Socioeconomic and demographic determinants of tobacco use in Kenya: A secondary data analysis of findings from the Kenya Demographic and Health Survey 2022
Bibliographic record
Abstract
INTRODUCTION: Tobacco use is a major public health crisis in Kenya, leading to over 6000 deaths annually. With a significant number of young people and adults using tobacco, the nation faces a rising health burden. The Kenyan government has implemented educational programs to curb consumption. This study analyzes data from the 2022 Kenya Demographic and Health Survey (KDHS) to assess changes in tobacco use from 2014 to 2022 and identify key demographic and socioeconomic determinants. METHODS: This study is a secondary data analysis of the 2022 Kenya Demographic and Health Survey (KDHS), a nationally representative survey of 46609 adults (aged 15-54 years). Data access was through the MEASURE DHS platform, ensuring ethical handling. A logistic regression model was used to estimate odds ratios of tobacco use, adjusting for socioeconomic and demographic factors. The analysis accounted for the survey's complex design using survey weights and clustering and was conducted in Stata 17 software. RESULTS: Between 2014 and 2022, overall tobacco use declined. Among men, prevalence dropped from 17.3% to 12.81% (25.95% decrease), and among women from 3.10% to 2.64% (14.84% decrease). While women's smoking slightly increased (0.18-0.35%), their smokeless use decreased (0.93-0.77%). Tobacco use was linked to age, marital status, residence, region, education level, and gender. Men's tobacco use odds increased with age, with those aged 20-24 years nearly five times more likely to use tobacco than those aged 15-19 years (AOR=4.44; 95% CI: 4.44-4.44). Married men were less likely to use tobacco than divorced, separated, or widowed men. CONCLUSIONS: The observed declines in tobacco use, especially among males, suggest that current tobacco control efforts are positively impacting public health. Given the financial strain of health costs, preventive interventions are crucial. Research on socioeconomic and demographic factors can guide targeted behavioral change strategies. Continued policy measures like increased tobacco taxation, raising the legal sale age, and enforcing advertising bans and smoke-free policies remain essential to further reduce tobacco's health burden in Kenya.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".