Characterizing adaptive capacity for the future heat-related cardiovascular morbidity burden in U.S. Metropolitan areas
Bibliographic record
Abstract
• By 2099, additional 36 days are projected above the min hospitalization temperature. • By 2099, excess heat-CVD burden is expected to increase at least 20-fold. • Risk scores are built for health, environment, economic/demographic, infrastructure. • Health risk scores explain the most model variance for current burden. • Environment risk scores predict the largest portion of change in burden over time. Exposure to excess heat is linked to increased risks of cardiovascular diseases (CVD). As temperatures increase globally, it is crucial to examine the potential increase in excess heat-related CVD (xHEAT-CVD) burden to inform strategies for adaptation. This study aimed to identify the contextual factors associated with future xHEAT-CVD burden among older adults across eighty U.S. metropolitan statistical areas (MSAs). The MSA-specific xHEAT-CVD risk for adults ≥ 65 years was estimated using hospitalization and temperature data from 2000 to 2017, with excess heat defined as temperatures above the minimum hospitalization percentile (T MHP ). Future xHEAT-CVD hospitalizations were estimated using temperature projections for 2025–2054, 2045–2074, and 2070–2099 under three climate scenarios. Area-level variables were used to identify demographic and economic status, health, environment, and infrastructure contexts and derive Urban Heat Health Risk (UHHR) scores using confirmatory factor analysis. The associations between adaptive capacity (the UHHR scores) and future xHEAT-CVD burden were examined. In 2070–2099 under the mildest scenario, 36 more days annually were projected to be ≥ T MHP , and xHEAT-CVD burden was projected to increase by at least 20.4-fold. Lower adaptive capacity was associated with greater increases in future xHEAT-CVD burden, over 9-fold increase per 1-unit increase in UHHR score (9.1, 95 % Confidence Intervals: 2.8–15.4). The historical xHEAT-CVD burden (2000–2017) was largely driven by the health context, whereas environment played a more important role in the future. Our findings suggest that drivers of the xHEAT-CVD burden may vary across time. Targeting the areas with the highest xHEAT-CVD burden at varying timeframes can help mitigate xHEAT-CVD burden more effectively.
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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) | 0.002 | 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 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".