The role of green areas in modifying heat-related circulatory and respiratory hospital admissions in Brazil
Bibliographic record
Abstract
Heat exposure is a growing public health concern, particularly in tropical regions like Brazil. However, the role of green spaces in mitigating heat-related health risks remains unclear. This study investigates the association between heat and circulatory and respiratory hospital admissions in Brazil (2008-2018) and assesses the potential mediating effect of different green space metrics. We conducted a three-stage analysis using a time-stratified quasi-Poisson regression model to estimate state-level heat-health associations. A meta-analysis was performed to obtain pooled national effects, followed by a meta-regression to assess the influence of green spaces. We considered four green metrics: NDVI, urban green area, natural forest, and total green area. Our results showed that extreme heat (99th percentile) significantly increased hospital admissions, with a 1 % increase in circulatory admissions for the overall population (RR: 1.01, 95 % CI: 1.00-1.03) and a 26% increase in respiratory admissions (RR: 1.26, 95 % CI: 1.22-1.30). Moderate heat (90th percentile) also increased respiratory admissions (RR: 1.13, 95 % CI: 1.11-1.15) but exhibited protective effects for specific subgroups in circulatory admissions. Regarding the mediating role of green areas, our findings suggest that while NDVI, urban green areas, and total green coverage mitigate heat-related health risks, their protective effects vary by age and sex. Under extreme heat conditions, a one standard deviation (SD) increase in urban green area was associated with a 0.6% (95%CI: 0.1 % - 1.1 %) reduction in circulatory admissions among individuals aged 65 and older. For heat-related respiratory admissions, under moderate heat conditions, a one SD increase in NDVI corresponded to a 3.4% (95 %CI: 2.1% - 4.7%) reduction in hospitalizations for the general population. Our findings suggest that while green areas can mitigate heat-related hospitalizations, their effectiveness depends on vegetation type, spatial distribution, and social vulnerability. These results emphasize the need for climate-adaptive urban planning and heat mitigation strategies tailored to local environmental and sociodemographic contexts.
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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 | 0.000 | 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 | 0.000 |
| 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".