Int. J. Environ. Res. Public Health 2014, 11(2), 1312-1340; doi:10.3390/ijerph110201312

Review
Diesel Exhaust Exposure and the Risk of Lung Cancer—A Review of the Epidemiological Evidence
Yi Sun *, Frank Bochmann , Annette Nold and Markus Mattenklott
Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA), Alte Heerstraße 111, Sankt Augustin 53773, Germany; E-Mails: frank.bochmann@dguv.de (F.B.); annette.nold@dguv.de (A.N.); markus.mattenklott@dguv.de (M.M.)
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed; E-Mail: yi.sun@dguv.de; Tel.: +49-2241-2312770; Fax: +49-2241-2312234.
Received: 25 September 2013; in revised form: 5 December 2013 / Accepted: 12 December 2013 /
Published: 27 January 2014

Abstract

: To critically evaluate the association between diesel exhaust (DE) exposure and the risk of lung cancer, we conducted a systematic review of published epidemiological evidences. To comprehensively identify original studies on the association between DE exposure and the risk of lung cancer, literature searches were performed in literature databases for the period between 1970 and 2013, including bibliographies and cross-referencing. In total, 42 cohort studies and 32 case-control studies were identified in which the association between DE exposures and lung cancer was examined. In general, previous studies suffer from a series of methodological limitations, including design, exposure assessment methods and statistical analysis used. A lack of objective exposure information appears to be the main problem in interpreting epidemiological evidence. To facilitate the interpretation and comparison of previous studies, a job-exposure matrix (JEM) of DE exposures was created based on around 4,000 historical industrial measurements. The values from the JEM were considered during interpretation and comparison of previous studies. Overall, neither cohort nor case-control studies indicate a clear exposure-response relationship between DE exposure and lung cancer. Epidemiological studies published to date do not allow a valid quantification of the association between DE and lung cancer.
Keywords:
diesel exhaust; diesel motor emissions; DME; epidemiology; review; lung cancer

1. Introduction

Diesel engines have been widely used for decades in various industrial sectors such as underground mining, construction, public transportation, ship loading in docks, agriculture, operation of machines and fire-fighting. Diesel exhaust (DE) emissions are composed of gases and a particulate phase containing thousands of chemicals. Their composition varies according to engine type, speed, air/fuel ratio, temperature, fuel and many other factors [1]. DE contains large quantities of carbonaceous particulates to which polynuclear aromatic hydrocarbons and other heterocyclic compounds are adsorbed. The latter are known to be mutagenic and carcinogenic in both animals and humans [2].

In June 2012, a working group of the International Agency for Research on Cancer concluded that there was sufficient evidence for the carcinogenicity of DE in humans [3]. However, these findings appear to be based upon selected epidemiological studies with certain important methodological limitations, particularly in the assessment of confounding effects and the assessment of DE exposures [4]. In order to evaluate critically the epidemiological evidence for the association between DE exposure and the risk of lung cancer, we conducted a systematic review of the international literature.

2. Methods

2.1. Literature Search

For comprehensive identification of original studies on the association between DE exposure and the incidence or mortality of lung cancer, searches were performed for the period between 1970 and 2013 in the following databases: MEDLINE, EMBASE, NIOSHTIC, CISDOC, Cochrane and the databases in TOXNET. Multipart search strategies were applied using “diesel” combined with the following search terms: “lung cancer”, “lung neoplasm?”, “work?”, “occupation?”, “epidemiol?”, “case control”, “cohort” or “risk”. Bibliographies and cross-referencing including comparison with reviews were additionally used for literature searches.

2.2. Quantification of DE Exposures Using MEGA-JEM

Previous studies on the effect of DE exposure focus mainly on risk estimation for jobs supposed to involve high and prolonged exposure to DE, such as those of professional drivers, railroad workers, heavy equipment operators, and so on. Although a large number of studies have been published, few are able to provide any information on the level of DE exposures in these jobs.

To allow an objective impression to be gained of the level of DE exposures in commonly exposed jobs, we created a job-exposure matrix for DE exposures based upon historical industrial hygiene data from the MEGA (Measurement data relating to workplace exposure to hazardous substances) database (see Table 1).

Table 1. DE exposures in common exposed jobs in Germany (MEGA-JEM).

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Table 1. DE exposures in common exposed jobs in Germany (MEGA-JEM).
Job Titles
(MEGA job title (1))
Exposure as Elemental Carbon (mg/m3) (2)
Before 1990 (3)1990–1993 (4)After 1993 (4)
Dock workers,0.190.050.03
Transportation equipment operators
(warehouse and loading work)
Heavy equipment operators0.260.080.03
Drivers of heavy construction vehicles
(shipping and transport within enterprises)
Highway maintenance0.130.040.02
Open-air mechanics
Highway workers
(repair and maintenance)
Mechanics (not open-air)0.180.090.03
Bus garage workers
Truck mechanics
(bench tests)
Truck drivers0.070.020.01
Heavy truck drivers
Professional drivers
Railroad workers
Bus drivers
Lorry drivers
Taxi drivers
(50% of exposure level of repair and maintenance)
Potash miner0.300.150.14

Notes: (1) Exposure data from MEGA are related to the listed job titles; (2) Exposure data are calculated from exposure data of total carbon (TC) using the known task related mean relation between EC and TC; (3) 90% percentile of the exposure data for the period 1990–1993; (4) 50% percentile of exposure data.

