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Identifying smoker subgroups with high versus low smoking cessation attempt probability: a decision tree analysis approach

journal contribution
posted on 01.04.2020, 00:00 authored by Hua YongHua Yong, Chandan KarmakarChandan Karmakar, Ron Borland, Shitanshu Kusmakar, Matthew Fuller-TyszkiewiczMatthew Fuller-Tyszkiewicz, John YearwoodJohn Yearwood
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.

History

Journal

Addictive behaviors

Volume

103

Article number

106258

Pagination

1 - 8

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0306-4603

Language

eng

Publication classification

C1 Refereed article in a scholarly journal