As world leaders meet in New York for the 81st session of the United Nations General Assembly, one question sits beneath many of the debates on the future of multilateralism: are our international institutions and rules still equipped for the world they are now being asked to govern?
The question is not whether international law remains relevant. It clearly does. The more difficult question is whether the system can keep pace with the speed and complexity of change.
The theme of this year's General Assembly, “Restoring Trust, Managing Transformation: A United Nations That Delivers for All”, reflects this exact tension. The agenda includes peace and security, UN reform, development financing, climate change, human rights, artificial intelligence, and the changing international economic order. The President of the General Assembly, H.E. Dr. Khalilur Rahman, has himself described the session as taking place at a moment of profound challenges to multilateral cooperation and a widening trust deficit.
For developing countries, particularly in Africa, these are not abstract institutional questions. They directly affect access to finance, trade, technology, policy space, and representation in the institutions that make decisions affecting their economies.
The answer should not be to discard established rules. It is to make international governance more capable of reviewing, learning, and adapting while preserving legal certainty and accountability.
A System Under Pressure
International trade offers a useful illustration. The General Agreement on Tariffs and Trade (GATT) established core disciplines for trade in goods, while the General Agreement on Trade in Services (GATS) extended the multilateral framework to services. Together with the wider WTO agreements, they created a rules-based system that has provided considerable predictability to international commerce.
But the global economy of today is not the economy of the Uruguay Round. Digital commerce, artificial intelligence, data flows, climate-related trade measures, industrial subsidies, and geopolitical competition have changed the fundamental questions that governments are asking of the trading system.
The WTO's dispute settlement experience is highly instructive. The Appellate Body has been unable to hear appeals because of continuing vacancies, having been functionally paralysed since December 2019 due to blocked judicial appointments. WTO members have recognised the need to address the resulting challenges and have continued discussions on reform.
This is more than a mere institutional problem. Dispute settlement is part of what gives a rules-based system credibility. Rules must not only exist; states must have confidence that they can be interpreted, applied, and enforced through institutions that remain functional. The wider lesson is that international legal regimes cannot assume that yesterday's institutional arrangements will remain adequate indefinitely.
Climate Change Shows the Importance of Learning and Delivery
The climate regime provides a different example. The Paris Agreement does not depend on a single negotiation settling every aspect of climate policy. It incorporates cycles of national action, reporting, review, and the Global Stocktake, allowing parties to assess collective progress and adjust their efforts over time. That is, in essence, an iterative approach to international governance.
But climate change also exposes the limits of adaptation without implementation. Governments can agree on targets, reporting frameworks, and long-term commitments; the difficult question is whether those commitments are adequately financed and translated into action.
At COP29, countries agreed on a new climate-finance goal of at least $300 billion annually for developing countries by 2035, while calling for efforts to scale climate finance from public and private sources towards $1.3 trillion annually by 2035. The significance goes beyond the figures. It illustrates the point at which international law and policy meet economic reality. A commitment that cannot be financed or implemented will eventually become a source of frustration rather than trust.
The same applies to the Sustainable Development Goals. The question for multilateralism is increasingly not whether the international community can agree on another set of targets, but whether countries have the financing, institutional capacity, and policy space to deliver them. The real test is therefore the movement from policy to financing and from financing to implementation.
Africa Cannot Be a Rule-Taker in an Adaptive System
For Africa, reform also raises a critical question of voice. African states have consistently called for greater representation in global decision-making and reforms to an international financial architecture that does not adequately reflect the circumstances and financing needs of developing economies.
This matters because representation and implementation are closely connected. A country has less ability to shape international rules when it has limited negotiating capacity, technical expertise, or access to the resources required to implement what it has agreed.
An adaptive multilateral system must therefore allow developing countries to participate not only in negotiating rules but also in reviewing how those rules operate and determining how they should evolve. That requires targeted investment in African policy institutions, legal and technical expertise, data systems, and regional cooperation. Greater flexibility without greater capacity could simply produce a system in which the most technically and financially powerful states remain best placed to shape the next generation of international rules.
AI Is Testing the Speed of International Law
Artificial intelligence makes this problem particularly visible. AI technologies are developing on a timescale that conventional treaty-making was not designed for. It is structurally difficult to negotiate a detailed international legal regime around technologies whose capabilities and risks may change substantially before the negotiation is complete.
The Global Digital Compact, adopted by UN Member States in 2024, recognises this reality. It calls for international cooperation on AI that is “agile and adaptable” and provides for an Independent International Scientific Panel on AI alongside a global dialogue on AI governance. It also stresses the need for developing countries to build the capacity to participate in and benefit from AI governance.
There is an important legal principle here: adaptability should not mean abandoning established rights or standards. The Global Digital Compact itself places digital cooperation within the framework of international law and human rights. The objective, therefore, should be flexibility in the means of regulation, without instability in fundamental principles.
Towards a More Iterative Multilateralism
These examples suggest that the next stage of multilateral reform should not focus only on creating new institutions. It should also ask whether existing legal frameworks contain sufficient capacity for review and adjustment.
New agreements, particularly in rapidly changing areas, should consider incorporating:
1)Regular and evidence-based review; 2)Mechanisms for updating technical standards without reopening an entire treaty (like established tacit acceptance procedures); 3)Functioning and credible dispute-resolution processes; 4)Meaningful participation by developing countries; and 5)Clearer links between international commitments, financing, and implementation.
This does not require constant renegotiation. Nor does it mean replacing binding rules with voluntary arrangements. It means recognising that international law can be both stable and capable of evolution.
Restoring Trust Through Results
The credibility of multilateralism will ultimately be judged less by the number of declarations adopted than by whether cooperation produces tangible results.
That is perhaps the most important challenge before the 81st General Assembly. UN reform, financial reform, climate commitments, AI governance, and development goals will all have limited value if they remain disconnected from the institutions, resources, and capacity needed to implement them.
For Africa and other developing economies, restoring trust must therefore mean more than being heard in international forums. It must mean having a meaningful role in shaping the rules, access to the financing required to implement them, and institutions capable of translating international commitments into national and regional outcomes.
International law should remain binding, predictable, and accountable. But predictability should not mean permanence, and adaptation should not mean uncertainty. The better approach is a multilateral system that can review what it has agreed, learn from how it has worked, correct what is no longer fit for purpose, and deliver on the commitments it makes.
If the objective of the 81st General Assembly is to restore trust while managing transformation, then the international system must do more than preserve the rules of the past. It must develop the capacity to learn from the present and adapt for the future.
