RESUMO
BACKGROUND: To evaluate the potential efficacy of increasing harm and relative addiction beliefs in discouraging e-cigarette use, we examined how adolescents' beliefs about e-cigarettes have changed over 6 years and how the predictive validity of these beliefs has changed over time. METHODS: Using data from the 2014-2019 National Youth Tobacco Survey (NYTS) (grades 6-12; N = 117,472), we evaluated the association between adolescents' beliefs about the harm and relative addiction of e-cigarettes and current e-cigarette use, as well as susceptibility to use. Logistic regressions and pairwise contrasts were used to analyze changes in these beliefs and determine how well these beliefs predict ever use, current use, and susceptibility to use over time. RESULTS: E-cigarette harm and relative addiction beliefs tended to increase over time. In most years, these beliefs were negatively associated with e-cigarette use, including ever use, current use, and susceptibility to use. Interactions between these beliefs were also observed in some years such that harm belief better predicted use when e-cigarettes were also perceived as more addictive. Survey year also interacted with health harm and relative addiction beliefs such that the predictive validity of these beliefs for e-cigarette use decreased over time. CONCLUSIONS: Beliefs about e-cigarette harm and relative addiction have increased over time and predict use of, and susceptibility to, e-cigarettes among US adolescents. However, the predictive validity of these beliefs has decreased over time. Future research should explore the reasons for the decreased predictive validity of health beliefs in e-cigarette use and identify constructs that predict adolescent e-cigarette use over and above general harm and relative addiction beliefs.
Assuntos
Sistemas Eletrônicos de Liberação de Nicotina , Produtos do Tabaco , Vaping , Humanos , Adolescente , Fumar , Estudos TransversaisRESUMO
Soybean Knowledge Base (http://soykb.org) is a comprehensive web resource developed for bridging soybean translational genomics and molecular breeding research. It provides information for six entities including genes/proteins, microRNAs/sRNAs, metabolites, single nucleotide polymorphisms, plant introduction lines and traits. It also incorporates many multi-omics datasets including transcriptomics, proteomics, metabolomics and molecular breeding data, such as quantitative trait loci, traits and germplasm information. Soybean Knowledge Base has a new suite of tools such as In Silico Breeding Program for soybean breeding, which includes a graphical chromosome visualizer for ease of navigation. It integrates quantitative trait loci, traits and germplasm information along with genomic variation data, such as single nucleotide polymorphisms, insertions, deletions and genome-wide association studies data, from multiple soybean cultivars and Glycine soja.