Evaluation of the association between health insurance status and healthcare utilization and expenditures among adult cancer survivors in the United States

Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm

Background

The Department of Rehabilitation Medicine is key to improving patients' quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models. The goal is to improve patient visit efficiency and optimize resource allocation through these predictive models.

Methods

Data were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. The steps included handling missing values, data normalization, and encoding conversion. The data were divided into 80% training and 20% test sets using the Scikit-learn library to ensure model independence and prevent overfitting. Performance comparisons among XGBoost, random forest, and logistic regression were conducted using metrics, including accuracy and receiver operating characteristic (ROC) curves. The imbalanced learning library's SMOTE technique was used to address the sample imbalance during model training. The model was optimized using a confusion matrix and feature importance analysis, and partial dependence plots (PDP) were used to analyze the key influencing factors.

Results

XGBoost achieved the highest overall accuracy of 80.21% with high precision and recall in Category 1. random forest showed a similar overall accuracy. Logistic Regression had a significantly lower accuracy, indicating difficulties with nonlinear data. The key influencing factors identified include distance to medical institutions, arrival time, length of hospital stay, and specific diseases, such as cardiovascular, pulmonary, oncological, and orthopedic conditions. The tiered diagnosis and treatment tool effectively helped doctors assess patients' conditions and recommend suitable medical institutions based on rehabilitation grading.

Conclusion

This study confirmed that ensemble learning methods, particularly XGBoost, outperform single models in classification tasks involving complex datasets. Addressing class imbalance and enhancing feature engineering can further improve model performance. Understanding patient preferences and the factors influencing medical institution selection can guide healthcare policies to optimize resource allocation, improve service quality, and enhance patient satisfaction. Tiered diagnosis and treatment tools play a crucial role in helping doctors evaluate patient conditions and make informed recommendations for appropriate medical care.

Are comorbidities associated with differences in healthcare charges among lung cancer patients in US hospitals? Focusing on variances by patient and socioeconomic factors

OBJECTIVE

The clinical aspects of lung cancer patients are well-studied. However, healthcare charge patterns have yet to be explored through a large-scale representative population-based sample investigating differences by socioeconomic factors and comorbidities.

AIM

To identify how comorbidities associated with healthcare charges among lung cancer patients.

METHODS

We examined the characteristics of the patient sample and the association between comorbidity status (diabetes, hypertension, or both) and healthcare charge. Multivariate survey linear regression models were used to estimate the association. We also investigated sub-group association through various patient and socioeconomic factors.

RESULTS

Of 212,745 lung cancer patients, 68.5% had diabetes and/or hypertension. Hospital charges were higher in the population with comorbidities. The results showed that lung cancer patients with comorbidities had 9.4%, 5.1%, and 12.0% (with diabetes, hypertension, and both, respectively) higher hospital charges than those without comorbidities. In sub-group analysis, Black patients also showed a similar trend across socioeconomic (i.e. household income and primary payer) and racial (i.e. White, Black, Hispanic, and Asian/Pacific Islander) factors.

DISCUSSION

Black patients may be significantly financially burdened because of the prevalence of comorbidities and low-income status. More work is required to ensure healthcare equality and promote access to care for the uninsured, low-income, and minority populations because comorbidities common in these populations can create more significant financial barriers.

The Role of Health Insurance Type and Clinic Visit on Hypertension Status Among Multiethnic Chicago Residents

PURPOSE

To investigate the joint relationship of health insurance and clinic visit with hypertension among underserved populations.

DESIGN

Population-based cohort study.

SUBJECTS

Data from 1092 participants from the Chicago Multiethnic Prevention and Surveillance Study (COMPASS) between 2013 and 2020 were analyzed.

MEASURES

Five health insurance types were included: uninsured, Medicaid, Medicare, private, and other. Clinic visit over past 12 months were retrieved from medical records and categorized into 4 groups: no clinic visit, 1-3 visits, 4-7 visits, >7 visits.

ANALYSIS

Inverse-probability weighted logistic regression was used to estimate odds ratios (OR) and 95% confidence interval (CI) for hypertension status according to health insurance and clinic visit. Models were adjusted for individual socio-demographic variables and medical history.

RESULTS

The study population was predominantly Black (>85%) of low socioeconomic status. Health insurance was not associated with more clinic visit. Measured hypertension was more frequently found in private insurance (OR = 6.48, 95% CI: 1.92-21.85) compared to the uninsured group, while 1-3 clinic visits were associated with less prevalence (OR = .59, 95% CI: .35-1.00) compared to no clinic visit. These associations remained unchanged when health insurance and clinic visit were adjusted for each other.

CONCLUSION

In this study population, private insurance was associated with higher measured hypertension prevalence compared to no insurance. The associations of health insurance and clinic visit were independent of each other.