Predict Health Care Accessibility for Texas Medicaid Gap
Date
2021-09-15
Authors
Zhang, Jinting
Wu, Xiu
Journal Title
Journal ISSN
Volume Title
Publisher
Multidisciplinary Digital Publishing Institute
Abstract
Medicaid is a unique approach in ensuring the below poverty population obtains free insurance coverage under federal and state provisions in the United States. Twelve states without expanded Medicaid caused two million people who were under the poverty line into health insecurity. Principal Component-based logistical regression (PCA-LA) is used to consider health status (HS) as a dependent variable and fourteen social-economic indexes as independent variables. Four composite components incorporated health conditions (i.e., “no regular source of care” (NRC), “last check-up more than a year ago” (LCT)), demographic impacts (i.e., four categorized adults (AS)), education (ED), and marital status (MS). Compared to the unadjusted LA, direct adjusted LA, and PCA-unadjusted LA three methods, the PCA-LA approach exhibited objective and reasonable outcomes in presenting an odd ratio (OR). They included that health condition is positively significant to HS due to beyond one OR, and negatively significant to ED, AS, and MS. This paper provided quantitative evidence for the Medicaid gap in Texas to extend Medicaid, exposed healthcare geographical inequity, offered a sight for the Centers for Disease Control and Prevention (CDC) to improve the Medicaid program and make political justice for the Medicaid gap.
Description
Keywords
medicaid gap, health access, principal components, logistical regression, Geography and Environmental Studies
Citation
Zhang, J., & Wu, X. (2021). Predict health care accessibility for Texas Medicaid gap. Healthcare, 9(9), 1214.
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This work is licensed under a Creative Commons Attribution 4.0 International License.