung carcinogen based on 

studies of occupationally exposed workers. Efforts to define lung cancer risk in these studies have 

been limited in part by lack of quantitative exposure estimates.

Objective: We conducted a retrospective cohort study to assess lung cancer mortality risk among 

U.S. trucking industry workers. Elemental carbon (EC) was used as a surrogate of exposure to 

engine exhaust from diesel vehicles, traffic, and loading dock operations.

Methods: Work records were available for 31,135 male workers employed in the unionized U.S. 

trucking industry in 1985. A statistical model based on a national exposure assessment was used to 

estimate historical work-related exposures to EC. Lung cancer mortality was ascertained through 

the year 2000, and associations with cumulative and average EC were estimated using proportional 

hazards models.

Results: Duration of employment was inversely associated with lung cancer risk consistent with 

a healthy worker survivor effect and a cohort composed of prevalent hires. After adjusting for 

employment duration, we noted a suggestion of a linear exposure–response relationship. For each 

1,000-μg/m3 months of cumulative EC, based on a 5-year exposure lag, the hazard ratio (HR) was 

1.07 [95% confidence interval (CI): 0.99, 1.15] with a similar association for a 10-year exposure lag 

[HR = 1.09 (95% CI: 0.99, 1.20)]. Average exposure was not associated with relative risk.

Conclusions: Lung cancer mortality in trucking industry workers increased in association with 

cumulative exposure to EC after adjusting for negative confounding by employment duration.

Key words: diesel exhaust, elemental carbon, lung cancer, occupational exposure, particulate 

matter, traffic. Environ Health Perspect 120:1301–1306 (2012). http://dx.doi.org/10.1289/

ehp.1204989 [Online 1 June 2012]
Garshick et al.

1302 volume 120 | number 9 | September 2012 • Environmental Health Perspectives

and change in employment status, job, and 

trucking terminal assignment. In one company (18.5% of the cohort) complete records 

were only available for workers still employed 

in 1993, so that company’s workers were only 

included if they were continuously employed 

from 1985 to 1993. Records for another company were not available before 1972, and work 

history before that date was based on job and 

terminal in 1972. Approval was obtained from 

the institutional review boards of Brigham 

and Women’s Hospital, Harvard School of 

Public Health, and VA Boston Healthcare 

System. Individual consent to participate was 

not required.

Mortality. Date of death and cause-specific mortality from 1985 through 2000 was 

obtained from the National Death Index using 

social security number, name, and date of birth 

as matching criteria. Because prolonged survival is rare, we included deaths with primary 

lung cancer, according to the International 

Classification of Diseases, 9th Revision (ICD-9) 

code 162 (World Health Organization 1978) 

and 10th Revision (ICD-10) codes C33-C34 

(World Health Organization 1993) as cases 

even if not the underlying cause of death. Men 

were included in the analysis if they worked 

for at least 1 year for one of the study companies. In addition, analyses were limited to 

men ≥ 40 years of age in 1985 because most 

lung cancer deaths (96%) occurred in this age 

group (n = 31,135).

Current exposure. Major jobs, diesel use, 

and numbers of workers in each job in 1985 

are presented in Table 1. An exposure assessment was conducted from 2001 through 2006. 

This assessment included 5-day sampling trips 

to 36 trucking terminals randomly chosen to 

be representative of the 139 large terminals 

(> 100 workers) in these companies (Davis 

et al. 2006; Smith et al. 2006). We measured 

cross-shift (8–12 hr) EC in particulate matter 

≤ 1.0 μm in aerodynamic diameter (PM1.0) 

the using the 5040 method from the National 

Institute for Occupational Safety and Health 

(1998). We collected > 4,000 EC measurements, including personal samples from dock 

workers and mechanics, and area measurements in loading docks, offices, and truck cabs. 

Ambient background conditions were measured at the periphery of the upwind terminal. 

Area measurements, including 214 loading 

dock samples, were collected at 44 smaller 

terminals (1–2 per trip) within 75 miles.

Separate exposure models were constructed for drivers and terminal workers. 

Drivers included pick-up and delivery (P&D) 

drivers, long-haul (LH) drivers, and hostlers 

(Davis et al. 2007) and terminal-based workers included dockworkers and mechanics 

(Davis et al. 2006). Models were validated 

with six repeat trips (Davis et al. 2009). 

Using structural equation modeling (SEM) 

for the terminal workers, personal EC exposure was a function of work area EC, and in 

turn, work area EC was a function of job, 

terminal characteristics related to size and ventilation, and upwind ambient (background) 

EC. Background EC was a function of local 

weather, distance from an interstate highway, 

land use, and U.S. census region. For P&D 

and LH drivers, home terminal ambient EC 

was a significant determinant of truck cab EC. 

In contrast to LH drivers who had air conditioned cabs, temperature was also a determinant of exposure for P&D drivers because 

cab EC was higher when the windows were 

predicted to be open. For drivers and hostlers, 

we determined factors scaling exposure based 

on ambient terminal EC. Office workers were 

assumed to have background exposures.

Historical exposure. Details of our historical model are presented elsewhere (Davis et al. 

2011). Historical trends in ambient terminal 

EC were modeled based on historical trends 

in the coefficient of haze (COH) available for 

1971 through 2000, a measurement of particulate matter based on optical density, highly 

predictive of ambient EC (R2 = 0.94) (Cass 

et al. 1984). We validated this approach using 

data from a 1988 through 1989 assessment of 

EC in the same industry that used the same 

methodology as our current assessment (Zaebst 

et al. 1991). The ratio of ambient EC from 

1988–1989 compared with 2001–2006 was 

identical to the ratio of COH data over the 

same time periods; this finding demonstrates 

that historical trends in COH and ambient 

EC were similar (Davis et al. 2011). Monthly 

ambient background EC estimates were determined for the 139 large terminals between 

1971–2000 using location specific temperature, windspeed, and other terminal-specific 

data as input into the SEM exposure model.

In addition to changes in ambient exposure, our historical model accounted for 

changes in job-related exposures based on comparison of 1988 through 1989 to 2001 through 

2006 measured values. For dockworkers who 

drove propane forklifts, the geometric mean of 

predicted EC in terminals that were sampled in 

1988–1989 and adjusted for background EC 

was 1.36 μg/m3 (Davis et al. 2011; Zaebst et al. 

1991). The value measured by Zaebst et al. 

(1991) was nearly identical (1.30 μg/m3), supporting the validity of our approach. The data 

reported by Zaebst et al. (1991) were also used 

to account for changes in the type of fuel used 

for forklifts, which included diesel in large terminals operated by three companies during the 

1980s through the mid-1990s, as well as gasoline and propane (Table 1). This approach provided multipliers that reflect historical changes 

in exposure for each job, including fuel type 

for dockworkers. We used these multipliers to 

estimate exposures for 1971 through 1989; we 

applied linear extrapolation for exposures that 

occurred from 1990 through 2000. 

Personal exposure. Data on historical 

job and terminal-specific monthly EC concentrations, which were determined by the 

exposure model, were summed by year to

estimate an individual’s cumulative exposure 

(μg/m3-months). Because workers may have 

had more than one job in a year, job-specific 

EC was weighted by the fraction of time spent 

in each job. Job-specific EC values before 1971 

(8% of total exposure time) were assigned 1971 

exposures because COH data were not available to estimate background. Combination 

workers (Table 1) were assumed to spend 50% 

of their time as a P&D driver and 50% as a 

dockworker. Fifty percent of exposure time 

was accounted for by work at the 139 larger 

terminals for which we had directly modeled 

exposure. EC predictions at one terminal were 

an order of magnitude higher than in others 

due to extreme weather values and other model 

input characteristics. Therefore, this terminal 

was assigned exposure values from another 

terminal in the same city. Based on available 

information, it is likely that nearly all terminals 

not directly modeled were smaller terminals 

(≤ 100 workers). From our exposure assessment, the mean difference between large and 

smaller terminal loading dock area EC measured during the same trip was 0, indicating no 

meaningful bias in applying our terminal exposure model. For exposure time without directly 

Table 1. Distribution of jobs in 1985 among men ≥ 40 years of age with ≥ 1 year of work (n = 31,135).

