The issue of risk selection is especially important for states that enroll blind and disabled beneficiaries of Supplemental Security Income (SSI) in Medicaid managed care. SSI beneficiaries have persistent needs for care, have a wide variety of chronic conditions, and often need atypical and complex services. Risk selection occurs when the health care needs of beneficiaries enrolled in a specific plan differ systematically from the needs of the overall beneficiary population and payments do not reflect those needs. We assess the extent of risk selection among managed care plans for SSI beneficiaries over the first three years of Tennessee's Medicaid managed care program, TennCare. Using claims data containing fee-for-service expenditures prior to enrollment in managed care, we find substantial evidence of persistent risk selection among plans. Results are robust to most alternative measures of risk selection for most plans.
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http://dx.doi.org/10.5034/inquiryjrnl_39.2.152 | DOI Listing |
Biol Direct
January 2025
Key Laboratory of Geriatrics of Jiangsu Province, Department of Geriatrics, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China.
Background: Despite the increasing body of evidence that mitochondrial activities implicate in chronic obstructive pulmonary disease (COPD), we are still far from a causal-logical and mechanistic understanding of the mitochondrial malfunctions in COPD pathogenesis.
Results: Differential expression genes (DEGs) from six publicly available bulk human lung tissue transcriptomic datasets of COPD patients were intersected with the known mitochondria-related genes from MitoCarta3.0 to obtain mitochondria-related DEGs associated with COPD (MitoDEGs).
Crit Care
January 2025
Department of Pediatric, West China Second University Hospital, Sichuan University, Chengdu, China.
Background: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research.
Methods: Data from pediatric patients undergoing ECMO were collected from the Chinese Society of Extracorporeal Life Support (CSECLS) registry database and local hospitals.
BMC Cancer
January 2025
Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, Henan, China.
Objectives: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).
Methods: DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed.
BMC Cancer
January 2025
Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, 530021, China.
Background And Objective: In clinical practice, CK19 can be an important predictor for the prognosis of HCC. Due to the high incidence and mortality rates of HCC, more effective and practical prognostic prediction models need to be developed urgently.
Methods: A total of 1,168 HCC patients, who underwent radical surgery at the Guangxi Medical University Cancer Hospital, between January 2014 and July 2019, were recruited, and their clinicopathological data were collected.
BMC Cancer
January 2025
Centre for Medical Education, Queen's University Belfast, Belfast City Hospital, Lisburn Road, Belfast, UK.
Background: Myelofibrosis (MF) is a clonal haematopoietic disease, with median overall survival for patients with primary MF only 6.5 years. The most frequent gene mutation found in patients is JAK2, causing constitutive activation of the kinase and activation of downstream signalling.
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