Artificial Intelligence (AI) is increasingly transforming economies, institutions, and governance systems across the globe. In Africa, this shift is unfolding rapidly: governments and private-sector actors are integrating AI-driven technologies into critical sectors such as finance, healthcare, education, telecommunications, security, and public administration as part of broader digital transformation agendas. These technologies offer significant opportunities to improve service delivery, expand financial inclusion, enhance productivity, and stimulate economic growth. Beyond improving efficiency and service delivery, many of these systems are increasingly being used to support or automate decisions that directly affect individuals, including credit scoring in finance, biometric identification, and digital identity systems. However, as AI systems become more embedded in decision-making processes, concerns are growing about the risks of algorithmic bias, unequal access, and the potential exclusion of already marginalised populations from the benefits of digital transformation.
Algorithmic bias is the tendency of AI systems to produce systematically prejudiced or unfair outcomes due to flawed assumptions in their programming, unrepresentative training data, or embedded societal inequalities. Algorithms learn from historical patterns and datasets, and may therefore replicate or even amplify existing disparities, particularly where certain groups are under-represented or excluded.
This challenge is particularly relevant in African contexts, where many AI systems are developed using datasets that may not adequately reflect African languages, cultures, identities, and socio-economic realities. The risks are potentially evident in areas such as language technologies, automated credit-scoring systems, and biometric identification tools, where inaccuracies or unequal outcomes may restrict access to opportunities, services, and resources. Beyond their technical implications, such outcomes raise broader concerns relating to non-discrimination, equality, privacy, and human dignity. As African countries continue to embrace AI-driven innovation, a fundamental question emerges: can digital transformation truly be inclusive if the systems driving it risk perpetuating structural bias?
Understanding the Risks of Algorithmic Bias in African Contexts
The risk of algorithmic bias is particularly relevant in Africa. Many AI systems currently deployed across the continent are trained predominantly on datasets generated outside Africa, often lacking sufficient representation of African languages, cultures, identities, and socio-economic realities. Consequently, these technologies risk reproducing and amplifying existing structural inequalities within African societies
A prominent example is seen in language representation. Africa is home to over 2000 languages, yet AI systems are optimised primarily for a small number of dominant global languages. UNESCO has highlighted the under-representation of African languages in AI training datasets, noting that many widely used AI systems perform poorly in African linguistic contexts. Speech recognition tools, virtual assistants, and translation systems repeatedly struggle to understand African accents, dialects and indigenous languages. For individuals who primarily communicate in their mother tongue, this can create barriers to accessing digital services and participating fully in the digital economy, thereby risk excluding millions of users from the benefits of emerging technologies.
Algorithmic decision-making is increasingly used in financial services, particularly in credit scoring and digital lending systems. Across countries such as South Africa, Kenya, and Nigeria, banks, credit bureaus, and fintech companies rely on algorithmic models to assess borrower risk and inform lending decisions. These systems have demonstrated significant potential to expand access to finance and improve the efficiency of credit allocation. For example, a 2025 study on digital lending in Kenya found that a credit-scoring model developed for a Nairobi-based lender achieved approximately 82.5% accuracy in distinguishing reliable borrowers from potential defaulters, demonstrating the potential effectiveness of algorithmic credit assessment. However, a significant proportion of the continent’s workforce operates within the informal economy, and lacks the formal financial histories on which traditional credit-scoring models rely. Consequently, many economically active individuals remain "credit invisible" or possess thin credit files that do not adequately reflect their financial behaviour or repayment capacity. This mismatch between conventional credit-scoring models and the realities of Africa's informal economies creates a potential risk of algorithmic bias.
Research from the Consultative Group to Assist the Poor (CGAP), a World Bank initiative, shows that algorithmic credit models may produce unfair or discriminatory outcomes where biases exist in the underlying data, model design, or proxy variables. In African contexts, these concerns may be amplified where large segments of the population lack formal financial histories, increasing the likelihood that automated systems will rely on incomplete or unrepresentative information when assessing creditworthiness.
Concerns have also emerged regarding the use of facial recognition technology. Research indicates that several facial recognition systems exhibit significantly higher error rates when identifying individuals with darker skin tones, with error rates reaching as high as 34% for darker-skinned women. This is not a marginal or hypothetical risk for Africa: a 2019 NIST study evaluating facial recognition algorithms across demographic groups found some of the highest false-positive error rates among people of West and East African descent, populations that make up much of the continent. The risk is compounded by how some of these systems have been developed and deployed on the continent: in Zimbabwe, for instance, the government's 2018 facial-recognition partnership with Chinese AI firm CloudWalk involved transferring biometric data on millions of citizens' faces abroad, with little regulatory oversight of how that data would be used or safeguarded. In contexts where facial technologies are deployed for surveillance or law enforcement, often in Africa with weaker biometric data protection, inaccurate identification may result in wrongful targeting and discrimination. These developments underscore a growing concern with significant implications for Africa's digital future: the risk of algorithmic bias and digital exclusion.
Beyond technical errors, these concerns raise broader questions about accountability, transparency, and the protection of fundamental human rights.
Human Rights Implications of Algorithmic Bias
The risks posed by the deployment of a biased AI system raise significant legal and human rights concerns within African states. Algorithmic decision-making systems may affect rights relating to equality, dignity, privacy, freedom from discrimination, and access to justice.
At the international level, the United Nations Universal Declaration of Human Rights (UDHR), a milestone instrument in the history of human rights, serves as a common standard of achievements for all peoples and all nations. It sets out, for the first time, fundamental human rights to be universally protected. The UDHR established the foundational principles of equality, non-discrimination, human dignity, and equal protection before the law. It also established the right to privacy as a fundamental human right. Although adopted long before the emergence of artificial intelligence, these principles remain relevant in assessing the fairness and societal implications of algorithmic decision-making systems.
Within the African context, these protections are reinforced by the African Charter on Human and Peoples’ Rights, a regional human rights instrument ratified by 54 of the 55 Member States of the African Union. The Charter guarantees the rights to equality before the law and equal protection of the law under Article 3, freedom from discrimination under Article 2, and respect for human dignity under Article 5. These rights are particularly relevant where algorithmic systems risk producing unequal outcomes that disproportionately affect certain groups or communities.
For instance, facial recognition systems that exhibit higher error rates for darker-skinned individuals may expose affected persons to discriminatory treatment and unequal access to services. Similarly, algorithmic credit-scoring systems that inadequately account for the realities of informal economies may unfairly disadvantage individuals and small businesses that lack conventional financial histories. In both cases, decisions that appear technologically neutral may, in practice, produce exclusionary outcomes that undermine principles of equality and non-discrimination.
The legal significance of such risks has been recognised beyond Africa. In SCHUFA Holding AG (Case C-634/21), the Court of Justice of the European Union (CJEU) held that automated credit-scoring decisions capable of determining an individual's access to credit may constitute automated decision-making with legal or similarly significant effects under the General Data Protection Regulation (GDPR), including decisions that affect a person’s rights or lead to exclusion of or discrimination against the individual. In doing so, the Court affirmed the need for safeguards against decisions based solely on automated processing, reinforcing the principles of transparency, human oversight, and the right to challenge decisions that materially affect individuals. Although decided within the European Union's legal framework, the judgement provides persuasive guidance for AI governance in Africa.
