Secondhand tobacco smoke exposure in selected public places (PM<sub>2.5</sub>and air nicotine) and non-smoking employees (hair nicotine) in Ghana
Bibliographic record
Abstract
BACKGROUND: Secondhand tobacco smoke (SHS) exposure is a global public health problem. Ghana currently has no legislation to prevent smoking in public places. To provide data on SHS levels in hospitality venues in Ghana the authors measured (1) airborne particulate matter <2.5 μm (PM(2.5)) and nicotine concentrations and (2) hair nicotine concentrations in non-smoking employees. Quantifying SHS exposure will provide evidence needed to develop tobacco control legislation. METHOD: PM(2.5) was measured for 30 min in 75 smoking and 13 non-smoking venues. Air nicotine concentrations were measured for 7 days in 8 smoking and 2 non-smoking venues. Additionally, 63 non-smoking employees provided hair samples for nicotine analysis. RESULT: Compared to non-smoking venues, smoking venues had markedly elevated PM(2.5) (median 553 [IQR 259-1038] vs 16.0 [14.0-17.0]μg/m(3)) and air nicotine (1.83 [0.91-4.25] vs 0.03 [0.02-0.04]μg/m(3)) concentrations. Hair nicotine concentrations were also higher in non-smoking employees working in smoking venues (median 2.49 [0.46-6.84] ng/mg) compared to those working in non-smoking venues (median 0.16 [0.08-0.79]ng/mg). Hair nicotine concentrations correlated with self-reported hours of SHS exposure (r=0.35), indoor air PM(2.5) concentrations (r=0.47) and air nicotine concentrations (r=0.63). CONCLUSION: SHS levels were unacceptably high in public places in Ghana where smoking is allowed, despite a relatively low-smoking prevalence in the country. This is one of the first studies to ascertain SHS and hair nicotine in Africa. Levels were comparable to those measured in American, Asian and European countries without or before smoking bans. Implementing a comprehensive smoke-free legislation that protects workers and customers from exposure to secondhand smoke is urgently needed in Ghana.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".