Job in 1985 Job duties n (%)

Long-haul driver (LH) Drives heavy-duty trucks between large terminals. Diesel first used 

1951–1957; transition complete 1954–1965, depending on company

10,825 (35)

Pick-up/delivery (P&D) Drives trucks locally; picks up and delivers cargo. Diesel first used 

1972–1978; transition complete 1980–1992, depending on company

5,866 (19)

Dockworker Loads/unloads cargo on dock using forklifts 5,710 (18)

Diesel forklifts in three companies starting in 1979–1986 through 

1994–1996 in large terminals

Combination worker Duties include P&D driver and dockworker 4,938 (16)

Mechanic Maintains trucks in large terminal shops 1,741 (6)

Hostler Drives a tractor or a specialized tractor to move trailers to and from 

freight dock and in yard

666 (2)

Clerk Cashiers, dock clerks, dispatchers, customer service representatives, 

and others in terminal offices

843 (3)

Other jobs Janitor, unionized manger, trainee, or not defined 546 (2)
Lung cancer and elemental carbon

Environmental Health Perspectives • volume 120 | number 9 | September 2012 1303

modeled estimates, exposure was assigned in 

one of three ways: for terminals still in existence, exposure was assigned using the nearest directly modeled larger terminal (25% of 

exposure time; 1,041 terminals); for terminals no longer in operation but with known 

region, exposure was assigned using the average 

exposure of directly modeled terminals in the 

same region (24% of exposure time; 1,583 

terminals), or national annual job averages if 

the location of the terminal was not known 

(1% of exposure time; 83 terminals). Although 

historical data were limited, it was assumed 

that gasoline forklifts were used before 1971. 

Exposures for dockworkers and combination 

workers for 1971 and afterward were assigned 

based on company-specific information on the 

use of diesel or propane forklift fuel. Workers 

were not assigned any EC exposure after retirement or before hire.

Statistical methods. Proportional hazard 

regression was used to estimate associations 

between lung cancer mortality and EC. To 

closely adjust for age and lung cancer secular 

trends, risk sets were generated using attained 

age in 1-year increments as the timeline, and an 

ordinal variable for calendar year (1985 through 

2000) was included in all models. Workers 

in the cohort were hired over many decades 

(from before 1960 until after 1980). Each 

period of work was associated with different 

unmeasured work practices and vehicle characteristics. Furthermore, individuals who entered 

follow-up (in 1985) at different ages had different risks of developing lung cancer during 

the follow-up period. To meet the assumptions of proportional hazards, we assigned separate baseline hazards based on decade of hire 

(< 1960, 1960–1969, 1970–1979, ≥ 1980) 

and age in 1985 (40 to < 50, 50 to < 60, 60 to 

< 70, ≥ 70 years). For example, the baseline 

hazard for a person 40 years of age in 1985 

(born in 1945) who began work in 1975 was 

the same as that for all workers in their 40s in 

1985 who were also hired in the 1970s, and 

the risk set for that individual would include all 

workers with the same attained age. All models 

were also adjusted for race and census region of 

residence (based on last known home address). 

We conducted sensitivity analyses with and 

without total years of employment as a timedependent covariate (modeled as either continuous or in quartiles) to assess its effect as a 

potential confounder. Previously in this cohort, 

Garshick et al. (2008) reported that lung cancer 

mortality was inversely associated with total 

duration of employment; this association is 

consistent with a survivor bias among those 

who remained at work.

Cumulative EC (μg/m3-months) and average EC (micrograms per cubic meter) were 

estimated for each worker from date of hire 

through the earlier of retirement date or 

through 31 December 2000. Time-dependent 

cumulative exposure was modeled as lags of 0, 

5, and 10 years, as continuous, and in quartiles. 

Time-dependent average exposure was modeled with lags of 0 and 5 years (continuous 

and in quartiles). For example, in a 5-year lag 

model, exposure in the current year and in 

the previous 4 years is not included in exposure calculations to exclude the contribution 

of more recent exposure to lung cancer mortality risk. Linear trend p-values were derived 

using an ordinal variable that was based on the 

median of each quartile. Penalized splines were 

incorporated into regression models to assess 

possible nonlinearity in mortality risk. Analyses 

were performed using SAS software (version 

9.2; SAS Institute Inc., Cary, NC) or R for 

Unix (R Foundation for Statistical Computing, 

Vienna, Austria).

Analyses were also performed that excluded 

workers who were employed for ≥ 1 year as a 

mechanic (n = 1,811). Mechanics experienced 

significant historical changes in job duties that 

weaken the validity of extrapolation of current 

exposure to historical estimates. Our exposure estimates reflect the current practice of 

performing minor repairs and maintenance 

whereas in the past major engine repairs were 

performed. In addition, the nature of exposure is different from that of other workers. 

