Abstract 12832: Risk of Extreme Wet Climate Conditions and Racial/Ethnic-Specific Cardiovascular Mortality in Southwest United States
Bibliographic record
Abstract
Introduction: Coastal regions are highly vulnerable to extreme climate conditions, which are known to have effects on adult cardiovascular disease mortality (CVDM). Racial/ethnic (R/E) minority populations (ie Non-Hispanic (NH) Black) are known to have a greater incidence of CVDM compared to their NH White counterparts. Consequently, there is a need to document associations between climate extremes and R/E-specific CVDM. Hypothesis: Extreme wet conditions associated with CVDM should vary by R/E, where strongest increased risk is expected for minority populations. Methods: Population-based study using sex, age (45-84 years), and R/E-specific (NH White, Black, Asian, & Hispanic/Latino) CVD deaths [ICD-10 (I00-99)] from CDC for Southwest US (California, Arizona, New Mexico) from Jan 1999 to Dec 2020. Climate exposure categories (mild, moderate, extreme dry/wet) were based on Palmer Drought Severity Index (PDSI), which integrates temperature and precipitation. With population estimates, CVDM rates were associated with PSDI categories using negative binomial regression upon adjusting for diabetes, smoking prevalence, sex, trend, month, state, unemployment rate, and air pollution (PM 2.5 ). Results: From 1999-2020, 1,307,418 (67.6% NH White, 16.1% Hispanic, 7.4% NH Asian, 8.9% NH Black) CVD deaths were recorded. CVDM was associated with a significant increased risk during extreme wet events (compared to extreme drought) only among NH Asian (adjusted rate ratio [RR] 1.08; 95% CI 1.06-1.09) and NH White (RR 1.02; 95% CI 1.01-1.025) in Southwest US (Figure). Insignificant interactions were observed between extreme wet and PM2.5 on CVDM by R/E. Conclusions: Extreme wet climate conditions were associated with an increased risk of CVDM among NH Asian and White adults. Both climate extreme events and R/E should be considered as predisposing risk factors for CVDM. Further studies are needed to assess such CVDM risk factors for other susceptible areas in the US.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".