Researchers from underrepresented groups leave research positions at a disproportionate rate. We aim to identify factors associated with self-efficacy in career advancement and career commitment among underrepresented post-doctoral fellows and early-career faculty.

Building Up is a cluster-randomized trial with 25 academic health institutions. In September-October 2020, 219 Building Up participants completed the pre-intervention assessment, which included questions on demographics, science identity, mentoring, self-efficacy in career advancement (i.e., advancement is open to me, confidence in career progression, confidence in overcoming professional barriers), and career commitment (i.e., intent to continue research training or studying in a field related to biomedical sciences). Using logistic and multinomial logistic regression, we identified characteristics independently associated with self-efficacy in career advancement and career commitment.

The cohort is 80% female, 33% non-Hispanic/Latinx Black, and 34% Hispanic/Latinx. Having mentors that address diversity was significantly associated with the belief that advancement is open to them (OR = 1.7). Higher science identity (OR = 4.0) and having mentors that foster independence (OR = 1.8) were significantly associated with confidence in career progression. Higher science identity was also significantly associated with confidence in overcoming professional barriers (OR = 2.3) and intent to continue studying in a field related to biomedical sciences (OR = 3.3). Higher age (OR = 2.3) and higher science identity (OR = 4.2) were significantly associated with intent to continue research training.

Science identity and mentoring play key roles in self-efficacy in career advancement and career commitment. These factors may contribute to retention of underrepresented early-career biomedical researchers.

Impaired motor and cognitive function can make travel cumbersome for People with Parkinson’s disease (PwPD). Over 50% of PwPD cared for at the University of Arkansas for Medical Sciences (UAMS) Movement Disorders Clinic reside over 30 miles from Little Rock. Improving access to clinical care for PwPD is needed.

To explore the feasibility of remote clinic-to-clinic telehealth research visits for evaluation of multi-modal function in PwPD.

PwPD residing within 30 miles of a UAMS Regional health center were enrolled and clinic-to-clinic telehealth visits were performed. Motor and non-motor disease assessments were administered and quantified. Results were compared to participants who performed at-home telehealth visits using the same protocols during the height of the COVID pandemic.

Compared to the at-home telehealth visit group (n = 50), the participants from regional centers (n = 13) had similar age and disease duration, but greater disease severity with higher total Unified Parkinson’s disease rating scale scores (Z = −2.218, p = 0.027) and lower Montreal Cognitive Assessment scores (Z = −3.350, p < 0.001). Regional center participants had lower incomes (Pearson’s chi = 21.3, p < 0.001), higher costs to attend visits (Pearson’s chi = 16.1, p = 0.003), and lived in more socioeconomically disadvantaged neighborhoods (Z = −3.120, p = 0.002). Prior research participation was lower in the regional center group (Pearson’s chi = 4.5, p = 0.034) but both groups indicated interest in future research participation.

Regional center research visits in PwPD in medically underserved areas are feasible and could help improve access to care and research participation in these traditionally underrepresented populations.

SPARK launched in 2016 to build a US cohort of autistic individuals and their family members. Enrollment includes online consent to share data and optional consent to provide saliva for genomic analysis. SPARK’s recruitment strategies include social media and support of a nation-wide network of clinical sites. This study evaluates SPARK’s recruitment strategies to enroll a core study population.

Individuals who joined between January 31, 2018, and May 29, 2019 were included in the analysis. Data include sociodemographic characteristics, clinical site referral, the website URL used to join, how the participant heard about SPARK, enrollment completion (online registration, study consents, and returning saliva sample), and completion of the baseline questionnaire. Logistic regressions were performed to evaluate the odds of core participant status (completing enrollment and baseline questionnaire) by recruitment strategy.

In total, 31,715 individuals joined during the study period, including 40% through a clinical site. Overall, 88% completed online registration, 46% returned saliva, and 38% were core participants. Those referred by a clinical site were almost twice as likely to be core participants. Those who directly visited the SPARK website or performed a Google search were more likely to be core participants than those who joined through social media.

