The Evolution of Tobacco Marketing to Women and Girls in sub-Saharan Africa
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".