ArticlePreventing chronic disease2018
Factors Predicting Client Re-Enrollment in Tobacco Cessation Services in a State Quitline.
Article in Preventing chronic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Reengagement for Long-Term Smoking-Cessation In Military Personnel, Retirees, Family Members (TRICARE): A Randomized Trial.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023Trial
- Predictors of Re-Engagement after Relapse in a Tobacco Quit Line Intervention: Secondary Analysis from a Randomized Clinical Trial.International journal of environmental research and public health · 2023Trial
- Do US quitsites present information related to providing services for LGBTQ individuals? An audit study.Tobacco prevention & cessation · 2024Article
- Outcomes Among People With Schizophrenia Participating in General-Population Smoking Cessation Treatment: An Observational Study.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2023Observational
- Quit outcomes among clients ineligible for cessation medication through the state quitline: a retrospective, observational study.BMC public health · 2018Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
introductionQuitlines 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.
methodsThis 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).
resultsCompared 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.
conclusionUnderstanding 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.