Associations of severe climate conditions and race/ethnic-specific ischemic heart disease mortality among middle-aged adults in the United States
Bibliographic record
Abstract
Abstract Ischemic heart disease (IHD) is the leading cause of death worldwide. In the United States, IHD deaths affect millions of adults, with substantial age and race/ethnic-specific variability. In recent years, emphasis has been placed on reducing the rate of IHD events among middle-aged adults. Non-Hispanic (NH) Black populations are known to have greater IHD mortality rates compared to other races/ethnicities. Researchers have established several biological, clinical, and socioeconomic IHD risk factors, but severe climate conditions have not been explored by race/ethnicity among middle-aged adults. Using generalized linear models, this study documents associations between the Palmer Drought Severity Index and race/ethnic-specific IHD mortality rates from January 1999 to December 2020 among middle-aged adults across four census regions of the United States. When controlling for covariates, we found that during months of severe droughts (in comparison to neutral phases) IHD mortality rates had an increased risk for NH Whites (RR 1.017, P = 0.017) and NH Blacks (RR 1.029, P = 0.015). Furthermore, we found that surface air temperature is a modifier, where during warm periods (⩾20 °C) throughout the United States, severe drought months exacerbated the risk of IHD mortality rates among NH White (RR 1.024, P = 0.007) and NH Blacks (RR 1.033, P = 0.039). Further studies are needed to understand the mechanism between severe climate conditions and race/ethnic-specific IHD events.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".