Prevalence of Smoking among Men in Ethiopia and Kenya: A Cross-Sectional Study
Bibliographic record
Abstract
While tobacco use remains the largest single cause of premature death in the industrialized countries, low-and-middle income countries are also experiencing a rising burden of the tobacco epidemic and are making various programmatic efforts to tackle the issue. Evidence-based policy making is critical to the long-term success of tobacco intervention programs and is reliant on regular monitoring of the trends and prevalence rates of tobacco use though population-based surveys, which are sparse for countries in eastern Africa. Therefore, in the present study we aimed to (1) estimate the trends in the prevalence of self-reported smoking status; and (2) explore the sociodemographic factors associated with smoking among adult men in Ethiopia and Kenya. Methods: Subjects were 26,919 adult men aged between 15 and 59 years from Ethiopia and Kenya. Nationally representative cross-sectional data on self-reported smoking and relevant sociodemographic indicators were collected from the Demographic and Health Surveys (DHS) conducted in these two countries. Data analysis was performed by descriptive, bivariate, and multivariable methods. Results: In Ethiopia, the prevalence rate of smoking increased from 8.5% in 2005 to 11.7% in 2011. While in Kenya, the rate declined albeit slowly from 22.9% in 2003 to 18.8% in 2008–2009 and 17% in 2014. The prevalence was significantly different in urban and rural areas. In majority of the surveys, prevalence of smoking was highest in the age group of 25–34 years. The prevalence of smoking varied widely across several socioeconomic characteristics. Conclusions: The findings indicate a high rate of smoking among men especially in urban areas, and call for policy actions to address the socioeconomic factors as a part of the policy to strengthen tobacco-control efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".