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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".