Air Pollution Related to Traffic and Chronic Respiratory Diseases (Asthma and COPD) in Africa
Bibliographic record
Abstract
Introduction: Chronic respiratory diseases (CRD) are obvious effects of air pollution and the third reason of death in developing countries. In Africa, air pollution from road traffic is one of the main causes of poor air quality. We set out to systematically review existing published researches on traffic related to air pollution and CRD, particularly asthma and Chronic Obstructive Pulmonary Disease (COPD) in Africa. Methods: A literature search of PubMed, Scholar and LISSA databases, published journals, reference articles, published up to 31 December 2016, has been done by using a research strategy procedure. Texts were reviewed for inclusion. Studies were included if they met the following criteria: 1) the relationship between asthma or COPD with ambient air pollution related to road traffic was studied and 2) the population included people from Africa or lived in Africa. Articles written in English and French were included. Results: Fifty-five articles were selected in this review, of which twenty-seven were on air pollution and CRD in Africa. The proximity of the residence or workplace to the traffic is associated with an increased risk of asthma with a dose-response relationship. The estimated prevalence of COPD varies between 2.7% and 38.5%. Conclusion: There is little research on traffic related to air pollution and CRD in Africa. Strategies to reduce traffic related to air pollution in African cities have been proposed in order to have a healthier ambient air.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".