Mortality Trends and Disparities in Cerebrovascular Disease Among Diabetic Population in the United States From 1999 to 2020: A CDC WONDER Analysis
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) significantly increases the risk of cerebrovascular disease (CeVD), a major cause of mortality and long-term disability. Despite improvements in healthcare, disparities in CeVD-related mortality among diabetic populations in the United States persist. METHODS: We conducted a retrospective analysis using the CDC WONDER database from 1999 to 2020 to assess mortality trends related to CeVD among adults aged ≥ 45 years with DM. Deaths were identified using ICD-10 codes I60-I69 (CeVD) and E10-E14 (DM). Age-adjusted mortality rates (AAMRs) were calculated, and trends were analysed using Joinpoint regression, stratified by age, race/ethnicity, geography, urbanisation, and place of death. RESULTS: A total of 689,846 CeVD-related deaths occurred in diabetic individuals. AAMR decreased from 36.9 in 1999 to 29.3 in 2020, with an average annual percentage change (AAPC) of -1.41%. However, a sharp rise was observed from 2018 to 2020 (APC 14.87%), indicating a concerning reversal in progress. The highest crude mortality rates were in the 75-84 age group, and the lowest in the 45-54 group. Black and Hispanic populations, rural residents, and those in the Southern United States had the highest mortality rates. The Northeast and Asian populations had the lowest, reflecting persistent disparities in access to care and preventive services. CONCLUSION: While CeVD mortality in diabetics declined over two decades, the recent reversal highlights emerging challenges, possibly due to healthcare disruptions and socioeconomic disparities. These findings underscore the need for targeted public health interventions to address inequities and improve outcomes in high-risk populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".