scholarly journals Predict Health Care Accessibility for Texas Medicaid Gap

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1214
Author(s):  
Jinting Zhang ◽  
Xiu Wu
Keyword(s):  
Insurance Coverage ◽  
Principal Component ◽  
Poverty Line ◽  
The United States ◽  
Health Condition ◽  
Quantitative Evidence ◽  
Regular Source ◽  
Control And Prevention ◽  
Unique Approach ◽  
Source Of Care

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.

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

2021 ◽  
Author(s):  
Jinting Zhang ◽  
Xiu Wu
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Good Health ◽  
Poverty Line ◽  
Component Analysis ◽  
Health Condition ◽  
Quantitative Evidence ◽  
Regression Algorithms ◽  
Logistical Regression ◽  
Scientific Judgment

Abstract 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.

Access to Medical Care for Children and Adolescents in the United States

PEDIATRICS ◽  
1990 ◽  
Vol 86 (5) ◽  
pp. 666-673 ◽  
Author(s):  
David L. Wood ◽  
Rodney A. Hayward ◽  
Christopher R. Corey ◽  
Howard E. Freeman ◽  
Martin F. Shapiro
Keyword(s):  
Health Care ◽  
Medical Care ◽  
Children And Adolescents ◽  
The United States ◽  
Emergency Rooms ◽  
Community Clinics ◽  
Hospital Outpatient ◽  
Regular Source ◽  
Outpatient Departments ◽  
Source Of Care

To evaluate access to health care for American children and adolescents, a telephone survey of a national random sample of households was conducted in which 2182 children 17 years or younger were studied. Approximately 10% had no medical insurance; 10% had no regular source of care; and 18% identified emergency rooms, community clinics, or hospital outpatient departments as their usual site of medical care. Children who were uninsured, poor, or nonwhite were less likely to have seen a physician in the past year (P < .001), and uninsured children were less likely to have up-to-date immunizations. Logistic regression analyses revealed that poor, uninsured, or nonwhite children less frequently had a regular source of care; more frequently used emergency rooms, community clinics, and hospital outpatient departments as their regular providers; and more frequently encountered financial barriers to health care. Low-income or nonwhite children had much less access to care compared with children from more affluent or white families, independent of insurance status or health status.

End-of-Life Care for an Undocumented Mexican Immigrant

2018 ◽  
Vol 33 (2) ◽  
pp. 63-64 ◽  
Author(s):  
Lilit Karapetyan ◽  
Om Dawani ◽  
Heather S. Laird-Fick
Keyword(s):  
United States ◽  
End Of Life ◽  
Health Care Access ◽  
Limited English Proficiency ◽  
Undocumented Immigrants ◽  
Insurance Coverage ◽  
Poverty Line ◽  
The United States ◽  
Immigrant Population ◽  
Long Term Treatment

The immigrant population in the United States has grown over the past years. Undocumented immigrants account for 14.6% of the uninsured population in the United States. Decisions about end-of-life treatment are often difficult to reach in the best of situations. We present a 43-year-old undocumented Mexican female immigrant with metastatic sarcomatoid squamous cell cervical cancer and discuss the barriers that she faced during her treatment. Limited English proficiency, living below the poverty line, low level of education, and lack access to Medicare, Medicaid, or other insurance coverage under the Affordable Care Act are major causes of decreased health-care access and service utilization by the immigrant population. Latinos are less likely to be referred to hospice by oncologists, and nearly a third of hospice agencies offer limited or no services to undocumented immigrants. Undocumented immigrants with terminal diagnoses generally do not have access to comprehensive or multidisciplinary follow-up treatment. Instead, one of their few options is to return to their home countries without any long-term treatment. This article discusses the many barriers and proposes areas for reform.

scholarly journals Poverty In The United States: A Continuing And Growing Problem

2011 ◽  
Vol 5 (12) ◽  
Author(s):  
Berch Haroian ◽  
Elizabeth C. Ekmekjian ◽  
Elias C. Grivoyannis
Keyword(s):  
Household Income ◽  
Insurance Coverage ◽  
Poverty Line ◽  
Health Insurance Coverage ◽  
The United States ◽  
Poverty Rate ◽  
Median Household Income ◽  
The Past ◽  
Area Of Interest ◽  
The Impact

<p class="Default" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">In recent years, the ability to deal with the problem of poverty in the US, in light of the new &ldquo;Federalism,&rdquo; is an area of interest to scholars. The poverty rate over the past 50 years has fluctuated from a high of 22.4% in 1959 to a low of 11.1% in 1973. Under George Bush&rsquo;s presidency, we again see an increase in the poverty rate to 12.7% in 2004. This paper provides an overview of poverty data for the 21<sup>st</sup> century, by region, race and age.<span style="mso-spacerun: yes;">&nbsp; </span>A discussion and comparison of median household income follows. Facts and figures are then provided/compared, tying in health care issues to income levels and citizenship/ethnicity. A brief introduction of the various attempts over the past years by the federal government to reduce the proportion of the American population that falls below the poverty line follows.<span style="mso-spacerun: yes;">&nbsp; </span>This section merely provides a listing of programs designed to satisfy social and equity considerations.<span style="mso-spacerun: yes;">&nbsp; </span>This paper does not provide the reader with the impact of these programs on the economy; a brief mention is provided to generate further thought and discussion.<span style="mso-spacerun: yes;">&nbsp; </span>The paper concludes with a summary of key elements of the above issues. The sole purpose is to provide an overview of historical data as concerns poverty, median household income and health insurance coverage. The ability to deal with the problem of poverty in the U S, is left for another paper.</span></span></p>