Algorithmic bias risks also present significant challenges to inclusion. One of the core promises of Africa’s digital transformation agenda is the expansion of access to opportunities, services, and participation in the digital economy. However, where AI systems are developed using datasets that inadequately represent African languages, cultures, and socio-economic realities, certain populations may be excluded from fully benefiting from technological innovation. Such exclusion risks widening existing social and economic disparities rather than reducing them.
Beyond concerns of fairness and inclusion is the question of accountability. Many algorithmic systems operate as so-called “black boxes”, making it difficult for affected individuals to understand how decisions are reached or to challenge adverse outcomes. The absence of transparency may undermine procedural fairness and weaken public trust in institutions that rely on automated decision-making. Accountability therefore requires that AI systems remain subject to appropriate human oversight, transparency obligations, and effective mechanisms for redress.
Privacy is another important human rights consideration in the deployment of AI systems. By their nature, AI technologies rely on the collection, processing, and analysis of vast amounts of personal data, raising significant concerns regarding the protection of individual privacy. Recognising the growing importance of data governance, the African Union Executive Council endorsed the African Union Data Policy Framework in 2022, providing a continental blueprint for the development of trusted, inclusive, and rights-based data ecosystems. Notably, 36 of the 55 African Union Member States have formally requested support from the African Union Commission to develop national data governance policies aligned with the Framework. Despite these developments, weak enforcement of data protection regimes in several African countries continues to heighten the risks of surveillance and misuse of personal data. These concerns are particularly pronounced in the context of biometric identification and facial recognition technologies, which process sensitive personal information and may expose individuals to heightened privacy risks if not properly regulated.
Recognising these challenges, UNESCO’s Recommendation on the Ethics of Artificial Intelligence emphasises that AI systems should be designed and deployed in ways that promote fairness, transparency, accountability, non-discrimination, and respect for human rights. These principles provide an important framework for African policymakers seeking to balance technological innovation with the protection of individual rights and societal interests.
Ultimately, the challenge presented by the risks of algorithmic bias is how AI technologies can be governed in a manner that advances innovation while safeguarding equality, dignity, inclusion, privacy, and accountability. As AI continues to shape economic and social outcomes across the continent, ensuring that human rights considerations remain central to AI governance will be essential to achieving an inclusive and equitable digital future.
Policy Pathways for Inclusive AI Governance
Addressing the risks of algorithmic bias requires the development of governance frameworks that ensure innovation advances alongside fairness, accountability, and inclusion. To achieve this, African governments, regulators, and private sector actors should consider the following policy priorities.
First, strengthen AI governance and regulatory frameworks. African countries should develop comprehensive AI governance frameworks that establish clear standards for fairness, accountability, transparency, and non-discrimination. In doing so, policymakers should adopt AI-specific governance measures that require organisations deploying high-risk AI systems to assess and mitigate potential discriminatory outcomes.
Secondly, African governments and institutions should invest in locally generated datasets that reflect African languages, cultures, identities and socio-economic realities. In doing so, governments should support the creation of high-quality, ethically sourced, and locally relevant datasets through investments in national data infrastructure, research and relevant digital resources. Collaboration among public institutions, universities, civil societies and the private sector is also essential to ensure that datasets are diverse, representative and collected in accordance with data protection and ethical standards.
Third, in line with UNESCO’s Recommendation on the Ethics of Artificial Intelligence, human rights principles should be integrated into AI governance frameworks from the design stage. Fairness, non-discrimination, accountability, and inclusion should form core pillars of African digital governance strategies.
Fourth, individuals affected by automated decision-making systems should be entitled to clear and understandable explanations of how such decisions are made, as transparency is essential to accountability and public trust. In addition, effective complaint and redress mechanisms should be established to allow individuals to challenge decisions that may be inaccurate, discriminatory, or otherwise harmful.
Finally, governments should strengthen the implementation and enforcement of data protection laws to address privacy risks associated with AI systems, while also building institutional and technical capacity. Robust safeguards are particularly necessary in relation to biometric technologies, facial recognition systems, and other applications involving the processing of sensitive personal data
By adopting these measures, African countries can harness the transformative potential of AI while minimising the risks of algorithmic bias and digital exclusion.
Authors: Thomas Van Huyssteen, Clinton Essang, Henry Urama, Carole Djuichou, Folashayo Adeniji, Terefe Gelibo, Winnie Awuor, Tochukwu Okereke
New primary research from the Tobacco Control Data Initiative (TCDI) demonstrates that tobacco and nicotine use is entrenched, uneven, and increasingly shaped by youth exposure, gender dynamics, product affordability, the rise of new and emerging products and weak cessation support.
The appeal is not accidental
World No Tobacco Day (WNTD) 2026 is anchored on the theme “Unmasking the appeal: countering tobacco and nicotine addiction". This focus is particularly urgent for tobacco control in Africa, where emerging product marketing increasingly targets the youth population. As the tobacco industry aggressively courts a new generation, this theme challenges us to shift the conversation from whether people know tobacco is harmful to a more pressing, systemic issue: Why do tobacco and nicotine products remain so accessible and appealing to young people across the continent despite their well-known health risks and addictive nature?
The TCDI Spot Check Prevalence Surveys (SCPS), conducted by Development Gateway: An IREX Venture (DG) in 2025 across 2 selected provinces in South Africa, Zambia, and the Democratic Republic of the Congo (DRC), offer a practical way to answer this question. These targeted surveys provide new evidence on how tobacco and nicotine use patterns are evolving across different local contexts. While many of the drivers of use are broadly similar across settings, including social norms, accessibility, and structural factors, there are important differences in the extent and expression of these influences. Based on observations from the spotcheck surveys, there appeared to be more similarities than differences across settings, which is consistent with the broader literature.
Across the three countries, the following findings emerged:
Specific communities continue to record high levels of daily cigarette use.
Smokeless tobacco remains the dominant threat in other areas.
Emerging products, such as e-cigarettes and nicotine pouches, are appearing unevenly across settings.
Second-hand smoke (SHS) exposure continues to harm millions of non-smokers.
At first glance, Africa’s tobacco use (9.5% prevalence) looks relatively low compared to the global average, where about one in five adults use tobacco. However, even low prevalence translates into millions of smokers in absolute terms. When you look beyond national numbers to examine sub-national “hotspots”, the reality is jarring. TCDI’s Spot Check findings underscore why timely, local data matters, as illustrated in Figure 1 below.
Figure 1: Tobacco and Nicotine use by Country: Current use among adults aged 15+ in selected provinces
These numbers represent localised snapshots or subnational findings rather than national averages. That distinction matters, but it is far from a limitation. While national data show how widespread a problem is at the population level, subnational data reveal how that problem is distributed across different regions and groups. These findings provide a strategic roadmap, revealing where tobacco control efforts need to be custom-tailored, where enforcement needs stronger teeth, and where prevention and cessation services are most urgently needed before addiction becomes entrenched.
