Racial/ethnic differences in cardiovascular outcomes in a universal healthcare system: insights from the CARTaGENE cohort
Bibliographic record
Abstract
Abstract Background While prior studies have shown racial/ethnic differences in cardiovascular (CV) outcomes within private or mixed health care systems, it remains uncertain whether inequalities in cardiovascular outcomes exist between different races and ethnicities in universal health care contexts. We aimed to determine whether there are racial/ethnicity disparities in long-term CV outcomes within a single-payer universal health care system. Methods The CARTaGENE study is a population-based prospective cohort study with enrollment of 19,996 individuals between 40–69 years in 2009, in the province of Quebec, Canada. Participants residing in four large metropolitan areas were randomly chosen from the provincial health insurance registry by strata of age, sex, and postal codes. Follow-up was available up to 2016. For this analysis, we retained only participants without prior known CV disease. The primary composite endpoint was time to the first CV event or intervention (CV death, acute coronary syndrome, heart failure, coronary revascularization, ischemic stroke, or peripheral vascular event or revascularization). We used unadjusted and adjusted Cox proportional hazard models to evaluate the association of self-defined race/ethnicity with the primary endpoint. Results There were 17,802 eligible participants with a mean age of 51 years (52.5% females) with 111,312 person-years of follow-up (median follow-up of 6.6 years). South Asian (SA) participants had the highest prevalence of diabetes mellitus (29%) and hypertension (32%). After adjustment for age and sex, SA ethnicity was associated with a 95% relative increase in risk for CV events, while East/Southeast Asian (ESA) ethnicity was associated with a 42% relative decrease in risk for CV events compared to White participants. After further adjustment for socioeconomic status and CV risk factors, ESA ethnicity remained associated with a similar decreased CV risk. In contrast, the association of SA ethnicity with increased CV risk was attenuated after full adjustment for baseline characteristics (Table 1). Conclusions Racial/ethnic disparities in long-term CV outcomes are present in a single-payer universal healthcare setting. ESA ethnicity was associated with a lower risk of long-term CV outcomes. Future studies are needed to corroborate the reduced risk of long-term major CV events associated with ESA ethnicity. Understanding the reasons related to potential CV protection with ESA ethnicity could facilitate endeavors to reduce long-term CV outcomes in other races/ethnicities. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): McGill Health University Center
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".