Near-Road Exposure to Air Pollution and Allergic Rhinitis: A Cross-Sectional Study among Vendors in Dakar, Senegal
Bibliographic record
Abstract
Introduction: The work environment is one of the main causes of allergic rhinitis. The majority of vendors in Dakar work in places close to roads that are very frequented by vehicles, exposing them to increased air pollution. The study determined the prevalence of allergic rhinitis and its associated risk factors in these vendors. Methods: This was a cross-sectional survey based on a structured questionnaire, conducted among vendors in the neighborhoods of HLM, Medina and Petersen in Dakar, Senegal. A total of 200 vendors were interviewed. Symptoms of allergic rhinitis were defined as the simultaneous presence of rhinorrhea, nasal congestion and sneezing in the absence of respiratory infection. A logistic regression analysis was performed to determine the relationship between socio-demographic characteristics, occupational factors, and allergic rhinitis. Results: Results of the study show a prevalence of 43% of allergic rhinitis among vendors. Multivariate analysis showed that the independent factors associated with allergic rhinitis in these vendors were age [OR: 3.28 (1.02 - 10.51)], working area [OR: 8.31 (2.39 - 28.95)], exposure to multiple sources of pollution [OR: 4.08 (1.43 - 11.63)], and recurrent cold [OR: 4.39 (1.15 - 16.85)]. Conclusion: The prevalence of allergic rhinitis was high among vendors in Dakar. Our data suggest that exposure to air pollution at the workplace in vendors could lead to allergic rhinitis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".