• Journal of Digital Convergence
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• v.16 no.9
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• pp.279-291
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• 2018

This study is designed to demonstrate risk factors of unmet medical care for people with cerebrovascular disease. To do this, statistical analysis was performed by using hierarchical logistic regression analysis with SPSS/WIN24.0 program using Korean Medical Panel data in 2014. In the final model of the hierarchical logistic regression analysis, which is based on Anderson's Model, adjusted for the factors of the predisposing and enabling factors, the explanatory variables affecting the unmet medical development are gender, economic activity, income level, the experience of lying in a sickbed, restriction on activity, subjective health condition, and the number of chronic diseases. Based on the results of this study, the practical and policy implications for the effective management and treatment of cerebrovascular disease should be included in the countermeasures for cerebrovascular disease, a strategy to reduce the unmet medical incidence of cerebrovascular disease, in order to meet the medical needs, the necessity of comprehensive measures considering various dimensions of variables and the influential variables of unmet medical emergence have been suggested for the necessity of making a detailed service manual that can improve accessibility to medical services.