The MEGA database is a large industrial hygiene database forming part of the Measurement System for Exposure Assessment of the German Social Accident Insurance Institutions (MGU). The database was established in 1972 and contains more than 2.4 million historical measurements of around 1,380 industrial chemical and biological agents. In total, around 4,000 historical measurements of DE exposures were entered in the database for the period from 1990 to 2000.

In this review, MEGA-JEM was used directly to estimate the exposure levels of jobs given in the results of previous published studies. If information on exposure duration is available, cumulative doses of DE exposure were quantified as “exposure level (MEGA-JEM) × median exposure duration”. Effect estimates published in previous studies were summarized in a scatter plot. Based on these values, exposure-response relationship between DE-exposure and lung cancer and their 95% CI were quantified by a linear regression analysis with the software package SigmaPlot 12.0. The inclusion of MEGA-JEM in this review will permit a direct comparison of previously published epidemiological evidence.

3. Results

In total, 42 cohort studies and 32 case-control studies were identified in which the association between DE exposure and lung cancer was examined.

3.1. Cohort Studies

In general, historical industrial hygiene data on DE exposure (based on the measurement of elemental carbon) were not available in published cohort studies. Therefore, exposure assessment was limited only to job titles in 37 of the 42 identified cohort studies. Five studies allow a quantitative assessment of DE exposure based on industrial hygiene measurement. Three studies [5,6,7] quantified the DE exposures based upon historical surrogate measurements of nitrogen dioxide, while two other studies were based either on current industrial hygiene measurement of total carbon [8] or on historical surrogate measurements of CO [9].

The effect of DE exposure upon lung cancer was evaluated with the focus primarily on the following job categories: professional drivers, highway maintenance workers, railroad workers, mechanics, workers at gasoline filling stations, heavy equipment operators, dock workers and miners (see Table 2).

The effect of DE exposure was evaluated in most studies by comparison of the lung cancer risk among workers in highly exposed jobs with an external population by use of the standardized mortality ratio (SMR), standardized incidence ratio (SIR) or proportional mortality ratio (PMR). Internal comparison was carried out in nine cohort studies [2,5,6,7,8,9,10,11,12]. All studies have large sample sizes. The possible confounding effect of smoking was adjusted in most of these studies (except the study by Bergdahl [7] and the study by Attfield [9]).

Boffetta et al. reported in an earlier study that railroad workers, heavy equipment operators, miners and truck drivers have higher mortality both for all causes and for lung cancer when compared with workers without exposure to DE [2]. Similar findings were also reported by Garshick et al. [11,13] and Larkin et al. [12]. However, a reanalysis of the US railroad study (originally published by Garshick [13]) indicates that the effect of DE exposure published in the early study appears to be unstable. The estimates of the effect vary strongly depending upon how the exposure was assessed and how confounders were considered in the analysis [14]. If the confounders were considered in a different manner, an exposure-response relationship between DE exposure and lung cancer is no longer observed. This early methodological disagreement in the US railroad study gives an example about how difficult previous evidence can be properly interpreted. This problem seems to be solved in a later published extended follow-up of this cohort [10]. Therefore, only the latest publication of this study [10] was considered in this review.

Table 2. Cohort studies on diesel exhaust exposure and lung cancer.

