Community health worker based interventions in secondary prevention of CVD in India (SPREAD): methods and baseline results
Bibliographic record
Abstract
Purpose: India has a high burden of cardiovascular diseases (CVD). Secondary prevention is sub-optimal, with several barriers to medication adherence and lifestyle changes. Community Health Workers (CHWs) work in infectious diseases, but not in CVD in low and middle-income countries. We aimed to test if CHWs could improve medication adherence at one year after an acute coronary syndrome. Methods: A multicentre randomized controlled trial at 14 sites. Consenting patients were randomized to CHW based care or standard care. The CHWs had 10th or 12th grade education and received training for 3 months. The tools developed in local languages include: (a) visual tool for adherence (a calendar to record drugs taken daily and lifestyle advice) and (b) patients' diary with details on heart disease, contact numbers and drug information. The CHW met patients at discharge, at 1, 3, 5, 7, 9, and 12 months. The CHW assessed patients' clinical status, medication adherence and lifestyle changes. CHWs elicited barriers and offered strategies related to medication adherence, tobacco, diet, physical activity and alcohol. Medication use was recorded as a percentage of drugs taken in the preceding month. With 800 patients, the trial has 90% power to detect a 10% improvement in adherence to medication (primary outcome) at one year. We will compare mean adherence using Student's t test and time to first discontinuation using Cox regression. Results: Over 11 months, we randomized 806 patients and follow up is ongoing. Participant's mean age is 56.4 (± 11.31), 82.8% are males, 66.0% are poor or low middle and 24.3% are from a rural area. At baseline, 35.2% used tobacco, 51.2% ate unhealthy food, 70.5% had little or no physical exercise and 19.2% used alcohol regularly. Known CVD risk factors were, hypertension 43.4%, diabetes 31.9%, and dislipidemia 6.6%. Before hospitalization, of the 429 (53.2%) who were prescribed CVD drugs, 29.8% missed them > once a week and 14.5% discontinued on their own. The diagnoses were: unstable angina 28.5%, NSTEMI 17.7% and STEMI 53.7%. Among STEMI patients 50.0% were thrombolysed and overall 58.9% had a coronary intervention. Drugs at discharge include anti-platelet (97.2%), ACE inhibitor or angiotensin receptor blocker (69.6%), beta blocker (69.0%) and lipid lowering (94.8%). Conclusions: This is the first trial evaluating CHWs in secondary prevention of CVD in India. We developed different tools to improve adherence, and trained a new cadre of health workers in CVD. The results and experience gained will help in implementing new strategies to contain CVD in similar settings.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".