The study of the correlation between pollutants and their interactions on the incidence of tuberculosis in Changping District based on distribution models
Bibliographic record
Abstract
BACKGROUND: Air pollution as a potential risk factor, mediator or moderator of TB incidence. Most of the studies focused on the provincial and urban areas, while Changping District, as a high incidence area and county of tuberculosis in Beijing, its relationship with pollutants is not clear, so this study aims to investigate the associations of air pollutants and their interactions on the number of new TB cases in Changping District, Beijing, China. METHODS: Data from Beijing Changping Institute for Tuberculosis Prevention and Treatment on monthly new TB cases from 2014 to 2022. Distributed lag nonlinear models are used to examine the associations of each 10 µg/m3 increase in PM10 and NO2 concentrations, along with extreme exposure episodes, with TB incidence. RESULTS: The cumulative relative risk (RR) of increasing pollutant concentrations is positively correlated with lag months for PM10, but the opposite result is observed for NO2. In terms of long and short-term relationships, increased PM10 concentration and extremely low NO2 concentration are associated with long-term hazardous for most subgroups, while extremely high PM10 and NO2 concentrations are associated with short-term hazardous. CONCLUSION: Increased concentrations of PM10 and NO2 having long-term or short-term effects on populations. Therefore, strengthening air quality monitoring and control is of great significance for the prevention of tuberculosis in Beijing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".