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Table 2. Cohort studies on diesel exhaust exposure and lung cancer.
AuthorPopulationFollow-up time periodExposure assessmentConfounder controlledStatistical methodJob title/exposureRR/SMR (95% CI)Quantification of exposure doses
Ahlberg et al. (1981) [15]35,960 drivers and 686,708 non-drivers1961–1973Job as professional driverAge, sex, local regionMantel-HaenszelDriver1.33
(1.13–1.56)
Impossible (exposure level and duration not available)
Attfield et al. (2012) [9]12,315 non-metal miners1947–1997Historical measurement of COAge, Work locationSMR Cox-modelHighest expo. (≥1,280 µg/m3-year)2.39
(0.82–6.94)
Possible (unit: µg/m3-year of respirable elemental carbon)
Balarajan et al. (1988) [16]3,392 professional drivers in London1950–1984Job as professional driver in 1939AgeSMRTruck driver1.59
(p < 0.05)
Impossible (exposure level and duration not available)
Taxi driver0.86
(p > 0.05)
Bus driver1.42
(p > 0.05)
Bender et al. (1989) [17]4,849 highway maintenance workers1945–1984Job as highway maintenance workerAgeSMRHighway maintenance0,69
(0.52–0.90)
Impossible (exposure level and duration not available)
Bergdahl et al. (2010) [7]8,321 iron ore miners1958–2000100,000 historical measurement of NO2Age and calendar periodSIR, Poisson regression>15 (ppm-year)0.87
(0.42–1.83)
Possible (unit: ppm-year of NO2)
Boffetta et al. (1988) [2]461,981 males aged 40–79 years1982–1984Longest job with DME exposureAge, smoking and other occupational exposuresMantel-HaenszelDE exposed1.18
(0.97–1.44)
Impossible (exposure level not available)
Truck driver1.24
(0.93–1.66)
Railroad worker1.59
(0.94–2.69)
Heavy equipment operator2.60
(1.12–6.06)
Boffetta et al. (2001) [18]All Swedish population employed without farmer1971–1989Job titles 1960–1970, DME yes/noAgeSIR, Poisson regressionDE low0.95
(0.92–0.98)
Impossible (exposure level and duration not available)
DE medium1.1
(1.08–1.21)
DE high1.3
(1.26–1.42)
Garshick et al. (1988) [13]55,407 US railroad workers1959–1980Job title in 1959 DME yes/noAgeCox-modelDE exposure
(1–4 years)
1.20
(1.01–1.44)
Impossible (exposure level not available)
DE exposure
(5–9 years)
1.24
(1.06–1.44)
DE exposure
(10–14 years)
1.32
(1.13–1.56)
DE exposure
(≥15 years)
1.82
(1.30–2.55)
Garshick et al. (2004) [19]54,973 US railroad workers1959–1996Job title in 1959 DME yes/noAge, year of employmentCox-modelDE exposed1.40
(1.30–1.51)
Impossible (exposure level not available)
Garshick et al. (2006) [10]39,388 US railroad workers1959–1996Job title in 1959 DME yes/noAge, SmokingCox-modelDE exposed1.22
(1.12–1.32)
Impossible (exposure level not available)
Conductor
(<5 years)
1.31
(1.12–1.51)
Conductor
(5–10 years)
1.23
(1.0–1.39)
Conductor
(10–15 years)
1.23
(1.08–1.39)
Conductor
(15–20 years)
1.16
(1.03–1.30)
Conductor
(≥20 years)
1.22
(1.02–1.47)
Garshick et al. (2008) [11]31,135 truck industry workers1985–2000Job title (ever employed ≥ 1 year)Age, race, smoking, healthy worker effectCox-modelLong-haul driver
(20 years)
1.40
(0.88–2.24)
Impossible (exposure level not available)
Pickup driver
(20 years)
2.21
(1.38–3.52)
Dockworker
(20 years)
2.02
(1.23–3.33)
Combination
(20 years)
2.34
(1.42–3.83)
Guberan et al. (1992) [20]6,630 professional drivers1949–1986Job documen-ted as profess-ional driverAgeSMR (SIR)Driver1.50
(1.23–1.81)
Impossible (exposure level and duration not available)
Guo et al. (2004) [6]All economically active Finns on 31 December 1970 (n = 1,180,231)1971–1995Work history documented in Population Census File, FIN-JEM (historical measurement of NO2)Smoking, asbestos, silica and socio-economic statusPoisson regressionDE low (0.1–1.9)0.98
(0.94–1.03)
Possible (unit: mg/m3-year)
DE middle (2.0–9.9)1.04
(0.94–1.03)
DE high (≥10)0.95
(0.94–1.03)
Gustafsson et al. (1986) [21]6,071 Swedishdock workers1961–1980Job as dock workerAgeSMR (SIR)Dock worker1.29
(1.02–1.63)
Impossible (exposure level and duration not available)
Haldorsen et al. (2004) [22]All Norwegians in 1970, age: 25-641971–1991Job titleAge, smokingSIRDriver1.58
(1.5–1.7)
Impossible (exposure level and duration not available)
Engine/motor operator workers1.34
(1.2–1.5)
Hansen (1993) [23]14,225 truck drivers1970–1980Self-reported job as truck driverin 1970AgeSMRTruck driver1.6
(1.28–1.98)
Impossible (exposure level and duration not available)
Howe et al. (1983) [24]43,826 retired railway workers1965–1977Job at time of retirement, DME yes/noAgeSMRDE probably exposed1.35
(p < 0.001)
Impossible (exposure level and duration not available)
Jakobsson et al. (1997) [25]96,438 professional drivers in Sweden1971–1984Job in 1970Age, smoking (indirect adjustment)SMRTaxi driver1.2
(1.0–1.4)
Impossible (exposure level and duration not available)
Long-distance lorry driver1.1
(0.9–1.3)
Short-distance lorry driver1.2
(1.0–1.7)
Järvholm et al. (2003) [26]20,728 drivers and 119,984 carpenters/electricians1971–1995 Job documented in health examinationAgeSMR (SIR)Equipment operator0.76
(0.58–0.97)
Impossible (exposure level and duration not available)
