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Record W2971878362 · doi:10.1371/journal.pone.0220223

Socioeconomic patterns of smoking cessation behavior in low and middle-income countries: Emerging evidence from the Global Adult Tobacco Surveys and International Tobacco Control Surveys

2019· article· en· W2971878362 on OpenAlexafffund
Nigar Nargis, Hua‐Hie Yong, Pete Driezen, Lazarous Mbulo, Luhua Zhao, Geoffrey T. Fong, Mary E. Thompson, Ron Borland, Krishna Palipudi, Gary A. Giovino, James F. Thrasher, Mohammad Siahpush

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchThai Health Promotion FoundationCenters for Disease Control and PreventionChinese Center for Disease Control and PreventionInternational Development Research CentreNational Cancer InstituteOntario Institute for Cancer ResearchU.S. Department of Health and Human Services
KeywordsTobacco controlSocioeconomic statusMedicineSmoking cessationEnvironmental healthResidenceDemographyOdds ratioLogistic regressionPublic healthPopulation

Abstract

fetched live from OpenAlex

INTRODUCTION: Tobacco smoking is often more prevalent among those with lower socio-economic status (SES) in high-income countries, which can be driven by the inequalities in initiation and cessation of smoking. Smoking is a leading contributor to socio-economic disparities in health. To date, the evidence for any socio-economic inequality in smoking cessation is lacking, especially in low- and middle-income countries (LMICs). This study examined the association between cessation behaviours and SES of smokers from eight LMICs. METHODS: Data among former and current adult smokers aged 18 and older came from contemporaneous Global Adult Tobacco Surveys (2008-2011) and the International Tobacco Control Surveys (2009-2013) conducted in eight LMICs (Bangladesh, Brazil, China, India, Mexico, Malaysia, Thailand and Uruguay). Adjusted odds ratios (AORs) of successful quitting in the past year by SES indicators (household income/wealth, education, employment status, and rural-urban residence) were estimated using multivariable logistic regression controlling for socio-demographics and average tobacco product prices. A random effects meta-analysis was used to combine the estimates of AORs pooled across countries and two concurrent surveys for each country. RESULTS: Estimated quit rates among smokers (both daily and occasional) varied widely across countries. Meta-analysis of pooled AORs across countries and data sources indicated that there was no clear evidence of an association between SES indicators and successful quitting. The only exception was employed smokers, who were less likely to quit than their non-employed counterparts, which included students, homemakers, retirees, and the unemployed (pooled AOR≈0.8, p<0.10). CONCLUSION: Lack of clear evidence of the impact of lower SES on adult cessation behaviour in LMICs suggests that lower-SES smokers are not less successful in their attempts to quit than their higher-SES counterparts. Specifically, lack of employment, which is indicative of younger age and lower nicotine dependence for students, or lower personal disposable income and lower affordability for the unemployed and the retirees, may be associated with quitting. Raising taxes and prices of tobacco products that lowers affordability of tobacco products might be a key strategy for inducing cessation behaviour among current smokers and reducing overall tobacco consumption. Because low-SES smokers are more sensitive to price increases, tobacco taxation policy can induce disproportionately larger decreases in tobacco consumption among them and help reduce socio-economic disparities in smoking and consequent health outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.281
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations60
Published2019
Admission routes2
Has abstractyes

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