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

Prevalence of Chronic Pain by Immigration Status and Latino Ethnicity

OBJECTIVE

The goal of this study was to evaluate the Latino paradox and healthy migrant hypotheses by estimating the association between the prevalence of chronic pain, immigration status, and Latino ethnicity.

METHODS

This cross-sectional study analyzed pooled data from 85,395 adult participants of the 2019-2021 National Health Interview Survey. The dependent variables were any pain and chronic pain in the past 3 months. The independent variables were immigration status (US born, naturalized citizen, non-citizen) and Latino ethnicity.

RESULTS

Chronic pain was prevalent for nearly a quarter of US born non-Latino adults (24%) and non-citizen non-Latino adults had the lowest prevalence at 8%. In multivariable adjusted models, US born non-Latino immigration status and ethnicity was associated with a higher probability of reporting chronic pain in the last 3 months compared to US born Latino adults (-3.0%; 95% CI = -4.4%, -1.6%), naturalized citizen non-Latino adults (-4.7%; 95% CI = -5.9%, -3.4%), naturalized citizen Latino adults (-6.7%; 95% CI = -8.5%, -4.9%), non-citizen non-Latino adults (-3.1%; 95% CI = -4.7%, -1.5%), and non-citizen Latino adults (-8.9%; 95% CI = -10.8%, -7.0%).

CONCLUSION

US Born non-Latino adults reported the highest prevalence of chronic pain and non-citizen Latino adults reported the lowest prevalence of chronic pain providing support for the Latino paradox and healthy migrant effect hypotheses.

Double burden of vulnerability for refugees: conceptualization and policy solutions for financial protection in Iran using systems thinking approach

INTRODUCTION

Iran is host to one of the world's largest and longest-standing refugee populations. Although Iran has initiated a basic health insurance scheme for refugees throughout the country since September 2015, the population coverage of this scheme is very low, and various factors have caused a significant percentage of refugees to still lack insurance coverage and often face financial hardships when receiving health services. In response, this study aimed to understand barriers to insurance coverage among refugees in Iran and propose effective policies that can address persistent gaps in financial protection.

METHODS

This qualitative study was conducted in two phases. First, a review of policy documents and interviews with participants were conducted to investigate the common barriers and facilitators of effective insurance coverage for refugees in Iran. Then, a systems thinking approach was applied to visualize the common variables and interactions on the path to achieving financial protection for refugees.

RESULTS

Findings showed that various factors, such as (1) household-based premium for refugees, (2) considering a waiting time to be eligible for insurance benefits, (3) determining high premiums for non-vulnerable groups and (4) a deep difference between the health services tariffs of the public and private service delivery sectors in Iran, have caused the coverage of health insurance for non-vulnerable refugees to be challenging. Furthermore, some policy solutions were found to improve the health insurance coverage of refugees in Iran. These included removing household size from premium calculations, lowering current premium rates and getting monthly premiums from non-vulnerable refugees.

CONCLUSIONS

A number of factors have caused health insurance coverage to be inaccessible for refugees, especially non-vulnerable refugees in Iran. Therefore, it is necessary to adopt effective policies to improve the health financing for the refugee with the aim of ensuring financial protection, taking into account the different actors and the interactions between them.

Cost of Public Health Insurance for US-Born and Immigrant Adults

Importance

The immigrant population in the US has low health insurance coverage. Surveys find that approximately one-half of the US population is opposed to public health insurance of immigrants, and there is a widely held belief that immigrants are a state fiscal liability.

Objective

To estimate the cost of providing public health insurance to immigrants in the US.

Design, Setting, and Participants

This serial cross-sectional study used restricted data from the 2011 to 2019 Medical Expenditure Panel Survey (and data from 2011-2020 in supplemental analyses). The data are nationally representative of the US civilian noninstitutionalized population. Participants included adults aged 19 to 64 years with family incomes below 138% of the Federal Poverty Level, the population that benefited from the Medicaid expansions. Data analysis was performed from November 2022 to August 2023.

Exposures

State Medicaid expansion.

Main Outcomes and Measures

The primary outcomes were insurance coverage, total health care expenditures, expenditures categorized by payment source (paid by self or family and paid by others), expenditures by major health care type (office based, inpatient, and prescription), and health care utilization (number of office-based visits, outpatient facility visits, emergency department visits, hospital discharges, dental care visits, home health clinician days, and prescription medicine refills). A difference-in-differences method was used to compare the health care cost and utilization by low-income, working-age US-born and immigrant adults in states that adopted the Patient Protection and Affordable Care Act (ACA) Medicaid expansions with the corresponding change in nonexpansion states before and after the policy implementation.

