Objectives/Goals: Cervical cancer is preventable through HPV vaccination and the detection/removal of precancerous lesions. Incidence and mortality rates have only decreased by 3–4% in the past decade. Despite having the tools to prevent all cervical cancers, they are not being fully utilized. Our goal is to identify barriers and design strategies to overcome them. Methods/Study Population: Women in urban (750) and rural (750) settings will be screened for the presence of high-oncogenic risk HPV (hrHPV) by self-vaginal swab, complete the Monitoring Blunting Style Scale, a validation scale to determine attentional style, and a structural barrier to care survey. A subset (Results/Anticipated Results: The study, launched in September 2024 at the Medicine Primary Care Clinic at UMC in New Orleans, has enrolled 16 women. Sample adequacy was high (82%), with 5 women having hrHPV present. Participants expressed high satisfaction and acceptance of the self-administered vaginal swab, with most samples demonstrating high quality. Surveys have been collected, and hrHPV-positive women have been referred for gynecological follow-up. Shreveport site will recruit women across over 20 rural clinical sites using a Mobile Health Unit to increase access in rural and underserved communities. Discussion/Significance of Impact: The baseline study will take 12–18 months. We will identify and address key barriers to follow-up gynecological care, including logistical issues (improving access and navigation), educational needs (developing culturally sensitive materials), and emotional support. We will create a care delivery model to eliminate cervical cancer in Louisiana.

Objectives/Goals: Access to accurate public health information is an essential component to ensuring health equity. We launched our social media channels on Instagram, Facebook, and TikTok to highlight, engage with, and bring culturally tailored and language appropriate health and research information to our target communities. Methods/Study Population: Monitoring engagement patterns with our content on each platform influenced the development of a range of innovative campaigns in both English and Spanish that were informed by our core values of inclusivity, trust-building, ongoing bidirectional communication, and co-creation. These three platforms were chosen to ensure reach and engagement with the different demographics within our target populations. The campaigns included those that provided relevant and accurate health information, highlighted the diversity of our team, uplifted our community partners, and gave voice to our community members. This content included health-related infographics, mini-documentary reels, video essays, interviews, and photos. Results/Anticipated Results: We assessed effectiveness, reach, and engagement based on the robustness of the analytics for each platform. Facebook content, the majority of which is in Spanish, appealed more to older, Latino community members. TikTok content appealed more to younger (under 35), primarily English-speaking community members, while Instagram appealed more to organizational partners and community health workers. A 2023 trendline analysis of average monthly Instagram content reach and interactions indicated a moderate-to-strong relationship between our tailored content and audience engagement. Storytelling techniques consistently outperformed other content types across platforms, and community partner collaboration drastically enhanced our visibility, reach, and further validated our approach. Discussion/Significance of Impact: Social media has become increasingly central to bidirectional information dissemination. Implementing tailored strategies and leveraging storytelling techniques is an effective means of engaging diverse audiences, enhancing public health communication, and building and maintaining trust by providing accurate, accessible information.

Objectives/Goals: Primary care physicians (PCPs) have limited awareness of assistive technology (AT) devices that can improve the daily functioning of older adults. This study aimed to assess the quality of the My Assistive Technology Guide (MATG), an informative web app, among PCPs and to describe their experiences using it. Methods/Study Population: In this pilot project, our team – comprising an established researcher, an undergraduate faculty member, and a graduate student – enrolled ten PCPs. In Phase I, the PCPs received training on how to use the MATG and were encouraged to utilize it for 30 days. At the end of this usage period, we implemented a concurrent parallel mixed-method design to collect both quantitative and qualitative data. Quantitative data were gathered using the User Mobile Application Scale (uMARS), while qualitative data was obtained through interviews. Data analysis involved descriptive statistics and thematic content analysis. Results/Anticipated Results: The mean score for the subjective quality of the MATG was high, 4.1 ± 1.1. The information domain received the highest rating, with a mean score of 4.6 ± 0.51, while the engagement domain received the lowest rating, at 3.3 ± 1.5. Overall, subjective quality was rated moderately high (mean 3.9, interquartile range 1.2), with perceived impact rated the highest at 4.8 ± 0.4. PCPs reported increased awareness, knowledge, attitude, intention, and behaviors to learn about AT and to inform and recommend AT devices to older adults. In addition, PCPs provided suggestions to improve the MGAT and its integration into their medical practice. Discussion/Significance of Impact: The results demonstrated the high quality and utility of the MATG, indicating that it could serve as a valuable resource for PCPs in addressing functional disabilities among older adults. Future research should evaluate the effectiveness of the MATG in enhancing older adults’ function in daily living activities.

