Critical appraisal of a mHealth-assisted community-based cardiovascular disease risk screening program in rural Kenya: an operational research study
Bibliographic record
Abstract
Community health workers (CHWs) can participate in the cascade of hypertension and diabetes management in low and middle-income countries (LMICs). Their services may be enhanced with mobile health (mHealth) tools. In this operational research study, we describe the AFYACHAT mHealth-assisted cardiovascular health screening program in rural Kenya. In this study, A CHW screened a convenience sample of adults ≥ 40 years old in rural Kenya for cardiovascular disease (CVD) risk using the two-way AFYACHAT mHealth instrument. AFYACHAT analyzes a patient's age, sex, smoking, diabetes and systolic blood pressure and provides a four-tiered 10-year CVD risk score. User acceptability was assessed by an end-of-study interview with the CWH. Automated error logs were analyzed. Patient satisfaction was measured with a six-question satisfaction questionnaire. Screened participants with high CVD risk were followed-up via telephone to explore any actions taken following screening. In 24 months, one CHW screened 1650 participants using AFYACHAT. The 10-year risk of CVD was <10% for 1611 (98%) patients, 10 to <20% for 26 (1.6%), 20 to <30% in 12 (0.7%), and ≥30% for 1 (0.1%). The point prevalence of hypertension and diabetes was 27% and 1.9%, respectively. Seventy-five percent of participants with elevated CVD risk sought further medical care. There was high acceptability, a 15% miscode error rate, and high participant satisfaction with the screening program. Our operational research outlines how AFYACHAT mHealth tool can assist CHW perform rapid CVD screening; this provides a model framework for non-communicable disease screening in LMICs.
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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 | 0.264 | 0.514 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".