Predict Health Care Accessibility for Texas Medicaid Gap
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Data Spatial Representation
2.3. PCA-LA Rationale
2.4. PCA-LA Analysis Procedure
- (1)
- Pearson correlation analysis in dependent variables. The bivariate Pearson correlation is used to estimate correlations among pairs of variables. The coefficients are revealed whether a statistically significant linear relationship exists between two continuous variables, as well as directions and strengths of a linear relationship. In the SPSS environment, the function of a bivariate is completed by choosing the correlate option of the analyzing menus [17].
- (2)
- PCA to extract major components. Based on correlation analysis, we performed nine explanatory variables to extract component factors using PCA. The dataset was examined using Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test of Sphericity. The KMO test compares the correlation statistics to identify if the variables include sufficient differences to extract unique factors. A KMO value of 0.56 for nine explanatory variables is more than the cutoff value of 0.7. The Bartlett’s Test of Sphericity (BTS) value of 0.0 was significant (p < 0.001), validating that correlation between variables does exist in the population. Communality is a common variance between 0 and 1, using the remaining variables as factors, which was used to determine if any variables should be excluded from the factor analysis. A 0.7 cutoff is used to determine the significance of explanatory variables [18]. Using an eigenvalue threshold greater than 1.0, four factors are identified that could explain a cumulative 64.7% of the variance after six iterations (Figure 3). A varimax rotation was used to assist in the interpretation of the PCA analysis. The rotated component matrix was examined for variables with a cutoff of 0.7.
- (3)
- Perform logistical regression on major components. Geographic disparities in health care are well noted [19,20,21,22], but seldom research has shown healthcare spending varies by geographical location in the Medicaid gap via statistical analysis. Even some ethnic groups may be under-represented in the Medicaid expansion population because they are more likely to live in states that have not expanded Medicaid [23,24]. Current studies highlighted the policy adjustment from the qualitative analysis [25,26,27,28] except for Spencer et al. (2019). Despite the Medicaid Gap population in North Carolina for health access was conducted in Statistical inference, counter-comparative multi-variated indexes were not involved [29]. In addition, potential risks of chronic diseases were not estimated in the research. Most importantly, multicollinearity issues are not mentioned, which is a phenomenon that undermines the statistical significance of an independent variable, increases the standard deviation of variables, as well as the inverse direction of coefficients [30,31,32]. Logistical regression is generally popular in the application of epidemiology [33,34,35,36]; most research investigated models without eliminating multicollinearity among variables. PCA is one of the effective ways to reduce dimensionality and minimize multicollinearity. Currently published articles of logistical regression based on PCA are focused on genome-wide association studies [37] and disease research, such as gestational diabetes mellitus [38] and nephropathy [39]. Health care accessibility for the Texas Medicaid Gap took advantage of principal component analysis (PCA) to eliminate multicollinearity negative effects and to compare comprehensive social-economic impacts between unadjusted conditions and adjusted conditions. Therefore, PCA-based LA health access analysis of the medical gap is beneficial to provide scientific evidence regarding whether implementing ACA or not, to balance Medicaid policies in the U.S.
- (4)
- T-tests and F-tests were used to validate the significance of the model. T-test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. In SPSS, the “Independent-Samples t-test” function is selected in the “compare mean” default of analyzing menu [40]. In addition, the F-test can be used for determining whether the variances of two groups differ from each other. In the SPSS environment, “One-way analysis of variance (ANOVA Analysis)” is selected as “Homogeneity of variance test” in “compare means” of “options” [41].
