Racial Disparities in Obesity‐Related Cardiovascular Mortality in the United States: Temporal Trends From 1999 to 2020
Bibliographic record
Abstract
Background Obesity is a major risk factor for cardiovascular disease, with differential impact across populations. This descriptive epidemiologic study outlines trends and disparities in obesity-related cardiovascular mortality in the US population between 1999 and 2020. Methods and Results The Multiple Cause of Death database was used to identify adults with primary cardiovascular death and obesity recorded as a contributing cause of death. Cardiovascular deaths were grouped into ischemic heart disease, heart failure, hypertensive disease, cerebrovascular disease, and other. Absolute, crude, and age-adjusted mortality rates (AAMRs) were calculated by racial group, considering temporal trends and variation by sex, age, and residence (urban versus rural). Analysis of 281 135 obesity-related cardiovascular deaths demonstrated a 3-fold increase in AAMRs from 1999 to 2020 (2.2-6.6 per 100 000 population). Black individuals had the highest AAMRs. American Indian or Alaska Native individuals had the greatest temporal increase in AAMRs (+415%). Ischemic heart disease was the most common primary cause of death. The second most common cause of death was hypertensive disease, which was most common in the Black racial group (31%). Among Black individuals, women had higher AAMRs than men; across all other racial groups, men had a greater proportion of obesity-related cardiovascular mortality cases and higher AAMRs. Black individuals had greater AAMRs in urban compared with rural settings; the reverse was observed for all other races. Conclusions Obesity-related cardiovascular mortality is increasing with differential trends by race, sex, and place of residence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".