Socio-economic and demographics determinants of tobacco use in Kenya: findings from the kenya demographic and health survey 2014
Bibliographic record
Abstract
INTRODUCTION: Every year, more than 6,000 Kenyans die of tobacco related diseases (79 men and 37 women die per week), while more than 220,000 children and more than 2,737,000 adults continue to use tobacco each day. Some suggest that these numbers will rise without concerted efforts to strengthen the implementation of tobacco control measures. To date, there remains much to be learned about what contributes to tobacco consumption in Kenya. This study analyses the socio-economic and demographic determinants of tobacco use in Kenya. METHODS: To analyze the determinants of tobacco use in Kenya, this study uses the 2014 Kenya Demographic and Health Survey. A logistic regression is used to estimate the probability of an individual smoking, given a set of socio-economic and demographic characteristics. RESULTS: Results suggest that the overall smoking and smokeless prevalence rate is 17.3% and 3.10% respectively among men. Women have low rates with smoking and smokeless prevalence standing at 0.18% and 0.93% respectively. However, for both genders, tobacco use is influenced by age, marital status, residence, region, educational status and gender. CONCLUSION: Socio-economic, demographic and geographic disparities on tobacco use should be explored in order to ensure prudent allocation of resources used for tobacco control initiatives. Allocation of resources for tobacco control including monitoring advertisements, sales to underage persons and general distribution of human resource for tobacco control should be based on socio-economic and demographic dynamics.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".