Why Spot Check surveys matter
National surveys remain essential for tracking country-level progress. However, they are resource-intensive, conducted infrequently, and often unable to capture the local social, economic, and behavioural reasons that shape tobacco use. Meanwhile, tobacco and nicotine markets can evolve far more quickly than large national surveys can keep pace with.
Spot Check surveys help fill this gap. By focusing on smaller geographic areas, they can be implemented more quickly and at a lower cost than large national surveys, while still producing policy-relevant evidence. They are not intended to replace large surveys such as the Global Adult Tobacco Survey (GATS), the Demographic and Health Survey (DHS), or the STEPS (STEPSwise Approach to NCD Risk Factor Surveillance). Rather, they are a practical complement, a way to identify local trends, generate proxy evidence for policy discussions, and guide targeted interventions.
The value becomes even greater when quantitative and qualitative evidence are integrated. Quantitative data show the scale and distribution of tobacco and nicotine use, while qualitative findings help explain the lived realities behind the numbers: why people start, why they continue, and why quitting is difficult even when the risks are well understood.
Tobacco use remains entrenched, but the pattern differs by place
South Africa shows a high-burden, mature tobacco market. In the Western Cape, more than four in ten adults in the survey reported current tobacco or nicotine use, while in KwaZulu-Natal the figure was just over three in ten.
Figure 2: Tobacco and Nicotine use in two selected provinces in South Africa, Zambia and the DRC
Cigarettes remain the dominant driver of nicotine addiction in South Africa, and smoking appears deeply entrenched in both KwaZulu-Natal and Western Cape provinces, where roughly 98% of smokers reported smoking daily, indicating that cigarette use is overwhelmingly habitual rather than occasional.
In Zambia, the burden is more geographically uneven. Current tobacco or nicotine use was 25.3% in Luapula, compared with 7.8% in Southern Province, with smokeless tobacco dominating the high-burden areas (i.e., Luapula). This pattern suggests that national or pooled averages can obscure high-burden provinces.
Figure 3: Tobacco and Nicotine use in two selected provinces in Zambia
In the DRC, a similar story unfolds, where Nord Ubangi recorded higher levels of current tobacco or nicotine use than Haut-Lomami (38.9% vs 25.3%). The findings also show a striking divide between smoked and smokeless tobacco. While smoked tobacco use is substantial in both provinces, smokeless tobacco is far more prevalent in Nord Ubangi than in Haut-Lomami.
Figure 4: Tobacco and Nicotine use in two selected provinces in the DRC
These contrasts reinforce a central lesson for tobacco control policy: there is no one-size-fits-all response. Broad, macro-level policy tools such as taxation, enforcement, smoke-free spaces, prevention and cessation are absolutely needed across countries; however, they must be tailored to address local product patterns and the populations most at-risk.
The next generation is already exposed
The WNTD 2026 focus on children and adolescents isn’t just a warning about future risks. TCDI’s latest Spot Check findings suggest that youth exposure and early initiation are already happening. Across the surveyed settings, the findings indicate a concerning trend toward early engagement with tobacco and nicotine, implying that tobacco products are becoming more ingrained in youth social contexts.
In South Africa, initiation occurs early, typically before the ages of 18 and 19, with worrying levels of use beginning below the age of 12 in some cases. In the Western Cape, the average age of initiation falls within the late teenage years. Among young people, aged 15-24, current tobacco or nicotine use was notably high in the Western Cape and lower, but still significant, in KwaZulu-Natal. Qualitative interviews with youth and community members revealed a distinct social cocktail. Tobacco use was linked not only to peer pressure and the myth that smoking is “cool” or “modern”, but also to intense boredom, alcohol use, stress, and a severe shortage of alternative recreational activities for young people. Collectively, these trends indicate that tobacco and nicotine consumption are becoming increasingly normalised within everyday youth social life, hence heightening the probability of prolonged use and dependence.
In the two surveyed provinces of Zambia, the average initiation age for manufactured cigarettes was around 19 years, while the mean age at first use of roll-your-own cigarettes was around 18 and 22 years for urban and rural areas, respectively. In many of these communities, tobacco use appears deeply woven into the social fabric, prompting early initiation among young people. Roll-your-own cigarettes are cheap, easily accessible to minors, and highly normalised, with explicit reinforcement from the behaviour of peers and sometimes even from community leaders. The availability of cheap tobacco products to younger individuals indicates that affordability and accessibility are facilitating early smoking initiation among adolescents and young people.
A similar pattern emerges in the DRC, where tobacco use often begins during early adolescence, roughly between the ages of 13 and 17, well below the legal age for purchasing tobacco products. In other words, there is a clear gap between existing regulations and the realities on the ground, with initiation happening long before young people are legally permitted to purchase tobacco. The data further reveal that average initiation occurs earlier in Nord-Ubangi than in Haut-Lomami. For Congolese youth, tobacco and nicotine use are rarely about image; it is a functional coping mechanism. Participants described smoking as a response to harsh daily realities, including coping with severe stress, reducing fatigue and boredom, staying warm during freezing night vigils, and serving as part of daily work routines. Although the legal age for purchasing cigarettes is 18 years, participants noted that tobacco products remained easily accessible within their communities.
Figure 5: Early Initiation: Why Prevention Must Start Early
Tobacco and Nicotine use often begins well before adulthood.
Figure 6: Why Young People Start Using Tobacco
The implication here is a major reality check for public health: preventing youth tobacco use requires more than simply telling young people that tobacco is harmful. The study shows that initiation commonly occurs in social and everyday environments where tobacco use is normalised, including schools, markets, transport hubs, ceremonies, youth social spaces, bars, homes and peer networks. This suggests that the appeal of tobacco is shaped not only by individual choice but also by social environments, economic realities, and routine exposure. Prevention efforts must therefore focus on the specific settings and situations where initiation occurs.
Emerging nicotine products show uneven uptake and growing public health concerns.
The data further highlighted a growing uptake of new and emerging nicotine and tobacco products (NENTPs). The clearest evidence comes from South Africa, particularly the Western Cape, where current e-cigarette use was estimated at 12.7% in the Western Cape compared with 1.9% in KwaZulu-Natal. A similar pattern was observed for Shisha use, which was higher in the Western Cape (12.1%) compared with KwaZulu-Natal (1.8%).
Although the prevalence of e-cigarette use remains low in Zambia and the DRC, uptake may increase over time due to globalisation and growing exposure to lifestyle influences from high-income settings, including through social media.
Qualitative findings help explain why early action matters. In South Africa, young people and stakeholders described vaping as modern, flavoured, enjoyable, and less harmful. Similarly, in Zambia and the DRC, these products have begun to appear in local markets and social conversations. They are marketed as fashionable, flavoured or “safer” than cigarettes. Therefore, there is a need to strongly enforce regulations before use becomes more widespread.
