Background: Representativeness in clinical trials (CT) serves as a metric of access to healthcare and reflects differences that may determine differential efficacy of medical interventions; thus, quantifying representativeness in CT participation is critical.
Methods: This retrospective, descriptive study utilized patient demographic data extracted from the largest Midwestern non-profit healthcare system. Using data between January 1, 2019 and December 31, 2021, a CT Participant Sample of 4,537 system patients who were active CT participants was compared to a CT Patient Population of 195,726 system patients receiving care by the PI of active CTs, which represented the target population. Chi-square goodness-of-fit tests were used to test differences in distributions of demographic variables between groups, indicating disparity in CT participation. Two metrics adapted from literature - participation incidence disparity (PID) and participation incidence ratio (PIR) - were calculated to quantify absolute and relative disparity in representativeness proportions, respectively. Descriptive approaches to assessing representativeness are also provided.
Results: Results showed significant differences by race/ethnicity (χ2 = 50.64; p < 0.0001), age categories (χ2 = 56.64; p < 0.0001), and insurance (χ2 = 41.29; p < 0.0001). PID and PIR metrics revealed reduced CT participation among non-White racial/ethnic groups and increased CT participation among White Non-Hispanic patients. Further, CT participants ≥80 or Worker's Compensation were underrepresented while those with Self-Pay insurance were overrepresented as CT participants.
Conclusions: Despite progress, continued efforts to not only enroll participants into CTs that are representative of the healthcare system and region, but also to better assess representativeness quantitatively are still needed.
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http://dx.doi.org/10.1016/j.conctc.2024.101274 | DOI Listing |
BMC Genomics
December 2024
Department of Entomology, University of Maryland, College Park, MD, 20742, USA.
Strong and shifting selective pressures of the Anthropocene are rapidly shaping phenomes and genomes of organisms worldwide. Crops expressing pesticidal proteins from Bacillus thuringiensis (Bt) represent one major selective force on insect genomes. Here we characterize a rapid response to selection by Bt crops in a major crop pest, Helicoverpa zea.
View Article and Find Full Text PDFJ Radiat Res
December 2024
Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8574, Japan.
The National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) is a database that stores anonymized information on medical receipts and health checkups in Japan. The NDB Open Data is a publicly accessible summary table of the NDB database. To reveal annual trends and regional disparities in radiotherapy utilization in Japan, we analyzed the NDB Open Data tables for a 9-year period from 2014 to 2022.
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
Office of the Assistant Secretary for Technology Policy/Office of the National Coordinator for Health Information Technology, Washington, DC 20201, United States.
Objectives: To develop indices of US hospital interoperability to capture the current state and assess progress over time.
Materials And Methods: A Technical Expert Panel (TEP) informed selection of items from the American Hospital Association Health IT Supplement survey, which were aggregated into interoperability concepts (components) and then further combined into indices. Indices were refined through psychometric analysis and additional TEP input.
Prev Chronic Dis
December 2024
Community Impact Office, Markey Cancer Center, University of Kentucky, Lexington.
Introduction: Kentucky has the highest all-site cancer incidence and death rate in the US. In 2021, the University of Kentucky Markey Cancer Center convened a steering committee to conduct a statewide community cancer needs assessment (CNA). The goal of the final CNA phase was to gather community input on prioritizing Kentucky's cancer-related needs and ways to address them.
View Article and Find Full Text PDFFront Neurosci
December 2024
Graduate Program in Cognitive Science, Yonsei University, Seoul, Republic of Korea.
Introduction: Functional magnetic resonance imaging (fMRI) data is highly complex and high-dimensional, capturing signals from regions of interest (ROIs) with intricate correlations. Analyzing such data is particularly challenging, especially in resting-state fMRI, where patterns are less identifiable without task-specific contexts. Nonetheless, interconnections among ROIs provide essential insights into brain activity and exhibit unique characteristics across groups.
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