Results

Among the study sample of 44 482 individuals (mean [SD] age, 38.5 [14.0] years; 25 221 female individuals [56.7%]; 34 052 [76.6%] US born), 46% of immigrant adults (1953 participants) and 70% of US-born adults (9396 participants) had insurance coverage in the pre-ACA period. Medicaid expansions increased insurance coverage of both groups by 7 percentage points (95% CI, 3 to 11 percentage points). The resulting change in health care increased total expenditures (self-paid plus insurer paid) by $660 (95% CI, $79 to $1242) and insurer-paid expenditures by $745 (95% CI, $141 to $1350) per US-born adult. For immigrant adults, the corresponding changes in total ($266; 95% CI, -$348 to $880) and insurer-paid ($308; 95% CI, -$352 to $968) expenditures were small and not statistically significant. Estimates suggest that providing insurance to immigrants costs the health care system approximately $3800 per person per year, less than one-half the corresponding cost ($9428 per person per year) for US-born adults.

Conclusions and Relevance

These findings suggest that the direct cost of providing public health insurance to immigrants is less than that for the US born, and immigrants' health care utilization, upon coverage, remains comparatively modest, thus refuting the notion that providing insurance to immigrants imposes a heavy fiscal burden.

Is use of a smuggler to cross the US-Mexico border associated with mental health problems among undocumented immigrants from Mexico?

Although numerous studies have found that Latine immigrants to the United States (US) have better health outcomes on average than persons born in the US, studies of persons living in Mexico have found that undocumented immigrants have worse health, especially those that were deported, compared to Mexican citizens that never migrated or migrated with authorization. However, the health outcomes of Mexican migrants using a smuggler to cross the US-Mexico border is a gap in the literature. We hypothesized that undocumented immigrant adults who used a smuggler to cross the US-Mexico border would be more likely to report mental health problems upon return to Mexico compared with undocumented immigrant adults that did not use a smuggler. We analyzed nationally representative, cross-sectional survey data of 1,563 undocumented immigrants currently living in Mexico. Most undocumented immigrants in the sample (87%) used a smuggler. Use of a smuggler by undocumented immigrant adults was associated with a 4.7% higher prevalence of emotional or psychiatric problems compared to undocumented immigrant adults that did not use a smuggler. We conclude that modality of ingress into the US is a risk factor for poorer mental health among undocumented immigrant adults.

Why Emergency Physicians Should Advocate for Suspension of Title 42 Restrictions on Asylum for US Immigrants

n/a

Assessment of Immigrants' Premium and Tax Payments for Health Care and the Costs of Their Care

IMPORTANCE

Some worry that immigrants burden the US economy and particularly the health care system. However, no analyses to date have assessed whether immigrants' payments for premiums and taxes that fund health care programs exceed third-party payers' expenditures on their behalf.

OBJECTIVE

To assess immigrants' net financial contributions to US health care programs.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional analysis used 2017 data from the Medical Expenditure Panel Survey (MEPS) and the Current Population Survey (CPS) and 2014 to 2018 data from the American Community Survey. The main analyses used data from the calendar year 2017. Data from the calendar years 2012 to 2016 were also reported. Data were analyzed from June 15, 2020, to August 14, 2022. Participants comprised 210 669 community-dwelling respondents to the MEPS and CPS (main analysis) and nursing home residents who were included in the American Community Survey (additional analysis).

EXPOSURES

Citizenship and immigration status.

MAIN OUTCOMES AND MEASURES

Total and per capita payments for premiums and taxes that fund health care as well as third-party payers' expenditures for health care in 2018 US dollars.

RESULTS

Among 210 669 participants, 51.0% were female, 18.3% were Hispanic, 12.3% were non-Hispanic Black, 60.3% were non-Hispanic White, and 9.2% were of other races and/or ethnicities. A total of 180 084 participants were respondents to the 2018 CPS, and 30 585 were respondents to the 2017 MEPS. Among the 180 084 CPS respondents, immigrants accounted for 14.1% (weighted to be nationally representative), with the subgroup of citizen immigrants accounting for 6.8%, documented noncitizen immigrants accounting for 3.7%, and undocumented immigrants accounting for 3.6%; US-born citizens constituted 85.9% of the population. Relative to US-born citizens, immigrants were more often age 18 to 64 years (79.6% vs 58.3%), of Hispanic ethnicity (45.0% vs 14.0%), and uninsured (16.8% vs 7.4%); similar percentages (51.4% vs 50.9%) were female. US-born citizens vs immigrants paid similar amounts in premiums and taxes ($6269 per capita [95% CI, $6185-$6353 per capita] vs $6345 per capita [95% CI, $6220-$6470 per capita]). However, third-party expenditures for immigrants' health care ($5061 per capita; 95% CI, $4673-$5448 per capita) were lower than their expenditures for the care of US-born citizens ($6511 per capita; 95% CI, $6275-$6747 per capita). Immigrants, in general, paid significantly more per person (net contribution, $1284; 95% CI, $876-$1691) than was paid on their behalf. Most of this surplus was accounted for by undocumented immigrants, whose contributions exceeded their expenditures by $4418 per person (95% CI, $4047-$4789 per person). US-born citizens collectively paid $67.2 billion (95% CI, -$2.3 to $136.3 billion) less in premiums and taxes than third-party payers paid for their care. This deficit was mostly offset by the $58.3 billion (95% CI, $39.8-$76.8 billion) net surplus of payments from immigrants, 89% of which ($51.9 billion; 95% CI, $47.5-$56.3 billion) was attributable to undocumented immigrants.

