Tobacco use in Haiti: findings from demographic and health survey
Bibliographic record
Abstract
INTRODUCTION: Although tobacco has harmful effects on the physical and mental health of individuals, its use remains significant, according to the World Health Organization. To understand this phenomenon, studies have been carried out in many countries around the world, while in Haiti where more than 5,000 people die each year due to tobacco use, little is known about the use of this substance. The aim of this study was to examine the prevalence and the factors associated with tobacco use in Haiti. METHODS: We used data from the 2016/17 Haitian Demographic Health Survey. Both descriptive and multivariate analyses were conducted using STATA 16.0 software to assess the prevalence and identify factors associated with tobacco use. Results were reported as adjusted odds ratios with 95% confidence intervals. Statistical significance was declared at p < 0.05. RESULTS: The prevalence of tobacco use was estimated at 9.8% (95% CI: 9.2-10.4) among men and 1.7% (95% CI: 1.5-1.9) among women. Although the prevalence of tobacco use was low among young people, it increased with age. Respondents aged 35 and above, with no formal education, non-Christians, divorced/separated/widowed, from poorest households, rural areas, "Aire Métropolitaine de Port-au-Prince" region, with high media exposure had a higher likelihood of tobacco use. CONCLUSION: The low prevalence of tobacco use among Haitian women and youth represents a public policy opportunity to prevent these vulnerable groups from starting smoking. Adult male smokers should also be targeted by appropriate policy to reduce the different health burdens associated with tobacco, both for the smokers and other people they may expose to passive smoking. Government and health sector stakeholders, along with community leaders, should create and enforce awareness strategies and rules to control advertisements that encourage irresponsible and health-risky consumption behaviors.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".