Based on our field observations, mechanics 

are exposed to EC for relatively brief periods 

when engines are intermittently run inside 

shops allowing for particle aging. Workers 

in other jobs on roadways and loading docks 

have exposures characterized by considerably 

longer periods of exposure to fresh exhaust 

from continuously running engines.

Results

Population. The mean cumulative years 

of work in the study cohort was 21.6 years, 

84.9% were Caucasian, and most lived in the 

South (37.5%) or Midwest (31.5%). (Table 2). 

Long-haul diesel trucks were first used during the 1950s in this industry, and nearly all 

workers began employment during or after this 

period. Most workers also began employment 

concurrent with or after the introduction of 

diesel P&D trucks first used during the 1970s. 

There were 4,003 dockworkers or combination workers with at least 1 year of diesel forklift exposure (median, 5.8 years; interquartile 

range, 2.9–9.0 years) in the 1980s and into 

the 1990s. The average age of hire was in the 

mid-30s (Table 2), most likely due to hiring 

policies requiring previous experience. There 

was a wide range of cumulative EC exposure 

(Table 3). The 75th percentile of estimated 

cumulative exposure was > 8 times that of the 

25th percentile for a 10-year lag, and almost 

4 or 5 times higher for no lag and 5-year lagged 

exposures, respectively.

Lung cancer mortality. Through 2000, 

there were 779 lung cancer cases (734 underlying cause) out of 4,306 deaths. In models adjusted for calendar year, race, and 

census region, we examined cumulative EC 

in quartiles without and with adjustment 

for employment duration (Table 4). When 

adjusted for race, calendar year, and census 

region only, lung cancer mortality was elevated 

for the upper three cumulative EC quartiles 

Table 2. Descriptive characteristics of the 31,135 

male trucking industry workers [mean ± SD or 

n (%)].

Characteristic Value

Age at hire (years) 36.0 ± 8.3

Age in 1985 (years) 49.1 ± 6.0

Years of work 21.6 ± 8.7

< 10 2,950 (9.5)

10 to < 20 10,443 (33.5)

20 to < 30 12,202 (39.2)

≥ 30 5,540 (17.8)

Race

Caucasian 26,430 (84.9)

African American 2,818 (9.1)

Other/unknown 1,887 (6.1)

Census regiona

Northeast 4,555 (14.6)

South 11,682 (37.5)

Midwest 9,818 (31.5)

West 4,759 (15.3)

Unknown 321 (1.0)

Decade of hire

< 1960 3,107 (10.0)

1960–1969 8,284 (26.6)

1970–1979 13,745 (44.2)

≥ 1980 5,999 (19.3)

aBased on last known home address.

Table 3. Distribution of cumulative EC (μg/m3-months) from hire date through retirement or through 

31 December 2000, whichever occurred first, in the entire cohort, excluding 1,811 mechanics.

Entire cohort (n = 31,135) Cohort excluding mechanics (n = 29,324)

Percentile No lag 5-year lag 10-year lag No lag 5-year lag 10-year lag

Maximum 24,130 24,130 23,106 15,242 15,074 10,341

99 8,725 7,729 6,482 6,623 5,588 4,543

95 4,829 4,305 3,579 4,044 3,599 2,997

90 3,560 3,131 2,595 3,057 2,723 2,283

75 2,076 1,803 1,436 1,891 1,635 1,305

50 1,061 860 596 990 801 553

25 530 371 167 508 351 155

10 271 136 2.0 260 128 0

5 167 48 0 161 44 0

Minimum 10 0 0 10 0 0
Garshick et al.

1304 volume 120 | number 9 | September 2012 • Environmental Health Perspectives

compared with the lowest quartile, but differences were not statistically significant [hazard ratio (HR) = 1.17–1.19 for 5-year lagged 

exposures, excluding mechanics]. Associations 

with cumulative EC exposure were stronger 

for all three lags after adjusting for duration 

of employment but did not display a monotonic dose response when estimated for the 

cohort as a whole. However, HRs for 5- and 

10-year lagged exposures increased with each 

quartile when mechanics were excluded, which 

resulted in estimated HR of 1.48 [95% confidence interval (CI): 1.05, 2.10] and 1.41 (95% 

CI: 0.95, 2.11) for the highest versus lowest 

quartiles of 5- and 10-year lagged exposures, 

respectively. Associations were weaker for 

average EC exposure (5-year lag shown as an 

example) in models adjusting for duration of 

employment (Table 4).

