Community-based screening for cardiovascular risk using a novel mHealth tool in rural Kenya
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: An increasing burden of cardiovascular disease (CVD) in low-resource settings demands innovative public health approaches. OBJECTIVES: To design and test a novel mHealth tool for use by community health workers (CHWs) to identify individuals at high CVD risk who would benefit from education and/or pharmacologic interventions. METHODS: We designed and implemented a novel two-way mobile phone application, "AFYACHAT," to rapidly screen for CVD risk in rural Kenya. AFYACHAT collects and stores SMS text message data entered by a CHW on a subject's age, sex, smoking, diabetes, and systolic blood pressure, and returns as SMS text message the category of 10-year CVD risk: "GREEN" (<10% 10 year risk of cardiovascular event), "YELLOW" (10 to <20%), "orange"(20 to <30%), or "RED" (≥30%). CHWs were equipped and trained to use an automated blood pressure device and the mHealth tool. RESULTS: Five CHWs screened 2,865 subjects in remote rural communities in Kenya over a 22 month period (2015-17). The median age of subjects was 50 (IQR 43 to 60) and 1581 (55%) were female. Point prevalence of hypertension (systolic blood pressure>140mmHg), diabetes, and tobacco use were 23%, 3.2%, and 22%, respectively. Overall, the 10-year risk of CVD among patients was <10% in 2778 (97%) patients, 10 to <20% in 65 (2.3%), 20 to <30% in 12 (0.4%), and ≥30% in 10 (0.2%). CONCLUSION: We have developed a mHealth tool that can be used by CHWs to screen for CVD risk factors, demonstrating proof-of-concept in rural Kenya.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it