Objectives/Goals: Summarize literature on parenting stress and treatment outcomes among postpartum women with opioid use disorder (OUD). Describe the causes of parenting stress identified by postpartum women who received medication for OUD (MOUD) and service providers. Discuss recommendations for parenting support services for postpartum women receiving MOUD in outpatient treatment settings. Methods/Study Population: We will conduct focus groups with postpartum women who received MOUD up to one-year after childbirth (2 groups; n  =  10) and service providers (e.g., obstetrics, psychiatry, pediatrics, primary care; 2 groups; n  =  10) to identify causes of and contributors to parenting stress to inform the adaptation of a parenting intervention for postpartum women receiving MOUD in an outpatient clinic setting. Participants will be recruited via flyers, email, and social media reach-outs, clinic staff and patient group meetings, and community-based outreach methods. Participants will be compensated for their participation. Focus groups will be audio-recorded and transcribed. Data will be analyzed via rapid analytic procedures using a summary template matrix. Results/Anticipated Results: We will use parenting-related stressors identified by mothers with substance use disorders in previous research to guide our interview questions. We expect to hear participants speak about their knowledge and experiences with stigma, guilt and shame, mental health symptoms, neonatal opioid withdrawal or neonatal opioid withdrawal syndrome (NOWS), fear of being reported to child protective services, and difficulties with mother–infant bonding and attachment. We will also ask participants about structural barriers that are known to increase parenting stress, such as housing instability, financial strain, and availability and cost of childcare. We will also report on new themes that emerge from the data that are shown to increase stress, challenge sobriety, and hinder continued engagement in the treatment. Discussion/Significance of Impact: Discontinuation of MOUD in the postpartum period is high and can lead to opioid recurrence and overdose. Outpatient treatment programs who offer psychiatric and behavioral health care, and parenting programs that target contributors of early postpartum parenting stress could improve health and MOUD treatment outcomes for mothers with OUD.

Objectives/Goals: Our goal for this project is to develop a metric that integrates the intersectional social and structural determinants of health and well-being into the existing policy development framework to impact the integration of such considerations on population mental health. Methods/Study Population: This project was developed from a case study module offered by the Translational Research Program at the University of Toronto. This course was designed to sharpen contextual inquiry skills and further develop a case through employing strategies, including outreach engagement with stakeholders, conducting informational interviews and formulating potential pathways forward based on the integration of insights from interdisciplinary perspectives. Results/Anticipated Results: The anticipated outcome would be improved mental health outcomes as measured by the Mental Health Commission of Canada’s Mental Health Indicators (Mental Health Commission of Canada, 2015) Discussion/Significance of Impact: Although there are established mental health indicators and policy development framework the two operate independently of each other. Our proposal bridge the gap between the sectors so that one may inform the other, and they can collectively formulate reflective and representative policies.

Objectives/Goals: We aim to predict and rank conserved, immunogenic targets within key malaria proteins using computational tools. These tools incorporate parasite protein diversity and regional HLA allele frequencies to prioritize antigens for further validation and inclusion in a malaria vaccine targeting circulating strains. Methods/Study Population: We identified 42 conserved malaria proteins with nonredundant functions for P. falciparum invasion and transmission as vaccine targets. Protein sequence datasets were constructed from samples collected in highly endemic areas. We predicted targets most likely to be presented to CD4+ and CD8+ T cells. We designed and used heuristic-based and AI-weighting models that integrated predicted binding affinities to HLA alleles, HLA allele frequency data, and sequence conservation to score and rank targets. We validated our model by comparing predicted epitope distributions with published in vitro and in vivo immunogenicity data available in the Immune Epitope Database and Tools repository. Results/Anticipated Results: We successfully predicted and ranked targets within the vaccine candidate proteins, identifying conserved and HLA-nonspecific targets that correspond to positive immunogenicity data, validating our approach. We are currently analyzing model performance by comparing predictions to over 5,800 experimentally validated P. falciparum targets from clinical trials and immune assays. We will evaluate each models’ accuracy and ability to prioritize targets and compare their performances as measured quantitatively by precision and area under the curve metrics. We expect the AI-based model to significantly outperform the heuristic approach, improving the identification of effective vaccine targets. Discussion/Significance of Impact: By incorporating parasite diversity and regional HLA allele frequencies, our approach addresses the challenge of directing the human immune response against genetically diverse P. falciparum strains in highly endemic areas. This strategy could significantly enhance malaria vaccine efficacy and can be adapted for use against other pathogens.