3. Results
3.1. Sample Description
3.1.1. Health Care Access
3.1.2. Demographic Status
3.1.3. Economic, Educational, and Marital Status
3.1.4. Space–Time Sample Change
3.2. Correlation
3.3. PCA Results
3.4. Logistical Regression Analysis
3.4.1. Health Conditions
3.4.2. Demographic Impacts
3.4.3. Education Impacts
3.4.4. Marital Status Impacts
3.5. The Result of the T-Test and F-Test
4. Discussion
5. Conclusions
5.1. Limitation
5.2. Implication
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Year | Above-Poverty | Medicaid | Medicaid Gap | Total |
---|---|---|---|---|
2013 | 638 | 6871 | 575 | 8084 |
2014 | 939 | 9912 | 874 | 11,725 |
2015 | 904 | 9481 | 764 | 11,149 |
2016 | 670 | 8070 | 509 | 9249 |
2017 | 732 | 8252 | 613 | 9597 |
2018 | 573 | 7370 | 472 | 8415 |
2019 | 2249 | 1483 | 1094 | 4826 |
2020 | 21 | 7 | 10 | 38 |
Total | 6726 | 51,446 | 4911 | 63,083 |
Predictors | Acronym | Value | AbovePoverty | Medicaid | Medicaid Gap | Interpretation |
---|---|---|---|---|---|---|
Health Condition | ||||||
No regular source of care | NRC | 1 | 74.0% | 25.1% | 32.7% | No (Good) |
No regular source of care | 2 | 26.0% | 74.9% | 67.3% | Yes (Bad) | |
Last check-up more than a year ago | LCT | 1 | 79.9% | 76.4% | 46.0% | No (Good) |
Last check-up more than a year ago | 2 | 20.1% | 23.6% | 54.0% | Yes (Bad) | |
Could not see doctor due to cost | NSD | 1 | 76.6% | 81.6% | 48.1% | No (Good) |
Could not see a doctor due to the cost | 2 | 23.4% | 18.4% | 51.9% | Yes (Bad) | |
Skippedmedicationduetocost | SMC | 1 | 44.5% | 51.1% | 23.3% | No (Good) |
Skipped medication due to cost | 2 | 55.5% | 48.9% | 76.7% | Yes (Bad) | |
Cardiovascular disease | CVD | 1 | 13.5% | 10.2% | 16.1% | Yes (Bad) |
Cardiovascular disease | 2 | 86.5% | 89.8% | 83.9% | No (Good) | |
Diabetes | DT | 1 | 18% | 31.7% | 42.6% | Yes (Bad) |
Diabetes | 2 | 82% | 68.3% | 47.4% | No (Good) | |
Current smoker | CS | 1 | 79.2% | 88.1% | 75.4% | No (Good) |
Current smoker | 2 | 20.8% | 11.9% | 24.6% | Yes (Bad) | |
Demographic | ||||||
Age structure | AS | 1 | 10.7% | 3.9% | 10.6% | Age (<=25) |
Age structure | 2 | 23.4% | 12.6% | 47.3% | 25 < Age <= 44 | |
Age structure | 3 | 17.8% | 9.4% | 21.0% | 44< Age <= 55 | |
Age structure | 4 | 48.1% | 74.2% | 21.2% | Age (>55) | |
Sex | SEX | 1 | 34.7% | 45.3% | 36.3% | Male |
Sex | 2 | 65.3% | 54.7% | 63.7% | female | |
Race | ||||||
White | RACE | 1 | 30.9% | 50.0% | 34.3% | White |
Hispanic | 2 | 44.7% | 31.3% | 54.0% | Hispanic | |
Black | 3 | 20.8% | 13.0% | 8.8% | Black | |
Other | 4 | 3.6% | 5.7% | 2.9% | Other | |
Economic condition | ||||||
Employment | EM | 1 | 30.3% | 54.2% | 50.5% | Yes (Good) |
Employment | 2 | 69.7% | 45.8% | 49.5% | No (Bad) | |
Living with dependent children | LDC | 1 | 68.5% | 81.5% | 44.5% | No (Good) |
Living with dependent children | 2 | 31.5% | 18.5% | 55.5% | Yes (Bad) | |
Education | ||||||
College graduate | ED | 1 | 45.1% | 44.2% | 28.6% | |
High school graduate/GED | 2 | 32.1% | 34.4% | 34.1% | ||
Did not finish high school | 3 | 22.7% | 21.4% | 37.3% | ||
Marital status | MS | 1 | 32.1% | 60.0% | 35.4% | married |
Marital status | 2 | 67.9% | 40.0% | 64.6% | single | |
