Respiratory effects of outdoor air pollution related to road traffic: Case of saleswomen working near Dantokpa’s market highways in Cotonou, Benin
Bibliographic record
Abstract
Introduction: Sales around roads, a common activity mainly carried out by women in African urban areas, exposes these professionals to outdoor air pollution (OAP). The study aimed to compare the frequency of respiratory symptoms and lung function in saleswomen exposed to OAP with controls. Materials and Methods: In a quasi-experimental longitudinal study here-elsewhere, we compared 115 saleswomen exposed to OAP linked to heavy traffic, with 115 matched controls. The intensity of traffic on the two sites and the daily sales time of the saleswomen were assessed. Respiratory symptoms were collected by questionnaire and pulmonary function assessed by spirometry. Symptom-free survival over 12 months was described by Kaplan-Meier and the comparison by log rank. Results: The two groups corresponded to the matching criteria. The saleswomen were exposed to higher levels of traffic intensity than the controls (p<0.000). Survival was better without cough, cold and sputum in controls. The spirometric parameters were more altered in saleswomen: FVC (p<0.001), FEV1 (p<0.001), DEP (p=0.0001), DEM 25-75 (P<0.0001). The latter reported longer durations of cough (p<0.000), cold (p=0.012), sputum (p=0.002) and dyspnea (p=0.040). Conclusion: Saleswomen exposed to road traffic presented more spirometric alterations and respiratory symptoms. It is urgent to ensure the awareness of these professionals on the health risks of this exposure and actions to reduce the OAP linked to traffic. The reinforcement of the environmental police is necessary as well as intervention studies for risk mitigation. If nothing is done, the city will face worrying prevalence of chronic, preventable respiratory disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".