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Record W3087004751 · doi:10.1016/s2214-109x(20)30311-9

The health and economic burden of smoking in 12 Latin American countries and the potential effect of increasing tobacco taxes: an economic modelling study

2020· article· en· W3087004751 on OpenAlexaboutno aff
Andrés Pichón-Rivière, Andrea Alcaraz, Alfredo Palacios, Belén Rodríguez, Luz Myriam Reynales-Shigematsu, Márcia Pinto, Marianela Castillo‐Riquelme, Esperanza Peña Torres, Diana Isabel Osorio, Leandro Huayanay, César Antonio Loza Munarriz, Belén Sáenz de Miera-Juárez, Verónica Gallegos-Rivero, Catherine De La Puente, María del Pilar Navía-Bueno, Joaquín Caporale, Javier Roberti, Sacha Alexis Virgilio, Federico Augustovski, Ariel Bardach

Bibliographic record

VenueThe Lancet Global Health · 2020
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansEnvironmental healthQuality-adjusted life yearYears of potential life lostDisease burdenMedicineTobacco controlEconomic costPovertyLife expectancyPublic healthDemographyEconomic growthCost effectivenessPopulationEconomicsPolitical science

Abstract

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BACKGROUND: Worldwide, smoking tobacco causes 7 million deaths annually, and this toll is expected to increase, especially in low-income and middle-income countries. In Latin America, smoking is a leading risk factor for death and disability, contributes to poverty, and imposes an economic burden on health systems. Despite being one of the most effective measures to reduce smoking, tobacco taxation is underused and cigarettes are more affordable in Latin America than in other regions. Our aim was to estimate the tobacco-attributable burden on mortality, disease incidence, quality of life lost, and medical costs in 12 Latin American countries, and the expected health and economic effects of increasing tobacco taxes. METHODS: In this modelling study, we developed a Markov probabilistic microsimulation economic model of the natural history, medical costs, and quality-of-life losses associated with the most common tobacco-related diseases in 12 countries in Latin America. Data inputs were obtained through a literature review, vital statistics, and hospital databases from each country: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Mexico, Paraguay, Peru, and Uruguay. The main outcomes of the model are life-years, quality-adjusted life-years, disease events, hospitalisations, disease incidence, disease cost, and healthy years of life lost. We estimated direct medical costs for each tobacco-related disease included in the model using a common costing methodology for each country. The disease burden was estimated as the difference in disease events, deaths, and associated costs between the results predicted by the model for current smoking prevalence and a hypothetical cohort of people in each country who had never smoked. The model estimates the health and financial effects of a price increase of cigarettes through taxes, in terms of disease and health-care costs averted, and increased tax revenues. FINDINGS: In the 12 Latin American countries analysed, we estimated that smoking is responsible for approximately 345 000 (12%) of the total 2 860 921 adult deaths, 2·21 million disease events, 8·77 million healthy years of life lost, and $26·9 billion in direct medical costs annually. Health-care costs attributable to smoking were estimated to represent 6·9% of the health budgets of these countries, equivalent to 0·6% of their gross domestic product. Tax revenues from cigarette sales cover 36·0% of the estimated health expenditures caused by smoking. We estimated that a 50% increase in cigarette price through taxation would avert more than 300 000 deaths, 1·3 million disease events, gain 9 million healthy life-years, and save $26·7 billion in health-care costs in the next 10 years, with a total economic benefit of $43·7 billion. INTERPRETATION: Smoking represents a substantial health and economic burden in these 12 countries of Latin America. Tobacco tax increases could successfully avert deaths and disability, reduce health-care spending, and increase tax revenues, resulting in large net economic benefits. FUNDING: International Development Research Centre (IDRC), Canada.

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 score0.061
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.029
GPT teacher head0.339
Teacher spread0.310 · 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

Citations80
Published2020
Admission routes1
Has abstractyes

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