CONCLUSIONS AND RELEVANCE

In this study, immigrants appeared to subsidize the health care of other US residents, suggesting that concerns that immigrants deplete health care resources may be unfounded.

Using machine learning to impute legal status of immigrants in the National Health Interview Survey

We describe a novel machine learning method of imputing legal status for immigrants using nationally representative survey data from the Survey of Income and Program Participation (SIPP) and the National Health Interview Survey (NHIS). K-nearest Neighbor (KNN) classifier and Random Forest (RF) Algorithm machine learning were described as novel imputation methods compared to established regression-based imputation. After validating the imputation methods using sensitivity, specificity, positive predictive value (PPV) and accuracy statistics, the Random Forest Algorithm was more accurate in identifying undocumented immigrants and minimized bias in both socio-demographic variables included in the imputation, and unobserved health variables relative to regression-based imputation and KNN.•

We developed a new machine learning method of imputing legal status for immigrants that can be used with nationally representative, publicly available data.

•

Our findings indicate that using machine learning to impute legal status of immigrants, specifically the Random Forest Algorithm, was more accurate in identifying undocumented immigrants and minimized bias relative to other imputation methods.

A healthy migrant effect? Estimating health outcomes of the undocumented immigrant population in the United States using machine learning

This paper investigated whether the commonly observed immigrant health advantage persists among undocumented immigrants in the U.S. and provides nationally representative evidence on the health of this vulnerable population. Data were derived from pooled cross-sections of the National Health Interview Survey (NHIS, 2000-2018). The legal status of foreign-born NHIS respondents is imputed using a non-parametric machine learning model built based on information from the 2004, 2008 and 2014 cohorts of the Survey of Income and Program Participation (SIPP). Multivariate logistic regression analysis indicated that, despite exposure to numerous additional risk factors, the undocumented population experienced a more pronounced Healthy Migrant Effect, with lower odds of reporting fair or poor self-rated health, any physician-diagnosed chronic conditions or being obese. The observed patterns in undocumented health outcomes may be related to the additional challenges and exclusionary policies associated with undocumented migration that could in turn lead to a more pronounced selection of healthy and resilient individuals.

A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review

Introduction: In biobanks, participants' biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI's role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in biobanking". Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle-Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study's goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.

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

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.

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

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.

Using social security number to identify sub-populations vulnerable to the health impacts from extreme heat in Florida, U.S

BACKGROUND

Some socioeconomically vulnerable groups may experience disproportionately higher risk of extreme heat illness than other groups, but no study has utilized the presence/absence of a social security number (SSN) as a proxy for vulnerable sub-populations.

METHODS

This study focused on the warm season from 2008 to 2012 in Florida, U.S. With a total number of 8,256,171 individual level health outcomes, we devised separate case-crossover models for five heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease), type of health care visit (emergency department (ED) and hospitalization), and patients reporting/not reporting an SSN. Each stratified model also considered potential effect modification by sex, age, or race/ethnicity.

RESULTS

Mean temperature raised the odds of five heat-sensitive health outcomes with the highest odds ratios (ORs) for heat-related illness. Sex significantly modified heat exposure effects for dehydration ED visits (Males: 1.145, 95 % CI: 1.137-1.153; Females: 1.110, 95 % CI: 1.103-1.117) and hospitalization (Males: 1.116, 95 % CI: 1.110-1.121; Females: 1.100, 95 % CI: 1.095-1.105). Patients not reporting an SSN between 25 and 44 years (1.264, 95 % CI: 1.192-1.340) exhibited significantly higher dehydration ED ORs than those reporting an SSN (1.146, 95 % CI: 1.136-1.157). We also observed significantly higher ORs for cardiovascular disease hospitalization from the no SSN group (SSN: 1.089, 95 % CI: 1.088-1.090; no SSN: 1.100, 95 % CI: 1.091-1.110).

CONCLUSIONS

This paper partially supports the idea that individuals without an SSN could experience higher risks of dehydration (for those 25-45 years), renal disease, and cardiovascular disease than those with an SSN.