Improving cardiovascular health at population level: 39 community cluster randomised trial of Cardiovascular Health Awareness Program (CHAP)
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of the community based Cardiovascular Health Awareness Program (CHAP) on morbidity from cardiovascular disease. DESIGN: Community cluster randomised trial. SETTING: 39 mid-sized communities in Ontario, Canada, stratified by location and population size. PARTICIPANTS: Community dwelling residents aged 65 years or over, family physicians, pharmacists, volunteers, community nurses, and local lead organisations. INTERVENTION: Communities were randomised to receive CHAP (n = 20) or no intervention (n = 19). In CHAP communities, residents aged 65 or over were invited to attend volunteer run cardiovascular risk assessment and education sessions held in community based pharmacies over a 10 week period; automated blood pressure readings and self reported risk factor data were collected and shared with participants and their family physicians and pharmacists. MAIN OUTCOME MEASURE: Composite of hospital admissions for acute myocardial infarction, stroke, and congestive heart failure among all community residents aged 65 and over in the year before compared with the year after implementation of CHAP. RESULTS: All 20 intervention communities successfully implemented CHAP. A total of 1265 three hour long sessions were held in 129/145 (89%) pharmacies during the 10 week programme. 15,889 unique participants had a total of 27,358 cardiovascular assessments with the assistance of 577 peer volunteers. After adjustment for hospital admission rates in the year before the intervention, CHAP was associated with a 9% relative reduction in the composite end point (rate ratio 0.91, 95% confidence interval 0.86 to 0.97; P = 0.002) or 3.02 fewer annual hospital admissions for cardiovascular disease per 1000 people aged 65 and over. Statistically significant reductions favouring the intervention communities were seen in hospital admissions for acute myocardial infarction (rate ratio 0.87, 0.79 to 0.97; P = 0.008) and congestive heart failure (0.90, 0.81 to 0.99; P = 0.029) but not for stroke (0.99, 0.88 to 1.12; P = 0.89). CONCLUSIONS: A collaborative, multi-pronged, community based health promotion and prevention programme targeted at older adults can reduce cardiovascular morbidity at the population level. Trial registration Current controlled trials ISRCTN50550004.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".