Evidence map›Paper›PMID 37920593›Full record

ArticlePreventive medicine reports2023

Probability and predictors of long-term smoking relapse among Chinese adult smokers: A longitudinal study.

Zhongmin Zhang, Mingzhao Huang, Ting Chen

Abstract read
In one paragraph

Article in Preventive medicine reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zhongmin ZhangSchool of Public Health, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, China.
Mingzhao HuangSchool of Public Health, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, China.
Ting ChenSchool of Public Health, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous smokers attempt to quit smoking, but most cessation efforts prove unsuccessful. Scarce evidence exists regarding predictors of long-term relapse in China. This study aims to evaluate the probability of relapse and examine factors may contribute to relapse among Chinese adults. A dynamic cohort of 6,036 observations on 2,378 adult quitters was constructed from the China Family Panel Studies in 2010, 2012, 2014, 2016 and 2018. The life table method was employed to calculate the probability of relapse for long-term smoking abstinence. Multivariate complementary log-log survival models were developed to examine the predictors of smoking relapse. We found that the probability of relapse decreased as the duration of abstinence increased, with rates of 49.07 %, 20.05 %, 10.29 %, and 6.63 % at 2, 4, 6, and 8 years of abstinence, respectively. The cumulative probability of relapse within 8 years was 65.89 %. Age ≥65 years, higher educational attainment, respiratory disease, and a satisfying lifestyle were associated with a reduced likelihood of relapse. Conversely, higher occupational prestige, alcohol drinking, cohabitant smoking, and greater future confidence were associated with an increased risk of relapse. These findings demonstrated that the probability of relapse decreased progressively over time, with most relapses occurring in the initial two years following quit attempts. Predictors of Chinese quitters' relapse behavior in our study were similar to those in previous studies. Drinking and cohabitant smoking were identified as strong predictors of relapse in this population.

Indexed as

AbstinenceCessationChinaRelapseSmoking

Identifiers

PMID37920593
PMCPMC10618515

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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.