BACKGROUND: Although it has been reported that low socioeconomic position (SEP) is associated with lung cancer, the extent to which this reflects SEP differences in cigarette smoking is unclear. We investigated how various modeling approaches for smoking might influence this observed association. METHODS: We used data from a case-control study conducted in Montreal, Canada (1996-2002), comprising 1203 subjects with incident lung cancer and 1513 population controls. SEP was measured by census-based and self-reported income, residential value, education level, and occupational class. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression, and Akaike's Information Criterion (AIC) was used to compare model fit. RESULTS: Associations were observed between SEP indicators and lung cancer, but gradually disappeared with more comprehensive adjustment for smoking. For comparisons of the highest to lowest categories of census-based income, the OR for lung cancer was 0.58 (95% CI = 0.32-1.05) when adjusting only for smoking status (never, former, current), but 0.97 (0.51-1.86) when adjusting for smoking status, cigarette-years, and time since cessation. For comparisons of highest to lowest levels of education, the ORs for lung cancer were 0.50 (0.38-0.65) and 0.76 (0.57-1.02), when making the least and most comprehensive adjustments for smoking, respectively. Similarly, comparing highly skilled with unskilled manual workers, the ORs were 0.78 (0.54-1.12) and 1.00 (0.68-1.47), respectively. With thorough smoking adjustment, associations between SEP indicators and lung cancer virtually disappeared, and SEP did not improve model fit. CONCLUSIONS: Previously reported associations of SEP with lung cancer may be attributable to incomplete adjustment for smoking. Our findings underline the importance of adjusting for several dimensions of smoking behavior to make correct inferences.
BACKGROUND: Although it has been reported that low socioeconomic position (SEP) is associated with lung cancer, the extent to which this reflects SEP differences in cigarette smoking is unclear. We investigated how various modeling approaches for smoking might influence this observed association. METHODS: We used data from a case-control study conducted in Montreal, Canada (1996-2002), comprising 1203 subjects with incident lung cancer and 1513 population controls. SEP was measured by census-based and self-reported income, residential value, education level, and occupational class. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression, and Akaike's Information Criterion (AIC) was used to compare model fit. RESULTS: Associations were observed between SEP indicators and lung cancer, but gradually disappeared with more comprehensive adjustment for smoking. For comparisons of the highest to lowest categories of census-based income, the OR for lung cancer was 0.58 (95% CI = 0.32-1.05) when adjusting only for smoking status (never, former, current), but 0.97 (0.51-1.86) when adjusting for smoking status, cigarette-years, and time since cessation. For comparisons of highest to lowest levels of education, the ORs for lung cancer were 0.50 (0.38-0.65) and 0.76 (0.57-1.02), when making the least and most comprehensive adjustments for smoking, respectively. Similarly, comparing highly skilled with unskilled manual workers, the ORs were 0.78 (0.54-1.12) and 1.00 (0.68-1.47), respectively. With thorough smoking adjustment, associations between SEP indicators and lung cancer virtually disappeared, and SEP did not improve model fit. CONCLUSIONS: Previously reported associations of SEP with lung cancer may be attributable to incomplete adjustment for smoking. Our findings underline the importance of adjusting for several dimensions of smoking behavior to make correct inferences.
Authors: Gwenn Menvielle; Thérèse Truong; Fatima Jellouli; Isabelle Stücker; Hermann Brenner; John K Field; H Dean Hosgood; Qing Lan; Maria Teresa Landi; Rayjean J Hung; Philip Lazarus; John McLaughlin; Hal Morgenstern; Joshua E Muscat; Alberto Ruano-Ravina; Ann G Schwartz; Adeline Seow; Margaret R Spitz; Adonina Tardon; Zuo-Feng Zhang; Danièle Luce Journal: Epidemiology Date: 2014-11 Impact factor: 4.822
Authors: Jan Hovanec; Jack Siemiatycki; David I Conway; Ann Olsson; Isabelle Stücker; Florence Guida; Karl-Heinz Jöckel; Hermann Pohlabeln; Wolfgang Ahrens; Irene Brüske; Heinz-Erich Wichmann; Per Gustavsson; Dario Consonni; Franco Merletti; Lorenzo Richiardi; Lorenzo Simonato; Cristina Fortes; Marie-Elise Parent; John McLaughlin; Paul Demers; Maria Teresa Landi; Neil Caporaso; Adonina Tardón; David Zaridze; Neonila Szeszenia-Dabrowska; Peter Rudnai; Jolanta Lissowska; Eleonora Fabianova; John Field; Rodica Stanescu Dumitru; Vladimir Bencko; Lenka Foretova; Vladimir Janout; Hans Kromhout; Roel Vermeulen; Paolo Boffetta; Kurt Straif; Joachim Schüz; Benjamin Kendzia; Beate Pesch; Thomas Brüning; Thomas Behrens Journal: PLoS One Date: 2018-02-20 Impact factor: 3.240
Authors: Thomas Behrens; Isabelle Groß; Jack Siemiatycki; David I Conway; Ann Olsson; Isabelle Stücker; Florence Guida; Karl-Heinz Jöckel; Hermann Pohlabeln; Wolfgang Ahrens; Irene Brüske; Heinz-Erich Wichmann; Per Gustavsson; Dario Consonni; Franco Merletti; Lorenzo Richiardi; Lorenzo Simonato; Cristina Fortes; Marie-Elise Parent; John McLaughlin; Paul Demers; Maria Teresa Landi; Neil Caporaso; David Zaridze; Neonila Szeszenia-Dabrowska; Peter Rudnai; Jolanta Lissowska; Eleonora Fabianova; Adonina Tardón; John K Field; Rodica Stanescu Dumitru; Vladimir Bencko; Lenka Foretova; Vladimir Janout; Hans Kromhout; Roel Vermeulen; Paolo Boffetta; Kurt Straif; Joachim Schüz; Jan Hovanec; Benjamin Kendzia; Beate Pesch; Thomas Brüning Journal: BMC Cancer Date: 2016-07-07 Impact factor: 4.430
Authors: Maureen Sanderson; Melinda C Aldrich; Robert S Levine; Barbara Kilbourne; Qiuyin Cai; William J Blot Journal: BMJ Open Date: 2018-09-10 Impact factor: 2.692