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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".