Effects of comprehensive smoke-free legislation on smoking behaviours and macroeconomic outcomes in Shanghai, China: a difference-in-differences analysis and modelling study
Notice bibliographique
Résumé
BACKGROUND: China has one of the highest levels of tobacco consumption globally, and there is no national smoke-free legislation. Although more than 20 Chinese cities have passed local smoke-free laws since 2008, evidence on their effectiveness in reducing smoking behaviours and their economic benefits is scarce. By exploiting a natural quasi-experiment, whereby a comprehensive public smoking ban was implemented in Shanghai in March, 2017, this study aims to assess the impact of the policy on individual smoking behaviours and quantify its effect on macroeconomic outcomes. METHODS: In this difference-in-differences analysis and modelling study, we used data on smoking behaviours from the 2012, 2014, 2016, and 2018 waves of the China Family Panel Studies. We used a difference-in-differences approach to investigate trends in smoking prevalence in respondents in Shanghai, relative to respondents from other direct-administered municipalities, provincial capital cities, and subprovincial municipalities (control group), after the implementation of a smoking ban in 2017. All respondents aged 18 years or older were included, with the exception of people who lived in Beijing and rural areas. The primary variable of interest in the difference-in-differences analysis was self-reported smoking status. Based on the difference-in-differences estimation of reduction in smoking prevalence, we then used a health-augmented macroeconomic model to estimate the potential macroeconomic gains if such a ban was implemented across China for the period 2017-35. FINDINGS: 14 688 respondents were included in the analysis: 5766 from Shanghai and 8922 from the control group. After the implementation of the smoking ban in Shanghai in 2017, smoking prevalence decreased by 2·2 percentage points (95% CI 2·1-2·3), equivalent to an 8·4% reduction in the number of current smokers. The smoking ban had a larger effect on men, people with a higher level of education, unmarried people, and younger people when compared with their respective counterparts. The modelling analysis showed that implementing a nationwide comprehensive public smoking ban similar to that in Shanghai would result in a 0·04-0·07% increase in the national gross domestic product in China between 2017 and 2035, outweighing the economic costs of smoking ban enforcement. INTERPRETATION: The smoking ban in Shanghai shows that a comprehensive public smoking ban with strict enforcement is effective in curbing smoking behaviours. Moreover, the implementation of a comprehensive public smoking ban across China would be cost-effective. FUNDING: National Social Science Fund of China.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| 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 ».