Objective: To investigate whether ethnicity was associated with differences in Medicaid eligibility, health care utilization, and direct medical costs in a systemic lupus erythematosus (SLE) population.
Methods: A retrospective analysis of California Medicaid claims data was conducted on patients with SLE. Patient eligibility and month-by-month utilization and costs were computed and compared across ethnic groups. Descriptive statistics are presented. A mixed regression model on patient-level data was used to verify the trends of the aggregate data, controlling for covariates. A survival regression model on time to ineligibility was used to show eligibility patterns adjusted for covariates.
Results: Hispanic patients were less likely to have a lengthy eligibility period as compared with other cohorts (approximately 50% versus 70% eligible at month 36, respectively). As treatment progressed, Hispanics generated lower total costs than other cohorts. Results for inpatient frequency, prescription costs, and outpatient/physician/supply (Part B) costs followed similar patterns. Mixed regression model findings revealed that when adjusted for age, sex, and aid program, total costs for Hispanic patients decreased as the length of care increased, in contrast to other ethnic groups. The interaction between ethnicity and treatment progression measured by quarter was significant (P <0.0001), but ethnicity as a main effect was not (P=0.091). This suggests that differences in total costs are small initially, but as the followup period extends, Hispanic patients experience lower total costs as compared with other ethnic groups.
Conclusion: These California Medicaid program data reinforce the importance of investigating treatment differences in ethnic groups with SLE.
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http://dx.doi.org/10.1002/art.20819 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFJ Occup Environ Med
January 2025
Departments of Public Health Sciences.
Objective: Estimate ever using marijuana in a sample of U.S. career first responders.
View Article and Find Full Text PDFInt J Radiat Biol
January 2025
Chungbuk National University College of Medicine, Cheongju, Republic of Korea.
Purpose: We aimed to identify the transcriptomic signatures of soft tissue sarcoma (STS) related to radioresistance and establish a model to predict radioresistance.
Materials And Methods: Nine STS cell lines were cultured. Adenosine triphosphate-based viability was determined 5 days after irradiation with 8 Gy of X-rays in a single fraction.
PLoS Comput Biol
January 2025
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches.
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