Relationships among green space, ambient fine particulate matter, and cancer incidence in Taiwan: A 16-year retrospective cohort study
Bibliographic record
Abstract
Green space and air pollution have been recognized as vital health determinants. There is a paucity of studies examining the interplay between green space, fine particulate matter (PM2.5), and the incidence of specific cancers. We aimed to explore the contributions of green space and ambient PM2.5 to the risk of specific cancers in terms of the most common cancers based on incidence or mortality rate in Taiwan and to ascertain the interaction between green space and PM2.5 and their role in cancer risk. This retrospective longitudinal cohort study included 407,415 participants. Data were obtained from the 2000–2015 Mei Jau Health Examination Database linked to the Taiwan Cancer Registry and Causes of Death datasets. All participants were aged ≥20 years and had no history of cancer. The environmental exposure were the normalized difference vegetation index (NDVI) and the 2-year average PM2.5 at baseline. Multivariate adjusted hazard ratios (HRs) were calculated using Cox proportional hazards models. We adjusted for covariates including demographics, anthropometrics, comorbidities, health behaviors, biochemical data, and environmental factors. During a median follow-up of 10.37 years, 11,576 cancer cases were reported. PM2.5 exposure increased the risk of all cancers (HR: 1.11, [95% CI: 1.06–1.15]), stomach cancer (HR: 1.27, [1.02–1.58]), endocrine gland cancer (HR: 2.13, [1.39–3.26]), breast cancer (HR: 1.12, [1.03–1.22]), and lung cancer (HR: 1.12, [1.01–1.24]). An increase in NDVI reduced the risk of prostate cancer (HR: 0.93, [0.88–0.99]) and lung cancer (HR: 0.95, [0.91–0.99]). NDVI influenced the incidence of prostate and all cancers by reducing PM2.5 concentrations. Long-term PM2.5 exposure is associated with an increased risk of some types of cancers. In contrast, an increase in environmental green space exposure is associated with lowering of the risk of prostate and lung cancer.
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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 | 0.001 | 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".