Excluding the mechanics, regression models 

incorporating penalized splines for cumulative 

EC indicated that lung cancer risk increased 

with increasing cumulative EC and did not 

statistically significantly depart from linearity. 

For each 1,000 μg/m3-months of cumulative 

EC, the estimated HR is 1.04 (95% CI: 0.97, 

1.11), 1.07 (95% CI: 0.99, 1.15), and 1.09 

(95% CI: 0.99, 1.20) for no exposure lag, a 

5-year exposure lag, and a 10-year exposure lag, 

respectively (Table 5). There was no association with average EC as a continuous covariate 

(p = 0.71 for the no lag and p = 0.69 for the 

5-year lag model) (data not shown).

Duration of employment was correlated 

with cumulative EC (r = 0.55–0.74, depending 

on lag), but lung cancer mortality risk statistically significantly decreased with duration. 

For example, in the 5-year lag cumulative EC 

model excluding mechanics, the HR per year 

of work was 0.97 (95% CI = 0.96, 0.99). The 

attenuation of relative risk estimates if employment duration was not included is consistent 

with negative confounding.

Discussion

We performed a retrospective assessment of 

EC exposure and lung cancer mortality in a 

large U.S. trucking industry cohort and found 

that estimated cumulative exposure to EC was 

positively associated with lung cancer mortality after adjusting for employment duration in 

addition to race, census region, and calendar 

year. Other than the mechanics, workers with 

occupational EC exposure were in driver- or 

loading dock–related jobs that involved regular and often continuous exposure to vehicle 

exhaust from diesel and nondiesel sources. 

Estimated relative risks were stronger when 

1,811 mechanics were excluded. There was a 

suggestion of a linear exposure–response relationship, and the strongest associations were 

observed with a 5- and 10-year cumulative 

exposure lag with a HR of 1.07 (95% CI: 

0.99, 1.15) and 1.09 (95% CI: 0.99, 1.20), 

respectively. Average exposure was not associated with lung cancer mortality.

Estimated relative risks decreased with 

employment duration, which suggests that 

duration was a surrogate for time-varying 

health status. This finding was likely due to bias 

caused by left truncation in a cohort composed 

of prevalent hires combined with a healthy 

worker survivor effect. Left truncation occurs 

when subjects who were hired before the start 

of follow-up are included. Inclusion of such 

prevalent hires and variability in susceptibility 

to exposure has been shown to cause downward bias between disease and exposure duration (Applebaum et al. 2011). Effects of left 

truncation were observed in a study of lung 

cancer and silica exposure where exposure was 

inversely associated with lung cancer mortality 

among prevalent hires, despite greater cumulative exposure (Applebaum et al. 2007). We 

also observed an inverse association between 

employment duration and lung cancer mortality in a cohort of prevalent hires in the U.S. 

railroad industry (Garshick et al. 2004).

The healthy worker survivor effect is commonly presumed to be small or absent for lung 

cancer. However, results from occupational 

lung cancer studies of arsenic, radiation, and 

other diesel-exposed workers suggest otherwise 

(Arrighi and Hertz-Picciotto 1996; Brown et al. 

2004; Cardis et al. 2007; Neumeyer-Gromen 

et al. 2009). In nuclear industry workers, there 

was no association between lung cancer mortality and cumulative radiation exposure (lag 10) 

without adjustment for duration (Cardis et al. 

2007). In diesel-exposed potash miners, adjusting for time since hire resulted in a stronger 

association between lung cancer mortality and 

exposure (Neumeyer-Gromen et al. 2009). In 

a study of autoworkers, Chevrier et al. (2012) 

assessed different analytical methods and also 

Table 4. Lung cancer HRs associated with each quartile of cumulative and average EC, with and without adjustment for duration of work.

Entire cohort (n = 31,135) Cohort excluding mechanics (n = 29,324)

Exposure

PersonyearsLung cancer 

deaths

HR (95% CI) 

unadjusted for 

duration of work

HR (95% CI) 

adjusted for 

duration of work

PersonyearsLung cancer 

deaths

HR (95% CI) 

unadjusted for 

duration of work

HR (95% CI) 

adjusted for 

duration of work

Cumulative EC (μg/m3

-months)

No lag

< 530 106,272 153 Reference Reference 105,868 153 Reference Reference

530 to < 1,061 106,208 194 1.13 (0.90, 1.42) 1.24 (0.98, 1.57) 105,312 193 1.13 (0.90, 1.42) 1.25 (0.99, 1.60)