Objectives/Goals: Urinary tract infections (UTIs) cause significant morbidity, and many patients require multiple courses of antibiotics increasing the risk of antibiotic resistance. We determined the prevalence of urinary antibiotic heteroresistance (HR), which has been associated with treatment failures in vivo, to three first-line antibiotics for UTIs. Methods/Study Population: Clinical urine Escherichia coli isolates from patients in metropolitan Atlanta, Georgia in August 2023 were collected as part of public health surveillance performed by the CDC-funded, Georgia Emerging Infections Program (EIP). Only the first E. coli isolate collected for each patient was included in this study. Antibiotic susceptibility was determined through medical record review. HR to nitrofurantoin, trimethoprim-sulfamethoxazole, and fosfomycin was determined by population analysis profiling (PAP), where broth dilutions of E. coli were plated on increasing concentrations of the antibiotic. HR was defined as survival of >1 in 106 cfu but fewer than 50% survival at 1X antibiotic breakpoint (bp), resistant as > 50% survival at 1X bp and susceptible as survival of Results/Anticipated Results: Among 355 patients, 21 (5.9%) were resistant or intermediate to nitrofurantoin and 92 (26%) were resistant to trimethoprim-sulfamethoxazole. Antibiotic susceptibility data were missing from 5(1.4%) and 11(3%) of isolates for nitrofurantoin and trimethoprim-sulfamethoxazole, respectively. Susceptibility testing was not routinely performed nor reported for fosfomycin, thus excluded. PAP revealed that of the total 355 isolates, 3(0.84%) were heteroresistant to nitrofurantoin, 17(4.8%) were heteroresistant to trimethoprim-sulfamethoxazole, and 27(7.6%) were heteroresistant to fosfomycin. Of the isolates found to be susceptible by standard testing, 1(0.3%) and 9(3.6%) were heteroresistant to nitrofurantoin and trimethoprim-sulfamethoxazole by PAP, respectively. Discussion/Significance of Impact: Despite low rates of HR to nitrofurantoin and trimethoprim-sulfamethoxazole (0.84%, 4.8%), HR to fosfomycin was more frequent (7.6%). Given that susceptibility is not generally performed for fosfomycin, this could have implications for including fosfomycin as a first-line treatment for E. coli UTIs.

Objectives/Goals: Both diabetes and obesity are major health issues in the USA. Though much focus is given on the impact of lifestyle modifications to control diabetes and obesity, more information needs to be established about the association of social determinants of health and them. This study explores these associations, focusing on the DMV area. Methods/Study Population: The data for this study were collected for 158 counties which cover two states Maryland and Virginia, and Washington DC from PolicyMap. PolicyMap is a geography information system (GIS) that aggregates different types of data from different sources for research purposes. County-level data on public health related to different SDoH like median age, and percentages proportions of different ethnicity (Hispanic or Latino), percentages of different race, and nativity (foreign born), gender (ratio of male to female), access to primary healthcare, social vulnerability index (SVI), and median household income was used in this study. Statistical methods like multiple regression, one way ANOVA, and Pearson’s correlation coefficients were used to determine which factors are associated with these two conditions. Results/Anticipated Results: For both diabetes prevalence and obesity, multiple linear regression model with backward elimination was used to select variables which associated with them. The backward elimination process selected the set of factors for which the adjusted R square was the highest. In both cases, median household income, median age of population, social vulnerability level, percentage of white population, and percentage of foreign-born population were found to be significant at 5% level of significant. Pearson’s correlation coefficients showed significant positive relationship between factors like obesity and diabetes, median age and access to health care, and negative relationship between obesity and foreign born. Income, healthcare access, and white population were found to be significantly different SVIs from ANOVA. Discussion/Significance of Impact: This research study found that some SDoH affect diabetes and obesity in the same direction. The association is positive for median age and negative for income, SVI, percentage of white population, and foreign born. The associations were found between actionable and non-actionable factors like percentage of white population with access to health care.

Objectives/Goals: To develop an informatics framework that will allow study of environmental effects on stillbirth at large scale (i.e., US-level) and leverage recent advances in machine learning and artificial intelligence to produce reproducible results that can be compared across multiple institutional settings. Methods/Study Population: Experimental exposure data are often available in “absolute time,” where a clinical event can be anchored using a timeline transformation. We associate each stillbirth event with a set of {ti…ti+1}L shapelets [1] associated with a location, L, and time intervals for the entire dataset. These shapeless are aggregated using a state-of-the-art shapelet classifier [2]. An autoencoder is used to reduce the dimensionality of the stillbirth classification and to cluster stillbirth events according to their corresponding exposure patterns. The stillbirth cluster can be analyzed for other nonexposure (i.e., genetic, SDoH, and demographics) factors, which may be enriched and/or depleted. Results/Anticipated Results: The framework we are developing leverages a shapelet-based approach to produce clusters of stillbirth events according to their corresponding exposure patterns. These clusters can be analyzed for depletion or enrichment of nonenvironmental factors. This analysis will inform how to formulate (or not) class models of exposure that can be more informative and have better predictive power than overall population models. Moreover, the finding of depletion and enrichment of physiological properties of the individuals may lead to novel physiological hypotheses to better understand the injury mechanisms that the environmental exposure profile produces. Discussion/Significance of Impact: Nearly 20,000 babies are stillborn in the USA each year [3]. Environmental exposures, usually studied as time averages over certain periods of time, have produced mixed results for stillbirth risk [4]. However, temporal profiles matter [1], and we argue that they can be assessed using shapelet technology.

