Socioeconomic Determinants of Cardiometabolic Diseases in Kenya: A Machine Learning Approach

Socioeconomic Determinants of Cardiometabolic Diseases in Kenya: A Machine Learning Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socioeconomic Determinants of Cardiometabolic Diseases in Kenya: A Machine Learning Approach Tatenda Duncan Kavu, Evans Omondi, Daniel Mwanga, Patrice Mawa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6408964/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Cardiometabolic diseases (CMDs) are major contributors to global morbidity and mortality, with significant disparities driven by socioeconomic factors. This study uses data collected in 2019 in Kenya by the AWI-Gen project, a collaborative initiative between the University of the Witwatersrand (Wits) and the INDEPTH Network. The work in Kenya was conducted within the Nairobi Urban Health and Demographic Surveillance System (NUHDSS), run by the African Population and Health Research Center (APHRC). The main objective is to apply machine learning methods to identify socioeconomic factors that influence rising CMD rates in Kenya, thereby guiding targeted interventions to mitigate CMDs. We performed translational research by leveraging the K-Prototypes clustering algorithm, a Random Forest classification model, and the SHAPley Additive Explanations (SHAP) technique to investigate and identify socioeconomic status factors influencing CMD risk. The K-Prototypes clustering model identified three groups: 31% of participants in the high-vegetable intake group, 34% in the overextended labour group, and 37% in the economically disadvantaged group had CMDs. The economically disadvantaged group, which had the highest prevalence of CMDs, also exhibited lower average years of schooling and a higher proportion of individuals with no formal education. Unemployment was notably high in this group (62%), with only 16% being self-employed, and asset ownership was significantly lower. Consuming vegetables and fruits more than three times a week reduced CMD risk, while education level, income, employment status, overall socioeconomic status, and the number of hours worked per week contributed to CMD risk in varying degrees. This study highlights that targeted socioeconomic interventions, including improved education and job creation, can mitigate CMD risk, especially among disadvantaged groups. Access to affordable, healthy food should be increased through subsidies, community gardening, and public-private partnerships that expand urban food markets. Healthcare coverage must be enhanced and out-of-pocket costs minimized, with government-funded mobile clinics reaching underserved populations. Public health campaigns can boost awareness of early detection and regular check-ups, further reducing CMD prevalence. Clustering Classification Cardiometabolic Diseases Socio-Economic Status Social Determinants of Health. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviews received at journal 28 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers invited by journal 14 May, 2025 Editor invited by journal 11 Apr, 2025 Editor assigned by journal 10 Apr, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6408964","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456590924,"identity":"bc6a3006-d975-4616-9a9c-1b00226ac6c3","order_by":0,"name":"Tatenda Duncan 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This study uses data collected in 2019 in Kenya by the AWI-Gen project, a collaborative initiative between the University of the Witwatersrand (Wits) and the INDEPTH Network. The work in Kenya was conducted within the Nairobi Urban Health and Demographic Surveillance System (NUHDSS), run by the African Population and Health Research Center (APHRC). The main objective is to apply machine learning methods to identify socioeconomic factors that influence rising CMD rates in Kenya, thereby guiding targeted interventions to mitigate CMDs. We performed translational research by leveraging the K-Prototypes clustering algorithm, a Random Forest classification model, and the SHAPley Additive Explanations (SHAP) technique to investigate and identify socioeconomic status factors influencing CMD risk. The K-Prototypes clustering model identified three groups: 31\\% of participants in the high-vegetable intake group, 34\\% in the overextended labour group, and 37\\% in the economically disadvantaged group had CMDs. The economically disadvantaged group, which had the highest prevalence of CMDs, also exhibited lower average years of schooling and a higher proportion of individuals with no formal education. Unemployment was notably high in this group (62\\%), with only 16\\% being self-employed, and asset ownership was significantly lower. Consuming vegetables and fruits more than three times a week reduced CMD risk, while education level, income, employment status, overall socioeconomic status, and the number of hours worked per week contributed to CMD risk in varying degrees. This study highlights that targeted socioeconomic interventions, including improved education and job creation, can mitigate CMD risk, especially among disadvantaged groups. Access to affordable, healthy food should be increased through subsidies, community gardening, and public-private partnerships that expand urban food markets. Healthcare coverage must be enhanced and out-of-pocket costs minimized, with government-funded mobile clinics reaching underserved populations. 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