Understanding research gaps and priorities for tobacco harm reduction in low-income and middle-income countries
Bibliographic record
Abstract
Introduction Tobacco use is among the leading preventable causes of premature death worldwide, with disproportionate effects in low-income and middle-income countries (LMICs). Global tobacco control efforts have shown inconsistent results, highlighting the need for innovative approaches, such as tobacco harm reduction (THR), to complement existing strategies. We aimed to identify gaps in THR research in the global and LMIC contexts. Methods We conducted a bibliometric review, using Scopus, to identify articles addressing THR published from January 2014 to August 2024. Research output was categorized by product type, geographical focus, volume of research output, and funding sources. A narrative synthesis was performed to outline research gaps and propose a strategic research agenda. Results THR research was dominated by e-cigarettes, primarily from high-income countries. Citations per 1 million smokers were highest in New Zealand (1,128.0), United Kingdom (634.3), United States (466.4), Australia (432.1), Switzerland (177.1), and South Korea (132.9). By contrast, rates were very low across Asia, Africa, and South America (range 1.8–53.5). Over the study period, research output increased only for e-cigarettes and heated tobacco products. Publicly funded research tended to focus on public health concerns, while private-sector research focused on product safety and efficacy. Conclusions THR research remains disproportionately concentrated in high-income countries and reflects a clear divergence between public and private research agendas. More research is needed to evaluate the long-term impacts, affordability, and real-world effectiveness of THR products using rigorous and standardized methodologies in diverse settings. Strengthening the evidence base in LMICs will be essential for developing affordable, accessible, and acceptable THR strategies tailored to local needs.
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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 | 0.064 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".