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Record W2602423745 · doi:10.1016/s2468-2667(17)30045-2

Implementation of key demand-reduction measures of the WHO Framework Convention on Tobacco Control and change in smoking prevalence in 126 countries: an association study

2017· article· en· W2602423745 on OpenAlexafffundabout
Shannon Gravely, Gary A. Giovino, Lorraine Craig, Alison Commar, Edouard Tursan d’Espaignet, Kerstin Schotte, Geoffrey T. Fong

Bibliographic record

VenueThe Lancet Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
FundersCanadian Institutes of Health ResearchHealth CanadaWorld Health Organization
KeywordsTobacco controlEnvironmental healthConventionMedicineTobacco useKey (lock)Political sciencePublic healthComputer sciencePathologyPopulationComputer security

Abstract

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BACKGROUND: The WHO Framework Convention on Tobacco Control (WHO FCTC) has mobilised efforts among 180 parties to combat the global tobacco epidemic. This study examined the association between highest-level implementation of key tobacco control demand-reduction measures of the WHO FCTC and smoking prevalence over the treaty's first decade. METHODS: We used WHO data from 126 countries to examine the association between the number of highest-level implementations of key demand-reduction measures (WHO FCTC articles 6, 8, 11, 13, and 14) between 2007 and 2014 and smoking prevalence estimates between 2005 and 2015. McNemar tests were done to test differences in the proportion of countries that had implemented each of the measures at the highest level between 2007 and 2014. Four linear regression models were computed to examine the association between the predictor variable (the change between 2007 and 2014 in the number of key measures implemented at the highest level), and the outcome variable (the percentage point change in tobacco smoking prevalence between 2005 and 2015). FINDINGS: Between 2007 and 2014, there was a significant global increase in highest-level implementation of all key demand-reduction measures. The mean smoking prevalence for all 126 countries was 24·73% (SD 10·32) in 2005 and 22·18% (SD 8·87) in 2015, an average decrease in prevalence of 2·55 percentage points (SD 5·08; relative reduction 10·31%). Unadjusted linear regression showed that increases in highest-level implementations of key measures between 2007 and 2014 were significantly associated with a decrease in smoking prevalence between 2005 and 2015). Each additional measure implemented at the highest level was associated with an average decrease in smoking prevalence of 1·57 percentage points (95% CI -2·51 to -0·63, p=0·001) and an average relative decrease of 7·09% (-12·55 to -1·63, p=0·011). Controlling for geographical subregion, income level, and WHO FCTC party status, the per-measure decrease in prevalence was 0·94 percentage points (-1·76 to -0·13, p=0·023) and an average relative decrease of 3·18% (-6·75 to 0·38, p=0·079). This association was consistent across all three control variables. INTERPRETATION: Implementation of key WHO FCTC demand-reduction measures is significantly associated with lower smoking prevalence, with anticipated future reductions in tobacco-related morbidity and mortality. These findings validate the call for strong implementation of the WHO FCTC in the WHO's Global Action Plan for the Prevention and Control of Non-communicable Diseases 2013-2020, and in advancing the UN's Sustainable Development Goal 3, setting a global target of reducing tobacco use and premature mortality from non-communicable diseases by a third by 2030. FUNDING: Health Canada, Canadian Institutes of Health Research, Ontario Institute for Cancer Research and Canadian Cancer Society Research Institute.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.151
GPT teacher head0.425
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations249
Published2017
Admission routes3
Has abstractyes

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