Respiratory Health Impacts of Outdoor Air Pollution and the Efficacy of Local Risk Communication in Quito, Ecuador
Bibliographic record
Abstract
Relatively few studies on the adverse health impacts of outdoor air pollution have been conducted in Latin American cities, whose pollutant mixtures and baseline health risks are distinct from North America, Europe, and Asia. This study evaluates respiratory morbidity risk associated with ambient air pollution in Quito, Ecuador, and specifically evaluates if the local air quality index accurately reflects population-level health risks. Poisson generalized linear models using air pollution, meteorological, and hospital admission data from 2014 to 2015 were run to quantify the associations of air pollutants and index values with respiratory outcomes in single- and multi-pollutant models. Significant associations were observed for increased respiratory hospital admissions and ambient concentrations of fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2), although some of these associations were attenuated in two-pollutant models. Significant associations were also observed for index values, but these values were driven almost entirely by daily O3 concentrations. Modifications to index formulation to more fully incorporate the health risks of multiple pollutants, particularly for NO2, have the potential to greatly improve risk communication in Quito. This work also increases the equity of the existing global epidemiological literature by adding new air pollution health risk values from a highly understudied region of the world.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".