SOCIOECONOMIC INEQUALITIES IN SELF-REPORTED TYPE 2 DIABETES SCREENING IN KENYA: EVIDENCE FROM THE 2015 KENYA STEPS SURVEY
Author(s)
Catherine Odhiambo, PhD.
PhD Student, University of Sheffield, Sheffield, United Kingdom.
PhD Student, University of Sheffield, Sheffield, United Kingdom.
OBJECTIVES: This study aimed to assess socioeconomic inequalities in self-reported diabetes screening and identify the key factors driving these disparities.
METHODS: We analysed nationally representative data from the 2015 Kenya STEPS survey (n=4,500). The outcome variable was self-reported blood glucose measurement by a health worker. Wealth status was measured using principal component analysis-derived index. We calculated concentration indices (CI) to measure socioeconomic inequality and employed Wagstaff decomposition analysis to determine the contribution of various factors to the inequality
RESULTS: The prevalence of self-reported diabetes screening was 12.3%. A significant pro-poor inequality was observed (CI = −0.384; p < 0.001), indicating that screening uptake was concentrated among poorer populations. The relative index of inequality (RII) was −2.022. Wealth status contributed the largest share of inequality (47.2%), followed by education level (24.7%) and employment status (12.3%). Combined, socioeconomic and demographic factors explained 50.8% of the total inequality.
CONCLUSIONS: This study revealed marked pro-poor inequality in diabetes screening in Kenya, largely driven by wealth, education, and employment. While this pattern suggests improved access among lower-income groups, it may reflect under-screening among wealthier individuals. Sustaining pro-poor equity while expanding universal and inclusive screening coverage should be a key focus for national diabetes prevention and control strategies.
METHODS: We analysed nationally representative data from the 2015 Kenya STEPS survey (n=4,500). The outcome variable was self-reported blood glucose measurement by a health worker. Wealth status was measured using principal component analysis-derived index. We calculated concentration indices (CI) to measure socioeconomic inequality and employed Wagstaff decomposition analysis to determine the contribution of various factors to the inequality
RESULTS: The prevalence of self-reported diabetes screening was 12.3%. A significant pro-poor inequality was observed (CI = −0.384; p < 0.001), indicating that screening uptake was concentrated among poorer populations. The relative index of inequality (RII) was −2.022. Wealth status contributed the largest share of inequality (47.2%), followed by education level (24.7%) and employment status (12.3%). Combined, socioeconomic and demographic factors explained 50.8% of the total inequality.
CONCLUSIONS: This study revealed marked pro-poor inequality in diabetes screening in Kenya, largely driven by wealth, education, and employment. While this pattern suggests improved access among lower-income groups, it may reflect under-screening among wealthier individuals. Sustaining pro-poor equity while expanding universal and inclusive screening coverage should be a key focus for national diabetes prevention and control strategies.
Conference/Value in Health Info
2026-09, ISPOR Asia Pacific 2026, Bangkok, Thailand
Value in Health, Volume 55, Issue S1
Code
HPR1
Topic
Health Policy & Regulatory
Topic Subcategory
Health Disparities & Equity
Disease
SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity)