Academic-community research partnerships focusing on addressing the social determinants of health and reducing health disparities have grown substantially in the last three decades. Early-stage investigators (ESIs), however, are less likely to receive grant funding from organizations like the National Institutes of Health, and we know little about the facilitators and barriers they face on their career journeys or the best ways to support them and their community research partnerships. This study examines ESIs’ experiences with a program that funded and supported their community-partnered pilot health disparities research.

Fourteen ESIs from five cohorts of pilot investigators participated in in-depth focus groups between April 2020 and February 2024. Two reviewers independently identified significant quotes and created codes. Thematic analysis was used to develop relevant themes.

The overarching theme was that the program was a launch pad for the ESIs’ research careers. Four distinct sub-themes contributing to the launch pad theme were: (1) ESI Growth & Adaptation; (2) Community and Support; (3) The Value of Collaboration and Partnership; (4) Need for Effective Mentorship. The results suggest the program offered ESIs and community partners substantial, unique support and resources, but challenges remained.

Future programs helping ESIs who conduct community-engaged research to launch their research careers should consider implementing tailored support while offering strategies to eliminate or reduce institutional barriers, including strengthening mentoring.

Persistent discrimination and identity threats contribute to adverse health outcomes in minoritized groups, mediated by both structural racism and physiological stress responses.

This study aims to evaluate the feasibility of recruiting African American volunteers for a pilot study of race-based stress, the acceptability of a mindfulness intervention designed to reduce racism-induced stress, and to evaluate preliminary associations between race-based stress and clinical, psychosocial, and biological measures.

A convenience sample of African Americans aged 18–50 from New York City’s Tri-state area underwent assessments for racial discrimination using the Everyday Discrimination Scale (EDS) and Race-Based Traumatic Stress Symptom Scale. Mental health was evaluated using validated clinical scales measuring depression, anxiety, stress, resilience, mindfulness, resilience, sleep, interpersonal connection, and coping. Biomarkers were assessed through clinical laboratory tests, allostatic load assessment, and blood gene expression analysis.

Twenty participants (12 females, 8 males) completed assessments after consent. Elevated EDS scores were associated with adverse lipid profiles, including higher cholesterol/high-density lipoprotein (HDL) ratios and lower HDL levels, as well as elevated inflammatory markers (NF-kB activity) and reduced antiviral response (interferon response factor). Those with high EDS reported poorer sleep, increased substance use, and lower resilience. Mindfulness was positively associated with coping and resilience but inversely to sleep disturbance. 90% showed interest in a mindfulness intervention targeting racism-induced stress.

This study demonstrated an association between discrimination and adverse health effects among African Americans. These findings lay the groundwork for further research to explore the efficacy of mindfulness and other interventions on populations experiencing discrimination.

Race/ethnicity and sleep disturbances are associated with dementia risk.

To explore racial–ethnic disparities in sleep disturbances, and whether race/ethnicity moderates the relationship between sleep disturbances and dementia risk among older adults.

We analysed ten annual waves (2011–2020) of prospective cohort data from the National Health and Aging Trends Study, a nationally representative USA sample of 6284 non-Hispanic White (n = 4394), non-Hispanic Black (n = 1311), Hispanic (n = 342) and non-Hispanic Asian (n = 108) community-dwelling older adults. Sleep disturbances were converted into three longitudinal measures: (a) sleep-initiation difficulty (trouble falling asleep within 30 min), (b) sleep-maintenance difficulty (trouble falling asleep after waking up early) and (c) sleep medication usage (taking medication to sleep). Cox proportional hazards models analysed time to dementia, after applying sampling weights and adjusting for sociodemographic characteristics and health.

Black, Hispanic and Asian respondents exhibited higher frequencies of sleep-initiation and sleep-maintenance difficulties, but had less sleep medication usage, compared with White older adults. Among Hispanic respondents, sleep-initiation difficulty was associated with significantly decreased dementia risk (adjusted hazard ratio (aHR) = 0.34, 95% CI 0.15–0.76), but sleep-maintenance difficulty was associated with increased dementia risk (aHR = 2.68, 95% CI 1.17–6.13), compared with White respondents. Asian respondents using sleep medications had a significantly higher dementia risk (aHR = 3.85, 95% CI 1.64–9.04). There were no significant interactions for Black respondents.

Sleep disturbances are more frequent among older Black, Hispanic and Asian adults, and should be considered when addressing dementia disparities. Research is needed to explore how certain sleep disturbances may elevate dementia risk across different racial and ethnic subgroups.

Social isolation has been implicated in the development of cognitive impairment, but research on this association remains limited among racial-ethnic minoritized populations. Our study examined the interplay between social isolation, race–ethnicity and dementia.

We analyzed 11 years (2011–2021) of National Health and Aging Trends Study (NHATS) data, a prospective nationally representative cohort of U.S. Medicare beneficiaries aged 65 years and older. Dementia status was determined using a validated NHATS algorithm. We constructed a longitudinal score using a validated social isolation variable for our sample of 6,155 community-dwelling respondents. Cox regression determined how the interaction between social isolation and race–ethnicity was associated with incident dementia risk.

Average longitudinal frequency of social isolation was higher among older Black (27.6%), Hispanic (26.6%) and Asian (21.0%) respondents than non-Hispanic White (19.1%) adults during the 11-year period (t = −7.35, p < .001). While a higher frequency of social isolation was significantly associated with an increased (approximately 47%) dementia risk after adjusting for sociodemographic covariates (adjusted hazard ratio [aHR] = 1.47, 95% CI [1.15, 1.88], p < .01), this association was not significant after adjusting for health covariates (aHR = 1.21, 95% CI [0.96, 1.54], p = .11). Race–ethnicity was not a significant moderator in the association between social isolation and dementia.