Health status | 1 | 67.3% | 82.1% | 27.5% | good | |
Health status | 0 | 32.7% | 17.9% | 72.5% | poor or fair |
Predictors | Acronym | NRC | LCT | NSD | SMC | CS | AS | SEX | RACE | EM | LDC | ED | MS | CVD | DT |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
No regular source of care | NRC | 1/0.00 | |||||||||||||
Last check up more than a year ago | LCT | −0.53/0.00 | |||||||||||||
Could not see doctor due to cost | NSD | 0.25/0.00 | 0.24/0.00 | ||||||||||||
Skipped medication due to cost | SMC | 0.10/0.00 | 0.01/0.04 | 0.27/0.00 | |||||||||||
Current smoker | CS | −0.08/0.00 | 0.10/0.00 | 0.14/0.00 | 0.06/0.00 | ||||||||||
Age Structure | AS | −0.17/0.00 | −0.27/0.00 | −0.16/0.00 | −0.23/0.00 | −0.03/0.00 | |||||||||
Sex | SEX | 0.04/0.00 | −0.07/0.00 | 0.11/0.00 | 0.02/0.00 | −0.05/0.00 | 0.01/0.0.3 | ||||||||
Race | RACE | 0.02/0.04 | 0.04/0.00 | 0.08/0.00 | 0.19/0.00 | 0.02/0.00 | −0.15/0.00 | 0.01/0.1 | |||||||
Employment | EM | 0.09/0.00 | −0.14/0.00 | −0.02/0.00 | 0.14/0.00 | −0.02/0.00 | 0.27/0.00 | 0.17/0.00 | −0.03/0.00 | ||||||
Living with dependent children | LDC | 0.11/0.00 | 0.16/0.00 | 0.16/0.00 | 0.17/0.00 | −0.00/0.03 | −0.44/0.00 | 0.13/0.00 | 0.11/0.00 | −0.17/0.00 | |||||
Education | ED | 0.18/0.00 | 0.13/0.00 | 0.15/0.00 | 0.29/0.00 | 0.10/0.00 | −0.04/0.00 | 0.04/0.00 | 0.13/0.00 | 0.06/0.00 | 0.16/0.00 | ||||
Marital status | MS | −0.07/0.00 | 0.47/0.00 | 0.10/0.00 | 0.17/0.00 | 0.12/0.00 | −0.14/0.00 | 0.10/0.00 | 0.10/0.00 | 0.06/0.00 | −0.09/0.00 | 0.06/0.00 | |||
CVD | CVD | −0.07/0.00 | 0.07/0.00 | −0.03/0.00 | −0.03/0.00 | −0.02/0.00 | −0.09/0.00 | 0.05/0.00 | −0.01/0.24 | −0.15/0.00 | 0.05/0.00 | −0.03/0.00 | 0.01/0.01 | ||
Diabetes | DT | −0.21/0.00 | 0.02/0.00 | −0.14/0.00 | 0.01/0.00 | 0.02/0.00 | −0.06/0.00 | −0.01/0.00 | −0.13/0.00 | −0.09/0.00 | 0.00/0.7 | −0.23/0.00 | 0.03/0.00 | 0.16/0.00 | |
Health status | HS | −0.19/0.00 | −0.04/0.00 | 0.22/0.00 | 0.20/0.00 | 0.05/0.00 | −0.1/0.00 | 0.02/0.00 | −0.1/0.00 | 0.12/0.00 | 0.04/0.00 | −0.21/0.00 | −0.11/0.00 | −0.03/0.00 | 0.29/0.00 |
Acronym | Extraction | Component | |||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | ||
LCT | 0.849 | 0.917 | 0.053 | 0.072 | −0.010 |
NRC | 0.878 | 0.903 | 0.236 | 0.078 | 0.032 |
ED | 0.672 | −0.030 | 0.816 | 0.023 | −0.073 |
SMC | 0.492 | 0.283 | 0.595 | −0.021 | 0.236 |
NSD | 0.274 | 0.257 | 0.417 | 0.070 | 0.171 |
AS | 0.792 | −0.168 | 0.159 | −0.840 | −0.185 |
LDC | 0.765 | −0.018 | 0.262 | 0.682 | −0.289 |
MS | 0.751 | 0.061 | −0.031 | 0.074 | 0.861 |
CS | 0.356 | −0.029 | 0.265 | −0.127 | 0.518 |
Unadjusted | Adjusted | PCA—Unadjusted | PCA—Adjusted | |||
---|---|---|---|---|---|---|
Variable/Factor | No. of Patients (%) | No. of Patients(N) | OR (95% CI) a | aOR (95% CI) b | aOR (95% CI) c | aOR (95% CI) d |
No regular source of care | ||||||
Above-poverty | ||||||
NO—1 | 74.0% | 4046 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 26.0% | 1419 | 1.9 [1.68, 2.19] * | 3.29 [2.1, 5.12] * | 1.7 [1.54, 1.9] * | 1.33 [1.16, 1.53] * |
Traditional Medicaid | ||||||
NO—1 | 25.1% | 12,582 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 74.9% | 37,468 | 1.02 [0.97, 1.08] | 1.56 [0.98, 2.48] | 1.56 [0.97, 2.52] | 1.18 [0.64, 2.17] |
