Demographic composition of National Institutes of Health Clinical and Translational Science Awards (CTSA) Program principal investigators, scholars, and trainees.

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.

Adaptive capacity and preparedness of Clinical and Translational Science Award Program hubs: Overview of an environmental scan.

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.

An analysis of the Clinical and Translational Science Award pilot project portfolio using data from Research Performance Progress Reports.

Introduction: Pilot projects ("pilots") are important for testing hypotheses in advance of investing more funds for full research studies. For some programs, such as Clinical and Translational Science Awards (CTSAs) supported by the National Center for Translational Sciences, pilots also make up a significant proportion of the research projects conducted with direct CTSA support. Unfortunately, administrative data on pilots are not typically captured in accessible databases. Though data on pilots are included in Research Performance Progress Reports, it is often difficult to extract, especially for large programs like the CTSAs where more than 600 pilots may be reported across all awardees annually. Data extraction challenges preclude analyses that could provide valuable information about pilots to researchers and administrators. Methods: To address those challenges, we describe a script that partially automates extraction of pilot data from CTSA research progress reports. After extraction of the pilot data, we use an established machine learning (ML) model to determine the scientific content of pilots for subsequent analysis. Analysis of ML-assigned scientific categories reveals the scientific diversity of the CTSA pilot portfolio and relationships among individual pilots and institutions. Results: The CTSA pilots are widely distributed across a number of scientific areas. Content analysis identifies similar projects and the degree of overlap for scientific interests among hubs. Conclusion: Our results demonstrate that pilot data remain challenging to extract but can provide useful information for communicating with stakeholders, administering pilot portfolios, and facilitating collaboration among researchers and hubs.

Downstream funding success of early career researchers for resubmitted versus new applications: A matched cohort.

BACKGROUND: Early career researchers face a hypercompetitive funding environment. To help identify effective intervention strategies for early career researchers, we examined whether first-time NIH R01 applicants who resubmitted their original, unfunded R01 application were more successful at obtaining any R01 funding within 3 and 5 years than original, unfunded applicants who submitted new NIH applications, and we examined whether underrepresented minority (URM) applicants differentially benefited from resubmission. Our observational study is consistent with an NIH working group's recommendations to develop interventions to encourage resubmission. METHODS AND FINDINGS: First-time applicants with US medical school academic faculty appointments who submitted an unfunded R01 application between 2000-2014 yielded 4,789 discussed and 7,019 not discussed applications. We then created comparable groups of first-time R01 applicants (resubmitted original R01 application or submitted new NIH applications) using optimal full matching that included applicant and application characteristics. Primary and subgroup analyses used generalized mixed models with obtaining any NIH R01 funding within 3 and 5 years as the two outcomes. A gamma sensitivity analysis was performed. URM applicants represented 11% and 12% of discussed and not discussed applications, respectively. First-time R01 applicants resubmitting their original, unfunded R01 application were more successful obtaining R01 funding within 3 and 5 years than applicants submitting new applications-for both discussed and not discussed applications: discussed within 3 years (OR 4.17 [95 CI 3.53, 4.93]) and 5 years (3.33 [2.82-3.92]); and not discussed within 3 years (2.81 [2.52, 3.13]) and 5 years (2.47 [2.22-2.74]). URM applicants additionally benefited within 5 years for not discussed applications. CONCLUSIONS: Encouraging early career researchers applying as faculty at a school of medicine to resubmit R01 applications is a promising potential modifiable factor and intervention strategy. First-time R01 applicants who resubmitted their original, unfunded R01 application had log-odds of obtaining downstream R01 funding within 3 and 5 years 2-4 times higher than applicants who did not resubmit their original application and submitted new NIH applications instead. Findings held for both discussed and not discussed applications.

An evaluation of the National Institutes of Health Early Stage Investigator policy: Using existing data to evaluate federal policy.

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.

Are Racial Identities of Multiracials Stable? Changing Self-Identification Among Single and Multiple Race Individuals.

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.

FRIENDSHIP CHOICES OF MULTIRACIAL ADOLESCENTS: RACIAL HOMOPHILY, BLENDING, OR AMALGAMATION?*

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.

Veterans' Location in Health Professional Shortage Areas: Implications for Access to Care and Workforce Supply.

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.

What race and ethnicity measure in pharmacologic research.

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.