The Evolution of Tobacco Marketing to Women and Girls in sub-Saharan Africa
Notice bibliographique
Résumé
Abstract Introduction Historically, tobacco use among women and girls in sub-Saharan Africa has been significantly lower than among men. However, recent trends show a concerning rise in smoking rates within this demographic. This shift necessitates a deeper examination of the role tobacco industry marketing plays in driving these changes. Focusing on five key countries—Nigeria, South Africa, Rwanda, Kenya, and Senegal—this research provides a comprehensive analysis of industry marketing tactics targeting women and girls in the region. Aims and Methods This study aims to investigate the evolving strategies used by the tobacco industry to market products to African women and girls. A mixed-methods approach was employed, combining a literature review, quantitative surveys, and qualitative semi-structured interviews. In addition, a historical analysis of tobacco industry documents and an evaluation of tobacco control laws and regulations in the five surveyed countries were conducted to gain deeper insights into industry practices. Results Findings from TIDs suggest that the tobacco industry has systematically targeted women for several decades, with a particular focus on young women aged 18-24. None of the surveyed countries currently have comprehensive laws addressing new and emerging products like e-cigarettes. Tobacco marketing was most commonly encountered in nightclubs, bars, lounges, and parties, with 32.8% of participants reporting exposure in these settings. Social media exposure varied across countries, while television shows and movies consistently showed high exposure rates (77.2%) across all five nations. Key informant interviews highlighted dominant themes such as brands targeting females, cultural perceptions of female tobacco use, femininity, autonomy, influencer marketing, digital strategies, harm reduction narratives, proximity marketing, peer and parental influences, and the perceived benefits of tobacco, particularly in terms of flavor, taste, and smell. Conclusion and Implications The tobacco industry uses sophisticated marketing strategies to enhance product appeal, particularly targeting women through emerging products, flavor manipulation, and harm reduction messaging. Proximity marketing in social settings has proven effective in increasing young women’s access to tobacco products. Critical regulatory gaps remain, particularly concerning e-cigarettes and other novel tobacco products. The adequacy and enforcement of existing TAPS regulations, especially those concerning digital media and cross-border advertising, need urgent attention. Countries should adopt proactive regulations that anticipate industry adaptations and reduce the need for frequent updates. TAPS bans must be extended to encompass emerging tobacco and nicotine products across both traditional and digital platforms. Additionally, regulations need to target proximity and harm-reduction marketing to safeguard young women and prevent the normalization of tobacco use among these vulnerable demographics.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».