Community-based screening for cardiovascular risk using a novel mHealth tool in rural Kenya. Issue 3 (1st July 2018)

• Record Type: Journal Article
• Authors: Mannik, Jennifer R.; Figol, Andrea; Churchill, Vanessa; Aw, James; Francis, Stacy; Karino, Ezekiel; Chesire, Julius Kibet; Opot, Danet; Ochieng, Benard; Hawkes, Michael T.
• Is Part Of: BMJ health & care informatics. Volume 25:Issue 3(2018)
• Page Start: 176
• Page End: 182
• Publication Date: 2018-07-01
• Subjects: mHealth -- low- and middle-income countries -- hypertension -- cardiovascular disease -- Kenya; Medical informatics -- Great Britain -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; Primary care (Medicine) -- Great Britain -- Data processing -- Periodicals; 362.10285
• DOI: 10.14236/jhi.v25i3.1012
• Languages: English
• ISSNs: 2632-1009

Abstract

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 mobile health (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 the CVD risk in rural Kenya. AFYACHAT collects and stores a short message system (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' (from 10% to <20%), 'ORANGE' (from 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 2865 subjects in remote rural communities in Kenya over a 22-month period (2015–2017). The median age of subjects was 50 (interquartile range 43–60) and 1581 (55%) were female. The point prevalence of hypertension (systolic blood pressure > 140 mmHg), 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, from 10% to <20% in 65 (2.3%), from 20% to <30% in 12 (0.4%) and ≥30% in 10 (0.2%). Conclusions: We have developed a mHealth tool that can be used by CHWs to screen for CVD risk factors, demonstrating the proof of concept in rural Kenya.