Diversity Differences Among Cardiovascular Fellowships Across Five Geographic Regions in the United States

Introduction Diversity and inclusion in cardiovascular fellowships are necessary for addressing the healthcare needs of diverse patient populations. However, regional disparities in the diversity of these programs persist, diminishing efforts to create a representative workforce. We observe the regional differences in the diversity of cardiovascular fellowship programs, focusing on gender, doctorate designation, and graduation within the United States (US) or other. We hypothesized that males, medical doctors (MD), and US graduates would be in majority across all regions. Methods Data for cardiovascular fellowships from the Fellowship and Residency Electronic Database Access (FREIDA) system for the matriculation year 2022-2023 was obtained to assess the representation of male vs female gender, MD vs osteopathic doctor (DO) designation, and US vs non-US graduate. We then compared these backgrounds to five defined regions (Midwest, Northeast, Southeast, Southwest, and West) in the United States to define representation for backgrounds across geographic areas. Statistical significance was determined by p<0.05 with the use of SAS Studio 3.8, version 9.4 (Cary, NC: SAS Institute, Inc.), and Wilson score for confidence intervals. Results We found significant disparities across all background factors for all regions. This includes that females, DOs, and non-US graduates were underrepresented among Midwest, Northeast, Southeast, Southwest, and West regions, and the p-value was <0.001 for all variations. Specifically for Midwest, the female frequency was 155 (23.81%; CI: 21, 27; p<0.001), DO frequency was 101 (15.51%; CI: 13, 19; p<0.001), and non-US graduate frequency was 206 (31.84%; CI: 28, 36; p<0.001). For Northeast, the female frequency was 231 (29.62; CI: 27, 33; p<0.001), DO frequency was 72 (9.22; CI: 7, 11; p<0.001), and non-US graduate frequency was 239 (30.68; CI 28, 34; p<0.001). For Southeast, the female frequency was 178 (25.99; CI: 23, 29; p<0.001), DO frequency was 67 (9.78; CI: 8, 12; p<0.001), and non-US graduate frequency 279 (41.46; CI: 38, 45; p<0.001). For Southwest, the female frequency was 74 (26.71; CI: 22, 32; p<0.001), DO frequency was 21 (7.58; CI 5, 11; p<0.001), and non-US graduate frequency was 110 (39.71; CI: 34,46; p<0.001). For West, the female frequency was 107 (31.75; CI 27, 37; p<0.001), DO frequency was 15 (4.45; CI: 3, 7; p<0.001), and non-US graduate frequency was 54 (16.07; CI: 13, 20; p<0.001). Conclusion We emphasize the regional disparities for females, DOs, and non-US graduates within cardiovascular fellowships in the past matriculation year. Understanding that we have not reached diversity goals allows for further reflection and implementation of targeted interventions and initiatives aimed at promoting equal opportunities for applicants. This is true for all regions of the United States. By addressing these disparities, fellowship programs can more effectively mirror the diverse patient populations they serve and foster a healthcare environment that is inclusive and accommodating. This, in turn, contributes to the overall enhancement of healthcare outcomes.

Abstract 5471: CNSigs: An R package for the identification of copy number signatures

Copy number aberrations (CNAs) are gains and losses of large genomic segments present across most cancer types and are a hallmark of cancer genomic alterations. However, the processes underlying CNAs and characteristic patterns of CNAs are poorly understood. Using single nucleotide variant (SNV) data, bioinformatic advances have identified underlying mutational signatures resulting from distinct mutational processes. Mutational signatures have led to a variety of discoveries, several of which are being investigated in clinical management of cancer. The development of algorithms able to uncover similar signatures for CNAs, rather than SNVs, is still in its infancy. Here we present an analysis package for the R programming language called CNSigs that allows for the robust and reproducible derivation of copy number signatures. Based on a list of extracted copy number features previously verified in ovarian cancer, we utilize mixed model approaches and non-negative matrix factorization to derive CNA signatures across cancer types. The development of a package to derive signatures from copy number data allows further investigation of underlying processes that may be responsible for these CNA fingerprints. To verify the reproducibility of the signatures, we derived signatures from two independent breast cancer datasets that use distinct copy number segmentation approaches. From these independent datasets we were able to recover the same signatures with high accuracy. We identified five signatures that are distinct from known breast cancer receptor-based or expression-based subtypes, yet reveal unique associations with underlying mutations, mutational processes, and transcriptional programs. To validate robustness, we applied the pipeline to 11 different cancer types from the TCGA dataset and showed that we were able to derive signatures from all of these cancer types of varying sample sizes. We identified certain signatures that cut across tumor types, while others are distinct to individual cancers. This work lays the groundwork for further analysis into the underlying molecular processes leading to these copy number signatures seen across cancer types. The CNSigs package also allows researchers to easily analyze their own samples to look for signatures in their copy number profiles and to compare these to signatures previously derived for their cancer type.

Citation Format: David Tallman, Sinclair Stockard, Zachary T. Weber, Sarah Asad, Katharine Collier, Elizabeth Adams, Jerome Bey, Daniel G. Stover. CNSigs: An R package for the identification of copy number signatures [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5471.

On-site occupational health nursing services: estimating the net savings: Part II

1. A primary management use of this quantitative assessment model is to estimate the overall net cost savings from operating an entire on-site occupational health nursing program. 2. Another management use of this model is to estimate the net cost savings contributed by individual occupational health nursing services included in the on-site program. 3. The exclusive focus of the model on the net cost savings arising from the direct substitution of on-site for off-site occupational health nursing services can be extended with the application of a break even analysis. 4. A sensitivity analysis can be applied to factors of the model to help overcome the effects of substantial uncertainty concerning their estimates.