Predictors of national health insurance membership among the poor with different education levels in Indonesia

Keyphrases

• Ability to Pay 6%
• Access to Care 6%
• Binary Logistic Regression 6%
• Education Level 100%
• Education Status 6%
• Employment Status 6%
• Gender Education 6%
• Gender Employment 13%
• Gender Status 6%
• Government Investment 6%
• Higher Education 13%
• Higher Education Levels 6%
• Indonesia 100%
• Inequity 6%
• Insurance Mechanism 6%
• Literacy 6%
• Low Education 6%
• Marital Status 20%
• Ministry of Health 6%
• National Health Insurance 100%
• Nationwide Survey 6%
• Participant Satisfaction 6%
• Pay Satisfaction 6%
• Poor People 13%
• Poor Population 40%
• Population Education 6%
• Primary Education 6%
• Republic of Indonesia 6%
• Residence Status 6%
• Secondary Dataset 6%
• Secondary Education 6%
• Socioeconomic Disparities 6%
• Universal Health Coverage 6%
• Urban Areas 6%
• Wealth Status 6%
• Weighted Sampling 6%
• Willingness to Pay 6%

Social Sciences

• Education and Employment 6%
• Education Level 100%
• Indonesia 100%
• Marital Status 20%
• National Health Insurance 100%
• Population Education 6%
• Primary Education 6%
• Secondary Education 6%
• Wealth 20%
• Willingness-to-Pay 6%

Economics, Econometrics and Finance

• Logit Model 6%
• National Health Insurance 100%
• Wealth 20%
• Willingness to Pay 6%