Quality of cardiovascular disease care in Ontario’s primary care practices: a cross sectional study examining differences in guideline adherence by patient sex
Bibliographic record
Abstract
BACKGROUND: Women are disproportionately affected by cardiovascular disease, often experiencing poorer outcomes following a cardiovascular event. Evidence points to inequities in processes of care as a potential contributing factor. This study sought to determine whether any sex differences exist in adherence to process of care guidelines for cardiovascular disease within primary care practices in Ontario, Canada. METHODS: This is a secondary analysis of pooled cross-sectional baseline data collected through a larger quality improvement initiative known as the Improved Delivery of Cardiovascular Care (IDOCC). Chart abstraction was performed for 4,931 patients from 84 primary care practices in Eastern Ontario who had, or were at high risk of, cardiovascular disease. Measures examining adherence to guidelines associated with nine areas of cardiovascular care (coronary artery disease, peripheral vascular disease (PVD), stroke/transient ischemic attack, chronic kidney disease, diabetes, dyslipidemia, hypertension, smoking cessation, and weight management) were collected. Multivariable logistic regression analysis was performed to evaluate sex differences, adjusting for age, physician remuneration, and rurality. RESULTS: Women were significantly less likely to have their lipid profiles taken (OR=1.17, 95% CI 1.03-1.33), be prescribed lipid lowering medication for dyslipidemia (OR=1.54, 95% CI 1.20-1.97), and to be prescribed ASA following stroke (OR=1.56, 95% CI 1.39-1.75). Women with PVD were significantly less likely to be prescribed ACE inhibitors and/or angiotensin receptor blockers (OR=1.74, 95% CI 1.25-2.41) and lipid lowering medications (OR=1.95, 95% CI 1.46-2.62) or ASA (OR=1.59, 95% CI 1.43-1.78). However, women were more likely to have two blood pressure measurements taken and to be referred to a dietician or weight loss program. Male patients with diabetes were less likely to be prescribed glycemic control medication (OR=0.84, 95% CI 0.74-0.86). CONCLUSIONS: Sex disparities exist in the quality of cardiovascular care in Canadian primary care practices, which tend to favour men. Women with PVD have a particularly high risk of not receiving appropriate medications. Our findings indicate that improvements in care delivery should be made to address these issues, particularly with regard to the prescribing of recommended medications for women, and preventive measures for men.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".