This reflects the central message of World No Tobacco Day (WNTD) 2026: addiction by design. The appeal of tobacco is not accidental; It is deliberately engineered. To "unmask the appeal" is to expose a highly calculated strategy. Nicotine products do not merely appear attractive to teenagers; they are meticulously designed to look that way. From candy-like flavours and sleek, high-tech product designs to lifestyle branding and targeted social media imagery, every element is designed to hook new users. Peer pressure and social trends further reinforce this appeal, particularly in areas where weak local regulations and lax enforcement allow these products to spread with little oversight.
The increasing use of smokeless tobacco among women needs more attention
Tobacco control messaging is typically designed for broad population reach and is not usually gender-specific, even in contexts where tobacco use is more common among men. The Spot Check findings corroborate existing evidence that tobacco use prevalence is higher among males than females. However, the results also highlight the importance of not overlooking the growing burden among women.
The DRC shows a distinct gendered pattern of tobacco and nicotine use. Women’s current use is higher in Nord Ubangi (NU) (25.2%) than in Haut Lomami (HL) (6.5%), with a similarly higher prevalence of smokeless tobacco in NU (25.2% versus 2.9% in HL). By contrast, cigarette smoking among women remains low in both provinces (3.73% and 5.4%, respectively). These findings suggest that tobacco use among women in the DRC is not mainly driven by cigarettes but more by other forms of tobacco consumption, especially smokeless products, with clear differences across provinces.
The reality in Zambia indicates a different gendered pattern. Men had higher current use overall, but smokeless tobacco in Luapula was concentrated among women: 15.9% of women reported current smokeless tobacco use compared with 2.7% of men. This finding suggests that a cigarette-focused lens can overlook important gender-specific forms of tobacco use.
South Africa adds another layer: stigma. The qualitative findings show that smoking among men is often normalised, while women who smoke may hide their use due to fear of moral judgement. This hidden use can discourage women from seeking support and make cessation services difficult to access.
A gender-sensitive tobacco control response should therefore do more than “include women” in general campaigns. It should address the specific products women use, the stigma that shapes whether they seek help, and the social meanings attached to tobacco use in different communities.
Second-hand smoke exposure makes this a public problem, not only an individual one
Second-hand smoke exposure is one of the strongest areas for policy action because it affects both users and non-users. Across the reports, exposure patterns differ, but the message is consistent: many people are unable to simply “opt out” of exposure to tobacco smoke.
South Africa reported very high exposure in outdoor public places, with 97.1% in KwaZulu-Natal and 89.4% in the Western Cape. Exposure within school environments was also extremely high.
In Zambia, household exposure was also quite high, with 89.3% of adults in both provinces reporting exposure to second-hand smoke at home. Exposure in enclosed public places was also substantial at 42.9%, while outdoor public places and schools recorded lower but still relevant levels.
The DRC reported widespread but comparatively lower levels of SHS exposure than those observed in South Africa and Zambia: 51.7% at home, 27.3% in enclosed public places, 31.5% in outdoor public places and 18.5% at school. Nord Ubangi showed higher exposure at home and in enclosed public places than Haut-Lomami.
These findings reinforce the urgent need for stronger smoke-free policies and more effective implementation across these three countries. Zambia’s recently passed Tobacco Control Bill provides an important opportunity to strengthen implementation and enforcement of smoke-free public places.
In South Africa, the pending Tobacco Products and Electronic Delivery Systems Control Bill could significantly strengthen smoke-free protections by moving toward 100% smoke-free indoor public places and selected outdoor areas. The Spot Check findings highlight the importance of finalising stronger legislation and improving the enforcement of existing protections. In the DRC, existing smoke-free policies cover many enclosed public places and facilities such as health and education settings. However, continued exposure points to the need for stronger enforcement and fuller protection in homes, schools, workplaces and community spaces.
Knowledge is not enough without systems that help people quit
One of the most striking findings across all three country reports is the gap between knowing tobacco is harmful and being able to quit. For decades, public health campaigns have operated on a single assumption: if you educate people, they will quit. The latest data proves that it is not so simple; information alone does not break the cycle of addiction.
In South Africa, knowledge was high: more than 90% of respondents in KwaZulu-Natal and over 86% in the Western Cape recognised that smoking causes serious illness. Yet prevalence remained high, and daily use among cigarette smokers was the norm.
In Zambia and the DRC, awareness was moderate but still meaningful. In Zambia, 73.8% of respondents (68.9% in Luapula and 79.3% in Southern Province) reported that tobacco use can cause serious illness, but they had limited knowledge of specific health risks beyond lung cancer. In the DRC, 72.4% of respondents were knowledgeable about the dangers of tobacco use, while only 51.6% reported being aware of the dangers of second-hand smoking.
The qualitative findings explain why awareness alone is insufficient. In South Africa, the reports describe a “cognitive compromise”: people know tobacco is harmful but rationalise continued use as stress relief, social belonging or cultural practice. In Zambia, participants described perceived benefits such as pleasure, coping, strength, and sexual performance, particularly around traditional tobacco products. In the DRC, participants described medicinal or protective beliefs, routine cues and social contexts that sustain use.
Cessation systems need a complete overhaul. In South Africa, they have completely fragmented the safety net. Quitting services are poorly publicized and financially out of reach for the average person, leaving vital tools like Nicotine Replacement Therapy (NRT) entirely inaccessible to the public-sector users who need them most. Move over to Zambia, and the infrastructure is similarly insufficient. Dedicated cessation services are rare, and the counselling that does exist is often siloed inside mental health departments, often a structural flaw that triggers intense social stigma around seeking help. Compounding the issue, NRT is virtually nonexistent. In the DRC, formal cessation services were largely absent, leaving smokers to rely on informal advice from health workers or local community actors.
This is precisely where the WNTD theme’s focus on addiction becomes so relevant. Countering the appeal of tobacco and nicotine products requires more than awareness campaigns. To save lives, we must transform the act of quitting from a solitary struggle into a system that enables cessation to be realistic, affordable, confidential, and fully supported.
Figure 7: What the findings mean for policy
These Spot Check findings point to six priorities for a WNTD 2026 message:
Conclusion: Unmasking appeal means acting on local evidence
The TCDI Spot Check Surveys show that tobacco and nicotine use are evolving differently across places, products and populations.
In South Africa, the challenge lies in a high-burden market where daily cigarette use remains entrenched, and newer products are rapidly gaining ground. In Zambia, the uneven provincial burden presents a challenge, particularly with Luapula showing higher use and smokeless tobacco requiring gender-sensitive attention. In the DRC, there are high levels of current use in Nord-Ubangi, early initiation, strong patterns of smokeless tobacco use, and an absence of formal cessation support.
