Abstract 9299: Associations Between Precipitation and Racial/Ethnic-Specific Adult Cardiovascular Disease Mortality During Wintertime in Northeast United States
Bibliographic record
Abstract
Introduction: Minority (i.e. non-Hispanic (NH) Blacks) populations tend to have higher rates of chronic diseases in comparison to NH Caucasians, leading to early cardiovascular disease (CVD) death. Studies suggest that winter precipitation (rain/snow) is associated with an increased risk of adult CVD mortality. Whether winter precipitation is associated with race/ethnic (RE)-specific CVD mortality remains unknown. Hypothesis: RE-specific associations between winter precipitation and CVD mortality would be strongest for NH Blacks. Methods: Using a population-based design, CDC data was obtained for Northeast US (New York, New Jersey, Pennsylvania). Region- and RE-specific (NH Blacks & Whites, Asian, Latino) adult (35-74 years) CVD mortality for winter months (December-February) from 2011-2019 were recorded based on ICD-10 codes (I00-I99). With population estimates, winter CVD rates were derived. Adjusting for the trend, temperatures, percent inactivity, obesity, college education, income, and region; negative binomial regression was used to associate winter precipitation and CVD rates. Results: From 2011-2019, 84,100 winter CVD adult deaths were recorded in Northeast US. A 1-inch rise in winter precipitation was significantly associated with an increased risk in CVD deaths only for NH Blacks (adjusted rate ratio [RR] 1.02; 95% CI 1.01-1.05) in Northeast US (Fig 1). Whereas the covariate, winter temperatures (per 1 o C rise) was significantly associated with a decrease in CVD only for NH Blacks (RR 0.99; 95% CI 0.98-0.99) and NH Whites (RR 0.99; 95% CI 0.98-0.99). Insignificant interactions were observed between winter precipitation and temperature. Conclusions: Winter rainy/snowy conditions are associated with increased risk of CVD events among NH Black adults. This suggests that climate effects on CVD may differ by RE and highlights the importance for incorporating RE diversity in combination with climatological influences when studying CVD risks.
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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 | 0.000 | 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) | 0.001 | 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 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".