Truck driver1.14
(0.87–1.46)
Johnston et al. (1997) [5]18,166 British coalminers1969–1992 historical measurement of NO, NO2Age, smokingCox-modelRisk/unit exposure1.23
(1.0–1.5)
Possible (unit: g/m3-hour)
Kaplan (1959) [27]6,506 deceased railroad workers in US1953–1958Job documented in medical recordAgeSMRRailroad worker0.88
(0.65–1.16)
Impossible (exposure level and duration not available)
Laden et al. (2007) [28]54,319 male employees in US1985–2000Job titleAgeSMRDriver1.1
(1.02–1.19)
Impossible (exposure level and duration not available)
Dockworker1.1
(0.94–1.30)
Lagorio et al. (1992) [29]1,446 workers of gasoline filling station1981–1991Employment durationAgeSMRFilling station worker1.06
(0.64–1.65)
Impossible (exposure level not available)
Larkin et al. (2000) [12]55,395 US railroad workers195–1976Job title in 1959 DME yes/noAge, smokingPoisson regressionEngineer/fireman1.17
(0.79–1.74)
Impossible (exposure level not available)
Brakemen/conductor1.08
(0.76–1.54)
Shop worker1.21
(0.80–1.83)
Luepker et al. (1978) [30]184,435 truck drivers3 months in 1976Union membershipAgeSMRTruck driver1.21
(p > 0.05)
Impossible (exposure level and duration not available)
Magnani et al. (1988) [31]All population in England and Wales1971–1971Decennial JEM for death cases, estimation of risk setAge social classSMRDE low0.98Impossible (exposure level and duration not available)
DE middle0.95
DE high0.96
Maizlish et al. (1988) [32]1,570 deceased highway workers1970–1983CalTRANS employeesAgePMRHighway worker0.98
(0.80–1.19)
Impossible (exposure level and duration not available)
Menck and Henderson (1976) [33]Estimated population at risk in 1971 in Los Angeles1968–1973Job documented in death certificatesAgeSMRTaxi driver3.44Impossible (exposure level and duration not available)
Truck driver1.65
Auto repair1.46
Transportation1.27
Milham (1983) [34]429,926 male and 25,066 female deaths1950–1979Job during most of lifetimeAgePMRRailroad worker1.2Impossible (exposure level and duration not available)
Machine operator1.4
Netterstrom (1988) [35]2,465 bus drivers1978–1984Job in 1978AgeSMRBus driver0.55
(0.33–0.99)
Impossible (exposure level and duration not available)
Neumeyer-Gromen et al. (2009) [8]5,862 potash miners1970–2001255 measurement of TC value in 1992Age, smokingSMR Poisson regression, Cox-modelDE exposure (<1.29)1.0Yes (unit: mg/m3-year)
Säverin et al. (1999) [36]DE exposure (1.26–2.04)1.13
(0.46–2.75)
DE exposure (2.04–2.73)2.47
(1.02–6.02)
DE exposure (2.73–3.90)1.50
(0.56–4.04)
DE exposure (>3.90)2.28
(0.87–5.97)
Nokso-Koivisto and Pukkala (1994) [37]8,391 locomotive drivers1953–1991Member of associationAgeSIRLocomotive driver0.86
(0.75–0.97)
Impossible (exposure level and duration not available)
Paradis et al. (1989) [38]2,134 bus drivers1962–1985Job in payrollAgeSMRBus driver1.01
(0.70–1.38)
Impossible (exposure level and duration not available)
Pukkala et al. (1983) [39]All population in Finland, (age: 35–69)1971–1975Job in 1970AgeSIRRailway driver0.58 (p > 0.05)Impossible (exposure level and duration not available)
Road transport1.06 (p > 0.05)
Raffle (1957) [40]London transport male staff1950–1953 Job in 1950AgeSMRBus driver1,4
(0.94–2.0)
Impossible (exposure level and duration not available)
Raffnson (1988) [41]295 marine engineers und 182 machinists1955–1982Job documented in the Register of EngineersAgeSMRMarine engineer2.05
(0.83–4.23)
Impossible (exposure level and duration not available)
Rafnsson and Gunnarsdottir (1991) [42]888 truck drivers and 726 taxi drivers alive in 19511951–1988Job documented in truck driver unionAgeSMRTruck driver2.14
(1.37–3.18)
Impossible (exposure level and duration not available)
Taxi driver1.39
(0.72–2.43)
Rushton et al. (1983) [43]8,490 transport maintenance workers1967–1975Last or present job documentedAgeSMRMaintenance Worker1.01
(0.82–1.22)
Impossible (exposure level and duration not available)
Schenker (1984) [44]2,519 railroad workers1967–1979Job title in retirement board, DME: Yes/NoAgeSMRDE exposed1.42
(0.92–1.92)
Impossible (exposure level and duration not available)
Stern et al. (1981) [45]1,558 motor vehicle examiners1944–1977Ever employed jobAgeSMRMotor vehicle examiner1.02
(0.6–2.0)
Impossible (exposure level and duration not available)
Stern et al. (1997) [46]Death of 15,843 construction operating engineers1988–1993Job titleAgePMRconstruction operating engineers1.14
(1.09–1.19)
Impossible (exposure level and duration not available)
Waller (1981) [47]Transport workers in London 420,699 man-years at risk1950–1974Job in 1950AgeSMRBus driver0.79
(0.73–0.85)
Impossible (exposure level and duration not available)
Waxweiler (1973) [48]4,944 potash miners, US1940–1967Ever employed in a potash firmAgeSMRPotash miner1.1
(0.69–1.66)
Impossible (exposure level and duration not available)
Wong et al. (1985) [49]34,156 construction workers in US1964–1978Heavy equipment operators ≥20 year, duration of union membershipAgeSMRUnion membership1,07
(1.00–1.15)
Impossible (exposure level not available)

