Patterns of Care and Outcomes Differ for Urban Versus Rural Patients With Newly Diagnosed Heart Failure, Even in a Universal Healthcare System
Bibliographic record
Abstract
BACKGROUND: Access to medical care differs between urban and rural residents, but the magnitude of these differences and whether they affect outcomes are unknown. We aimed to determine whether outcomes differ for patients with incident heart failure (HF) by urban-rural status. METHODS AND RESULTS: This cohort study used administrative data from Alberta, Canada. Patients with incident HF were identified from April 1, 1999, to December 31, 2005, and followed for 1-year. Multivariable logistic regression was used to assess differences in 1-year outcomes after initial HF diagnosis in patients living in rural versus urban settings. We identified 72 043 patients with incident HF (mean age, 72±14; male sex, 50%) of whom 12 173 (17%) died and 29 074 (39%) were hospitalized within 1 year. Although crude all-cause 1-year mortality rates were higher in urban than in rural residents (17.3% versus 15.6%, P<0.001), after adjustment for comorbidities, no significant differences were observed (adjusted odds ratio [aOR], 0.95; 95% CI, 0.90 to 1.00). However, sex-specific analyses indicated that urban men had a significantly lower risk of mortality than rural men (aOR, 0.89; 95% CI, 0.83 to 0.96). In contrast, no difference was observed between urban and rural women (aOR, 1.02; 95% CI, 0.94 to 1.10). Urban patients were more likely to have office-based physician visits in the first year after HF diagnosis (aOR, 1.09; 95% CI, 1.02 to 1.17) and exhibited lower rates of hospitalization (aOR, 0.71; 95% CI, 0.68 to 0.74) and emergency department visits (aOR, 0.62; 95% CI, 0.60 to 0.65) than rural patients. CONCLUSIONS: Even within a universal healthcare system, there are differences in outcomes after HF diagnosis based on location of residence. Urban patients with HF are more likely to receive outpatient care and less likely to be hospitalized or present to the emergency department in the first year after diagnosis than rural patients with HF.
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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 | 0.000 | 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 | 0.001 | 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".