Occupational risks and lung cancer burden for Chinese men: a population-based case–referent study
Menée en population chinoise, cette étude (1 208 cas et 1 069 témoins) analyse l'association entre une exposition à un facteur de risque d'origine professionnelle et le risque de cancer du poumon
Objective We aimed to fill in the gap of knowledge on the lung cancer burden resulting from occupational exposures among Chinese men through a population-based case–referent study. Methods Detailed information on lifestyle and full occupational histories of 1,208 male lung cancer incident cases and 1,069 age-matched male community referents were obtained through interviews during 2004–2006. The associations between lung cancer risk and exposures to specific or group of agents that were confirmed or suspected occupational carcinogens were analyzed. Results After adjustment of smoking and other potential confounding factors, significant odds ratio of lung cancer was observed for workers employed in major industrial divisions of “construction” (1.37, 95% CI: 1.00–1.89) and “financing, insurance, real estate, and business services” (0.48, 95% CI: 0.23–0.97), as well as in the occupational groups of “bricklayers, carpenters, and other construction workers” (1.49, 95% CI: 1.07–2.06). Significantly elevated odds ratios were found for occupational exposures to silica dust (1.75, 95% CI: 1.16–2.62), welding fumes (1.74, 95% CI: 1.13–2.68), diesel exhaust (2.18, 95% CI: 1.23–3.84), and man-made mineral fibers (7.45, 95% CI: 1.63–34.00), while a significantly reduced risk (OR = 0.67, 95% CI: 0.47–0.95) was linked to cotton dust. The population attributable fraction of lung cancer was 3.2% (95% CI: 0.1–7.3%) for construction workers and 9.5% (95% CI: 4.8–15.1%) for the four significant specific exposures. Conclusions Our study indicates that previous exposure to occupational carcinogens remains an important determinant of lung cancer burden for Hong Kong Chinese men. However, results obtained from this study should be confirmed by future analyses based on job exposure matrix.