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Little has been published on the demographic composition of the clinical and translational science research workforce within the Clinical and Translational Science Awards (CTSA) Program despite the well-documented need for greater diversity in the biomedical research workforce. Analyses of workforce demographic reveal that women and members of underrepresented groups remain persistently underrepresented in the CTSA hub and training components principal investigators. In contrast, in the CTSA Program career development and training programs, females have greater representation as participants, and non-Whites were better represented in training programs.

The ability of research networks and individual institutions to effectively and efficiently prepare, respond, and adapt to emergent challenges is essential for the biomedical research enterprise. At the beginning of 2021, a special Working Group was formed by individuals in the Clinical and Translational Science Award (CTSA) consortium and approved by the CTSA Steering Committee to explore “Adaptive Capacity and Preparedness (AC&P) of CTSA Hubs.” The AC&P Working Group took a pragmatic Environmental Scan (E-Scan) approach of utilizing the diverse data that had been collected through existing mechanisms. The Local Adaptive Capacity framework was adapted to illustrate the interconnectedness of CTSA programs and services, while exposing how the demands of the pandemic forced them to quickly pivot and adapt. This paper presents a synopsis of the themes and lessons learned that emerged from individual sections of the E-Scan. Lessons learned from this study may improve our understanding of adaptive capacity and preparedness at different levels, as well as help strengthen the core service models, strategies, and foster innovation in clinical and translational science research.

Underrepresented minorities have higher attrition from the professoriate and have experienced greater negative impacts of the COVID-19 pandemic. The purpose of this study was to compare the impact of COVID-19 on the lives of 196 early-career physician-scientists versus PhD researchers who are underrepresented in biomedical research. Participants in the Building Up study answered questions on the impact of the COVID-19 pandemic on their personal and professional lives, and a mixed-methods approach was used to conduct the analysis. While most participants experienced increases in overall stress (72% of PhD researchers vs 76% of physician-scientists), physician-scientists reported that increased clinical demands, research delays, and the potential to expose family members to SARS-CoV-2 caused psychological distress, specifically. PhD researchers, more than physician-scientists, reported increased productivity (27% vs 9%), schedule flexibilities (49% vs 25%), and more quality time with friends and family (40% vs 24%). Future studies should consider assessing the effectiveness of programs addressing COVID-19-related challenges experienced by PhD researchers and physician-scientists, particularly those from underrepresented backgrounds.

A previous report found an association of topic choice with race-based funding disparities among R01 applications submitted to the National Institutes of Health (‘NIH’) between 2011 and 2015. Applications submitted by African American or Black (‘AAB’) Principal Investigators (‘PIs’) skewed toward a small number of topics that were less likely to be funded (or ‘awarded’). It was suggested that lower award rates may be related to topic-related biases of peer reviewers. However, the report did not account for differential funding ecologies among NIH Institutes and Centers (‘ICs’). In a re-analysis, we find that 10% of 148 topics account for 50% of applications submitted by AAB PIs. These applications on ‘AAB Preferred’ topics were funded at lower rates, but peer review outcomes were similar. The lower rate of funding for these topics was primarily due to their assignment to ICs with lower award rates, not to peer-reviewer preferences.

Several interrelated workforce and population trends have intensified the need to increase the number of nurse practitioners (NPs) in primary care. The Advanced Nursing Education Expansion (ANEE) program was created to address health workforce needs by increasing the number of students enrolled full-time in NP and nurse-midwifery programs. Using data from ANEE grant performance measures, we found that nearly 65% of ANEE-supported graduates practice in primary care and 44.8% work in medically underserved communities. Results from this study demonstrate that even short-term federal investments have an impact on the expansion of the primary care health workforce.

Objective To describe the distribution of Veterans in areas of the United States where there are potentially inadequate supplies of health professionals, and to explore opportunities suggested by this distribution for fostering health workforce flexibility. Data Sources County-level data from the 2015–2016 Health Resources and Services Administration's (HRSA's) Area Health Resources Files (AHRF) were used to estimate Veteran populations in HRSA-designated health professional shortage areas (HPSAs). This information was then linked to 2015 VA health facility information from the Department of Veterans Affairs. Study Design Potential Veteran populations living in Shortage Area Counties with no VHA facilities were estimated, and the composition of these populations was explored by Census division and state. Principal Findings Nationwide, approximately 24 percent of all Veterans and 23 percent of Veterans enrolled in VHA health care live in Shortage Area Counties. These estimates mask considerable variation across states. Conclusions An examination of Veterans residing in Shortage Area Counties suggests extensive maldistribution of health services across the United States and the continued need to find ways to improve health care access for all Veterans. Effective avenues for doing so may include increasing health workforce flexibility through expansion of nurse practitioner scopes of practice.

To assist new scientists in the transition to independent research careers, the National Institutes of Health (NIH) implemented an Early Stage Investigator (ESI) policy beginning with applications submitted in 2009. During the review process, the ESI designation segregates applications submitted by investigators who are within 10 years of completing their terminal degree or medical residency from applications submitted by more experienced investigators. Institutes/centers can then give special consideration to ESI applications when making funding decisions. One goal of this policy is to increase the probability of newly emergent investigators receiving research support. Using optimal matching to generate comparable groups pre- and post-policy implementation, generalized linear models were used to evaluate the ESI policy. Due to a lack of control group, existing data from 2004 to 2008 were leveraged to infer causality of the ESI policy effects on the probability of funding applications from 2011 to 2015. This article addresses the statistical necessities of public policy evaluation, finding administrative data can serve as a control group when proper steps are taken to match the samples. Not only did the ESI policy stabilize the proportion of NIH funded newly emergent investigators but also, in the absence of the ESI policy, 54% of newly emergent investigators would not have received funding. This manuscript is important to Research Evaluation as a demonstration of ways in which existing data can be modeled to evaluate new policy, in the absence of a control group, forming a quasi-experimental design to infer causality when evaluating federal policy.

Using the National Longitudinal Study of Adolescent Health (Add Health), we utilize the concepts of homophily, blending, and amalgamation to describe the possible friendship patterns of multiracials. Homophily occurs when multiracials are most likely to choose other multiracials as friends. Blending occurs when friendship patterns of multiracials are somewhere in-between those of their monoracial counterparts. Amalgamation consists of friendship patterns that are similar to one of their monoracial counterparts. All groups exhibit signs of amalgamation such that non-white multiracials resemble Blacks, and White multiracials resemble whites except for Black-White multiracials. Black-Whites, Asian-Whites, and Asian-Blacks also exhibit signs of blending, while only Native American multiracials show signs of homophily. Multiracials have different experiences depending on their specific racial composition, and while they seem to bridge the distance between racial groups, their friendship patterns also fall along Black and White lines.

Objective. Our general objective of this study is to furtherassess the predictive validity of Apgar scores on infant mortality using a national-level data setallowing for race/ethnic-specific variation. Method. This analysis is based on the 1989–1991NCHS Linked Birth/Infant Death files. Multivariate, multinomial logistic regression modelswere constructed adjusting for maternal behavioral and health risks, socioeconomic and demographicfactors, and infant characteristics. Results. After adjusting for maternal riskfactors, infants with low Apgar scores experienced 86 times the odds of neonatal death relative tohigh Apgar score infants while infants with medium Apgar scores experienced 10 times the odds ofneonatal death relative to the high Apgar referent. After controlling for maternal risk factors,birth weight and gestational age, the effect of low Apgar scores on neonatal mortality risk relativeto high Apgar scores is reduced (from OR = 86.1 to OR = 31.2), but still remains a strong predictor.Fully adjusted, race/ethnic-specific models show that the predictive value oflow Apgar on neonatal mortality risk is strongest for Mexican Americans (OR = 44.2) versusNon-Hispanic Whites and Blacks (OR = 36.2 and 20.4 respectively). Conclusion Ourresults support the conclusion that Apgar scores continue to be strong predictors of infant survival,independent of birth weight, gestational age, and a large number of maternal risk factors.They are also shown to be powerful predictors within different race/ethnic groups, solidifying theirimportance as both a diagnostic tool and descriptive health characteristic across various U.S.populations.

Using the National Longitudinal Study of Adolescent Health (Add Health), we estimate the determinants and direction of change in individual racial identification among multiracial and monoracial adolescents as they transition to young adulthood. We find that while many multiracials subsequently identify as monoracials, sizable numbers of monoracials also subsequently become multiracials. Native American-whites appear to have the least stable identification. We find strong support that socioeconomic status, gender, and physical appearance shape the direction of change for multiracials, and that black biracials are especially compelled to identify as monoracial blacks.

Advances in genomic technology have put the utility of collecting racial and ethnic data into question. Some researchers are optimistic about the potential of moving toward "personalized medicine" by using a person's genome to administer medications. Genetics will not erase the importance of race and ethnicity because race and ethnicity do not measure genetic composition. Unlike genes, race and ethnicity are social constructs; 2 persons with identical genetic makeup may self-identify as being of different race or ethnic origin. Race and ethnic categories have been subject to change over time; a person's self-identification may vary according to the context, wording, and format of the question asked. Despite the fluid nature of the concept, self-identified race and ethnicity can capture something that genes cannot, namely, aspects of culture, behavior, diet, environment, and features of social status that commonly used measures of socioeconomic status, such as income, education, and occupation, cannot measure.