1,061 to < 2,076 106,149 209 1.14 (0.89, 1.47) 1.30 (0.99, 1.70) 102,534 202 1.13 (0.87, 1.46) 1.30 (0.99, 1.72)

≥ 2076 106,039 223 0.98 (0.74, 1.29) 1.16 (0.86, 1.57) 86,600 193 1.02 (0.76, 1.36) 1.24 (0.89, 1.71)

p for trend 0.37 0.92 0.63 0.71

5-year lag

< 371 106,359 122 Reference Reference 105,513 122 Reference Reference

371 to < 860 106,223 193 1.18 (0.92, 1.51) 1.30 (1.01, 1.68) 104,909 191 1.18 (0.92, 1.52) 1.31 (1.01, 1.71)

860 to < 1,803 106,114 208 1.16 (0.88, 1.53) 1.35 (1.01, 1.81) 102,496 202 1.17 (0.88, 1.55) 1.38 (1.02, 1.87)

≥ 1,803 105,972 256 1.12 (0.83, 1.52) 1.36 (0.98, 1.89) 87,397 226 1.19 (0.86, 1.63) 1.48 (1.05, 2.10)

p for trend 0.97 0.39 0.61 0.16

10-year lag

< 167 106,409 114 Reference Reference 104,329 112 Reference Reference

167 to < 596 106,213 183 1.04 (0.79, 1.37) 1.14 (0.86, 1.52) 104,487 179 1.06 (0.80, 1.40) 1.17 (0.88, 1.57)

596 to < 1,436 106,120 205 1.01 (0.74, 1.37) 1.18 (0.85, 1.64) 102,712 202 1.05 (0.77, 1.45) 1.26 (0.90, 1.78)

≥ 1,436 105,926 277 1.03 (0.72, 1.45) 1.25 (0.86, 1.82) 88,786 248 1.12 (0.78, 1.61) 1.41 (0.95, 2.11)

p for trend 0.96 0.39 0.57 0.15

Average exposure, 5-year lag (μg/m3

)

< 3.6 106,304 146 Reference Reference 105,930 146 Reference Reference

3.6 to < 5.4 106,153 211 1.15 (0.93, 1.43) 1.15 (0.93, 1.43) 105,950 211 1.15 (0.93, 1.43) 1.15 (0.93, 1.43)

5.4 to < 7.9 106,093 221 1.11 (0.89, 1.39) 1.12 (0.90, 1.40) 105,512 221 1.11 (0.89, 1.39) 1.12 (0.89, 1.40)

≥ 7.9 106,117 201 1.06 (0.84, 1.34) 1.08 (0.85, 1.36) 82,922 163 1.11 (0.87, 1.43) 1.13 (0.88, 1.44)

p for trend 0.97 0.88 0.61 0.53

All models adjusted for race, calendar year of follow-up, and census region.
Lung cancer and elemental carbon

Environmental Health Perspectives • volume 120 | number 9 | September 2012 1305

showed evidence of a healthy worker survivor 

effect for lung cancer. 

Average exposure was not significantly 

associated with lung cancer risk. Estimated 

trucking industry EC exposures declined 

considerably over the course of the study and 

also varied based on location, job, and the 

period of diesel forklift use. Using modelbased data to estimate trucking industry 

exposure trends (Davis et al. 2011), the 

median EC exposure over a shift for LH 

drivers in 1971–1980, 1981–1990, and 

1991–2000 was 5.88 μg/m3, 4.26 μg/m3, and 

2.01 μg/m3, respectively. For P&D drivers, 

in warm weather (windows open), estimated 

median levels were 9.59 μg/m3, 6.97 μg/m3, 

and 2.77 μg/m3, respectively, and in cold 

weather (windows closed), corresponding 

median levels were 4.15 μg/m3, 2.95 μg/m3, 

and 1.64 μg/m3, respectively. In the 1980s, 

the estimated median EC exposure for 

dockworkers working with diesel forklifts was 

29.86 μg/m3, whereas for propane forklifts it 

was 1.43 μg/m3. Therefore, average exposure 

intensity is unlikely to be an accurate surrogate 

for cumulative exposure and pulmonary dose 

of particulate over time in the study cohort.

