Comparison of satellite-based exposure metrics to assess exposure to residential greenness in a nationwide cohort of coronary artery bypass graft (CABG) surgery patients in Israel
Bibliographic record
Abstract
PDS 70: Green space, Johan Friso Foyer, Floor 1, August 28, 2019, 1:30 PM - 3:00 PM Introduction/Aims: Greenness has been associated with health benefits but the impacts of specific vegetation forms and mechanisms remain unclear. We evaluated three satellite-derived measures of residential greenness among a cohort of Coronary Artery Bypass Grafting (CABG) patients to assess associations with physical activity 1-year post surgery. The Normalized Difference Vegetation Index (NDVI) is widely used in epidemiological studies, but varies with vegetation density, soil color/moisture, and has limited ability to capture spatial heterogeneity in urban settings. The Soil-adjusted Vegetation Index (SAVI), corrects for soil brightness when vegetative cover is low, while Linear Spectral Unmixing (LSU), measures the relative contribution (%) of different sources, and estimates the percent of greenness coverage. Methods: NDVI, SAVI and % of green spaces in a 300m radius surrounding homes of (N=846) patients were derived from Landsat images at 30m spatial resolution. Logistic regression models estimated the association between the exposure measures and physical activity 1-year post surgery. Results: Presence of urban area vegetation in Israel was reflected by NDVI ~>0.21 based upon manual visual assessment, corresponding to the highest NDVI quartile (Q-4) and a mean of 30% greenness in a buffer (Q-4 using LSU). NDVI was more highly correlated with SAVI (r=0.87) than LSU (r=0.66). Compared to NDVI, exposures estimated by SAVI and LSU resulted in 21.5% and 35.5% of patients changing exposure quartiles. Associations with physical activity for NDVI and SAVI respectively resulted in an adjusted odds ratio (95%CI) per interquartile range difference of 1.67 (1.28-2.19) and 1.59 (1.28-2.10) for NDVI and SAVI, respectively. Comparing (with LSU) patients in areas with >30% greenspace resulted in an adjusted odds ratio of 2.12 (1.30-3.46). Conclusions: Use of alternate greenness metrics (e.g. SAVI) may improve precision in arid areas. In a cohort of CABG patients, residential greenness was associated with increased physical activity at 1 year post-surgery.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".