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Record W4288438278 · doi:10.1136/tc-2022-057383

Economic research in waterpipe tobacco smoking: reflections on data, demand, taxes, equity and health modelling

2022· editorial· en· W4288438278 on OpenAlexfundno aff
Dima Bteddini, Rima Nakkash, Ali Chalak, Mohammed Jawad, Yousef Khader, Niveen M E Abu-Rmeileh, Aya Mostafa, Ruba Abla, Sameera Awawda, Ramzi G. Salloum

Bibliographic record

VenueTobacco Control · 2022
Typeeditorial
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsExciseTobacco controlPrice elasticity of demandPublic economicsEquity (law)Thematic analysisTax revenueTobacco industryGovernment revenueBusinessRevenueHealth economicsEnvironmental healthEconomicsPublic healthQualitative researchHealth careMedicineEconomic growthPolitical scienceAccounting

Abstract

fetched live from OpenAlex

Economic evaluation of tobacco control policies is common in high-income settings and mainly focuses on cigarette smoking. Evidence suggests that increasing the excise tax of tobacco products is a consistently effective tool for reducing tobacco use and is an efficient mechanism for increasing government revenues. However, less research has been conducted in low/middle-income countries where other tobacco forms are common. This paper presents insights from our work on the economics of waterpipe tobacco smoking conducted in the Eastern Mediterranean Region where waterpipe smoking originated and is highly prevalent. The specific areas related to economics of waterpipe smoking considered herein are: price elasticity, taxation, government revenue, expenditure and healthcare costs. This paper aims to provide practical guidance for researchers investigating the economics of waterpipe tobacco with potential implications for other novel tobacco products. We present lessons learnt across five thematic areas: data, demand, taxes, equity and health modelling. We also highlight knowledge gaps to be addressed in future research. Research implications include designing comprehensive assessment tools that investigate heterogeneity in waterpipe smoking patterns; accounting for cross-price elasticity of demand with other tobacco products; exploring the change in waterpipe tobacco smoking in response to a tax increase and analysing the equity impact of waterpipe tobacco control interventions.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.265
GPT teacher head0.482
Teacher spread0.216 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2022
Admission routes1
Has abstractyes

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