Regional variations in ambulatory care and incidence of cardiovascular events
Bibliographic record
Abstract
BACKGROUND: Variations in the prevalence of traditional cardiac risk factors only partially account for geographic variations in the incidence of cardiovascular disease. We examined the extent to which preventive ambulatory health care services contribute to geographic variations in cardiovascular event rates. METHODS: We conducted a cohort study involving 5.5 million patients aged 40 to 79 years in Ontario, Canada, with no hospital stays for cardiovascular disease as of January 2008, through linkage of multiple population-based health databases. The primary outcome was the occurrence of a major cardiovascular event (myocardial infarction, stroke or cardiovascular-related death) over the following 5 years. We compared patient demographics, cardiac risk factors and ambulatory health care services across the province’s 14 health service regions, known as Local Health Integration Networks (LHINs), and evaluated the contribution of these variables to regional variations in cardiovascular event rates. RESULTS: Cardiovascular event rates across LHINs varied from 3.2 to 5.7 events per 1000 person-years. Compared with residents of high-rate LHINs, those of low-rate health regions received physician services more often (e.g., 4.2 v. 3.5 mean annual family physician visits, p value for LHIN-level trend = 0.01) and were screened for risk factors more often. Low-rate LHINs were also more likely to achieve treatment targets for hypercholes-terolemia (51.8% v. 49.6% of patients, p = 0.03) and controlled hypertension (67.4% v. 53.3%, p = 0.04). Differences in patient and health system factors accounted for 74.5% of the variation in events between LHINs, of which 15.5% was attributable to health system factors alone. INTERPRETATION: Preventive ambulatory health care services were provided more frequently in health regions with lower cardiovascular event rates. Health system interventions to improve equitable access to preventive care might improve cardiovascular outcomes.
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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 | 0.003 |
| 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) | 0.001 | 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".