Exposure to vehicle exhaust particulate 

was estimated based on the assessment of EC 

mass in PM1.0. We conducted a source apportionment study using particle-phase organic 

molecular markers in personal and work area 

samples at a freight terminal in an urban 

area in 2003 (Sheesley et al. 2008a). These 

data indicated that for the LH drivers, P&D 

drivers, dockworkers, mechanics, and in the 

terminal yard and in an urban background 

site, most (≥ 80%) of the EC was from diesel 

exhaust sources, with a smaller percentage 

from spark ignition vehicles. These findings 

are consistent with other source apportionment studies indicating that diesel vehicles 

significantly contributed to EC in the United 

States during the period of the study, particularly in urban areas (Schauer 2003).

A unique feature of this study was the 

quantitative approach linking historical estimates of EC with job title and trucking terminal location information from work history 

records. We conducted a national exposure 

assessment at representative work locations 

in the participating companies. Statistical 

exposure models were developed that identified 

determinants of EC exposure, and these factors 

were used to estimate exposure nationally and 

historically (Davis et al. 2011). In contrast, previous studies of this industry estimated exposures based on diesel vehicle miles, emission 

rates, and fuel efficiency factors (Bailey et al. 

2003; Steenland et al. 1998).

We conducted analyses with and without 

mechanics because we had less confidence in 

the historical extrapolation of their EC exposures since their job duties changed over 

time as major truck repairs were outsourced. 

Additionally, the patterns and potentially the 

composition of exposures for this job group 

were different from the others (McDonald 

et al. 2011). Misclassification of exposure 

would make it more difficult to detect a relationship between EC and lung cancer mortality with mechanics included. In addition, we 

hypothesized that because of their intermittent 

exposure, mechanics have less exposure to fresh 

particles that may be more hazardous than aged 

particles, thereby reducing their lung cancer 

risk. Short-lived reactive oxygen species (ROS) 

present in traffic-related PM and ultrafine particles resulting in DNA damage is a mechanism 

whereby traffic-related exposures may increase 

lung cancer risk. ROS activity has been found 

to be associated with exposure to traffic-related 

PM and ultrafine particles, potentially explaining the greater risk in persons such as drivers 

and dockworkers who are in jobs with more 

continuous exposures to fresh exhaust (de Kok 

et al. 2006; Li et al. 2003).

Our results are consistent with a large 

body of literature that supports a relationship between diesel exhaust exposure and lung 

cancer risk in occupationally exposed workers (Diesel Working Group 1995; Office of 

Research and Development 2002). These studies have not included historical quantitative 

estimates of exposure. The positive association 

between cumulative EC exposure and lung cancer estimated in the present analysis is consistent 

with our previous assessments of lung cancer 

mortality by job title in this cohort (Garshick 

et al. 2008) and is similar to results from a case–

control study conducted by Steenland et al. 

(1990) in the same industry in 1982–1983. 

Further, our findings are consistent with an 

analysis that demonstrated a linear relationship 

between PM mass and lung cancer mortality 

based on estimates of PM mass exposure from 

particulate air pollution, second hand smoke, 

and active smoking (Pope et al. 2011).

Several other studies have measured diesel 

exhaust exposures and estimated lung cancer 

mortality in miners. A study of potash miners exposed to diesel exhaust with cumulative 

exposure based on occupational measurements 

was suggestive of a positive exposure–response 

relationship (Neumeyer-Gromen et al. 2009). 

Attfield et al. (2012) reconstructed historical 

EC exposures over a 30–50 year period for a 

cohort mortality study of 12,315 U.S. nonmetal miners. In comparison to the trucking 

industry, the duration of underground mine 

exposure was relatively short (a mean of 8 

years compared with a mean of 21.6 years 

in the present study) but more intense, with 

mean EC exposures of 128 μg/m3 compared with the historical trucking industry’s 

lower cross-shift estimates noted previously. 

Cumulative exposure quartiles reported by 

Attfield et al. (2012) for underground miners 

ranged from < 108 μg/m3-years (equivalent to 

1,296 μg/m3-months) to ≥ 946 μg/m3-years 

(equivalent to 11,352 μg/m3-months) in 

a 15-year exposure lag model, which overlaps with our estimates for trucking industry 

cumulative EC. For example, for the 10-year 

lag exposures excluding the mechanics, the 

75th percentile was 1,305 μg/m3-months and 

the maximum was 10,341 μg/m3-months 

(Table 3). We converted the 10-year lag 

regression coefficient (indicating risk per 

1,000 μg/m3-months) for trucking industry 

EC exposure in Table 5 to the same units 

used in Attfield et al. (2012) by multiplying by 12 months. This calculation resulted 

in an HR of 2.77 (95% CI: 0.85, 9.00) per 

1,000 μg/m3-years. Attfield et al. (2012) 

found that the corresponding HR, based on 

a 15-year lag, was 4.06 per 1,000 μg/m3-years 

(95% CI: 2.11, 7.83), indicating overlap 

between the relative risks estimated for the 

two study cohorts.

