BackgroundRacial and ethnic disparities in stage at cancer diagnosis remain a major contributor to cancer-related mortality in the United States. Whether the magnitude of these disparities differs across cancers with distinct screening paradigms remains unclear.MethodsWe conducted a retrospective cohort study using the National Cancer Database (2006-2021) to examine racial and ethnic differences in distant-stage presentation among adults diagnosed with lung, breast, or colorectal cancer. Multivariable logistic regression models with robust standard errors were used to estimate adjusted odds ratios (ORs) for distant-stage disease. Interaction terms between race/ethnicity and cancer type were included to assess whether disparities differed across cancers. To facilitate cross-cancer comparisons, we also estimated adjusted predicted probabilities and absolute risk differences.ResultsAmong 3,386,771 patients, racial and ethnic disparities in distant-stage presentation varied substantially across cancer types. In lung cancer, compared with Non-Hispanic White (NHW) patients, the odds of distant-stage disease were modestly higher among Non-Hispanic Black (NHB) (OR, 1.02; 95% CI, 1.00-1.03), Hispanic (OR, 1.12; 95% CI, 1.10-1.14), and Non-Hispanic Other (NHO) patients (OR, 1.19; 95% CI, 1.16-1.22), corresponding to absolute increases of 0.5, 2.8, and 4.3 percentage points, respectively. In breast cancer, NHB patients experienced substantially higher odds of distant-stage presentation (OR, 1.61; 95% CI, 1.58-1.64), representing the largest disparity observed across all cancers. In colorectal cancer, NHB patients also had higher odds of distant-stage disease (OR, 1.14; 95% CI, 1.12-1.16), whereas Hispanic and NHO patients had lower odds and absolute probabilities compared with NHW patients.ConclusionsRacial and ethnic disparities in distant-stage presentation vary markedly across cancers. The largest inequities occur among Non-Hispanic Black patients with breast cancer despite the presence of long-standing population screening programs, suggesting that mechanisms beyond screening access-such as delays in diagnostic follow-up, barriers to care navigation, and structural inequities-contribute to persistent disparities in early cancer detection.
Oluwasegun A. Akinyemi, Keyline Moreno, O. Oyebanji et al.· The American surgeon· 0 citations
Artificial Intelligence (AI) has the potential to revolutionize medicine, particularly in the field of cardiology. There are significant diagnostics and treatments variabilities in the field of cardiovascular medicine that affects racial and ethnic racially and ethnically diverse populations as well as female patients across all age groups. The efforts put forth towards the development of AI and precision medicine within the cardiovascular practice do not fully account for existing variations in cardiovascular care delivery. AI models and precision medicine tools that were created with uncomprehensive data primarily drawn from White populations risk embedding historical differences into clinical decision-support systems. This paper outlines the integrative approach taken to review the current variabilities that persist within younger adults (<65 years) and older adults (≥ 65 years) who have cardiovascular disease. Additionally, genetic factors, limited access to care, health literacy, lack of insurance coverage and adherence are examined, as these are frequently cited as major contributors to health care adverse outcomes but remain under-researched and unresolved even with the expansion of Medicaid. For instance, Black patients experience higher prevalence of heart failure (HF) and hypertension, especially transthyretin amyloid cardiomyopathy HF, with Black women being disproportionately affected due to higher structural, environmental and clinical factors. Also, racially and ethnically diverse children with CVDs have higher odds of mortality than their White counterparts. The integration of AI in cardiovascular medicine must first be preceded by an active effort to restructure systems and reduce variable outcomes. Future research must prioritize diverse genomic datasets and equitable comprehensive representation in clinical trials. These initiatives are better served if they are driven by institutions that historically serve racially and ethnically diverse populations and communities to better enhance inclusion and fairness in electronic medical record keeping. Accordingly, cardiovascular medical practices and technology can progress forward with AI and precision medicine models that are both equitable and accurate.
Mikayla N. Harris, Parth A. Desai, Eric Yalley et al.· Frontiers in Artificial Inte...· 0 citations
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