Comparing Cardiovascular Mortality Estimates From Global Burden of Disease and From the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiological Research
Bibliographic record
Abstract
BACKGROUND: Several sources of data, including the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiological Research (CDC WONDER) and the Global Burden of Disease (GBD) data set, report causes of mortality in the United States. While CDC WONDER contains data based on death certificate codes, the GBD mortality data undergo additional processing, such as cause-of-death reassignment before reporting. Potential differences in reported mortality from cardiovascular disease in the United States between these 2 data sources have not been characterized. METHODS: US nationwide cardiovascular cause-of-death data for each year between 2000 and 2019 were obtained from the GBD and the Multiple Cause-of-Death files using CDC WONDER in this longitudinal study. In addition to mortality from cardiovascular disease, mortality from key components of cardiovascular disease, including ischemic heart disease, stroke, and atrial fibrillation/flutter, was determined from each data set. Absolute and crude mortality rates per 100 000 are reported for each data set. Percent differences in cardiovascular mortality from GBD and CDC WONDER and percent changes in cardiovascular mortality across years were calculated. RESULTS: In 2019, GBD reported 957 455 (95% uncertainty interval, 855 065-1 013 175) cardiovascular deaths, while CDC WONDER reported 859 290 cardiovascular deaths in the United States. Between 2000 and 2019, the reported crude mortality rates from cardiovascular causes in GBD decreased from 327 (297-341) to 292 (261-309), a reduction of 10.7%, and decreased in CDC WONDER from 335 (334-335) to 267 (266-267), a reduction of 20.3%. In 2019, the reported mortality rates for components of cardiovascular disease were higher in GBD compared with CDC WONDER for ischemic heart disease (percent difference, 54.5%), stroke (percent difference, 26.1%), and atrial fibrillation/flutter (percent difference, 25.0%). CONCLUSIONS: There are prominent differences in reported cardiovascular mortality between GBD and CDC WONDER data.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".