Sex Disparities in Post-Acute Myocardial Infarction Pharmacologic Treatment Initiation and Adherence
Bibliographic record
Abstract
BACKGROUND: The prevalence of the use of secondary prevention cardiovascular medications is lower among women than men, but it is unclear if this is a result of lower treatment initiation among women or lower treatment adherence. We aimed to map the treatment pathway for survivors of acute myocardial infarction (AMI) by sex and age. METHODS AND RESULTS: This retrospective population-based cohort study used linked administrative data sets in British Columbia (2004-2011), which include health care, prescription drugs, sociodemographic, and mortality information. The study cohort included all individuals admitted to hospital for AMI in 2007-2009 and survived for 1 year after hospital discharge. Patients were evaluated for whether they initiated and then subsequently filled prescriptions angiotensin-converting enzyme inhibitors, β-blockers, and statins. More than two thirds of AMI survivors initiated treatment on all appropriate medications, given their contraindications, within 2 months of discharge. Younger men were significantly more likely than younger women to initiate appropriate treatment (adjusted odds ratio, 1.38; 95% confidence interval, 1.10-1.75). By the end of 1 year after discharge, only one third of all AMI survivors filled all appropriate prescriptions for at least 80% of the year. There was no significant difference in adherence to medication therapy between women and men. CONCLUSIONS: The majority of AMI survivors either discontinue treatment or do not refill their prescriptions consistently. Women <55 years are significantly less likely to be on optimal therapy by the end of 1 year after discharge, which is driven by a sex disparity in treatment initiation and not treatment adherence.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".