Medicaid Gap | ||||||
NO—1 | 32.7% | 1561 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 67.3% | 3213 | 1.89 [1.66, 2.16] * | 5.8 [4.01, 8.44] * | 1.47 [1.27, 1.7] * | 1.37 [1.18, 1.6] * |
Last check-up more than a year ago | ||||||
Above-poverty | ||||||
NO—1 | 79.9% | 5154 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 20.1% | 1294 | 0.36 [0.24, 0.54] * | 0.35 [0.22, 0.55] * | 1.7 [1.54, 1.9] * | 1.33 [1.16, 1.53] * |
Traditional Medicaid | ||||||
NO—1 | 76.4% | 38,826 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 23.6% | 11,991 | 0.65 [0.61, 0.69] * | 1.78 [0.67, 4.71] | 1.56 [0.97, 2.52] | 1.18 [0.64, 2.17] |
Medicaid Gap | ||||||
NO—1 | 46% | 2068 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 54% | 2427 | 1.07 [0.94, 0.1.22] | 0.24 [0.17, 0.35] * | 1.47 [1.27, 1.7] * | 1.37 [1.18, 1.6] * |
Could not see a doctor due to the cost | ||||||
Above-poverty | ||||||
NO—1 | 76.6% | 5129 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 23.4% | 1567 | 0.45 [0.4, 0.5] * | 0.60 [0.48, 0.75] * | ||
Traditional Medicaid | ||||||
NO—1 | 81.6% | 1210 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 18.4% | 273 | 0.55 [0.4, 0.76] * | 0.52 [0.35, 0.78] * | ||
Medicaid Gap | ||||||
NO—1 | 48.1% | 2350 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
YES—2 | 51.9% | 2539 | 0.37 [0.32, 0.42] * | 0.38 [0.32, 0.44] * | ||
Age | ||||||
Above-poverty | ||||||
Age (<=25)—1 | 10.7% | 718 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Age (>25 and <=44)—2 | 23.4% | 1574 | 0.26 [0.2, 0.35] * | 0.44 [0.28, 0.67] * | 0.26 [0.2, 0.35] * | 0.26 [0.19, 0.35] * |
Age (>44 and <=55)—3 | 17.8% | 1197 | 0.11 [0.08, 0.14] * | 0.30 [0.2, 0.47] * | 0.11 [0.08, 0.14] * | 0.11 [0.08, 0.15] * |
Age (>55)—4 | 48.1% | 3237 | 0.15 [0.11, 0.2] * | 0.23 [0.15, 0.36] * | 0.15 [0.11, 0.2] * | 0.08 [0.06, 0.11] * |
Traditional Medicaid | ||||||
Age (<=25)—1 | 3.9% | 58 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Age (>25 and <=44)—2 | 12.6% | 187 | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] |
Age (>25 and <=44)—3 | 9.4% | 140 | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] |
Age (>55)—4 | 74.2% | 1105 | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] | 0.0 [0.0, 0.0] |
Medicaid Gap | ||||||
Age (<=25)—1 | 10.6% | 519 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Age (>25 and <=44)—2 | 47.3% | 2321 | 0.55 [0.42, 0.73] * | 0.58 [0.42, 0.8] * | 0.55 [0.42, 0.73] * | 0.51 [0.38, 0.69] * |
Age (>25 and <=44)—3 | 21.0% | 1032 | 0.28 [0.21, 0.37] * | 0.41 [0.29, 0.57] * | 0.28 [0.21, 0.37] * | 0.28 [0.2, 0.38] * |
Age (>55)—4 | 21.2% | 1039 | 0.21 [0.16, 0.28] * | 0.32 [0.22, 0.45] * | 0.21 [0.16, 0.28] * | 0.18 [0.14, 0.25] * |
Education | ||||||
Above-poverty | ||||||
Education (College Graduate) | 45.1% | 3033 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Education (High School Graduate/GED) | 32.1% | 2157 | 0.61 [0.54, 0.69] * | 0.81 [0.63, 1.02] | 0.61 [0.54, 0.69] * | 0.8 [0.68, 0.93] ** |
Education (Did not finish High School) | 22.7% | 1528 | 0.42 [0.37, 0.48] * | 0.71 [0.55, 0.92] * | 0.42 [0.37, 0.48] * | 0.63 [0.53, 0.75] * |
