Related Experiment Video
Updated: Feb 3, 2026

Screening of Tobacco Genotypes for Phytophthora nicotianae Resistance
Published on: April 15, 2022
Factors Predicting Client Re-Enrollment in Tobacco Cessation Services in a State Quitline
Uma S Nair1,2, Benjamin R Brady1, Patrick A O'Connor3
1Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona.
Introduction:
Quitlines are an integral part of tobacco treatment programs and reach groups of smokers who have a wide range of barriers to cessation. Although tobacco dependence is chronic and relapsing, little research exists on factors that predict the likelihood of clients re-engaging and reconnecting with quitlines for treatment. The objective of this study was to describe factors that predict the re-enrollment of clients in Arizona's state quitline.
Methods:
This was a retrospective analysis of data collected from clients (N = 49,284) enrolled in the Arizona Smokers' Helpline from January 2011 through June 2016. We used logistic regression to analyze predictors of re-enrollment in services after controlling for theoretically relevant baseline variables (eg, nicotine dependence, smokers in the home) and follow-up variables (eg, program use, quit outcome).
Results:
Compared with clients who reported being quit after their first enrollment, clients who reported not being quit were almost 3 times as likely to re-enroll (odds ratio = 2.89; 95% confidence interval, 2.54-3.30). Other predictors were having a chronic condition or a mental health condition, greater nicotine dependence, and lower levels of social support. Women and clients not having other smokers in the home were more likely to re-enroll than were men and clients not living with other smokers.
Conclusion:
Understanding baseline and in-program factors that predict client-initiated re-enrollment can help quitlines tailor strategies to proactively re-engage clients who may have difficulty maintaining long-term abstinence.
More Related Videos
09:25Methods to Evaluate Cytotoxicity and Immunosuppression of Combustible Tobacco Product Preparations
Published on: January 10, 2015
20:24Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
Related Concept Videos
Predicting Molecular Geometry
Primary Healthcare Services
In 1978, international leaders convened in Alma-Ata, Kazakhstan, for what would be a pivotal event in global health. The Alma-Ata Declaration was the first to call...
Preventive Healthcare Services
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Transcription Factors
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...