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Ownership of mainstream media devices and digital access tools predict health insurance coverage status among Kenyans: A cross-sectional study of the Kenya Demographic and Health Survey (KDHS) 2022 dataset

Aldousari, E.; Alhajeri, M.; Kithinji, D.

2025-06-20 public and global health
10.1101/2025.06.18.25329901 medRxiv
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BackgroundLow health insurance coverage, poverty, and reduced donor funding impede access to healthcare in Kenya. The Social Health Authority (SHA) unveiled in Kenya in October 2024 can provide the healthcare financing solutions needed through targeted interventions to improve health insurance coverage. This study sought to identify modifiable factors that influence health insurance coverage status in Kenya to provide the basis for targeted interventions by SHA. MethodsThe Household Recode, Individual Recode, and Mens Recode datasets from the Kenya Demographic and Health Survey conducted between February and July 2022 were combined to form the dataset. Proportions of individuals with and without health insurance as well as those with the various potential determinants were calculated. The associations between the potential determinants and health insurance coverage status were estimated using bivariate analysis through Pearsons Chi-square test. Multivariable logistic regression facilitated the identification of determinants of health insurance coverage in Kenya. ResultsData of 14232 participants aged 15-54 years whose health insurance coverage status were indicated were analyzed. The participants were mainly female (66%), in good health (79%), literate (75%), relatively poor (56%), connected to electricity (55%), and radio listeners (61%). The rate of health insurance coverage was 34%, with 93% of them covered by NHIF. Out of the 16 potential predictors (chi-square range = 21-2694, p < 0.0001) included in the logistic regression model, only six were significant predictors of health insurance coverage: education level, wealth index, ownership of a solar panel, and television, mobile phone, and computer ownership (14-47% difference in odds). ConclusionHealth insurance coverage remains low in Kenya. Education levels, economic status, and media factors are significant determinants of health insurance coverage. SHA can leverage the identified determinants to strengthen interventions that worked for NHIF and address the challenges that impede health insurance uptake among Kenyans in the informal sector.

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