Comparison of Use of Health Care Services and Spending for Unauthorized Immigrants vs Authorized Immigrants or US Citizens Using a Machine Learning Model

Wilson, Fernando A.; Zallman, Leah; Pagán, José A.; Ortega, Alexander N.; Wang, Yang; Tatar, Moosa; Stimpson, Jim P.

doi:10.1001/jamanetworkopen.2020.29230

Cited by 16 publications

(19 citation statements)

References 24 publications

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“…Most were retrospective cohort studies, 75 76 77 78 79 80 81 82 83 84 85 86 87 while six used prospective cohort studies, 88 89 90 91 92 93 four used a cross-sectional design, 94 95 96 97 and one used a survey for primary data collection. 98 Most studies used the EHR and administrative databases to collect data but three used surveys, 87 96 98 two used public datasets, 78 95 one used mobile phone data, 97 one used images, 90 and one used data from social media. 92 All studies were adult based with the exception of one study examining families.…”

Section: Resultsmentioning

confidence: 99%

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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Section: Resultsmentioning

confidence: 99%

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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“…This new model has a fast convergence rate and better computational accuracy when using a data set that contains the medical screening information from UIs compared with models developed using other techniques. In addition, concerning other machine-learning models, 15 this is the first machine learning model designed for assisting in the analysis of the health statuses of UIs.…”

Section: Discussionmentioning

confidence: 99%

“…For this application regarding the health information of UIs, NMF has at least one major advantage over traditional multivariable regression models; that NMF is more easily adapted to missing input information, which results from the difficulties in gathering this health information discussed in the previous sections. 15 In terms of how NMF is applied, this method first blindly decomposes the target matrix X into two non-negative matrices, U and V ; then it iteratively solves the objective function

to find the minimum, where

denotes the matrix Frobenius norm; finally, the training process was completed once the iterations met the convergence criteria.…”

Section: Methodsmentioning

confidence: 99%

A Novel Application of Non-Negative Matrix Factorization to the Prediction of the Health Status of Undocumented Immigrants

Wells

Yang

et al. 2021

Health Equity

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Introduction: Undocumented immigrants (UIs) in the United States are less likely to be able to afford health insurance. As a result, UIs often lack family doctors and are rarely involved in annual screening programs, which makes estimating their health status remarkably challenging. This is especially true if the laboratory results from limited screening programs fail to provide sufficient clinical information. Methods: To address this issue, we have developed a machine learning model based on the non-negative matrix factorization technique. The data set we used for model training and testing was obtained from the 2004 cost-free hepatitis B screening program at the Omni Health Center located in Plano, Texas. Total 300 people were involved, with 199 identified as UIs. Results: People in the UIs group have higher cholesterol (219.6 mg/dL, p =0.038) and triglycerides (173.2 mg/dL, p =0.03) level. They also have a lower hepatitis B vaccination rate (38%, p =0.0247). No significant difference in hepatitis B (+) was found ( p =0.8823). Using 16 individual clinical measurements as training features, our newly developed model has a 67.56% accuracy in predicting the ratio of cholesterol to high-density lipoprotein; in addition, this newly developed model performs 9.1% better than a comparable multiclass logistic regression model. Conclusions: Elderly UIs have poorer health status compared with permanent residents and citizens in the United States. Our newly developed machine learning model demonstrates a powerful support tool for designing health intervention programs that target UIs in the United States.

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“…Most estimates of health care spending or utilization among undocumented immigrants do not control for factors such as age, health status, or health insurance status. Without controlling for such factors, health care spending for undocumented immigrants appears to be two to four times lower than that of the legally present population (Flavin et al, 2018;Wilson et al, 2020). However, controlling for such factors indicates that health care spending among undocumented immigrants is only 25 percent lower than among legally present residents (Wilson et al, 2020).…”

Section: Health Care Spendingmentioning

confidence: 93%

“…Without controlling for such factors, health care spending for undocumented immigrants appears to be two to four times lower than that of the legally present population (Flavin et al, 2018;Wilson et al, 2020). However, controlling for such factors indicates that health care spending among undocumented immigrants is only 25 percent lower than among legally present residents (Wilson et al, 2020). Therefore, we applied this estimate of health care spending among undocumented immigrants in our modeling.…”

Section: Health Care Spendingmentioning

confidence: 99%

Expanding Insurance Coverage to Undocumented Immigrants in Connecticut

Rao¹,

Girosi²,

Eibner³

2022

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About RANDThe RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. To learn more about RAND, visit www.rand.org. Research IntegrityOur mission to help improve policy and decisionmaking through research and analysis is enabled through our core values of quality and objectivity and our unwavering commitment to the highest level of integrity and ethical behavior. To help ensure our research and analysis are rigorous, objective, and nonpartisan, we subject our research publications to a robust and exacting quality-assurance process; avoid both the appearance and reality of financial and other conflicts of interest through staff training, project screening, and a policy of mandatory disclosure; and pursue transparency in our research engagements through our commitment to the open publication of our research findings and recommendations, disclosure of the source of funding of published research, and policies to ensure intellectual independence. For more information, visit www.rand.org/about/principles. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.

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Comparison of Use of Health Care Services and Spending for Unauthorized Immigrants vs Authorized Immigrants or US Citizens Using a Machine Learning Model

Abstract: This cross-sectional study examines health care utilization and expenditures that are attributable to unauthorized immigrants vs authorized immigrants or US-born individuals.

Cited by 16 publications

References 24 publications

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

A Novel Application of Non-Negative Matrix Factorization to the Prediction of the Health Status of Undocumented Immigrants

Expanding Insurance Coverage to Undocumented Immigrants in Connecticut

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