Tobacco use and associated risk factors in Burkina Faso: results from a population-based cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Tobacco is a leading preventable cause of non-communicable diseases (NCDs). Studies characterizing the prevalence of tobacco use in low-income countries are lacking. This study describes the prevalence of tobacco use in Burkina Faso and its associated factors. METHODS: Data from the 2013 Burkina Faso World Health Organization (WHO) Stepwise approach to Surveillance (STEPS) were analyzed. The prevalence of any tobacco product use, cigarette smoking, and other tobacco use was calculated. Logistic regression analyses identified factors associated with tobacco use. Overall, 4691 people were included in this analysis. RESULTS: The prevalence of any tobacco use was 19.8% (95% CI: 18.4-21.2). Tobacco use was higher for men (29.2% [27.0-31.5]) than women (11.8% [10.3-13.4]). The prevalence of smoked tobacco was 11.3% (10.3-12.4), with a significantly higher prevalence among men (24.5% [22.1-27.0]) than women (0.1% [0.01-0.3]). The overall prevalence of other tobacco use was 8.9% (7.4-10.7), with lower values for men (5.6% [4.1-7.2]) compared to women (11.7% [9.4-14.1]). Tobacco smoking among men was significantly associated with an increased age and alcohol consumption. The analysis of risk factors for other tobacco use stratified by gender showed that age, education, residence, and alcohol consumption were significantly associated with consumption for women, and age and alcohol consumption for men. CONCLUSION: Tobacco use is common in Burkina Faso. To effectively reduce tobacco use in Burkina Faso, a comprehensive tobacco control program should consider associated factors, such as gender, age, and alcohol consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".