The message for World No Tobacco Day 2026 is therefore clear: countering tobacco and nicotine addiction requires more than exposing industry tactics alone. It also means confronting the local conditions that make tobacco use attractive and sustainable, including social acceptance, affordability, easy access, weak enforcement, stigma, and inadequate cessation support.
At the same time, the findings point to a clear opportunity for action. With timely local data, stronger policy enforcement, youth-focused prevention, and accessible cessation support, countries can move from knowing the problem to changing the conditions that sustain it.
These findings should, however, be interpreted with caution. Differences in survey design, scope of the study, product definitions, and product coverage across countries may affect direct comparability. In addition, the growing illicit tobacco trade and rapid evolution of emerging products may mean the actual exposure and use patterns are broader. Nonetheless, the overall direction of the evidence remains concerning and points to the need for stronger and more responsive tobacco control responses.
Acknowledgements
The authors acknowledge the support and contributions of the Tobacco Control Data Initiative (TCDI) team in the development of this blog for World No Tobacco Day (WNTD) 2026. Special appreciation goes out to the country teams and research teams whose work on the Spot Check Prevalence Survey provided the evidence that informed this analysis. These insights generated through the initiative continue to strengthen engagement with evidence-based tobacco control policies across the continent.
Artificial intelligence (AI) now sits at the centre of global development discourse and rightly so. Few technologies in recent history have the capacity either to accelerate human development or profoundly restructure the social and economic systems that have defined modern civilisation. Despite the urgency of these challenges, the appropriate governance framework for managing this powerful and often unpredictable technology remains unsettled. Governments, multilateral organisations, and private sector actors broadly agree that effective governance is essential for ensuring that AI systems deliver societal benefits while mitigating harm. However, key questions remain contested: What form should governance take? Who should lead the process—states, private firms, international institutions, or multi-stakeholder coalitions? And how can governance keep pace with technological innovation without limiting its potential?
Complicating the issues further are powerful geopolitical and economic incentives. Nations increasingly view AI leadership as a strategic priority tied to economic competitiveness and national security. In such a context, AI governance is often perceived not as an essential element for responsible innovation but as a potential barrier to technological advancement.
While historical precedents for governing AI-like technologies are limited, the broader challenge of regulating emerging technologies within complex innovation ecosystems is not new. One particularly relevant domain is data governance—the set of rules, standards, and institutional arrangements that govern how data are collected, processed, shared, and used. Over the past two decades, data governance has emerged as a well-developed policy field, shaping global debates around privacy, digital rights, cross-border data flows, digital taxation and platform accountability.
Indeed, contemporary discussions around AI governance have in many ways evolved out of earlier debates on data governance. The rapid emergence of generative AI systems around 2022 accelerated this shift, bringing renewed attention to how data, algorithms, and computational power interact to shape economic and social outcomes.
Against this backdrop, this article highlights critical lessons from data governance that can inform the global search for effective AI governance frameworks. Drawing on the experience of digital policy development over the past decade, it outlines pathways for moving beyond conceptual debates toward actionable governance models capable of balancing AI’s enormous potential with its significant risks.
Is There a Drift Between AI Governance and Data Governance?
While the extent to which data governance influences AI governance is not debatable, there are emerging views that the two fields are addressing separate issues. One side of the argument indicates that the two are inseparable. From this perspective, AI systems are fundamentally dependent on data, meaning that governance interventions aimed at regulating data flows, quality, and ownership are inherently also regulating AI outcomes. According to this viewpoint, data governance primarily concerns the input phase in the digital value chain, while AI governance addresses the outputs and impacts of algorithmic systems. Taken together, the two phases constitute a continuous governance framework spanning the entire lifecycle of data-driven technologies.
However, AI development has not linearly followed the best ideal of data governance, as AI training data sidesteps data privacy conditions and even broader intellectual property. Furthermore most current efforts to govern AI devote relatively little attention to issues related to data, including major initiatives such as the EU AI Act and US President Joe Biden’s executive order on AI. This suggests the emergence of a conceptual drift between the two fields. While closely related, AI governance is increasingly being treated as a distinct domain with its policy priorities and institutional arrangements.
Figure 1: Trends in Usage of AI and Data Governance in the Literature
Evidence of this shift can be observed in the evolution of policy discussions and terminology. We used the Google Ngram Viewer to track this evolution, as shown in Figure 1. The concept of “AI governance” appeared in policy discussions around 2018 with the regulatory concerns largely embedded within broader analysis of data governance which was already a decade-old field at the time. However, the rapid development of generative AI systems has catalysed a growing body of scholarship and policy initiatives explicitly focused on AI governance. Table 1 also highlights the key domains of each approach, which are interrelated but not overlapping.
Domain
Data Governance
AI Governance
Primary Focus
Privacy, ownership, and flow.
Algorithmic bias, safety, and agency.
Core Mechanism
Consent and encryption.
Model transparency and red teaming.
Regulatory Aim
Protecting the input (The Person).
Controlling the output (The Intelligence).
In this sense, while data governance and AI governance remain closely linked, there are growing reasons to treat them as analytically distinct policy domains. First, the scale and potential impact of AI technologies raise unique governance concerns that extend beyond traditional data regulation. Managing risks associated with autonomous systems implications for the labour market and large-scale algorithmic decision-making for human rights and political governance. Second, while data governance primarily addresses the management of data resources, AI governance increasingly focuses on the behaviour, accountability, and impact of algorithms systems themselves.
Recognising this distinction is important for designing governance frameworks that are sufficiently comprehensive without conflating different policy challenges. A critical review of decades of data governance reflects mixed performance with key success stories sitting side by side with an enormous unmet governance gap and sometimes unintended negative effects. The experience of data governance offers several important lessons for policymakers seeking to design effective oversight frameworks. We highlight five key lessons below.
What AI Governance Can Learn from Data Governance
Governance Models Reflect Geopolitical Interests
Digital governance frameworks are rarely neutral. Rather, they often reflect broader geopolitical dynamics and competing visions of the global digital order. The evolution of data governance regimes over the past decade illustrates this trend clearly, with distinct regulatory models emerging across major digital powers. The European Union has emphasised rights-based regulation focused on privacy and data protection; the United States has largely favoured market-driven innovation with limited federal oversight; while China has pursued a state-centric model that integrates data governance with national security and industrial policy.
AI governance is likely to follow a similar trajectory. Diverging governance frameworks are already emerging, shaped by differences in political institutions, economic priorities, and national security strategies. These fragmented approaches have important implications for the global digital ecosystem. In particular, the extraterritorial reach of major regulatory regimes—such as the EU’s digital regulations—can impose significant compliance and capacity burdens on developing countries that often lack the institutional and technical resources to meet complex regulatory requirements.
At the same time, fragmented governance regimes risk creating regulatory silos that encourage firms to relocate data processing and unethical AI development to jurisdictions with weaker oversight. Such dynamics can undermine global efforts to ensure responsible AI development and equitable digital governance.
