Sociodemographic patterns of health insurance coverage in Namibia

• Hoofdauteurs: Sophie H. Allcock (Auteur), Elizabeth H. Young (Auteur), Manjinder S. Sandhu (Auteur)
• Formaat: Boek
• Gepubliceerd in: BMC, 2019-01-01T00:00:00Z.
• Onderwerpen: article
• Online toegang: Connect to this object online.
• Notes: 10.1186/s12939-019-0915-4; 1475-9276
• Keywords: Health insurance; Namibia; Education; Women; Wealth; Public aspects of medicine; RA1-1270
• Source: International Journal for Equity in Health, Vol 18, Iss 1, Pp 1-11 (2019)

Abstract Introduction Health insurance has been found to increase healthcare utilisation and reduce catastrophic health expenditures in a number of countries; however, coverage is often unequally distributed among populations. The sociodemographic patterns of health insurance in Namibia are not fully understood. We aimed to assess the prevalence of health insurance, the relation between health insurance and health service utilisation and to explore the sociodemographic factors associated with health insurance in Namibia. Such findings may help to inform health policy to improve financial access to healthcare in the country. Methods Using data on 14,443 individuals, aged 15 to 64 years, from the 2013 Namibia Demographic and Health Survey, the association between health insurance and health service utilisation was investigated using multivariable mixed effects Poisson regression analyses, adjusted for sociodemographic covariates and regional, enumeration area and household clustering. Multivariable mixed effects Poisson regression analyses were also conducted to explore the association between key sociodemographic factors and health insurance, adjusted for covariates and clustering. Effect modification by sex, education level and wealth quintile was also explored. Results Just 17.5% of this population were insured (men: 20.2%; women: 16.2%). In fully-adjusted analyses, education was significantly positively associated with health insurance, independent of other sociodemographic factors (higher education RR: 3.98; 95% CI: 3.11-5.10; p < 0.001). Female sex (RR: 0.83; 95% CI: 0.74-0.94; p = 0.003) and wealth (highest wealth quintile RR: 13.47; 95% CI: 9.06-20.04; p < 0.001) were also independently associated with insurance. There was a complex interaction between sex, education and wealth in the context of health insurance. With increasing education level, women were more likely to be insured (p for interaction < 0.001), and education had a greater impact on the likelihood of health insurance in lower wealth quintiles. Conclusions In this population, health insurance was associated with health service utilisation but insurance coverage was low, and was independently associated with sex, education and wealth. Education may play a key role in health insurance coverage, especially for women and the less wealthy. These findings may help to inform the targeting of strategies to improve financial protection from healthcare-associated costs in Namibia.