Being a core participant may be associated with the “personal” connection and support provided by a clinical site and/or site staff, as well as greater motivation to seek research opportunities. Findings from this study underscore the value of adopting a multimodal recruitment approach that combines social media and a physical presence.

Under enrollment of participants in clinical research is costly and delays study completion to impact public health. Given that research personnel make decisions about which strategies to pursue and participants are the recipients of these efforts, we surveyed research staff (n = 52) and participants (n = 4,144) affiliated with SPARK (Simons Foundation Powering Autism for Knowledge) – the largest study of autism in the U.S. – to understand their perceptions of effective recruitment strategies.

In Study 1, research personnel were asked to report recruitment strategies that they tried for SPARK and to indicate which ones they would and would not repeat/recommend. In Study 2, SPARK participants were asked to indicate all the ways they heard about the study prior to enrollment and which one was most influential in their decisions to enroll.

Staff rated speaking with a SPARK-study-team member (36.5%), speaking with a medical provider (19.2%), word of mouth (11.5%), and a live TV news story (11.5%) as the most successful strategies. Participants most often heard about SPARK via social media (47.0%), speaking with a medical provider (23.1%), and an online search (20.1%). Research personnel’s and participants’ views on effective recruitment strategies often differed, with the exception of speaking with a medical provider.

Results suggest that a combination of strategies is likely to be most effective in reaching diverse audiences. Findings have implications for the selection of strategies that meet a study’s specific needs, as well as recruitment-strategy “combinations” that may enhance the influence of outreach efforts.

Addressing social determinants of health (SDOH) is fundamental to improving health outcomes. At a student-run free clinic, we developed a screening process to understand the SDOH needs and resource utilization of Milwaukee’s uninsured population.

In this cross-sectional study, we screened adult patients without health insurance (N = 238) for nine traditional SDOH needs as well as their access to dental and mental health care between October 2021 and October 2022. Patients were surveyed at intervals greater than or equal to 30 days. We assessed correlations between SDOH needs and trends in patient-reported resource usefulness.

Access to dental care (64.7%) and health insurance (51.3%) were the most frequently endorsed needs. We found significant correlations (P ≤ 0.05) between various SDOH needs. Notably, mental health access needs significantly correlated with dental (r = 0.41; 95% CI = 0.19, 0.63), medications (r = 0.51; 95% CI = 0.30, 0.72), utilities (r = 0.39; 95% CI = 0.17, 0.61), and food insecurity (r = 0.42; 95% CI = 0.19, 0.64). Food-housing (r = 0.55; 95% CI = 0.32, 0.78), housing-medications (r = 0.58; 95% CI = 0.35, 0.81), and medications-food (r = 0.53; 95% CI = 0.32, 0.74) were significantly correlated with each other. Longitudinal assessment of patient-reported usefulness informed changes in the resources offered.

Understanding prominent SDOH needs can inform resource offerings and interventions, addressing root causes that burden under-resourced patients. In this study, patient-reported data about resource usefulness prompted the curation of new resources and volunteer roles. This proof-of-concept study shows how longitudinally tracking SDOH needs at low-resource clinics can inform psychosocial resources.

There is an urgent need to address pervasive inequities in health and healthcare in the USA. Many areas of health inequity are well known, but there remain important unexplored areas, and for many populations in the USA, accessing data to visualize and monitor health equity is difficult.

We describe the development and evaluation of an open-source, R-Shiny application, the “Health Equity Explorer (H2E),” designed to enable users to explore health equity data in a way that can be easily shared within and across common data models (CDMs).

We have developed a novel, scalable informatics tool to explore a wide variety of drivers of health, including patient-reported Social Determinants of Health (SDoH), using data in an OMOP CDM research data repository in a way that can be easily shared. We describe our development process, data schema, potential use cases, and pilot data for 705,686 people who attended our health system at least once since 2016. For this group, 996,382 unique observations for questions related to food and housing security were available for 324,630 patients (at least one answer for all 46% of patients) with 65,152 (20.1% of patients with at least one visit and answer) reporting food or housing insecurity at least once.