In principle, a multilateral governance approach offers the most effective pathway for managing these challenges. Harmonised frameworks can reduce regulatory fragmentation and ensure that compliance expectations do not differ dramatically across jurisdictions. Yet achieving such alignment remains difficult, particularly when major powers view digital governance as an extension of strategic competition.
Despite these constraints, progress toward a coordinated global framework remains both possible and necessary. Even within a tiered or pluralistic governance landscape, international agreements can establish baseline principles and minimum standards that guide national regulation. Such global frameworks would not replace national policies but could provide a common foundation upon which countries—particularly developing economies—can build stronger and more context-appropriate AI governance systems. The African Data Policy Framework is an example of such a tiered framework that allows for national governments to set their data policies with standards established at the regional levels.
2. Regulation Alone Does Not Resolve Power Imbalances
Experience with data governance demonstrates that no regulatory model is without unintended consequences. For instance, the European Union’s General Data Protection Regulation (GDPR) strengthened global data protection standards, but it also created compliance barriers for many firms and governments in developing countries. While these pressures have encouraged the adoption of domestic data protection laws across Africa and other regions, they have also exposed variation in institutional readiness and technical capacity across the continent.
Other governance models illustrate similar trade-offs. China’s state-centered approach to digital governance has become a reference point for governments seeking stronger control over digital infrastructure and online information flows, sometimes at the expense of digital and human rights. Meanwhile, the largely market-driven approach in the United States has enabled rapid technological innovation but has also concentrated significant power in large private technology firms, creating challenges for competition and limiting the growth of African local digital enterprises.
Each model therefore delivers important benefits while simultaneously creating new layers of exclusion or barriers for certain actors. AI technologies will inevitably produce winners and losers across economies and labour markets. Ensuring that governance frameworks account for these distributional impacts will therefore be crucial. AI governance will likely require similar complementary interventions beyond policy and regulatory frameworks.
For example, policies that support workers displaced by automation—such as reskilling programmes, social protection measures, or even forms of universal basic income—may become increasingly important components of broader AI governance strategies. International cooperation will also be necessary to ensure that developing economies are not left behind in the global AI transition.
The experience of digital governance in Europe provides a useful illustration. The European Union’s broader data governance in Africa initiatives have combined regulation with investments in infrastructure, institutional capacity, and policy development. These efforts have helped strengthen governance capabilities not only within Europe but also indirectly in Africa, where they have supported the development of emerging data governance frameworks.
3. Aligning Actors in AI Governance is the most critical
Data governance debates have often been characterised by a division among key stakeholders. Public sector and civil society organisations have tended to emphasise safeguards—focusing on issues such as digital privacy, data protection, data localisation, and digital rights. In contrast, private sector actors have largely prioritised enabling conditions for innovation, including investments in digital infrastructure, expanded data sharing, and regulatory flexibility that allows new technologies to scale.
These differing priorities are not inherently problematic, as they reflect the legitimate interests and perspectives of different actors within the digital ecosystem. However, the lack of early cooperation among stakeholders has often led to fragmented policy environments, with diverse groups advocating for regulatory approaches that sometimes conflict with each other . In many jurisdictions, this dynamic has slowed the development of coherent data governance frameworks and created uncertainty for both policymakers and market participants.
AI governance has an opportunity to avoid some of these challenges by fostering earlier alignment among key actors. This multi-stakeholder approach has been a defining feature of many successful digital governance initiatives. Due to the complexity and rapid evolution of AI technologies, no single institution or sector possesses the expertise or authority necessary to regulate them effectively. Governments play a critical role in setting legal frameworks and ensuring accountability, but private sector actors hold much of the technical knowledge and infrastructure that underpin AI systems. At the same time, civil society organisations and academic institutions provide essential oversight, research, and advocacy to ensure that governance frameworks protect public interests.
Bringing these actors together early in the policy process can help reduce fragmentation, improve policy legitimacy, and create governance models that are both adaptive and practical. In the context of AI—where technological change often outpaces regulatory capacity—such collaborative governance approaches may prove particularly important for ensuring that innovation proceeds in ways that are both responsible and socially beneficial.
4. Regulating digital platforms presents a highly complex and evolving challenge
Regulating large digital platforms presents a fundamental challenge for modern governance systems due to several factors. First, many of these platform institutions command economic resources, technical expertise, and global reach that exceed those of nation-states. Their transnational operations allow them to navigate regulatory environments strategically, often avoiding litigation or shifting operations across jurisdictions with relative ease. As a result, governments frequently find themselves attempting to regulate actors whose scale and influence exceed traditional regulatory frameworks.
Second, a common ethos within the digital innovation ecosystem has long been the “build first, fix later” approach—prioritising rapid technological development while addressing risks only after they materialise. While this model has accelerated innovation, it has also exposed significant governance gaps, particularly when technologies scale globally before appropriate safeguards are in place.
For this reason, early regulatory engagement can be advantageous, even when regulatory frameworks are imperfect at the outset. Waiting for technologies to fully mature before introducing governance mechanisms may allow harmful dynamics to become deeply entrenched and difficult to reverse. However, early intervention inevitably carries trade-offs. Initial regulatory frameworks may contain design flaws, unintended consequences, or implementation challenges.
Policymakers must therefore accept that some degree of policy experimentation is unavoidable. Effective governance in rapidly evolving technological environments requires regulators to tolerate a certain level of error and adjustment. The objective should not be to design perfect rules from the outset, but rather to create governance systems that can learn and adapt over time.
The experience of data governance provides a valuable lesson in this regard: that big platform firms are difficult to control and regulate for developing countries. The leading role of the EU in strong data governance systems supports many African countries to also implement data governance frameworks. A similar global leader is required to ensure better regulation of AI emerging from these same platform firms.
Conclusion
Getting AI governance right will be critical to social and economic development in the coming decades. Despite the complexity and ambiguity of the AI policy landscape, data governance knowledge can provide significant guidance. This analysis highlights key lessons—both successes and persistent challenges—from data governance that can inform emerging approaches to AI governance.
Effective AI governance will require flexibility, continuous learning, and timely policymaking. Historically, governance frameworks have often lagged behind technological innovation. However, in the case of AI, such delays carry particularly significant consequences. Slow or reactive policy responses risk amplifying social and economic harms while missing opportunities to shape AI development in ways that advance public welfare. Proactive, adaptive governance is therefore essential to ensure that AI systems contribute to inclusive and sustainable development both now and in the future.
The Cancer-Tobacco Burden in Nigeria Following the conclusion of Cancer Prevention Week 2026, it is important to highlight one of the most preventable drivers of cancer in Nigeria: Tobacco use.
Globally, tobacco use has been identified as the leading preventable cause of cancer, responsible for 15% of all new cancer cases. Tobacco smoke contains 7,000 chemicals and at least 70 known carcinogens that directly damage human cells and trigger cancers of the lung, mouth, throat, stomach, cervix and several other organs. Tobacco-related cancers contribute significantly to mortality, with an estimated 40,000 annual cancer deaths in women and 30,000 deaths among men. These risks are compounded by the rapid emergence of new and emerging nicotine and tobacco products such as shisha, e-cigarettes, heated tobacco products and nicotine pouches.
Nigeria is already experiencing a growing cancer burden, with an estimated 120,000 new cases and over 72,000 deaths recorded annually. Although national programs have focused on diagnosis and treatment, tobacco-related prevention has garnered minimal attention, despite smoking being a significant factor in the country's increasing cancer rates. Tobacco use prevalence in Nigeria is at 3.4%, while cigarette smoking remains the most common form of consumption. The risk of tobacco-related cancer extends far beyond direct smokers. Secondhand smoke (SHS), which is the tobacco smoke inhaled by non-smokers, exposes non-smokers in homes, workplaces, markets, motor parks and public transportation to the measurable harmful impact of tobacco use.
Beyond health risks, tobacco consumption imposes significant direct and indirect economic costs on Nigerians. The entire yearly economic burden of tobacco use was estimated to be ₦634 billion (roughly USD 2.1 billion) in 2019. The direct cost of treatment was estimated at ₦GN526 billion (US$1.7 billion), nearly one-tenth of all healthcare costs in Nigeria. Indirect costs, which include employee absenteeism, reduced or lost productivity due to tobacco-related illness or disability and premature death, were estimated at NGN 107 billion (approximately US$0.4 billion). The estimated cost of treating lung cancer per case was over $3 million in 2019.
The illicit tobacco trade further worsens the problem by fuelling low prices and flooding markets with products that evade graphic health warnings. The tobacco control data reports that approximately 17 billion cigarettes are sold annually, costing Nigerians US$ 931 million in 2019 (over N300 billion) in revenue loss. This revenue loss occurs when tobacco products are smuggled, under-declared or illegally manufactured, allowing them to evade excise duties, VAT, import tariffs and tax stamp requirements mandated under Nigeria’s tax and regulatory system.
Why Tobacco Remains a Major Cancer Driver in Nigeria
Tobacco Control is not fully integrated into the cancer prevention strategy: Nigeria relies on the National Tobacco Control Act (2015), which is critically outdated due to regulatory gaps on emerging products and weak enforcement, and the National strategic cancer control plan 2023-2027. However, neither document explicitly links tobacco reduction targets to measurable cancer-reduction outcomes. This disconnect implies a lack of coordination between Nigeria's cancer and tobacco control policies, hindering the implementation of coordinated preventive measures. One implication of this disconnect is weak enforcement of extant laws. For example, despite the existence of smoke-free laws through the NTCA of 2015, its 2019 regulations, and the national tobacco control enforcement plan of 2025, WHO assessments indicate that Nigeria has not yet achieved the highest level of enforcement. This weakened enforcement maintains involuntary exposure, which contradicts cancer control efforts by increasing cancer risk among non-smokers.
There is no regulation for new or emerging tobacco and nicotine products. While Nigeria’s National Tobacco Control Act (2015) is technically outdated, it does provide a framework for tobacco regulation covering emerging nicotine products such as e-cigarettes, but shisha and heated tobacco products are currently not regulated. Hence, enforcement and regulatory clarity remain nonexistent. This regulatory gap risks creating new cohorts of tobacco and nicotine products, thereby increasing long-term cancer risks.
Prevalence of illicit trade: When untaxed or under-taxed products are widely available, tobacco remains affordable, undermining efforts to reduce consumption and structurally embedding future tobacco-related cancer incidence in the population.
Implications of Tobacco-related Cancers
Nigeria’s total economic burden attributed to tobacco was estimated at US$2.1 billion (₦2.8 trillion) in 2019.1 Tobacco-driven cancers have severe economic implications, draining household incomes through catastrophic out-of-pocket health expenses, lost productivity due to illness and premature death of the working population, and overwhelming Nigeria’s fragile healthcare system, where only 8 oncology centres serve over 200 million people. Socially, tobacco-related cancers fracture families, as caregivers sometimes abandon livelihoods, deepening poverty cycles, which further exacerbates the economic burden on households already struggling with healthcare costs and loss of income.
Recommendations:
To effectively lower the incidence of cancer, tobacco control must be repositioned and implemented as a major cancer prevention tool. In line with the National Cancer Control Plan of 2026-2030, cancer control programmes should be integrated into other health programmes, especially those addressing tobacco use, as it is a major risk factor for cancer.
The Federal Ministry of Health and the National Institute for Cancer Research and Treatment should incorporate goals related to reducing lung, cervical, oral, breast, and other cancers caused by tobacco into Nigeria's National Cancer Control Plan (NCCP) for cancer prevention, with clear and measurable indicators, and designate a body responsible for monitoring and tracking these indicators.
The Federal Ministry of Health in collaboration with the National Orientation Agency, should develop and implement “Tobacco Causes Cancer” media awareness campaigns to link tobacco use to the most prevalent cancers in Nigeria, using local data and stories of survivors as case studies.
The National Tobacco Control Committee (NATOCC) and the Tobacco Control Unit (TCU) under the Federal Ministry of Health, along with State Commissioners for Health, should strongly enforce 100% smoke-free policies in high-exposure informal settings (motor parks, markets, public transport, etc.), which have been identified as having high levels of SHS exposure, as part of a plan to reduce cancer risk.
The Federal Competition and Consumer Protection Commission (FCCPC), which is responsible for enforcing graphic health warnings, should mandate large, rotating, cancer-specific graphic warnings about lung, oral, breast, and other cancers on the front and back of all tobacco and new emerging nicotine products, including shisha packaging and vape packaging.
The government should urgently raise Tobacco Taxes to lower future cancer incidences. Excise taxes should be aligned with WHO best practices to prevent initiation among youth as well as lower future cancer incidence. High Price through increased taxes is one of the most effective tobacco-cancer prevention strategies.
The government should create a system that connects tobacco use information with the national cancer registry to calculate how much cancer is caused by tobacco each year, helping to track tobacco cancer incidence and outcomes to shape better policies.
The Nigerian government needs to immediately operationalise effective anti-illicit trading systems by enhancing borders and track-and-trace systems to prevent cheap tobacco products from undermining cancer prevention policies.
Conclusion
Repositioning tobacco control as a cancer prevention priority offers Nigeria one of the most cost-effective opportunities to save lives, reduce healthcare costs, and protect future generations. Tobacco control policies should be treated not only as a public health measure but also as a core cancer prevention strategy within national cancer planning. Stronger taxation, enforcement of smoke-free environments, regulation of emerging nicotine products, and improved surveillance systems can significantly reduce tobacco-attributable cancers in the years ahead.
Preventing cancer must start long before a diagnosis. By acting now to reduce tobacco exposure, Nigeria can prevent thousands of avoidable cancer deaths and move closer to a healthier, more productive population.
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