Older adults from racial-ethnic minoritized populations experienced a higher longitudinal frequency of social isolation. However, race–ethnicity did not moderate the positive association observed between social isolation and dementia. Future research is needed to investigate the underlying mechanisms contributing to racial-ethnic disparities in social isolation and to develop targeted interventions to mitigate the associated dementia risk.

Severe weather events exacerbate existing health disparities due to poorly managed non-communicable diseases (NCDs). Our objective is to understand the experiences of staff, providers, and administrators (employees) of Federally Qualified Health Centers (FQHCs) in Puerto Rico and the US Virgin Islands (USVI) in providing care to patients living with NCDs in the setting of recent climate-related extreme events.

We used a convergent mixed-methods study design. A quantitative survey was distributed to employees at 2 FQHCs in Puerto Rico and the USVI, assessing experience with disasters, knowledge of disaster preparedness, the relevance of NCDs, and perceived gaps. Qualitative in-depth interviews explored their experience providing care for NCDs during recent disasters. Quantitative and qualitative data were merged using a narrative approach.

Through the integration of quantitative and qualitative data, we recognize: (1) significant gaps in confidence and preparedness of employees with a need for more training; (2) challenges faced by persons with multiple NCDs, especially cardiovascular and mental health disorders; and (3) most clinicians do not discuss disaster preparedness with patients but recognize their important role in community resilience.

With these results, we recommend strengthening the capacity of FQHCs to address the needs of their patients with NCDs in disasters.

This study explores the transformative effects of the Community Plunge, an educational program at the Wake Forest University School of Medicine (WFUSOM), on healthcare delivery, community engagement, and trainee perspectives. It addresses the broader context of health outcomes, where clinical care only accounts for 20%, emphasizing the critical role of social determinants of health (SDOH) and individual behaviors in the remaining 80%.

WFUSOM’s Community Plunge, established in 2002, involves a guided tour of the community, discussions with residents, and debriefing sessions. Qualitative interviews with 20 clinicians were conducted to extract key themes and insights.

The study identified several key outcomes. First, participants gained crucial insights into the community’s history, structural challenges, and prevalent SDOH, enhancing their understanding of the diverse patient populations they serve. Second, the program positively influenced clinician attitudes, fostering empathy, reducing paternalism, and promoting holistic patient care. Third, participants expressed a desire for increased community involvement and reported career trajectory changes toward advocacy and volunteerism. However, challenges such as time constraints were acknowledged.

The study advocates for collaborative efforts to enhance the program’s impact, including proactive measures to ensure respectful engagement during community tours. It positions the Community Plunge as an innovative, scalable, and transformative strategy for experiential SDOH exposure, crucial for the evolving social consciousness of healthcare learners.

Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities.

We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels.

The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning.

As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

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.

Modified Mini-Mental State Examination (3MSE) is often used to screen for dementia, but little is known about psychometric validity in American Indians.

We recruited 818 American Indians aged 65–95 for 3MSE examinations in 2010–2013; 403 returned for a repeat examination in 2017–2019. Analyses included standard psychometrics inferences for interpretation, generalizability, and extrapolation: factor analysis; internal consistency-reliability; test-retest score stability; multiple indicator multiple cause structural equation models.

This cohort was mean age 73, majority female, mean 12 years education, and majority bilingual. The 4-factor and 2nd-order models fit best, with subfactors for orientation and visuo-construction (OVC), language and executive functioning (LEF), psychomotor and working memory (PMWM), verbal and episodic memory (VEM). Factor structure was supported for both research and clinical interpretation, and factor loadings were moderate to high. Scores were generally consistent over mean 7 years. Younger participants performed better in overall scores, but not in individual factors. Males performed better on OVC and LEF, females better on PMWM. Those with more education performed better on LEF and worse on OVC; the converse was true for bilinguals. All differences were significant, but small.

These findings support use of 3MSE for individual interpretation in clinic and research among American Indians, with moderate consistency, stability, reliability over time. Observed extrapolations across age, sex, education, and bilingual groups suggest some important contextual differences may exist.

This study evaluated whether food insecurity (US Adult Food Security Survey) was associated with chronic pain (≥ 3 months) and high-impact chronic pain (i.e. pain that limits work and life) among US adults.

Cross-sectional analysis.

Nationally representative sample of non-institutionalised adults in the USA.

79 686 adults from the National Health Interview Survey (2019–2021).

Marginal, low and very low food security were associated with increased prevalence odds of chronic pain (OR: 1·58 (95 % CI 1·44, 1·72), 2·28 (95 % CI 2·06, 2·52) and 3·37 (95 % CI 3·01, 3·78), respectively) and high-impact chronic pain (OR: 1·28 (95 % CI 1·14, 1·42), 1·55 (95 % CI 1·37, 1·75) and 1·90 (95 % CI 1·65, 2·18), respectively) in a dose–response fashion (P-trend < 0·0001 for both), adjusted for sociodemographic, socio-economic and clinically relevant factors. Participation in Supplemental Nutrition Assistance Program (SNAP) and age modified the association between food insecurity and chronic pain.

These findings illustrate the impact of socio-economic factors on chronic pain and suggest that food insecurity may be a social determinant of chronic pain. Further research is needed to better understand the complex relationship between food insecurity and chronic pain and to identify targets for interventions. Moreover, the consideration of food insecurity in the clinical assessment of pain and pain-related conditions among socio-economically disadvantaged adults may be warranted.

Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors.

This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets.

The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index.

The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.