What are the factors associated with alcohol, cigarette and marijuana use among adolescents in Africa? Evidence from the Global School-based Health Survey
Bibliographic record
Abstract
OBJECTIVES: To provide comprehensive estimates of the prevalence of psychoactive substance use, specifically alcohol, cigarettes and marijuana, and factors associated with their use among school-going adolescents in 25 African countries. DESIGN AND METHODS: We used a pooled cross-sectional dataset from the publicly available Global School-based Health Survey (GSHS) from 25 African countries. We used descriptive statistics to estimate the prevalence of alcohol, cigarette and marijuana use as well as their dual use among adolescents aged 11-16 years. Additionally, we used logistic regressions to model factors associated with the use of each substance, with adjusted Odds Ratios (aORs) and their 95% Confidence Intervals (CIs) as the measures of association. SETTING AND PARTICIPANTS: The study focused on school-going adolescents aged 11-16 years in 25 African countries that have conducted the GSHS between 2003 and 2017. OUTCOME MEASURES: The key outcome measure is the proportion of adolescents who have used a specific substance in the past 30 days. These substances include: (1) alcohol, (2) cigarettes, (3) marijuana, (4) alcohol and cigarettes, (5) cigarettes and marijuana and (6) alcohol and marijuana. RESULTS: The prevalence of alcohol use among adolescents was 9.5% (95% CI 8.4% to 10.7%), that of cigarette smoking was 6.2% (95% CI 5.0% to 7.6%), and it was 3.4% (95% CI 2.7% to 4.2%) for marijuana. The prevalence of dual use of alcohol and cigarettes was 3.1% (95% CI 2.4% to 3.9%), that of alcohol and marijuana was 2.0% (95% CI 1.5% to 2.5%), and it was 1.4% (95% CI 1.1% to 1.8%) for cigarettes and marijuana. The prevalence of cigarette smoking was significantly higher among boys than girls. However, there was no statistically significant difference in the prevalence of alcohol or marijuana by sex. Having parents who smoke any tobacco products, being bullied, missing school without permission and experiencing sadness and hopelessness were positively associated with being a current user, irrespective of substance type. CONCLUSIONS: There is a need for comprehensive, current data on substance use among adolescents. Interventions that tackle bullying, reduce school absenteeism, build resilience against difficult situations and increase self-efficacy to resist the use of these substances have the potential to curb substance use among adolescents in Africa.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".