Re: "Occupational exposure and lung cancer risk: a population-based case-referent study in Sweden"

Exposure-Lag-Response in Longitudinal Studies: Application of Distributed-Lag Nonlinear Models in an Occupational Cohort

Prolonged exposures can have complex relationships with health outcomes, as timing, duration, and intensity of exposure are all potentially relevant. Summary measures such as cumulative exposure or average intensity of exposure may not fully capture these relationships. We applied penalized and unpenalized distributed-lag nonlinear models (DLNMs) with flexible exposure-response and lag-response functions in order to examine the association between crystalline silica exposure and mortality from lung cancer and nonmalignant respiratory disease in a cohort study of 2,342 California diatomaceous earth workers followed during 1942–2011. We also assessed associations using simple measures of cumulative exposure assuming linear exposure-response and constant lag-response. Measures of association from DLNMs were generally higher than those from simpler models. Rate ratios from penalized DLNMs corresponding to average daily exposures of 0.4 mg/m³ during lag years 31–50 prior to the age of observed cases were 1.47 (95% confidence interval (CI): 0.92, 2.35) for lung cancer mortality and 1.80 (95% CI: 1.14, 2.85) for nonmalignant respiratory disease mortality. Rate ratios from the simpler models for the same exposure scenario were 1.15 (95% CI: 0.89, 1.48) and 1.23 (95% CI: 1.03, 1.46), respectively. Longitudinal cohort studies of prolonged exposures and chronic health outcomes should explore methods allowing for flexibility and nonlinearities in the exposure-lag-response.

Spatial Analysis of Regional Factors and Lung Cancer Mortality in China, 1973-2013

Background: China's lung cancer crude death rate has increased 6.9-fold from 1973 to 2014. During this time, the country experienced extremely rapid economic growth and social change. It is important to understand the effects of risk factors on lung cancer mortality (LCM) for better allocation of limited resources of cancer prevention and control in China.Methods: Using three nationwide mortality surveys from 1973 to 2005, Global Health Data Exchange data in 2013, three nationwide smoking surveys from 1984 to 2013, four population censuses from 1964 to 2000, and other datasets, we have compiled datasets and developed spatial random effect models to assess the association of various area-level-contributing factors on LCM. Spatial scan statistics are used to detect high-risk clusters of LCM.Results: LCM is higher in urban and more industrialized areas (RR = 1.17) compared with those in rural areas. The level of industrial development's effect is higher for men, which accounts for about 70% of all LCM. Smoking is positively associated with regional variation of LCM rates, and the effect is higher for women than for men.Conclusions: The geographic pattern of high LCM in China is different from that of Western countries. LCM is positively associated with higher socioeconomic status, with more urbanized areas at a higher level of industrial development.Impact: There is a need to further explore additional risk in the high-risk clusters. The study is about China, but this situation may happen in other countries experiencing rapid industrialization and other developing countries. Cancer Epidemiol Biomarkers Prev; 26(4); 569-77. ©2017 AACRSee all the articles in this CEBP Focus section, "Geospatial Approaches to Cancer Control and Population Sciences."