ArticleInfection and drug resistance2025
The Association of Latent Tuberculosis Infection with Air Pollutant Exposure, Meteorological and Other Factors: A Retrospective Study in Eastern China of College Students.
Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- The association between exposure to air pollutants and latent tuberculosis infection prevalence in the elderly population: a population-based cross-sectional study from China.Journal of global health · 2026Article
- Analysis of the current status and influencing factors of latent tuberculosis infection among new students in Taishan District in 2025.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The associations between meteorological factors, air pollutant indicators, and latent tuberculosis infection (LTBI) have not yet been confirmed. This study aimed to assess the association of meteorological factors, air pollutant indicators, and other factors with LTBI among college students. Methods: We selected 5,193 freshmen randomly who originated from key tuberculosis areas in nine colleges in Nanjing. We ranked the importance of independent variables using Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest models. We then conducted a multi-model analysis after incorporating them into the prediction model. In addition, we adopted a calibration curve to determine the quality of the model. A nomogram was used to evaluate the possibility of using multiple models to predict LTBI risk. Results: We found that higher outdoor PM Conclusion: Higher outdoor PM
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