Among the three cohort studies employing historical measurements of nitro compounds as surrogate indicators of DE exposures [5,6,7], a weak association (OR = 1.23, 95% CI: 1.0–1.5) between DE exposure and lung cancer can be demonstrated only in the study by Johnston et al. [5]. In the other two cohort studies [6,7], no relationship between DE exposure and lung cancer could be observed. Main strengths of these studies are large sample size, quantitative exposure estimations and consideration of smoking as a confounder in the analysis. However, some important limitations make the interpretation of these studies difficult. These include the population based setting and incomplete assessment of work history in the study by Guo et al. [6], and the missing consideration of occupational cofounders (such as respirable silica) in the analysis of the other two mining cohorts [5,7]. Since it is generally questionable if nitro compounds can be used as surrogate to measure DE exposures, the evidences provided by these studies are rather limited.

The German potash miner study [8] is the first study which quantified DE exposures by measuring carbon compounds. This study has a sample size of 5,862 workers with a follow-up duration of 30 years. After adjustment for age and smoking, the study demonstrates a clear exposure-response relationship between DE exposures and lung cancer mortality. However, in a recent reanalysis of this study, Möhner et al. [50] pointed out that a part of cohort members in this study were previously employed as uranium miners. These workers may have had a high exposure to respirable silica and radon daughters in their work history. If these subjects were excluded from the data analysis, an exposure-response relationship between DE exposure and lung cancer can no longer be observed. This finding leads to a further reanalysis of this cohort in which employment in external mines or industries was controlled [51]. The final results give no evidence of an association between DE exposure and lung cancer. Strengths of this study are large sample size and extensive control of both occupational and non-occupational confounders in the analysis [50,51]. Historical DE exposures were estimated based on the current industrial hygiene measurements.

In contrast to the German potash miner study, the US Miners study demonstrates an extremely high effect of DE exposure (up to 5-fold), although the initial analysis of this cohort did not reveal a clear relationship between DE exposure and lung cancer [9]. Main strengths of this study are large sample size (more than 12,000 workers with an average follow-up duration of about 23 years), quantitative assessment of DE exposures by measuring carbon compounds and the adjustment of smoking as a confounder in a nested case-control analysis [52]. However, some findings reported in this study need more clarification. For example, it is unclear why “surface only workers” (SMR = 1.33) have the same risk as the “ever underground workers” (SMR = 1.21) in the initial analysis, although DE exposure among “underground workers” was about 500 times higher than “surface workers”. This finding seems to be contradictory with the final reported high effect of DE exposures. Possible limitations of this study have been discussed by Morfeld [53] and Gamble et al. [54] regarding the completeness of follow-up, essential exposure misclassification, inadequate control of occupational confounder and improper statistical methods used.

In order to compare previously published cohort studies objectively and to allow an overall judgement of the association between DE exposure and lung cancer, we calculated the historical DE exposure in previous studies by means of the MEGA-JEM. Due to limited exposure information (limited information on job title or exposure duration), cumulative doses of DE exposures are only available for six cohort studies (Table S1, Supplementary Information). The results of these studies are summarized in Figure 1. Overall, no exposure-response relationship between DE exposure and lung cancer can be demonstrated.

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Figure 1. Effects of DE-exposures on the risk of lung cancer given in previously published cohort studies.

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Figure 1. Effects of DE-exposures on the risk of lung cancer given in previously published cohort studies.
Ijerph 11 01312 g001 1024

3.2. Case-Control Studies

In total, 25 population or hospital-based case-control studies, six nested case-control studies and 1 industry-based case-referent study were identified (see Table 3). Most of these studies have large sample sizes and adjustment of the possible confounding effect of smoking in the analysis.

Assessments of DE exposures were limited in most of these studies on job title (with different definitions) or dichotomous categorization (ever/never exposed). Quantitative or semi-quantitative assessment of DE exposure was carried out in only six studies, with use of different exposure assessment methods [51,52,55,56,57,58]. Overall, a consistently increased risk of lung cancer was reported for jobs supposed to have high DE exposures. An exposure-response relationship was also presented in most studies. However, due to the different exposure assessment methods used, direct comparison between these studies is difficult.

Table 3. Case-control studies on diesel exhaust exposure and lung cancer.

