Occupation and Lung Cancer: Results from a New Zealand cancer registry-based case-control study

Transcription

1 Occupation and Lung Cancer: Results from a New Zealand cancer registry-based case-control study Marine Corbin, David McLean, Andrea t Mannetje, Evan Dryson, Chris Walls, Fiona McKenzie, Milena Maule, Soo Cheng, Chris Cunningham, Hans Kromhout, Paolo Boffetta, Aaron Blair, Neil Pearce Centre for Public Health Research Massey University Wellington

2 Occupational Epidemiology: Cohort and Case-Control Studies

3 Case-Control Study Design PAST PRESENT Exposed/ non-exposed Exposed/ non-exposed Cases Controls

4 Case-Control Studies: Potential sources of bias Controls must represent the population from which the cases are drawn (selection bias). Difficult to obtain accurate and unbiased measures of past exposures (information bias). The temporal sequence between exposure and disease may be difficult to establish (reverse causality). Not suitable for investigating rare exposures.

5 The OLCANZ study design O incident lung cancer cases O notified to the NZ Cancer Registry O aged O population controls selected from the Electoral Roll O telephone or face-to-face interview O lifetime occupational history O lifestyle factors

6 The OLCANZ study population

7 Response rate 53% for cases and 48% for controls Non-respondent cases: Both clinician and GP did not provide consent OR Patient refused Non-respondent controls: Contact could not be established OR Control refused We found similar reponse rates in previous studies of this project, and found that male controls, those of younger age and those in the lowest class of the occupational class were less likely to participate. We therefore adjusted all the models for sex, age and socio-economic status.

8 The statistical model Unconditional logistic regression Explanatory variables: Confounders: O Occupation O Industry O Sex O Age O Smoking O Ethnicity O Socioeconomic status

9 A priori high risk occupations and industries Increased risk occupations for lung cancer cases/ Controls OR 95%CI Butchers 8/1 8.77* Welder and flame-cutter 10/ Sawmill, Wood Panel and Related Wood-Processing Plant Operators 5/4 4.63* Rubber and Plastics Products Machine Operators 6/3 4.27* Presser 5/ Electric and Electronic Equipment Assembler 7/ Heavy truck drivers 25/ * Increased risk industries for lung cancer Petroleum, Coal, Chemical and Associated Product Manufacturing 36/ * * p 0.05

10 Semi-Bayes (SB) adjustment for multiple comparisons Are these findings due to chance? SB adjustment shrinks the outlying odds ratios towards the overall mean (of the odds ratios of all occupations/industries). The larger the individual variance of the odds ratios, the stronger is the shrinkage, i.e. the shrinkage is stronger for less reliable estimates based on small numbers.

11 Effect of SB adjustment by occupation Life science and health associate professionals (32/32) Nursing associate professionals (25/9) Enrolled nurse (25/7) Bartender (15/11) Care Giver (20/10) Cattle Farmer, Cattle Farm Worker (7/3) Fishery Workers, Hunters and Trappers (11/8) Bricklayers and Stonemasons (7/3) Bricklayer and/or Block layer (7/3) Other craft and related trades workers (29/27) Plant and machine operators and assemblers 204/233) Timber Processing Machine Operator (8/5) Stationary machine operators and assemblers (148/150) Metal and mineral products processing machine operators (15/5) Rubber and Plastics Products Machine Operators (12/4) Plastics Machine Operator (8/2) Textile bleaching, dyeing and cleaning machine operators (20/13) Food and related products processing machine operators (63/46) Meat and fish processing machine operators (45/27) Electrical machinery assemblers (14/12) Heavy Truck or Tanker Driver (31/26) Elementary occupations (incl residuals) (128/146) Labourers and related elementary service workers (128/146) Labourers (51/54) Builder's labourer (20/11) Standard SB-adjusted

12 Lung cancer incidence by employment duration <2 years 2-10 years >10 years p value for OR OR OR linear trend Timber processing machine operators Occupation/Industry Textile products machine operators <0.01 Textile bleaching, dyeing and cleaning machine operators Textile product manufacturing industry Heavy truck drivers <0.01 Road transport <0.01 Road freight transport <0.01

13 OLCANZ: Main findings (1) Occupations and Industries Wood workers Timber processing machine operators OR (95%CI) 4.63 ( ) Log sawmilling and timber 2.85 dressing industry ( ) Metal workers Metal and mineral products processing machine operators Drivers Heavy truck drivers Road transport industry Road freight transport industry 4.10 ( ) 2.24 ( ) 1.78 ( ) 3.02 ( ) Durationresponse association Possible Exposures Wood dust: IARC Group 1 carcinogen Asbestos, metal fumes and dust Diesel and gasoline exhaust respectively IARC Group 2A and Group 2B

14 OLCANZ: Main findings (2) Occupations and Industries OR (95%CI) Durationresponse association Possible Exposures Meat/Fish workers Meat and fish processing machine operators Textile workers Textile products machine operators Textile bleaching, dyeing and cleaning machine operators Textile product manufacturing industry 2.17 Exposure to blood, ( ) urine, faecal matter and other biological agents 1.55 ( ) 2.35 ( ) 1.89 ( ) Exposure to organic solvents and textile dyes?

15 Association of lung cancer with wood dust exposure NZ JEM category Cases Controls Adjusted # OR (95% CI) Never exposed Ever exposed High exposure* ( ) 1.46 ( ) # adjusted for sex, age, ethnicity, smoking and SES * >50% exposed to levels in excess of 0.5 mg/m 3

16 Association of lung cancer with asbestos exposure NZ JEM category Cases Controls Adjusted # OR (95% CI) Never exposed Medium exposure High exposure* ( ) 2.58 ( ) # adjusted for sex, age, ethnicity, smoking and SES * >50% exposed to levels in excess of 1 f/ml

17 Conclusions These analyses give insight into the occupational risk patterns of lung cancer in New Zealand. There are increased risks of lung cancer associated with certain occupations and industries including wood workers, metal workers, meat workers, textile workers and drivers. Further analyses should be conducted to determine which particular carcinogenic exposures occur in these occupations and how their intensity affects the risk of lung cancer.

18 Acknowledgements This project was funded by: O The New Zealand Health Research Council O The Department of Labour O Lotteries Health Research O The Cancer Society of New Zealand O Accident Compensation Corporation (ACC)