Growing Income-Related Disparities in Cardiovascular Hospitalizations Among People With Diabetes, 1995–2019: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular risk reduction is an important focus in the management of people with diabetes. Although event rates have been declining over the long term, they have been observed to plateau or reverse in recent years. Furthermore, the impact of income-related disparities in cardiovascular events is unknown. The objective of this study is to evaluate age-, sex-, and income-related trends in cardiovascular hospitalization rates among people with diagnosed diabetes. RESEARCH DESIGN AND METHODS: We calculated rates of hospitalization for acute myocardial infarction, stroke, heart failure, and lower-extremity amputation in annual cohorts of the entire population of Ontario, Canada, with diagnosed diabetes, from 1995 to 2019. Event rates were stratified by age, sex, and income level. RESULTS: We studied nearly 1.7 million people with diabetes. The rate of acute myocardial infarction declined throughout the 25-year study period (P < 0.0001), such that the rate in 2019 was less than half the rate in 1995. Rates of stroke (P < 0.0001), heart failure (P < 0.0001), and amputation (P < 0.0001) also changed over time, but hospitalization rates stabilized through the 2010s. This apparent stabilization concealed a growing income-related disparity: wealthier patients showed continued declines in rates of these outcomes during the decade, whereas rates for lower-income patients increased (P for interaction < 0.0001 for all four outcomes). CONCLUSIONS: During a quarter-century of follow-up, cardiovascular hospitalization rates among people with diabetes fell. However, the apparent stabilization in rates of stroke, heart failure, and amputation in recent years masks the fact that rates have risen for lower-income individuals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".