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Table 3. Case-control studies on diesel exhaust exposure and lung cancer.
AuthorDesignPopulationExposure assessmentConfounder controlledStatistical methodJob title/exposureORQuantification of exposure doses
(95% CI)
Benhamou et al. (1988) [59]Population based case-control study 1,625 cases and 3,091 controlsEver employed as professional driverAge, smokingConditional logistic regressionMotor vehicle driver1.42
(1.07–1.89)
Impossible (exposure level and duration not available)
Transport equipment operator1.35
(1.05–1.75)
Miner2.14
(1.07–4.31)
Farmers1.24
(0.94–1.62)
Boffetta et al. (1990) [60] Population based case-control study 2,584 cases and 5,099 controlsSelf reported exposure (yes/no)Age, race, smoking, education and asbestosLogistic regressionProbable DE exposure1,49
(0,72–3,11)
Impossible (exposure level not available)
(≥30 years)
Truck driver 1,83
(0,31–10,73)
(1–15 years)
Truck driver 0,94
(0,41–2,15)
(16–30 years)
Truck driver 1,17
(0,40–3,41)
(>30 years)
Brüske-Hohlfeld et al. (1999) [61]Population based case-control study3,498 cases and 3,541 controlsInterview on work historyAge, smoking and AsbestosConditional logistic regressionDE exposed1,43
(1,23–1,67)
Impossible (exposure level and duration not available)
Buiatti et al. (1985) [62] Population based case-control study 376 cases and 892 controlsEver employed job transportationAge and smokingLogistic regressionTransportation1.1 (0.7–1.6)Impossible (exposure level and duration not available)
Taxi driving1.8 (1.0–3.4)
Train conductor1.4 (0.5–3.9)
Burns (1991) [63] Population based case-control study 5,935 cases and 3,956 controls with colon cancerTelephone interview on work history, job title Age and smokingLogistic regressionAutomobile repair1.56
(0.85–2.87)
Impossible (exposure level and duration not available)
Railroad1.37
(0.70–2.66)
Bus and truck transport1.20
(0.82–1.75)
Coggon et al. (1984) [64]Population based case-control study598 cases and 1,180 controlsJob in death certificate DME (yes/no)Age, sex and residenceLogistic regressionHigh DE jobs1.1 (0.7–1.8)Impossible (exposure level and duration not available)
Damber and Larsson (1987) [65]Population based case-control study 589 cases and 1,035 controlsSelf reported work historyAge and smokingLogistic regressionProfessional driver 1.36
(0.97–1.91)
Impossible (exposure level not available)
(>1 years)
Professional driver 1.47
(0.97–2.20)
(>10 years)
Professional driver 1.61
(1.01–2.57)
(>20 years)
Decoufle et al. (1977) [66] Hospital based case-control study Cases and controls were selected among 13,949 patientsJob titleAge and smokingunclearBus driver1.81 (p < 0.05)Impossible (exposure level and duration not available)
Taxi driver0.82 (p < 0.05)
Truck driver1.07 (p < 0.05)
Elci et al. (2003) [67]Hospital based case-control study1,354 cases and 1,519 controlsJob titleAge and smokingLogistic regressionDriver1.4 (1.1–2.0)Impossible (exposure level and duration not available)
Highway construction1.5 (1.1–2.5)
Emmelin (1993) [55] Industry based case-referent study 50 cases and 154 controls (dock workers)Job as dock worker. Index for DME exposureAge and smoking Conditional logistic regressionLow DEreferenceImpossible (exposure level and duration not available)
Medium DE1.6
(0.5–5.1)
High DE2.9
(0.8–10.7)
Garshick et al. (1987) [68] Nested case-control study Deceased railroad workers. 1,256 cases and 2,385 controls Expert evaluation for jobs, exposure durationAge, smoking and asbestosLogistic regressionRailroad1.55
(1.09–2.21)
Impossible (exposure level not available)
(>20 years)
DE exposed 1.41
(1.06–1.88)
(>20 years)
Gustavsson et al. (1990) [56]Nested case-control study 20 cases and 120 controlsIndex for exposure level, exposure durationAge and asbestosConditional logistic regressionIndex value 1ReferenceImpossible (exposure level and duration not available)
(0–10)
Index value 21.34
(1.09–1.64)
(10–20)
Index value 31.81
(1.20–2.71)
(20–30)
Index value 4 (>30)2.43
(1.32–4.47)
Gustavsson et al. (2000) [57]Population based case-referent study 1,042 cases and 1,274 controlshistorical measurement of NO2Age, smoking, radonLogistic regression0–0.530.67
(0.42–1.08)
DME was calculated as cumulative NO2 exposure (mg/m3-year)
0.54–1.411.14
(0.77–1.67)
1.42–2.37 1.01
(0.67–1.53)
≥2.381.62
(1.13–2.31)
Hall et al. (1984) [69] Hospital based case-control study 502 cases and 502 controlsInterview on job titleAge, smoking and social statusMantel-HaenszelBus driver5.5
(0.8–36.0)
Impossible (exposure level and duration not available)
Truck driver1.4
(0.7–2.6)
Railroad worker2.6
(0.5–12.8)
Heavy equipment3.5
(1.0–11.8)
Hansen et al. (1998) [70] Population based case-control study 37,597 cases and 37,597 controls Job title documented in National Bureau of StatisticsAge and sexConditional logistic regressionTaxi driver1.6
