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S1 Table -

journal contribution

posted on 2021-12-08, 18:24 authored by Nayoung Kim, Wei-Yin Loh, Danielle E. McCarthy

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targeted prevention strategies targeted prevention interventions showing higher rates predictors included demographics past 30 days novel machine learning nationally representative cross na &# 239 machine learning models health warning messages 1 %- 12 0 %- 12 identifying factors associated xlink "> adolescents stratified adolescents based age ), geography tobacco school education tobacco industry promotions efficient ml modeling 2017 adolescents reported using tobacco control policies tobacco risk prediction adolescent tobacco susceptibility control factors tobacco risk global adolescents susceptibility ),risk subpopulations adolescent subgroups adolescent characteristics tobacco susceptibility tobacco risks tobacco promotion tobacco product tobacco policy tobacco marketing tobacco initiation smokeless tobacco smoked tobacco income ),weighted n southeast asia sampling weights reported exposure particularly vulnerable may benefit findings emphasize evident across complex ways 97 countries 69 low 6 %)5 %)15 years

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