Fragmented Democracy: Medicaid, Federalism, and Unequal Politics, by Jamila Michener

López-Santana, Mariely

doi:10.1093/publius/pjz026

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…Beyond the statistical significance of the Medicaid gap, we are further aware that the deep root of the decline of Medicaid expansion is not about financial limits, but about the political contest. We called "Politics of Medicaid", which means democratic states asserting Medicaid importance when republic states holding Medicaid cuts [28]. When standing at marginal in a polity, the Medicaid gap has to stop to think about how policy and political vulnerability [29].…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, the deep root relies on federalism. Medicaid is the epitome of how federalism produces inequity by constructing geographical disparity in access to vital healthcare resources [28]. As a boon, Medicaid is a program that saves thousands of lives each year and rendered tens of millions of Americans with free security of health insurance [30].…”

Section: Discussionmentioning

confidence: 99%

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Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap

Zhang¹,

Wu²

2021

Preprint

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Background12 states without expanded Medicaid caused 2 million people who were under the poverty line across the U.S to be in Medicaid limbo and not eligible for subsidized health plans on the Affordable Care Act insurance exchanges. In order to amplify geographic equity, this paper aims to explore the health access for Medicaid gaps in Texas. MethodsPrincipal Component-based logistical regression algorithms (PCA-LA) is provided data visualization and comparison in between unadjusted and adjusted Medicaid programs. Initially, Principal Component Analysis (PCA) eliminated well-known multiplicity problems between explanatory variables in the application of epidemiology. Optimized the traditional logistical Regression (LR), the PCA-LA method, is considered health status (HS) as a dependent variable with 0 (“poor” health) and 1 (“good” health), fourteen social-economic indexes as independent variables. ResultsAfter Principal Component Analysis (PCA), 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 1 OR, and negatively significant to ED, AS, and MS due to less than 1 OR. ConclusionsThis 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 raise researchable direction of the Medicaid program and make a timely scientific judgment of Texas healthcare accessibility.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap

Zhang¹,

Wu²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We called it the “Politics of Medicaid”, which means democratic states asserting Medicaid importance while republic states are holding Medicaid cuts [ 50 ]. When standing at marginal in a polity, the Medicaid gap has to stop to think about how policy and political vulnerability [ 51 ]. Indeed, the deep root relies on federalism.…”

Section: Discussionmentioning

confidence: 99%

Predict Health Care Accessibility for Texas Medicaid Gap

Zhang

2021

Healthcare

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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.

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Fragmented Democracy: Medicaid, Federalism, and Unequal Politics, by Jamila Michener

Cited by 2 publications

References 3 publications

Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap

Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap

Predict Health Care Accessibility for Texas Medicaid Gap

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