Association of Fine Particulate Matter and Residential Greenness With Risk of Pulmonary Tuberculosis Retreatment: Population-Based Retrospective Study
Bibliographic record
Abstract
Background: The evidence on the association of fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) with pulmonary tuberculosis (PTB) retreatment is limited. There are no data on whether greenness exposure protects air pollution-related PTB retreatment in patients with prior PTB. Objective: In a population-based retrospective study, we aimed to investigate the influence of PM2.5 and residential greenness on the risk of PTB retreatment. Methods: A total of 26,482 patients with incident PTB, registered in a mandatory web-based reporting system between 2012 and 2019 in Zhengzhou, China, were included in the analysis. The exposure to PM2.5 was assessed based on the China High Air Pollutants dataset, and the level of greenness was estimated using the Normalized Difference Vegetation Index (NDVI) values. The associations of PTB retreatment with exposure to PM2.5 and greenness were evaluated, respectively, considering the local socioeconomic level indicated by the nighttime light index. Results: Among the 26,482 patients (mean age 46.86, SD 19.52 years) with a median follow-up time of 1523 days per patient, 1542 (5.82%) PTB retreatments were observed between 2012 and 2019. Exposure to PM2.5 was observed to be significantly associated with the increased risk of PTB retreatment in fully adjusted models with a hazard ratio of 1.97 (95% CI 1.34-2.83) per 10 μg/m3 increase in PM2.5. Patients living in the regions with relatively high quartiles of NDVI values had a 45% lower risk of PTB retreatment than those living in the regions with the lowest quartile for the 500 m buffers (hazard ratio 0.55, 95% CI 0.40-0.77). Such a protective effect of residential greenness was more pronounced among patients living in lower nighttime light areas. The strength of the association between PM2.5 exposure and the risk of PTB retreatment was attenuated by greenness. No significant association was observed between NDVI and the incidence of drug resistance. Conclusions: Long-term exposure to PM2.5 might be a risk factor for PTB retreatment, while an increased level of residential greenness was found to be associated with reduced risks of PTB retreatment. Our results suggest strengthening the control of ambient air pollution and improving residential greenness may contribute to the reduction of PTB retreatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".