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Prevalence and factors associated with health insurance coverage in resource-poor urban settings in Nairobi, Kenya: a cross-sectional study

OBJECTIVE: To determine the prevalence of health insurance and associated factors among households in urban slum settings in Nairobi, Kenya. DESIGN: The data for this study are from a cross-sectional survey of adults aged 18 years or older from randomly selected households in Viwandani slums (Nairob...

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Detalles Bibliográficos
Autores principales: Otieno, Peter O, Wambiya, Elvis Omondi Achach, Mohamed, Shukri F, Donfouet, Hermann Pythagore Pierre, Mutua, Martin K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6924758/
https://www.ncbi.nlm.nih.gov/pubmed/31843827
http://dx.doi.org/10.1136/bmjopen-2019-031543
Descripción
Sumario:OBJECTIVE: To determine the prevalence of health insurance and associated factors among households in urban slum settings in Nairobi, Kenya. DESIGN: The data for this study are from a cross-sectional survey of adults aged 18 years or older from randomly selected households in Viwandani slums (Nairobi, Kenya). Respondents participated in the Lown scholars’ study conducted between June and July 2018. SETTING: The Lown scholars’ survey was nested in the Nairobi Urban Health and Demographic Surveillance System in Viwandani slums in Nairobi, Kenya. PARTICIPANTS: A total of 300 randomly sampled households participated in the survey. The study respondents comprised of either the household head, their spouses or credible adult household members. PRIMARY OUTCOME MEASURE: The primary outcome of this study was enrolment in a health insurance programme. The households were classified into two groups: those having at least one member covered by health insurance and those without any health insurance cover. RESULTS: The prevalence of health insurance in the sample was 43%. Being unemployed (adjusted OR (aOR) 0.17; p<0.05; 95% CI 0.06 to 0.47) and seeking care from a public health facility (aOR 0.50; p<0.05; 95% CI 0.28 to 0.89) was significantly associated with lower odds of having a health insurance cover. The odds of having a health insurance cover were significantly lower among respondents who perceived their health status as good (aOR 0.62; p<0.05; 95% CI 1.17 to 5.66) and those who were unsatisfied with the cost of seeking primary care (aOR 0.34; p<0.05; 95% CI 0.17 to 0.69). CONCLUSIONS: Health insurance coverage in Viwandani slums in Nairobi, Kenya, is low. As universal health coverage becomes the growing focus of Kenya’s ‘Big Four Agenda’ for socioeconomic transformation, integrating enabling and need factors in the design of the national health insurance package may scale-up social health protection.