The estimated benefits of increasing cigarette prices through taxation on the burden of disease and economic burden of smoking in Nigeria: A modeling study
Bibliographic record
Abstract
BACKGROUND: Globally, tobacco consumption continues to cause a considerable burden of preventable diseases. Although the smoking prevalence in Nigeria may be declining over the last years, the absolute number of active smokers remains one of the highest in Africa. Little is known about the disease burden and economic costs of cigarette smoking in Nigeria. Consequently, there is an evidence gap to inform the design and implementation of an effective policy for tobacco control. METHODS: We applied a microsimulation model to estimate the burden attributable to smoking in terms of morbidity, mortality, disability-adjusted life-years (DALYs), and direct medical costs and indirect costs (e.g., productivity loss costs, informal caregivers' costs). We also modeled the health and economic impact of different scenarios of tobacco price increases through taxes. RESULTS: We estimated that smoking is responsible for approximately 29,000 annual deaths in Nigeria. This burden corresponds to 816,230 DALYs per year. In 2019, the total economic burden attributable to tobacco was estimated at ₦ 634 billion annually (approximately U$D 2.07 billion). If tobacco cigarettes' prices were to be raised by 50% through taxes, more than 30,000 deaths from smoking-attributable diseases would be averted in 10 years, with subsequent savings on direct and indirect costs of ₦597 billion and increased tax revenue collection of ₦369 billion. CONCLUSION: In Nigeria, tobacco is responsible for substantial health and economic burden. Increasing tobacco taxes could reduce this burden and produce net economic benefits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".