Cardiac medication prescribing and adherence after acute myocardial infarction in Chinese and South Asian Canadian patients
Bibliographic record
Abstract
BACKGROUND: Failure to adhere to cardiac medications after acute myocardial infarction (AMI) is associated with increased mortality. Language barriers and preference for traditional medications may predispose certain ethnic groups at high risk for non-adherence. We compared prescribing and adherence to ACE-inhibitors (ACEI), beta-blockers (BB), and statins following AMI among elderly Chinese, South Asian, and Non-Asian patients. METHODS: Retrospective-cohort study of elderly AMI survivors (1995-2002) using administrative data from British Columbia. AMI cases and ethnicity were identified using validated ICD-9/10 coding and surname algorithms, respectively. Medication adherence was assessed using the 'proportion of days covered' (PDC) metric with a PDC ≥ 0.80 indicating optimal adherence. The independent effect of ethnicity on adherence was assessed using multivariable modeling, adjusting for socio-demographic and clinical characteristics. RESULTS: There were 9926 elderly AMI survivors (258 Chinese, 511 South Asian patients). More Chinese patients were prescribed BBs (79.7% vs. 73.1%, p = 0.04) and more South Asian patients were prescribed statins (73.5% vs. 65.2%, p = 0.001). Both Chinese (Odds Ratio [OR] 0.53; 95%CI, 0.39-0.73; p < 0.0001) and South Asian (OR 0.78; 95%CI, 0.61-0.99; p = 0.04) patients were less adherent to ACEI compared to Non-Asian patients. South Asian patients were more adherent to BBs (OR 1.3; 95%CI, 1.04-1.62; p = 0.02). There was no difference in prescribing of ACEI, nor adherence to statins among the ethnicities. CONCLUSION: Despite a higher likelihood of being prescribed evidence-based therapies following AMI, Chinese and South Asian patients were less likely to adhere to ACEI compared to their Non-Asian counterparts.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".