(1.2–2.2)
Impossible (exposure level and duration are not available)
Bus and truck driver1.3
(1.2–1.5)
Hayes et al. (1989) [71] Population based case-control study 1,444 cases and 1,893 controlsInterview, motor exhaust-related jobs, employment durationAge, smoking and study areaLogistic regressionTruck driver1.5
(1.1–1.9)
Impossible (exposure level not available)
(≥10 years)
Bus driver1.6
(0.9–2.8)
(≥10 years)
Mechanics1.7
(0.9–3.4)
(≥10 years)
Heavy equipment1.3
(0.6–3.1)
(≥10 years)
Kauppinen (1993) [72]Nested case-control study136 cases and 408 controlsJEM for job title, DME (yes/no)Age, smokingConditional logistic regressionDE exposed1.70
(0.55–5.20)
Impossible (exposure level and duration not available)
Lerchen et al. (1987) [73] Population based case-control study 506 cases and 771 controlsHigh risk jobs ever exposed?Age, sex, race and smokingLogistic regressionEngineer and fireman0.6
(0.1–3.3)
Impossible (exposure level and duration not available)
Diesel engine mechanic0.6
(0.2–2.0)
ME exposure0.6
(0.2–1.6)
Milne et al. (1983) [74]Population based case-control study925 cases and 6,420 cancer controlsJob title in death certificatesAge and sexLogistic regressionTransportation1.1Impossible (exposure level and duration not available)
Möhner et al. (2013) [51] Nested case-control study 68 cases and 340 controls255 measurement of TC value in 1992Age, smoking, external employmentConditional logistic regression1st quartilereferenceYes (unit: μg/m3-year)
2nd quartile0.90
3rd quartile1.16
4th quartile 0.78
Olsson et al. (2011) [75] Pooled analysis of 11 case-control studies13,304 population cases and 16,282 controls Logistic regressionExposure index > 34.51.31
(1.19–1.43)
Impossible (exposure level not available)
Parent et al. (2007) [76] Population based case-control study857 cases and 1,882 controls Logistic regressionDE exposure1.2
(0.8–1.8)
Impossible (exposure level not available)
Pfluger and Minder (1994) [77]Population based case-control studyDeceased chauffeursJob title in death certificatesAge and smokingPoisson regressionChauffeur1.48
(1.30–1.68)
Impossible (exposure level and duration not available)
Richiardi et al. (2006) [78]Population based case-control study595 cases and 845 controlsJob title, DME (yes/no)Age, sex, smoking and other occupational exposuresLogistic regressionDE exposure1.04
(0.79–1.37)
Impossible (exposure level and duration not available)
Siemiatycki et al. (1988) [79]Hospital based case-control study857 cases and 1,523 controlsInterview on work history, expert judgement on DE exposureAge, race, social status, smoking and blue/white collar jobMantel-HaenszelDE exposed1.2
(0.8–1.5)
Impossible (exposure level and duration not available)
Silverman et al. (2012) [52]Nested case-control study 198 cases and 562 controls from 8 mining companies1,156 measurement of EC value during 1998–2001 Age, sex, race, smokig and history of respiratory diseaseConditional logistic regressionDE exposureReferenceYes (unit: μg/m3-year)
(0–19)
DE exposure0.87
(0.48–1.59)
(19–246)
DE exposure1.50
(0.67–3.36)
(246–964)
DE exposure1.75
(0.77–3.97)
(≥964)
Soll-Johanning et al. (2003) [80]Nested case-control study 153 cases and 606 controlsJob as bus driver Age and smokingConditional logistic regression20+ years of employment0.63
(0.32–1.14)
Impossible (exposure level not available)
Steenland et al. (1990) [81]Population based case-control study 996 cases and 1,085 controlsInterview next of kin, longest job as truck driverAge, smoking and asbestosMultivariate analysisTruck driver1.55
(0.97–2.47)
Impossible (exposure level not available)
(≥18 year)
Truck mechanic1.50
(0.59–3.40)
(≥18 year)
Swanson et al. (1993) [82] Population based case-control study 3,797 cases and 1,966 controls (colon cancer)Interview relatives, last job title, employment durationAge, race and smokingLogistic regressionIndustrial maintenance
(20+ years)
1.5
(0.8–2.9)
Impossible (exposure level not available)
Automobile mechanics
(20+ years)
1.5
(0.7–3.0)
Machine operators
(20+ years)
1.9
(1.0–3.9)
Heavy truck driver
(20+ years)
2.5
(1.4–4.4)
Light truck driver
(20+ years)
2.1
(0.9–4.6)
Villeneuve et al. (2011) [58]Population based case-control study 1,681 cases and 2,053 controlsExpert evaluation for jobsAge, smoking, location, silica and asbestosLogistic regressionCumul. expo. 1. tertile0.93
(0.75–1.17)
Impossible (exposure level and duration not available)
Cumul. expo. 2. tertile1.03
(0.83–1.29)
Cumul. expo. 3. tertile1.12
(0.89–1.40)
Wegman and Peters (1978) [83]Population based case-control study100 cases and 100 controls of CNS cancerTele. Interview relatives on job titleNoLogistic regressionTransportation equipment operator1.26
(0.28–5.84)
Impossible (exposure level and duration not available)

