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Impact of China National Tobacco Company’s ‘Premiumization’ Strategy: longitudinal findings from the ITC China Surveys (2006–2015)

2018· article· en· W2889504255 on OpenAlexafffund
Steve S. Xu, Shannon Gravely, Gang Meng, Tara Elton‐Marshall, Richard J. O’Connor, Anne C K Quah, Guoze Feng, Yuan Jiang, Grace J Hu, Geoffrey T. Fong

Bibliographic record

VenueTobacco Control · 2018
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Tobacco Research UnitOntario Institute for Cancer ResearchCentre for Addiction and Mental HealthWestern UniversityPublic Health OntarioUniversity of TorontoUniversity of Waterloo
FundersChinese Center for Disease Control and PreventionCenters for Disease Control and PreventionChina National Tobacco CorporationCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchUniversity of WaterlooNational Cancer InstituteOntario Institute for Cancer Research
KeywordsChinaTobacco controlCohortTobacco industryMedicineDemographyBusinessGeographyPublic healthInternal medicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: designed to encourage smokers to trade up to more expensive brands, mainly by promoting the concept that higher class cigarettes are better quality and less harmful. This study is the first evaluation of the strategy's impact on: (1) prevalence of premium brand cigarettes (PBC), mid-priced brand cigarettes (MBC) and discount brand cigarettes (DBC) over 9 years, from 3 years pre-strategy (2006) to 6 years post-strategy (2015); and (2) changes in reasons for choosing PBCs, MBCs and DBCs. METHODS: A representative cohort of adult Chinese smokers (n=9047) in seven cities who participated in five waves of the International Tobacco Control (ITC) China Survey: pre-implementation (Wave 1 (2006; n=3452), Wave 2 (2007-2008; n=3586)); mid-implementation (Wave 3 (2009; n=4172)); and post-implementation (Wave 4 (2011-2012; n=4070), Wave 5 (2013-2015; n=2775)). Generalised estimating equations were conducted to examine changes in prevalence of PBCs, MBCs and DBCs, and reasons for brand choice from pre-implementation to post-implementation. RESULTS: From pre-implementation to post-implementation, there was an increase in prevalence of PBCs (5.4% to 23.2%, p<0.001) and MBCs (40.0% to 50.4%, p<0.001), and a decrease in DBCs (54.6% to 26.5%, p<0.001). There was an increase in smokers who chose their current brand because they believed it to be less harmful, both for MBC smokers (+13.0%, p=0.001) and PBC smokers (+9.0%, p=0.06). There was an increase for smokers in all brand classes for choosing their current brand because they were 'higher in quality' and because of affordable price, but the greatest increase was among PBC smokers (+18.6%, p<0.001 and +34.9%, p<0.001, respectively). CONCLUSIONS: Our findings demonstrate that the rising trend in Chinese smokers' choice of 'less harmful', 'higher quality' and 'affordable' cigarettes, particularly PBCs, is likely due to CNTC's aggressive marketing strategies. Strong tobacco control policies that prohibit CNTC's marketing activities are critical in order to dispel erroneous beliefs that sustain continued smoking in China, where the global tobacco epidemic is exerting its greatest toll.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.028
GPT teacher head0.323
Teacher spread0.295 · 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.

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

Citations12
Published2018
Admission routes2
Has abstractyes

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