As mentioned above, there is no one exposure metric unique to diesel exhaust; therefore, 

we selected EC mass in PM1.0 as our marker. 

Although our source apportionment analyses 

support this choice, using EC also incorporates 

exposure to other mobile sources (Sheesley 

et al. 2008b). In addition, it is also possible 

that other parameters related to vehicle exhaust 

particulate matter are relevant to the assessment of health risk, such as particle number or 

surface area (Wittmaack 2007). As the biologic 

mechanisms are not known, we do not propose 

that EC serves as a lung carcinogen, but serves 

as a marker of exposure to components associated with combustion sources.

An additional limitation is the lack of 

exposure information before employment in 

the four participating companies. In a survey mailed in 2003 to a sample of active and 

retired workers (Jain et al. 2006), workers may 

have had up to 10 additional years of trucking 

industry exposure, thereby underestimating 

actual exposure and potentially reducing apparent risk associated with estimated exposure. 

Potential overestimation of exposures using 

our exposure model for workers at very small 

terminals with little trucking activity would 

have a similar effect. Additionally, our cumulative exposure metric has the implicit assumption that effects of long-term low-intensity 

Table 5. Lung cancer hazard per 1,000 μg/m3-months 

associated with cumulative EC for exposure with 

no lag, a 5-year lag, and a 10-year lag, excluding 

mechanics.

Coefficient (SE)

1,000 μg/m3

-months

Relative 

hazard 95% CI

No lag 0.0345 (0.0349) 1.04 0.97, 1.11

5-year lag 0.0665 (0.0379) 1.07 0.99, 1.15

10-year lag 0.0849 (0.0501) 1.09 0.99, 1.20

Adjusted for race, census region, calendar year of follow-up, and duration of employment. For the no lag, 5-year 

lag, and 10-year log, p = 0.32, 0.08, and 0.09, respectively.
Garshick et al.

1306 volume 120 | number 9 | September 2012 • Environmental Health Perspectives

exposures are equivalent to effects of shorter-term high-intensity exposure resulting in a 

comparable estimate of cumulative exposure 

in terms of μg/m3-month. The overlap in estimated lung cancer risks in the current trucking 

industry cohort and the underground miner 

cohort study reported by Attfield et al. (2012) 

supports this assertion.

Although we obtained detailed work 

records and carefully assessed exposure, an 

additional limitation is a lack of information on personal risk factors for lung cancer. 

Although cigarette smoking is a major risk factor, the degree that it is a confounder depends 

on its differential association with exposure 

(Blair et al. 2007). Since workers in the study 

cohort were all unionized trucking industry 

employees, they were generally similar with 

regards to socioeconomic status which is a 

known correlate of smoking habits. Smoking 

rates also vary by age and birth cohort and 

all analyses were adjusted for these factors. 

In a 2003 questionnaire survey, we obtained 

information regarding smoking habits. In 

our previous assessment of lung cancer risk 

in this cohort based on job title (Garshick 

et al. 2008), indirect adjustment (Axelson and 

Steenland 1988) for smoking did not meaningfully influence estimated relative risks for 

lung cancer. Adjustment for differences in 

smoking habits based on job title increased 

relative risk estimates among P&D drivers, 

dockworkers, and combination workers by 

4–8% and decreased relative risks among LH 

drivers by 15%. In addition, if short-term 

workers have different behaviors (e.g., smoke 

more heavily) than longer-term workers, this 

could contribute to negative confounding 

in analyses unadjusted for duration. Drivers 

have a commercial license and undergo medical certification that could also indicate that 

healthier workers continue working.

To conclude, our results suggest lung cancer mortality increases with cumulative EC 

exposures in trucking industry workers in jobs 

with regular exposures to particulates from 

pre-2007 diesel exhaust and other mobile 

sources. Our exposure assessment indicates 

that there has been substantial success in 

reducing trucking industry EC exposures and 

therefore we predict a reduction in future 

lung cancer risk in this industry.

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