CKD-Associated Cardiovascular Mortality in the United States: Temporal Trends From 1999 to 2020
Bibliographic record
Abstract
Rationale & Objective: Chronic kidney disease (CKD) is associated with an increased risk of cardiovascular (CV) mortality, but there are limited data on temporal trends disaggregated by sex, race, and urban/rural status in this population. Study Design: Retrospective observational study. Setting & Participants: The Centers for Disease Control and Prevention Wide-Ranging, Online Data for Epidemiologic Research database. Exposure & Predictors: Patients with CKD and end-stage kidney disease (ESKD) stratified according to key demographic groups. Outcomes: Etiologies of CKD- and ESKD-associated mortality between 1999 and 2000. Analytical Approach: Presentation of age-adjusted mortality rates (per 100,000 people) characterized by CV categories, ethnicity, sex (male or female), age categories, state, and urban/rural status. Results: Between 1999 and 2020, we identified 1,938,505 death certificates with CKD (and ESKD) as an associated cause of mortality. Of all CKD-associated mortality, the most common etiology was CV, with 31.2% of cases. Between 1999 and 2020, CKD-related age-adjusted mortality increased by 50.2%, which was attributed to an 86.6% increase in non-CV mortality but a 7.1% decrease in CV mortality. Black patients had a higher rate of CV mortality throughout the study period, although Black patients experienced a 38.6% reduction in mortality whereas White patients saw a 2.7% increase. Hispanic patients experienced a greater reduction in CV mortality over the study period (40% reduction) compared to non-Hispanic patients (3.6% reduction). CV mortality was higher in urban areas in 1999 but in rural areas in 2020. Limitations: Reliance on accurate characterization of causes of mortality in a large dataset. Conclusions: Among patients with CKD-related mortality in the United States between 1999 and 2020, there was an increase in all-cause mortality though a small decrease in CV-related mortality. Overall, temporal decreases in CV mortality were more prominent in Hispanic versus non-Hispanic patients and Black patients versus White patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".