To facilitate the comparison of previously published case-control studies, we assessed the DE exposure quantitatively by means of the MEGA-JEM. Due to limited exposure information, cumulative doses of DE-exposures can only be quantified for eight case-control studies (Table S2, Supplementary Information). The results of these studies are summarized in Figure 2. Similar to previously published cohort studies, case-control studies do not show a clear exposure-response-relationship.

Ijerph 11 01312 g002 200
Figure 2. Effects of DE-exposures on the risk of lung cancer given in previously published case-control studies.

Click here to enlarge figure

Figure 2. Effects of DE-exposures on the risk of lung cancer given in previously published case-control studies.
Ijerph 11 01312 g002 1024

4. Discussion

The possible association between DE and lung cancer, which constitutes an important occupational health question, has long been the subject of debate. Interpretation of epidemiological evidence faces a series of methodological challenges.

Lack of exposure information appears to be the major problem in interpreting human epidemiological data. The low volume of data documenting past exposures is due to the fact that no standardized method of measuring diesel fumes existed before the late 1980s. From an industrial hygiene prospective, it was not clear which substance to measure during assessment of occupational exposure to DE. Diesel fumes are composed of gases (nitrogen oxides, carbon monoxide) and various hydrocarbons bound to a carbon core. Early studies have reported levels of particulate, but such particulates are generated by many sources other than diesel engines [84]. Attention has also been focused on polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs in the exhaust. However, there are no standard methods of measuring PAHs, and PAHs are also emitted by sources other than diesel engines [84].

In the late 1980s, a standardized method of measuring diesel fumes by quantifying elemental carbon was introduced. Since then, systematic industrial hygiene measurements have been begun in some industrialized countries. However, a long time is needed for sufficient measurement data to be collected for use in epidemiological research. Most of the epidemiological studies published to date therefore provide no fundamental basis for an objective assessment of DE exposures.

In this review, we identified only two recent studies containing industrial hygiene measurement data for carbon compounds. In all remaining studies, the exposure assessments are based on expert judgements. A given job may be classified as having high exposure by one expert, but low by another [14,85]. Previous studies indicate that the differences in expert opinion have a strong influence on the estimated exposure-response relationship between DE exposure and lung cancer [14,85]. This problem makes the interpretation and comparison of previously published epidemiological studies difficult.

To facilitate an objective comparison of previously published epidemiological studies, we created a JEM for DE exposures based upon a large number of standardized industrial hygiene measurements conducted since the late 1980s. Three calendar periods were considered in the JEM, since most of the technical changes occurred during the period between 1990 and 1993. The values in the MEGA-JEM were considered in the interpretation of the epidemiological studies published to date. We found that conflicting findings were reported not only between studies, but also within studies. It is very common for jobs associated with higher exposure (according to the exposure value given in Table 1) to be reported as having lower risks than jobs with lower exposure, even within the same study. Since many studies indicated only job titles without detailed information on the exposure duration, direct comparison of the effect estimates was limited. To solve this problem, we summarized only studies with complete exposure information (both job title and exposure duration) and presented the results in Figure 1 and Figure 2. Overall, neither cohort nor case-control-studies show exposure-response relationship between DE exposure and lung cancer.

Caution should be exercised during interpretation of these studies. Previous cohort studies often compare workers in certain job categories with a standard population without adjustment for important confounders, while case-control studies generally employ a population-based design which is less suitable for detecting weak associations related to DE exposures. For some of the early epidemiological studies, latency may also be too short to attribute lung cancer to DE exposure. The use of different definitions of job titles in the analysis (longest job, ever employed jobs, census job, job in death certificates or at the time of medical examination, etc.) and the related cross-contamination with current and previous occupational history may also have a strong influence on the estimated effects. This problem was clearly demonstrated in the cohort of German potash miners, for which the study results were strongly dependent upon whether previous work history in the uranium mining industry was considered in the analysis [50]. The JEM-approach used in this review has also some weaknesses. First, the exposure duration in most studies is given only in categories. Therefore, the use of the center of such category gave only a very crude estimate for the mean or the median of exposure duration. Furthermore, the JEM used in this review is based on German industrial hygiene measurement data. The data collected in Germany may not be representative for all industrialized countries. Since diesel engines were introduced into the workplace at variable rates over time by industry and country, the use of MEGA-JEM in this review may lead to some uncertainty in the exposure assessment. However, despite the exposure-assessment methods used (expert judgement, measuring nitro compounds, measuring carbon compound, MEGA-JEM) no consistent findings of an association between DE exposures and lung cancer can be demonstrated.

5. Conclusions

Overall, the previously published epidemiological evidence did not clearly support an exposure-response relationship between DE exposure and lung cancer. In fact, the limited exposure information available in previous studies does not even allow a valid estimation of an association between DE exposure and lung cancer. However, such an association cannot be ruled out. Causality of weak association is often difficult to establish, since it is susceptible to all forms of possible design bias. Due to the limited epidemiological evidence to date, well designed studies in an industrial context are still needed, for which detailed exposure assessment methods and adequate control for confounders are recommended.

Author Contributions

All authors participate in drafting the article or revising it critically for important intellectual content; and give final approval of the version to be submitted and revised.

Conflicts of Interest

The authors declare no conflict of interest.

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