Factors affecting tobacco smoking in Ethiopia: evidence from the demographic and health surveys
Bibliographic record
Abstract
BACKGROUND: Tobacco smoking is growing at an alarming rate in the developing world and sub-Saharan Africa. Although Ethiopia has a relatively low rate in the region, it is not immune to the tobacco epidemic. The government of Ethiopia passed an anti-tobacco bill in 2015 that includes measures governing tobacco consumption, advertising, packaging, and labeling. To effectively address the challenge of tobacco control, the government should consider a number of aspects of tobacco production and consumption, such as local production in rural areas, as well as the complementarity nature of tobacco and khat use. METHODS: Using the World Bank's Demographic and Health Surveys (2011 and 2016), this paper analyzes the key determinants of tobacco smoking in Ethiopia, emphasizing possible differences in various social contexts, across regions. More specifically, we assess the association between khat use and tobacco smoking while controlling for various observed individual-level, household-level, and community-level covariates. Using GPS data, we are able to capture the neighboring effects of smoking behavior in community clusters bordering other administrative regions as well as differences in smoking patterns between lowland and highland residents. We utilize a multilevel modeling framework and use a two-stage residual inclusion estimation method that accounts for the endogeneity of khat and tobacco use. RESULTS: The results suggest that chewing khat and geographic regions are statistically significant determinants of tobacco smoking even after controlling for various socioeconomic and demographic factors. Altitude information analysis suggests that people living in lowlands are more likely to smoke compared to those living in highland areas. Additional analysis including interactions between regions and khat use indicate wide inter-regional variations in tobacco smoking by khat users. We also extend our analysis by interacting khat use with religious adherence. Results indicate a wide variation in tobacco smoking by khat chewers across different religious groups. CONCLUSIONS: To effectively control tobacco smoking of the diverse communities in Ethiopia, policymakers should consider a multi-pronged policy approach that combines various policy tools that account for regional variation, the local social contexts, as well as the complementary nature of smoking and khat chewing practices.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".