PURPOSES: (1) Determine the association of multiple cancers with smoking, focusing on cancers with an uncertain association; and (2) illustrate quantitative bias analysis as applied to registry data, to adjust for misclassification of smoking and residual confounding by alcohol and obesity. METHODS: New Zealand 1981 and 1996 censuses, including smoking questions, were linked to cancer registry data giving 14.8 million person-years of follow-up. Rate ratios (RR) for current versus never smokers, adjusting for age, sex, ethnicity and socioeconomic factors were calculated and then subjected to quantitative bias analysis. RESULTS: RR estimates for lung, larynx (including ear and nasosinus), and bladder cancers adjusted for measured confounders and exposure misclassification were 9.28 (95 % uncertainty interval 8.31-10.4), 6.14 (4.55-8.30), and 2.22 (1.94-2.55), respectively. Moderate associations were found for cervical (1.82; 1.51-2.20), kidney (1.29; 1.07-1.56), liver cancer (1.75; 1.37-2.24; European only), esophageal (2.14; 1.73-2.65), oropharyngeal (2.30; 1.94-2.72), pancreatic (1.68; 1.44-1.96), and stomach cancers (1.42; 1.22-1.66). Protective associations were found for endometrial (0.67; 0.56-0.79) and melanoma (0.72; 0.65-0.81), and borderline association for thyroid (0.76; 0.58-1.00), colon (0.89; 0.81-0.98), and CML (0.66; 0.44-0.99). Remaining cancers had near null associations. Adjustment for residual confounding suggested little impact, except the RRs for endometrial, kidney, and esophageal cancers were slightly increased, and the oropharyngeal and liver (European/other) RRs were decreased. CONCLUSIONS: Our large study confirms the strong association of smoking with many cancers and strengthens the evidence for protective associations with thyroid cancer and melanoma. With large data sets, considering and adjusting for residual systematic error is as important as quantifying random error.
PURPOSES: (1) Determine the association of multiple cancers with smoking, focusing on cancers with an uncertain association; and (2) illustrate quantitative bias analysis as applied to registry data, to adjust for misclassification of smoking and residual confounding by alcohol and obesity. METHODS: New Zealand 1981 and 1996 censuses, including smoking questions, were linked to cancer registry data giving 14.8 million person-years of follow-up. Rate ratios (RR) for current versus never smokers, adjusting for age, sex, ethnicity and socioeconomic factors were calculated and then subjected to quantitative bias analysis. RESULTS: RR estimates for lung, larynx (including ear and nasosinus), and bladder cancers adjusted for measured confounders and exposure misclassification were 9.28 (95 % uncertainty interval 8.31-10.4), 6.14 (4.55-8.30), and 2.22 (1.94-2.55), respectively. Moderate associations were found for cervical (1.82; 1.51-2.20), kidney (1.29; 1.07-1.56), liver cancer (1.75; 1.37-2.24; European only), esophageal (2.14; 1.73-2.65), oropharyngeal (2.30; 1.94-2.72), pancreatic (1.68; 1.44-1.96), and stomach cancers (1.42; 1.22-1.66). Protective associations were found for endometrial (0.67; 0.56-0.79) and melanoma (0.72; 0.65-0.81), and borderline association for thyroid (0.76; 0.58-1.00), colon (0.89; 0.81-0.98), and CML (0.66; 0.44-0.99). Remaining cancers had near null associations. Adjustment for residual confounding suggested little impact, except the RRs for endometrial, kidney, and esophageal cancers were slightly increased, and the oropharyngeal and liver (European/other) RRs were decreased. CONCLUSIONS: Our large study confirms the strong association of smoking with many cancers and strengthens the evidence for protective associations with thyroid cancer and melanoma. With large data sets, considering and adjusting for residual systematic error is as important as quantifying random error.
Authors: Liesbeth Sondermeijer; Lieke G E Lamboo; Anne C de Waal; Tessel E Galesloot; Lambertus A L M Kiemeney; Michelle van Rossum; Katja H Aben Journal: Dermatology Date: 2019-09-10 Impact factor: 5.366
Authors: Keum Ji Jung; Daeyoun Won; Christina Jeon; Soriul Kim; Tae Il Kim; Sun Ha Jee; Terri H Beaty Journal: BMC Genet Date: 2015-05-09 Impact factor: 2.797
Authors: Tony Blakely; Linda J Cobiac; Christine L Cleghorn; Amber L Pearson; Frederieke S van der Deen; Giorgi Kvizhinadze; Nhung Nghiem; Melissa McLeod; Nick Wilson Journal: PLoS Med Date: 2015-07-28 Impact factor: 11.069