Traditional Medicaid | ||||||
Education (College Graduate) | 44.2% | 658 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Education (High School Graduate/GED) | 34.4% | 513 | 0.7 [0.51, 0.96] ** | 0.62 [0.42, 0.92] * | 0.7 [0.51, 0.96] ** | 0.86 [0.48, 1.54] |
Education (Did not finish High School) | 21.4% | 319 | 0.61 [0.43, 0.87] * | 0.6 [0.38, 0.95] * | 0.61 [0.43, 0.87] * | 0.62 [0.33, 1.17] |
Medicaid Gap | ||||||
Education (College Graduate) | 28.6% | 1400 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Education (High School Graduate/GED) | 34.1% | 1673 | 0.87 [0.74, 1.03] | 0.84 [0.68, 1.02] | 0.87 [0.74, 1.03] | 0.88 [0.73, 1.06] * |
Education (Did not finish High School) | 37.3% | 1830 | 0.62 [0.53, 0.73] * | 0.63 [0.52, 0.78] * | 0.62 [0.53, 0.73] * | 0.72 [0.6, 0.86] * |
Marital Status | ||||||
Above-poverty | ||||||
Married—1 | 32.1% | 2147 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Single—2 | 67.9% | 4550 | 0.69 [0.61, 0.77] * | 0.88 [0.71, 1.1] | 0.69 [0.61, 0.77] * | 0.63 [0.55, 0.72] * |
Traditional Medicaid | ||||||
Married—1 | 60.0% | 30,769 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Single—2 | 40.0% | 20,504 | 1.54 [1.47, 1.61] * | 0.61 [0.39, 1.0] ** | 1.54 [1.47, 1.61] * | 0.52 [0.31, 0.88] ** |
Medicaid Gap | ||||||
Married—1 | 35.4% | 1731 | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) | 1.00 (referent) |
Single—2 | 64.6% | 3155 | 1.0 [0.89, 1.15] | 0.93 [0.78, 1.09] | 1.0 [0.89, 1.15] * | 0.85 [0.73, 0.98] ** |
Components | Equal Variances | t | df | Sig. | Lower CI | Lower CI |
---|---|---|---|---|---|---|
Health conditions | assumed | −4.029 | 4103 | 0.000 | −0.224 | −0.077 |
not assumed | −3.906 | 2099 | 0.000 | −0.227 | −0.075 | |
Demographic | assumed | 8.987 | 4103 | 0.000 | 0.212 | 0.33 |
not assumed | 9.068 | 2287 | 0.000 | 0.212 | 0.33 | |
Education | assumed | −13.368 | 4103 | 0.000 | −0.504 | −0.375 |
not assumed | −13.151 | 2164 | 0.000 | −0.505 | −0.374 | |
Marital Status | assumed | 2.143 | 4103 | 0.032 | 0.006 | 0.14 |
not assumed | 2.113 | 2176 | 0.035 | 0.005 | 0.142 |
Components | Linear Term | df | Mean Square | F | Sig. |
---|---|---|---|---|---|
Combined | 716 | 0.352 | 1.998 | 0.000 | |
Health conditions | Weighted | 1 | 3.350 | 18.990 | 0.000 |
Deviation | 715 | 0.348 | 1.974 | 0.000 | |
Demographic | Weighted | 1 | 16.408 | 93.014 | 0.000 |
Deviation | 715 | 0.330 | 1.871 | 0.000 | |
Education | Weighted | 1 | 35.476 | 201.105 | 0.000 |
Deviation | 715 | 0.303 | 1.720 | 0.000 | |
Marital Status | Weighted | 1 | 0.950 | 5.386 | 0.000 |
Deviation | 715 | 0.352 | 1.993 | 0.000 |
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Zhang, J.; Wu, X. Predict Health Care Accessibility for Texas Medicaid Gap. Healthcare 2021, 9, 1214. https://doi.org/10.3390/healthcare9091214
Zhang J, Wu X. Predict Health Care Accessibility for Texas Medicaid Gap. Healthcare. 2021; 9(9):1214. https://doi.org/10.3390/healthcare9091214
Chicago/Turabian StyleZhang, Jinting, and Xiu Wu. 2021. "Predict Health Care Accessibility for Texas Medicaid Gap" Healthcare 9, no. 9: 1214. https://doi.org/10.3390/healthcare9091214