Inequality in green space distribution and its association with preventable deaths across urban neighbourhoods in the UK, stratified by Index of Multiple Deprivation
Bibliographic record
Abstract
BACKGROUND: This study investigated inequalities in the distribution of green space (GS) and the association between inequalities in amounts of GS and preventable deaths across urban neighbourhoods with different Index of Multiple Deprivation (IMD) scores in the UK. METHODS: Data on preventable deaths, IMD, percentage of grassland and woodland, urban/rural, population size, and density were sourced for each of 6791 middle-layer super output areas (MSOAs) in England, 410 MSOAs in Wales, 1279 intermediate zones (IZs) in Scotland, and 890 super output areas (SOAs) in Northern Ireland (NI). While appreciating the potential for ecological fallacy we related area-based measures of deprivation to deaths. Concentration curves, Lorenz dominance tests, and negative binomial regression models were used to analyse the data. RESULTS: In urban areas of England, Scotland, and NI, the percentage of grassland was significantly lower among the more deprived neighbourhoods (Lorenz test, p<0.0001). In England, a 1% increase in grassland area was associated with a 37% reduction in annual preventable deaths among the most deprived urban MSOAs (incidence rate ratio (IRR) 0.63, 95% CI 0.52 to 0.76). In NI and Scotland, a 1% increase in grassland area was associated with a 37% (IRR 0.63, 95% CI 0.43 to 0.91) and 41% (IRR 0.59, 95% CI 0.42 to 0.81) reduction in 5-year accumulated preventable deaths in the most deprived urban SOAs/IZs, respectively. CONCLUSIONS: Results suggest that investment in GS in urban areas may be an important public health prevention strategy. There is evidence that investments in the most deprived urban neighbourhoods where the highest inequality currently exists would see the largest effect on preventable deaths.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".