Sharing common measures of the environment across continents: challenges and opportunities for global studies of aging
Bibliographic record
Abstract
OBJECTIVES: Environmental exposures are modifiable risk factors for accelerated aging, but research is frequently limited to individual countries due to inconsistent exposure assessment. Cross-national data provide broader perspectives but add methodological complexities. This study evaluated spatial and temporal patterns of 5 environmental measures assigned to older adult pseudopopulations in 8 countries to highlight opportunities and challenges for aging epidemiologic studies. METHODS: Through the Gateway to Global Aging Data project, we harmonized measures of air pollution (PM2.5, NO2, O3) and natural spaces (greenspace, blue space) for longitudinal aging cohorts in Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, and the United States. Global exposure data (1990-2021) derived from satellite observations, spatiotemporal models, and deterministic simulations were linked to 10,000 population-weighted points representing adults >50 years per country. We characterized urbanicity and area-level deprivation and examined spatial/temporal patterns to inform environmental aging research. RESULTS: Exposure levels and variability differed within and between countries. Greenspace and NO2 exhibited high within-country variation (200% higher within-country vs between-country standard deviations), whereas PM2.5 and O3 had larger across-country differences (300% higher between-country than within-country standard deviation). Temporal trends were generally consistent across countries, though unique patterns emerged (e.g., increasing PM2.5 and O3 levels in India and a radical drop in greenspace in Chile). Correlations were most consistent between NO2, greenspace, and area-level deprivation, though they varied (0.1 to 0.7). DISCUSSION: Harmonized measures facilitate cross-country comparisons of environmental exposures but require careful consideration of confounding by place and time and differential measurement to ensure robust inferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".