This population-based case–control study was conducted in Germany with 208 histological confirmed male cases (response rate 9.2%) between 1998 and 2000. The study region covered a population of about 2.7 million people in South-West Germany, comprising the cities of Heidelberg, Mannheim, Ludwigshafen, Darmstadt, and Heilbronn. Cases and controls were restricted to Germans aged ≤80 who were registered as citizens in the study region. 702 population controls were selected randomly from the population registries of the study area and were originally 1:3 frequency-matched for age (response rate 62.4%). After checking the clinical-pathological records, 28 patients had to be excluded due to other diagnoses or recurrence of an earlier tumour. Ethical clearance was received by the ethical committee of the University of Heidelberg and written consent was obtained from the participants through collaborating physicians.
Risk factors were obtained with face-to-face interviews using a detailed standardized questionnaire . Information on smoking, alcohol consumption and occupational exposure was collected with a comprehensive, standardized questionnaire which has been used in almost identical form in previous large studies [21, 22].
SES was assessed in terms of education and grouped in three levels according to the years of school attended following the German educational system: nine years or less (“Hauptschule”), 10 years (“mittlere Reife”) and more than 10 years (“(Fach-)Hochschulreife”).
Smoking data were assessed by lifetime smoking periods for which daily, weekly and monthly tobacco consumption of cigarettes (rare uses of cigars, cigarillos and pipes were added according to their average weight relative to that of cigarettes) and were used to calculate pack-years of smoking, i.e. the cumulative number of cigarette smoked (1 pack-year corresponds to 20 cigarettes/day for one year, being equivalent to nearly 7300 cigarettes). Pack-years were included as a log-transformed continuous variable, which showed statistically the best model fit using the fractional polynomial technique . Time since smoking cessation was included as binary variable “having stopped smoking at least 2 years before diagnosis/interview”.
Alcohol consumption was calculated from the daily, weekly or monthly consumption 10 years before interview for all common alcoholic beverages, assuming the following ethanol content: beer 5%, wine, fruit wine or sparkling wine 10%, aperitif and liquors 20% and spirits 40%. Average daily consumption was included as an untransformed continuous variable, again following the fractional polynomial technique. More detailed information about the study population and the assessment of smoking and alcohol consumption can be found elsewhere .
A detailed life-time occupational history section collected data on every occupation since the time point people started their working phase, including start date, end date, job title, industry and nature of work. More details about the assessment of the occupational history can be found elsewhere [8, 9, 11, 12]. Each job title was coded according to the International Standard Classifications for Occupation (ISCO) and converted from ISCO-68 to ISCO-88  as the latter one served as a basis for the application of the previously published occupational indices used in our study here .
As the occupational indices used in this paper were previously only published in German language, some construction details for the indices will be given here for a better understanding. In the work done by Kroll and colleagues, the occupational burden is measured via JEMs that were constructed specifically for Germany and matched to data using the International Classification of Occupations of 1988 (ISCO-88) by the International Labour Organization . In these JEMs, 100% of all ISCO-88 2-digit codes, 94.8% of the 3-digit codes and 78.5% of the 4-digit codes are represented. The JEMs were based on data from a large scale representative survey on working conditions for 20.000 employees in Germany. The German survey was based on the European Working Conditions Survey conducted regularly since the 1980s in all Member States of the European Union  on demand of the European Commission [26, 27]. The JEMs were constructed using hierarchical linear regression models (HLM) using summary scores for job exposures in three domains based on 39 individual job characteristics . The levels for the multi-level estimation were defined by the 4-digit codes of the ISCO-88 classification and the respondents of the survey. 5 dimensions of occupational burden were analysed: Ergonomic Stress (ES), Environmental Pollution (EP), Mental Stress (MS), Social Stress (SS) and Temporal Loads (TL). The individual scores for the items were summed up for each dimension to build the indices.
The Overall Job Index (OJI) is defined as the sum of all these dimensions. A Physical Job Index (PJI) was constructed using ES and EP only, whereas a Psycho-Social Index (PSI) includes MS, SS and TL. An additional index considering only those jobs with a likely exposure to smoke, dust, gases and vapors was summarized as Carcinogenic Agent Index (CAI). Thus, the items of the CAI are a subset of the items of the PJI, as the PJI is a subset of the OJI. The indices are controlled for respondent characteristics such as age, gender, working hours and experience on the job. They were validated externally using data of the German Health Update 2009  and the German Socio-Economic Panel Study . The indices’ values refer to deciles of jobs according to the conducted German survey in ascending order: jobs in index group 1 were among those with the lowest occupational burden (like draftsmen, bookkeepers and teachers), index group 10 refers to a particularly heavily loaded group (like miners, bricklayers and metal and machinery workers) in comparison to all occupational groups. In the original concept, the indices were categorized in three levels : “high” (index values 9–10), “middle” (index values 3–8) and “low” (index values 1–2). Due to the broad interval of deciles included in the middle level of the job indices, we divided it into “upper-middle” (6–8) and “lower middle” (3–5).
In our study we matched the indices both with all jobs mentioned in the lifetime job history and with the longest job. For the analyses, the indices were used as ordinal and categorical variables.
To illustrate which exposures might play a role in jobs which are associated to a high value of CAI, we linked exposure information independently collected through a substance check list (SCL) via year of exposure and year of job period: we summarized the working hours of cases and controls respectively per year and substance and linked them to the CAI via year of exposure and year of job period. Since in the same year a person could potentially have been exposed to different substances, parallel exposures to multiple substances reported in the SCL during the same year were registered. To account for different intensities, we used the reported hours of exposure per substance by cases and controls as exposure unit.
Odds Ratios (OR) and 95%-Confidence Intervals (CI) were assessed by conditional logistic regression models conditioned on age (five years age groups) . The models were adjusted for smoking cessation, tobacco and alcohol consumption and the occupational indices. The statistical software package SAS (version 9.2) was used for the analysis.