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Record W2599234343 · doi:10.1136/bmjopen-2016-013817

Tobacco control environment: cross-sectional survey of policy implementation, social unacceptability, knowledge of tobacco health harms and relationship to quit ratio in 17 low-income, middle-income and high-income countries

2017· article· en· W2599234343 on OpenAlexafffund
Clara K Chow, Daniel J. Corsi, Anna Gilmore, Annamarie Kruger, Ehimario Igumbor, Jephat Chifamba, Yang Wang, Wei Li, Romaina Iqbal, Prem Mony, Rajeev Gupta, Krishnapillai Vijayakumar, Viswanathan Mohan, Rajesh Kumar, Omar Rahman, Khalid Yusoff, Noor Hassim Ismail, Katarzyna Zatońska, Yüksel Altuntaş, Annika Rosengren, Ahmad Bahonar, Afzalhussein Yusufali, Gilles R. Dagenais, Scott A. Lear, Rafael Díaz, Álvaro Avezum, Patricio López‐Jaramillo, Fernando Laņas, Sumathy Rangarajan, Koon Teo, Martin McKee, Salim Yusuf

Bibliographic record

VenueBMJ Open · 2017
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsSimon Fraser UniversityInstitut universitaire de cardiologie et de pneumologie de QuébecOttawa HospitalPopulation Health Research Institute
FundersMedical Research CouncilBritish Heart FoundationCancer Research UKMcMaster UniversityCanadian Institutes of Health ResearchEconomic and Social Research CouncilHamilton Health Sciences
KeywordsMedicineTobacco controlCross-sectional studyEnvironmental healthLow and middle income countriesPublic healthTobacco useControl (management)Survey data collectionDeveloping countryEconomic growthNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: This study examines in a cross-sectional study 'the tobacco control environment' including tobacco policy implementation and its association with quit ratio. SETTING: 545 communities from 17 high-income, upper-middle, low-middle and low-income countries (HIC, UMIC, LMIC, LIC) involved in the Environmental Profile of a Community's Health (EPOCH) study from 2009 to 2014. PARTICIPANTS: Community audits and surveys of adults (35-70 years, n=12 953). PRIMARY AND SECONDARY OUTCOME MEASURES: Summary scores of tobacco policy implementation (cost and availability of cigarettes, tobacco advertising, antismoking signage), social unacceptability and knowledge were associated with quit ratios (former vs ever smokers) using multilevel logistic regression models. RESULTS: Average tobacco control policy score was greater in communities from HIC. Overall 56.1% (306/545) of communities had >2 outlets selling cigarettes and in 28.6% (154/539) there was access to cheap cigarettes (<5cents/cigarette) (3.2% (3/93) in HIC, 0% UMIC, 52.6% (90/171) LMIC and 40.4% (61/151) in LIC). Effective bans (no tobacco advertisements) were in 63.0% (341/541) of communities (81.7% HIC, 52.8% UMIC, 65.1% LMIC and 57.6% LIC). In 70.4% (379/538) of communities, >80% of participants disapproved youth smoking (95.7% HIC, 57.6% UMIC, 76.3% LMIC and 58.9% LIC). The average knowledge score was >80% in 48.4% of communities (94.6% HIC, 53.6% UMIC, 31.8% LMIC and 35.1% LIC). Summary scores of policy implementation, social unacceptability and knowledge were positively and significantly associated with quit ratio and the associations varied by gender, for example, communities in the highest quintile of the combined scores had 5.0 times the quit ratio in men (Odds ratio (OR) 5·0, 95% CI 3.4 to 7.4) and 4.1 times the quit ratio in women (OR 4.1, 95% CI 2.4 to 7.1). CONCLUSIONS: This study suggests that more focus is needed on ensuring the tobacco control policy is actually implemented, particularly in LMICs. The gender-related differences in associations of policy, social unacceptability and knowledge suggest that different strategies to promoting quitting may need to be implemented in men compared to women.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.114
GPT teacher head0.463
Teacher spread0.349 · 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

Citations83
Published2017
Admission routes2
Has abstractyes

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