Residential greenness and mortality in oldest-old women and men in China: a longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Exposure to natural vegetation, or greenness, might affect health through several pathways, including increased physical activity and social engagement, improved mental health, and reductions in exposure to air pollution, extreme temperatures, and noise. Few studies of the effects of greenness have focused on Asia, and, to the best of our knowledge, no study has assessed the effect on vulnerable oldest-old populations. We assessed the association between residential greenness and mortality in an older cohort in China. METHODS: We used five waves (February, 2000-October, 2014) of the China Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort representative of the general older population in China. We assessed exposure to greenness through satellite-derived Normalised Difference Vegetation Index (NDVI) values in the 250 m and 1250 m radius around the residential address for each individual included in the study. We calculated contemporaneous NDVI values, cumulative NDVI values, and changes in NDVI from the start of the study over time. The health outcome of the study was all-cause mortality, excluding accidental deaths. Mortality rate ratios were estimated with Cox proportional hazards models, adjusted for age, sex, ethnicity, marital status, geographical region, childhood and adult socioeconomic status, social and leisure activity, smoking status, alcohol consumption, and physical activity. FINDINGS: Among 23 754 individuals (mean age at baseline 93 years [SD 7·5]) totaling 80 001 person-years, we observed 18 948 deaths during 14 years of follow-up, between June, 2000, and December, 2014. Individuals in the highest quartile of contemporaneous NDVI values had 27% lower mortality than those in the lowest quartile for the 250 m radius (hazard ratio [HR] 0·73, 95% CI 0·70-0·76), and 30% lower mortality for the 1250 m radius (0·70, 0·67-0·74). No clear association was observed for cumulative NDVI measurements and mortality. We did not detect an association between area-level changes in NDVI and mortality. INTERPRETATION: Our research suggests that proximity to more green space is associated with increased longevity, which has policy implications for the national blueprint of ecological civilisation and preparation for an ageing society in China. FUNDING: Bill & Melinda Gates Foundation, US National Institute on Aging, US National Institute of Health, Natural Science Foundation of China, UN Population Fund, China Social Sciences Foundation, and Hong Kong Research Grants Council.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".