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Identifying smoker subgroups with high versus low smoking cessation attempt probability: A decision tree analysis approach.
Yong, Hua-Hie; Karmakar, Chandan; Borland, Ron; Kusmakar, Shitanshu; Fuller-Tyszkiewicz, Matthew; Yearwood, John.
Afiliação
  • Yong HH; School of Psychology, Deakin University, Geelong, Australia. Electronic address: hua.yong@deakin.edu.au.
  • Karmakar C; School of Information Technology, Deakin University, Geelong, Australia.
  • Borland R; School of Psychological Sciences, University of Melbourne, Melbourne, Australia.
  • Kusmakar S; School of Information Technology, Deakin University, Geelong, Australia.
  • Fuller-Tyszkiewicz M; School of Psychology, Deakin University, Geelong, Australia.
  • Yearwood J; School of Information Technology, Deakin University, Geelong, Australia.
Addict Behav ; 103: 106258, 2020 04.
Article em En | MEDLINE | ID: mdl-31884376
BACKGROUND: Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES). METHODS: Data come from the Australian arm of the ITC project, a longitudinal cohort study of adult smokers followed up approximately annually. Reported wanting to quit smoking, worries about smoking negative health impact, quitting self-efficacy and quit intentions assessed in 2005 were used as predictors and reported quit attempts at the 2006 follow-up survey were used as the outcome for the initial model calibration and validation analyses (n = 1475), and further cross-validated using the 2012-2013 data (n = 787). RESULTS: DTA revealed that while all four predictor variables conjointly contributed to the identification of subgroups with high versus low smoking cessation initiation probability, quit intention was the most important predictor common across all SES strata. The relative importance of the other predictors showed differences by SES. CONCLUSIONS: Modifiable characteristics of smoker subgroups associated with making a quit attempt and any variations by SES can be successfully identified using a decision tree analysis approach, to provide insights as to who might benefit from targeted intervention, thus, underscoring the value of this approach to complement the conventional regression-based approach.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Classe Social / Árvores de Decisões / Abandono do Hábito de Fumar / Fumantes Tipo de estudo: Etiology_studies / Health_economic_evaluation / Incidence_studies / Observational_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Adolescent / Adult / Female / Humans / Male / Middle aged País/Região como assunto: Oceania Idioma: En Revista: Addict Behav Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Classe Social / Árvores de Decisões / Abandono do Hábito de Fumar / Fumantes Tipo de estudo: Etiology_studies / Health_economic_evaluation / Incidence_studies / Observational_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Adolescent / Adult / Female / Humans / Male / Middle aged País/Região como assunto: Oceania Idioma: En Revista: Addict Behav Ano de publicação: 2020 Tipo de documento: Article