Long term outcomes of cluster randomized trial to improve cardiovascular health at population level: The Cardiovascular Health Awareness Program (CHAP)
Bibliographic record
Abstract
STUDY QUESTION: The Cardiovascular Health Awareness Program (CHAP) cardiovascular risk reduction program consisted of sessions run by local volunteers in local pharmacies during which cardiovascular risk was assessed, healthy lifestyle and preventive care was promoted, and the participants were oriented to local resources to support changes in modifiable risk factors. A clustered randomized trial implemented in September 2006 across 39 communities targeting community-dwelling individuals 65 years and older showed a significant reduction in hospitalization one year after its implementation (rate ratio of 91 [95% confidence interval (CI): 86%-97%]). This study explores the impact of CHAP in the first five years. METHODS: Using health administrative data housed at the Institute for Clinical Evaluative Sciences, we established a closed cohort consisting of all individuals eligible in these communities at the study onset whom we followed over time. We assessed hospitalizations and survival using a negative binomial model for count data and Cox regression to assess time to first event, accounting for the clustered design. The primary outcome was the rate of cardiovascular-related hospitalizations defined as congestive heart failure, stroke or acute myocardial infarction. RESULTS: Most estimates pointed to an advantage for the intervention arm, but only all-cause mortality reached statistical significance (hazard ratio [95% CI] = 0.955 [0.914-0.999]). The hospitalization cardiovascular-related hospitalization rate ratio was (0.958, 95% CI: 0.898-1.022) in favour of the intervention communities, translating to an estimated 408 averted hospitalizations over the five-year period. There was no evidence of the effect of time from start of intervention. CONCLUSIONS: The consistent direction of the outcomes in favour of the intervention arms suggests that CHAP likely had a meaningful impact on reducing cardiovascular-related morbidity and mortality. Given the low cost of the intervention, further development of CHAP should be pursued.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".