Objectives: We examined the impact of access to care characteristics on health care use patterns among those veterans dually eligible for Medicare and Veterans Affairs (VA) services.
Methods: We used a retrospective, cross-sectional design to identify veterans who were eligible to use VA and Medicare health care in calendar year 1999. We analyzed national VA utilization and Medicare claims data. We used descriptive and multivariable generalized ordered logit analyses to examine how patient, geographic, and environmental factors affect the percent reliance on VA and Medicare inpatient and outpatient services.
Results: Of the 1.47 million veterans in our study population with outpatient use, 18% were VA-only users, 36% were Medicare-only users, and 46% were both VA and Medicare users. Among veterans with inpatient use, 24% were VA only, 69% were Medicare only, and 6% were both VA and Medicare users. Multivariable analysis revealed that veterans who were black or had a higher VA priority were most likely to rely on the VA. Patient with higher risk scores were most likely to rely on a combination of VA and Medicare health care. Patients who lived farther from VA hospitals were less likely to rely on VA health care, particularly for inpatient care. Patients living in urban areas with more health care resources were less likely to rely on VA health care.
Conclusions: VA health care provides an important safety net for vulnerable populations. Targeted approaches that carefully consider the simultaneous impacts of VA and Medicare policy changes on minority and high-risk populations are essential to ensure veterans have access to needed health care.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/01.mlr.0000244657.90074.b7 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFAnnu Rev Clin Psychol
January 2025
3Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
Most people with mental health needs cannot access treatment; among those who do, many access services only once. Accordingly, single-session interventions (SSIs) may help bridge the treatment gap. We conducted the first umbrella review synthesizing research on SSIs for mental health problems and service engagement in youth and adults.
View Article and Find Full Text PDFACS Sens
January 2025
Department of Physics and Astronomy, Franklin College of Arts and Sciences, The University of Georgia, Athens, Georgia 30602, United States.
Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia.
Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community.
View Article and Find Full Text PDFJMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!