Green space exposure and childhood health – a question of moderation or mediation?
Bibliographic record
Abstract
S07: Of moderators and mediators: Complex relationships between greenness, air pollution, noise, and health behaviors in driving health outcomes, Beatrix Theater, August 27, 2019, 10:30 AM - 12:00 PM Background. An increasing number of studies indicate that exposure to urban greenspace has various positive health effects, including improved birth outcomes and childhood development. Different pathways are suggested for the positive health effects, including regulating ecosystem services, such as reduction of air pollution and noise. Methods. Using a small subset of our study population in the metropolitan area of Vancouver, Canada, we assessed multiple spatially co-varying environmental exposures during pregnancy up to birth. To analyse whether any positive effect of greenspace exposure on birthweight could be mediated by various pathways of influence, we used structural equation modelling where air pollution, noise, and blue spaces were entered as a latent variable. Two different metrics for assessing greenspace exposure were examined– the Normalised Difference Vegetation Index (NDVI), based on remote sensing images of 250m resolution (MODIS), and a segmented index developed from Google Street View (GSV), indicating different types of vegetation, such as trees, grass, and flowers. Air pollution was derived from land use regression models of NO2 and noise was assessed from existing predictive models. Blue space was derived from GSV as the sum of the proportion of images reflecting lakes, rivers, waterfalls, and sea. Premature births were excluded and all models were adjusted for gestational age. Results. We found no direct effect of green space when using the average value of the GSV-index, but in the models using NDVI we found a small direct effect (total effect estimate=0.061, SE=0.033) and a non-significant indirect effect (p=0.070). Conclusions. Our results suggest that any positive effect on birth weight of green space exposure may be due to a moderation of harmful exposures, rather than a mediation effect. Future studies should include larger sample sizes as well as area-level social indicators. Further analysis of different effects depending on greenspace metric is also warranted.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".