Long-term exposure to PM2.5 and cardiorespiratory mortality: an ecological small-area study in five cities in Colombia
Bibliographic record
Abstract
Long-term exposure to the fine particulate matter (PM2.5) is a risk factor for cardiorespiratory mortality. However, little is known about its distribution and health impact in large cities in low-middle-income countries where population exposure has increased during the last decades. This ecological study evaluated the association between PM2.5 concentration and adult cardiorespiratory mortality at the intraurban census sector (CS) level of Colombia's five most populated cities (2015-2019). We estimated incidence rate ratios (IRR; per 5µg/m3) by fitting negative binomial regressions to smoothed Bayesian mortality rates (BMR) on PM2.5 predicted from land use regression (LUR) models, adjusting for CS demographic structure, multidimensional poverty index, and spatial autocorrelation. CS median PM2.5 ranged from 8.1µg/m3 in Bucaramanga to 18.7µg/m3 in Medellín, whereas Bogotá had the highest variability (IQR = 29.5µg/m3) and cardiorespiratory mortality (BMR = 2,560 per 100,000). Long-term exposure to PM2.5 increased cardiorespiratory mortality in Bucaramanga (IRR = 1.15; 95%CI: 1.02; 1.31), without evidence of spatial clustering, and cardiovascular (IRR = 1.06; 95%CI: 1.01; 1.12) and respiratory (IRR = 1.07; 95%CI: 1.02; 1.13) mortality in Medellín. Cardiorespiratory mortality spatially clustered in some Colombian cities and was associated with long-term exposure to PM2.5 in urban areas where the LUR models had the highest predictive accuracy. These findings highlight the need to incorporate high-quality, high-resolution exposure assessments to better understand the health impact of air pollution and inform public health interventions in urban environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".