Preventive medicine
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Preventive medicine · Apr 2021
Identifying emerging predictors for adolescent electronic nicotine delivery systems use: A machine learning analysis of the Population Assessment of Tobacco and Health Study.
Intervention strategies to prevent adolescents from using electronic nicotine delivery systems (ENDS) should be based on robust predictors of ENDS use that may differ from predictors of conventional cigarette use. Literature points to the need for uncovering emerging predictors of ENDS use. This study identified emerging predictors of adolescent ENDS use using machine learning (ML) techniques. ⋯ ML models appear to be a promising method to identify unique population-level predictors for U. S. adolescent ENDS use behaviors. More research is warranted to investigate emerging predictors of ENDS use and experimentally examine the mechanism by which these emerging predictors affect ENDS use behavior across different spectrum of populations.
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Diets high in sodium have long been known to raise blood pressure, which, in turn, increases the risk of cardiovascular disease. Though authoritative recommendations have been made in the past several decades for federal policies and programs to reduce sodium consumption, measures adopted to date have not been effective. We recommend a comprehensive public health approach to reduce sodium in the food supply and prevent thousands of unnecessary deaths and billions of dollars in health-care costs each year.