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A machine learning approach to identify drivers of e-cigarette dependence Nominated Principal Investigator Michael Chaiton Independent Scientist, Centre for Addiction and Mental Health [email protected] Knowledge User Peter Selby Centre for Addiction and Mental Health Co-investigators Susan J Bondy Adam G Cole Tara E Elton-Marshall Hayley A Hamilton Sean Hill Scott Leatherdale Nikolaos Mitsakakis Robert M Schwartz Wei Wang Project Summary Understanding person-level drivers of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these drivers due to the reliance on cross-sectional data, small sample sizes in many studies, lack of generalizability, and limitations of traditional data analyses
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When all three factors were considered together, the association between genetic risk for smoking and brain volume disappeared, while the link between each of those and smoking behaviors remained