Choosing Policies

2019 ◽  
pp. 252-271
Author(s):  
Robert L. Klitzman
Keyword(s):  
New Technologies ◽  
Assisted Reproductive Technologies ◽  
Insurance Coverage ◽  
Self Regulation ◽  
The United States ◽  
Professional Organizations ◽  
Control And Prevention ◽  
Donor Agencies ◽  
American Society

The United States regulates assisted reproductive technologies far less than do other Western countries, most of which have more nationalized health insurance. US states vary widely in whether they have any laws and, if so, what. Governmental agencies (e.g., Food and Drug Administration, Centers for Disease Control and Prevention) and professional organizations (e.g., American Medical Association, American Society of Reproductive Medicine) have begun addressing several areas but could potentially do more. Improved national and professional policies are needed regarding several areas, including egg and sperm donation, egg donor agencies, numbers of embryos transferred into wombs, gestational surrogacy, oversight of providers, insurance coverage, and data collection. Doctors generally perceive problems in the field but argue that industry self-regulation, rather than government policy, is adequate. Yet many providers fail to follow current guidelines and regulations. Moreover, new technologies continue to develop, including gene editing of embryos through CRISPR and mitochondrial replacement therapy (so-called three-parent babies). More data and research are crucial on current use of procedures and long-term medical and psychological follow-up of patients, egg donors, gestational surrogates, and offspring, to evaluate, for instance, the effectiveness of egg freezing and longitudinal follow-up of children born through these procedures.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

scholarly journals Reluctance to Accept Palliative Care and Recommendations for Improvement: Findings From Semi-Structured Interviews With Patients and Caregivers

2021 ◽  
pp. 104990912110126
Author(s):  
Valeria Cardenas ◽  
Anna Rahman ◽  
Yujun Zhu ◽  
Susan Enguidanos
Keyword(s):  
Palliative Care ◽  
Controlled Trial ◽  
Insurance Coverage ◽  
Healthcare Providers ◽  
Health Condition ◽  
Insurance Companies ◽  
Structured Interviews ◽  
Care Services ◽  
Home Based ◽  
Palliative Care Services

Background: Despite some insurance plans now paying for home-based palliative care, recent reports have suggested that insurance coverage for palliative care may be insufficient in expanding patient access to home-based palliative care. Aim: To identify patients’ and caregivers’ perceived barriers to home-based palliative care and their recommendations for overcoming these barriers. Design: We conducted a qualitative study using semi-structured individual interviews. Our interview protocol elicited participants’ perspectives on home-based palliative care services; positive and negative aspects of the palliative program explanation; and suggestions for improving messaging around home-based palliative care. Setting/Participants: Twenty-five participants (patients, proxies, and their caregivers) who were eligible for a randomized controlled trial of home-based palliative care were interviewed by telephone. Results: Themes related to home-based palliative care referral barriers included reluctance to have home visits, enrollment timing, lack of palliative care knowledge, misconceptions about palliative care, and patients’ self-perceived health condition. Themes related to recommendations for overcoming these obstacles included ensuring that palliative care referrals come from healthcare providers or insurance companies and presenting palliative care services more clearly. Conclusion: Findings reinforce the need for additional palliative care education among patients with serious illness (and their caregivers) and the importance of delivering palliative care information and referrals from trusted sources.

scholarly journals Users’ Beliefs Toward Physical Distancing in Facebook Pages of Public Health Authorities During COVID-19 Pandemic in Early 2020

2021 ◽  
pp. 109019812110144
Author(s):  
Soon Guan Tan ◽  
Aravind Sesagiri Raamkumar ◽  
Hwee Lin Wee
Keyword(s):  
Public Health ◽  
Health Belief Model ◽  
United States Public Health ◽  
Health Belief ◽  
The United States ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Control And Prevention ◽  
Belief Model ◽  
The Health Belief Model

This study aims to describe Facebook users’ beliefs toward physical distancing measures implemented during the Coronavirus disease (COVID-19) pandemic using the key constructs of the health belief model. A combination of rule-based filtering and manual classification methods was used to classify user comments on COVID-19 Facebook posts of three public health authorities: Centers for Disease Control and Prevention of the United States, Public Health England, and Ministry of Health, Singapore. A total of 104,304 comments were analyzed for posts published between 1 January, 2020, and 31 March, 2020, along with COVID-19 cases and deaths count data from the three countries. Findings indicate that the perceived benefits of physical distancing measures ( n = 3,463; 3.3%) was three times higher than perceived barriers ( n = 1,062; 1.0%). Perceived susceptibility to COVID-19 ( n = 2,934; 2.8%) was higher compared with perceived severity ( n = 2,081; 2.0%). Although susceptibility aspects of physical distancing were discussed more often at the start of the year, mentions on the benefits of intervention emerged stronger toward the end of the analysis period, highlighting the shift in beliefs. The health belief model is useful for understanding Facebook users’ beliefs at a basic level, and it provides a scope for further improvement.

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