Cardiovascular medication utilization and adherence among adults living in rural and urban areas: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Rural residents face numerous barriers to healthcare access and studies suggest poorer health outcomes for rural patients. Therefore we undertook a systematic review to determine if cardiovascular medication utilization and adherence patterns differ for rural versus urban patients. METHODS: A comprehensive search of major electronic datasets was undertaken for controlled clinical trials and observational studies comparing utilization or adherence to cardiovascular medications in rural versus urban adults with cardiovascular disease or diabetes. Two reviewers independently identified citations, extracted data, and evaluated quality using the STROBE checklist. Risk estimates were abstracted and pooled where appropriate using random effects models. Methods and reporting were in accordance with MOOSE guidelines. RESULTS: Fifty-one studies were included of fair to good quality (median STROBE score 17.5). Although pooled unadjusted analyses suggested that patients in rural areas were less likely to receive evidence-based cardiovascular medications (23 studies, OR 0.88, 95% CI 0.79, 0.98), pooled data from 21 studies adjusted for potential confounders indicated no rural-urban differences (adjusted OR 1.02, 95% CI 0.91, 1.13). The high heterogeneity observed (I(2) = 97%) was partially explained by treatment setting (hospital, ambulatory care, or community-based sample), age, and disease. Adherence did not differ between urban versus rural patients (3 studies, OR 0.94, 95% CI 0.39, 2.27, I(2) = 91%). CONCLUSIONS: We found no consistent differences in rates of cardiovascular medication utilization or adherence among adults with cardiovascular disease or diabetes living in rural versus urban settings. Higher quality evidence is needed to determine if differences truly exist between urban and rural patients in the use of, and adherence to, evidence-based medications.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".