automatic cigarette dispenser A wall-mounting by National Machines of London raise the lever on the right and drop a small handful of tobacco into the tobacco chamber Pres • a • Lite

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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

Retailers often describe IQOS as less harmful, smokeless and useful for quitting smoking

D., Xu, F., Huang, A., Derakhshandeh, R., Rao, P., Whitlatch, A., Cheng, J., Keith, R

Approximately one in four cancer cases can potentially be prevented through healthy eating and physical activity