Particulate Matter Air Pollution Effects on Pulmonary Tuberculosis Activation in a Semi-Desert City on the US-Mexican Border
Bibliographic record
Abstract
In this paper, we assessed the association (relative risk, RR) between the exposure to PM10 and PM2.5 (as a continuous variable and as categories of low or high pollution exposure) on the incidence of pulmonary tuberculosis (PTB) in Mexicali, Baja California, Mexico. We used a weekly, lagged multiple Poisson regression model. We observed a 10-week delayed effect for PM10 and PM2.5 in all PTB cases and in male cases with PTB. An 11-week delayed effect occurred in the female PTB cases. For all the PTB cases, the RR rose by 2.4% (95% CI: 2.1, 2.6, p<0.10) for each 10 µg/m3 increase of PM10 in the continuous exposure and by 3.6% (CI: 3.3, 4.0, p<0.05) in the high pollution exposure category, and by 3.2% (CI: 2.9, 3.4, p<0.05) for each 10 µg/m3 increase of PM2.5 in the continuous exposure and by 3.9% (CI: 3.6, 4.3, p<0.05) in the high pollution exposure category. In men, the RR rose by 2.8% (CI: 2.5, 3.1, p<0.10) for each 10 µg/m3 increase of PM10 in the continuous exposure and by 4.6% (CI: 4.2, 5.0, p<0.05) in the high pollution exposure category, and by 3.4% (CI: 3.1, 3.7, p<0.05) for each 10 µg/m3 increase of PM2.5 in the continuous exposure and by 4.2% (CI: 3.8, 4.6, p<0.05) in the high pollution exposure category. In women, the RR rose by 5.1% (CI: 4.7, 5.5, p<0.05) for each 10 µg/m3 increase of PM10 in the continuous exposure and by 5.3% (CI: 4.7, 5.8, p<0.10) in the high pollution exposure category, and by 4.3% (CI: 3.8, 4.8, p<0.10) for each 10 µg/m3 increase of PM2.5 in the continuous exposure and by 5.3% (CI: 4.8, 5.9, p<0.10) in the high pollution exposure category. PM air pollution appears to associate with the incidence of PTB in the population of Mexicali.
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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 | 0.000 |
| 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) | 0.001 | 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".