The consequences of tobacco tax on household health and finances in rich and poor smokers in China: an extended cost-effectiveness analysis
Notice bibliographique
Résumé
BACKGROUND: In China, there are more than 300 million male smokers. Tobacco taxation reduces smoking-related premature deaths and increases government revenues, but has been criticised for disproportionately affecting poorer people. We assess the distributional consequences (across different wealth quintiles) of a specific excise tax on cigarettes in China in terms of both financial and health outcomes. METHODS: We use extended cost-effectiveness analysis methods to estimate, across income quintiles, the health benefits (years of life gained), the additional tax revenues raised, the net financial consequences for households, and the financial risk protection provided to households, that would be caused by a 50% increase in tobacco price through excise tax fully passed onto tobacco consumers. For our modelling analysis, we used plausible values for key parameters, including an average price elasticity of demand for tobacco of -0·38, which is assumed to vary from -0·64 in the poorest quintile to -0·12 in the richest, and we considered only the male population, which constitutes the overwhelming majority of smokers in China. FINDINGS: Our modelling analysis showed that a 50% increase in tobacco price through excise tax would lead to 231 million years of life gained (95% uncertainty range 194-268 million) over 50 years (a third of which would be gained in the lowest income quintile), a gain of US$703 billion ($616-781 billion) of additional tax revenues from the excise tax (14% of which would come from the lowest income quintile, compared with 24% from the highest income quintile). The excise tax would increase overall household expenditures on tobacco by $376 billion ($232-505 billion), but decrease these expenditures by $21 billion (-$83 to $5 billion) in the lowest income quintile, and would reduce expenditures on tobacco-related disease by $24·0 billion ($17·3-26·3 billion, 28% of which would benefit the lowest income quintile). Finally, it would provide financial risk protection worth $1·8 billion ($1·2-2·3 billion), mainly concentrated (74%) in the lowest income quintile. INTERPRETATION: Increased tobacco taxation can be a pro-poor policy instrument that brings substantial health and financial benefits to households in China. FUNDING: Bill & Melinda Gates Foundation and Dalla Lana School of Public Health.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».