H2E can be used to support dynamic and interactive explorations that include rich social and environmental data. The tool can support multiple CDMs and has the potential to support distributed health equity research and intervention on a national scale.

There is no consensus on how to determine appropriate financial compensation for research recruitment. Selecting incentive amounts that are reasonable and respectful, without undue inducement, remains challenging. Previously, we demonstrated that incentive amount significantly impacts participants’ willingness to complete various hypothetical research activities. Here we further explore this relationship in a mock decentralized study.

Adult ResearchMatch volunteers were invited to join a prospective study where interested individuals were given an opportunity to view details for a study along with participation requirements, then offered a randomly generated compensation amount between $0 and $50 to enroll and participate. Individuals agreeing to participate were then asked to complete tasks using a remote mobile application (MyCap), for two weeks. Tasks included a weekly survey, a daily gratitude journal and daily phone tapping task.

Willingness to participate was 85% across all incentive levels but not significantly impacted by amount. Task completion appeared to increase as a function of compensation until a plateau at $25. While participants described the study as low burden and reported that compensation was moderately important to their decision to join, only 31% completed all study tasks.

While offering compensation in this study did not have a strong effect on enrollment rate, this work provides insight into participant motivation when joining and participating in studies employing mobile applications.

Individuals reside within communities influenced by various social determinants impacting health, which may harmonize or conflict at individual and neighborhood levels. While some experience concordant circumstances, discordance is prevalent, yet poorly understood due to the lack of a universally accepted method for quantifying it. This paper proposes a methodology to address this gap.

We propose a systematic approach to operationalize concordance and discordance between individual and neighborhood social determinants, using household income (HHI) (continuous) and race/ethnicity (categorical) as examples for individual social determinants. We demonstrated our method with a small dataset that combines self-reported individual data with geocoded neighborhood level. We anticipate that the risk profiles created by either self-reported individual data or neighborhood-level data alone will differ from patterns demonstrated by typologies based on concordance and discordance.

In our cohort, it was revealed that 20% of patients experienced discordance between their HHIs and neighborhood characteristics. Additionally, 38% reside in racially/ethnically concordant neighborhoods, 23% in discordant ones, and 39% in neutral ones.

Our study introduces an innovative approach to defining and quantifying the notions of concordance and discordance in individual attributes concerning neighborhood-level social determinants. It equips researchers with a valuable tool to conduct more comprehensive investigations into the intricate interplay between individuals and their environments. Ultimately, this methodology facilitates a more accurate modeling of the true impacts of social determinants on health, contributing to a deeper understanding of this complex relationship.

Screening for health-related social needs (HRSNs) within health systems is a widely accepted recommendation, however challenging to implement. Aggregate area-level metrics of social determinants of health (SDoH) are easily accessible and have been used as proxies in the interim. However, gaps remain in our understanding of the relationships between these measurement methodologies. This study assesses the relationships between three area-level SDoH measures, Area Deprivation Index (ADI), Social Deprivation Index (SDI) and Social Vulnerability Index (SVI), and individual HRSNs among patients within one large urban health system.

Patients screened for HRSNs between 2018 and 2019 (N = 45,312) were included in the analysis. Multivariable logistic regression models assessed the association between area-level SDoH scores and individual HRSNs. Bivariate choropleth maps displayed the intersection of area-level SDoH and individual HRSNs, and the sensitivity, specificity, and positive and negative predictive values of the three area-level metrics were assessed in relation to individual HRSNs.

The SDI and SVI were significantly associated with HRSNs in areas with high SDoH scores, with strong specificity and positive predictive values (∼83% and ∼78%) but poor sensitivity and negative predictive values (∼54% and 62%). The strength of these associations and predictive values was poor in areas with low SDoH scores.

While limitations exist in utilizing area-level SDoH metrics as proxies for individual social risk, understanding where and how these data can be useful in combination is critical both for meeting the immediate needs of individuals and for strengthening the advocacy platform needed for resource allocation across communities.