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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.
Jinting Zhang; Xiu Wu. Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap. 2021, 1 .
AMA StyleJinting Zhang, Xiu Wu. Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap. . 2021; ():1.
Chicago/Turabian StyleJinting Zhang; Xiu Wu. 2021. "Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap." , no. : 1.
As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).
Jinting Zhang; Xiu Wu; T. Chow. Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties. International Journal of Environmental Research and Public Health 2021, 18, 5541 .
AMA StyleJinting Zhang, Xiu Wu, T. Chow. Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties. International Journal of Environmental Research and Public Health. 2021; 18 (11):5541.
Chicago/Turabian StyleJinting Zhang; Xiu Wu; T. Chow. 2021. "Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties." International Journal of Environmental Research and Public Health 18, no. 11: 5541.
As COVID-19 run rampant in high-density housing sites, it is important to use real-time data tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research is appropriately analyzed the disparities between spatial-temporal clusters, expectation Maximization clustering (EM) and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to South-east Texas analysis in GWR modeling. The sensitive period took place in the last two quarters in 2020. We explored Postgre application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform Principal Component Analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility and satisfy for the need of Emergency Operations Plan (EOP).
Jinting Zhang; Xiu Wu; T. Edwin Chow. Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality Impacts on Texas Counties. 2021, 1 .
AMA StyleJinting Zhang, Xiu Wu, T. Edwin Chow. Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality Impacts on Texas Counties. . 2021; ():1.
Chicago/Turabian StyleJinting Zhang; Xiu Wu; T. Edwin Chow. 2021. "Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality Impacts on Texas Counties." , no. : 1.
Since COVID-19 is extremely threatening to human health, it is significant to determine its impact factors to curb the virus spread. To tackle the complexity of COVID-19 expansion on a spatial–temporal scale, this research appropriately analyzed the spatial–temporal heterogeneity at the county-level in Texas. First, the impact factors of COVID-19 are captured on social, economic, and environmental multiple facets, and the communality is extracted through principal component analysis (PCA). Second, this research uses COVID-19 cumulative case as the dependent variable and the common factors as the independent variables. According to the virus prevalence hierarchy, the spatial–temporal disparity is categorized into four quarters in the GWR analysis model. The findings exhibited that GWR models provide higher fitness and more geodata-oriented information than OLS models. In El Paso, Odessa, Midland, Randall, and Potter County areas in Texas, population, hospitalization, and age structures are presented as static, positive influences on COVID-19 cumulative cases, indicating that they should adopt stringent strategies in curbing COVID-19. Winter is the most sensitive season for the virus spread, implying that the last quarter should be paid more attention to preventing the virus and taking precautions. This research is expected to provide references for the prevention and control of COVID-19 and related infectious diseases and evidence for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.
Xiu Wu; Jinting Zhang. Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR). Environmental Science and Pollution Research 2021, 28, 43732 -43746.
AMA StyleXiu Wu, Jinting Zhang. Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR). Environmental Science and Pollution Research. 2021; 28 (32):43732-43746.
Chicago/Turabian StyleXiu Wu; Jinting Zhang. 2021. "Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR)." Environmental Science and Pollution Research 28, no. 32: 43732-43746.
In March 2020, the United States government began a series of measures designed to dramatically restrict immigration as part of its response to the global health crisis caused by the coronavirus pandemic. This included Title 42, which deported asylum seekers immediately and prevented them from applying for asylum. These measures worsened an already precarious situation at the US–Mexico border for an estimated 60,000 asylum seekers who were prevented, by the Trump administration’s ‘Remain in Mexico’ (aka MPP) policy enacted in January 2019, from remaining in the United States while they awaited their asylum hearings. In-depth interviews, participant observation, and social media analysis with humanitarian and legal advocates for asylum seekers living in a camp at the border in Matamoros, Mexico reveal that COVID-19’s impacts are not limited to public health concerns. Rather, COVID-19’s impacts center on how the Trump administration weaponized the virus to indefinitely suspend the asylum system. We argue that the Matamoros refugee camp provides a strategic vantage point to understand the repercussions of state policies of exclusion on im/mobility and survival strategies for asylum seekers. Specifically, we use the analytical lenses of the politics of im/mobility, geographies of exclusion, and asylum seeker resilience to identify how COVID-19 has shaped the im/mobility and security of the camp and its residents in unexpected ways. At the same time, our research illustrates that camp residents exercise im/mobility as a form of political visibility to contest and ameliorate their precarity as they find themselves in conditions not of their choosing.
Sarah Blue; Jennifer Devine; Matthew Ruiz; Kathryn McDaniel; Alisa Hartsell; Christopher Pierce; Makayla Johnson; Allison Tinglov; Mei Yang; Xiu Wu; Sara Moya; Elle Cross; Carol Starnes. Im/Mobility at the US–Mexico Border during the COVID-19 Pandemic. Social Sciences 2021, 10, 47 .
AMA StyleSarah Blue, Jennifer Devine, Matthew Ruiz, Kathryn McDaniel, Alisa Hartsell, Christopher Pierce, Makayla Johnson, Allison Tinglov, Mei Yang, Xiu Wu, Sara Moya, Elle Cross, Carol Starnes. Im/Mobility at the US–Mexico Border during the COVID-19 Pandemic. Social Sciences. 2021; 10 (2):47.
Chicago/Turabian StyleSarah Blue; Jennifer Devine; Matthew Ruiz; Kathryn McDaniel; Alisa Hartsell; Christopher Pierce; Makayla Johnson; Allison Tinglov; Mei Yang; Xiu Wu; Sara Moya; Elle Cross; Carol Starnes. 2021. "Im/Mobility at the US–Mexico Border during the COVID-19 Pandemic." Social Sciences 10, no. 2: 47.
A spatial-temporal panel dataset was collected from 101 countries during 2006–2016. Using partial correlation (PC) and ordinary correlation (OR) analyses, this research examines the relationship between ecological footprint (EF) and subjective well-being (SWB) to measure environmental impacts on people’s happiness. Gross domestic product (GDP), urbanization rate (UR), literacy rate (LR), youth life expectancy (YLE), wage and salaried workers (WSW), political stability (PS), voice accountability (VA) are regarded as control variables. Total bio-capacity (TBC), ecological crop-land footprints (ECL), ecological grazing-land footprint (EGL), and ecological built-up land footprint (EBL) have significant positive influences on SWB, but ecological fish-land (EFL) has significant negative influences on SWB. Ecological carbon footprint (ECF) is significantly negatively related to SWB in developed countries. An increase in the amount of EF factors is associated with a country’s degree of development. Political social–economic impacts on SWB disguised environmental contribution on SWB, especially CBF impacts on SWB. The use of PC in examining the association between SWB and EF helps bridge a knowledge gap and facilitate a better understanding of happiness.
Jinting Zhang; F. Zhan; Xiu Wu; Daojun Zhang. Partial Correlation Analysis of Association between Subjective Well-Being and Ecological Footprint. Sustainability 2021, 13, 1033 .
AMA StyleJinting Zhang, F. Zhan, Xiu Wu, Daojun Zhang. Partial Correlation Analysis of Association between Subjective Well-Being and Ecological Footprint. Sustainability. 2021; 13 (3):1033.
Chicago/Turabian StyleJinting Zhang; F. Zhan; Xiu Wu; Daojun Zhang. 2021. "Partial Correlation Analysis of Association between Subjective Well-Being and Ecological Footprint." Sustainability 13, no. 3: 1033.