Objectives/Goals: Understanding systematic review results help prioritise more valuable studies. However, evaluating whether a systematic review has conclusively answered a question is difficult, and it is unclear which tools are available for such assessments. Thus, we mapped the extent of methods for determining the conclusiveness of systematic review results. Methods/Study Population: We searched Medline (Ovid), EMBASE (Ovid), and Web of Science to find papers with methods to determine whether systematic review results were conclusive or should be updated. The characteristics of primary references for included methods are presented. We classified and summarized available methods. Results/Anticipated Results: A total of 58 unique methods were identified. Many have been published since 2010 and often did not include a worked example. We found 25 mathematical methods for the conclusiveness of meta-analyses, which included cumulative meta-analysis, fail-safe number, fragility index, prediction and machine learning model, simulation-based power, conditional power, and graphical approaches. There were 15 methods for the conclusiveness of cumulative meta-analyses, such as quality control approach, trial sequential analysis, sequential meta-analysis, and law of iterated logarithm. And, 18 methods assessed the conclusiveness of systematic reviews: GRADE framework, the strength of a body of evidence approach, methods for assessing the need to update a systematic review, and methods for specific clinical domains. Discussion/Significance of Impact: We found a wide range of methods that can be used to determine the conclusiveness of systematic review results. End-users of systematic reviews can review our results to find the most appropriate methods for their contexts and decisions.

Objectives/Goals: This study aims to evaluate common features of mobile health (mHealth) apps and their role in helping caregivers make health decisions for children. Methods/Study Population: A scoping review of literature on caregivers’ use of mHealth apps (published since 2008) was conducted across 5 databases (i.e., Embase, PubMed, CINAHL, Clinicaltrials.gov, and IEEE Xplore). Selected papers were categorized based on app purposes, target users, and mHealth agile development phases. Common features were also identified and analyzed along with users’ pros and cons. Further, primary feature requests were summarized to inform future development. Results/Anticipated Results: This review included 62 studies. Most apps were about maternity and infant care and specific diseases. Major users were caregivers and pregnant women. Around 20% of papers covered multiple phases in the mHealth agile development lifecycle. The effectiveness/clinical trial (phase III) was the most common. E-learning, personalization and customization, and health tracking features were the three most common features of mHealth apps included in this review. More positive feedback was found regarding features than concerns. Caregivers perceived apps as helpful and empowered them to make informed decisions. Concerns were mainly over 1) technical issues, 2) inappropriate design, and 3) ambiguous terms. Requested new features included content comprehensiveness, user engagement, and usage flexibility. Discussion/Significance of Impact: To our knowledge, this is the first review to investigate the usability of mHealth app features in this area. The results offer feasible strategies for developers to improve the effectiveness of apps for caregiver decision-making.

Objectives/Goals: To determine the acceptability of a mobile/school-based diabetes and prevention clinic to overcome transportation barriers and improve attendance at endocrinology appointments for youth with type 1 diabetes, type 2 diabetes, MODY diabetes, and obesity living in socially vulnerable communities Methods/Study Population: This study utilized a 3-phase implementation process focused on feasibility, demand, and acceptability. Phase 1: identified high-need areas using patient volume and disease control metrics. Collaborated with school districts to define needs, enrolled them through a memorandum of understanding, and subsequently recruited and consented existing patients attending these schools. Phase 2 piloted the program to refine logistics and workflows. Semi-structured interviews and surveys were conducted with caregivers to assess program acceptability and satisfaction. Phase 3 is ongoing and will track clinical outcomes including glycemic control, appointment attendance, hospital admissions, emergency department visits, and self-management metrics. Results/Anticipated Results: Demand for the program was high with 91% (10/11) of approached districts agreeing to participate. District enrollment was staggered, starting with 1 district in August 2023, expanding to 5 in September, 7 in October, and reaching 8 districts by May 2024. From August 2023 to July 2024, the school-based mobile clinic conducted 355 individual clinical encounters addressing diabetes and obesity. The encounters included 180 for type 1 diabetes, 69 for type 2 diabetes, 8 for MODY diabetes, and 98 for obesity. Interviews and surveys with 36 caregivers are ongoing to evaluate program satisfaction. Discussion/Significance of Impact: The data suggest that this innovative healthcare delivery model is feasible. District-level enrollment demonstrates a demand for the program. Results from the interviews and surveys will further characterize the program’s acceptability among caregivers and lay the groundwork for future efficacy testing.

Objectives/Goals: Longitudinal studies capture dynamic host–environment interactions crucial to microbiome research. Commonly used mixed-effects models can struggle with small clinical samples, like many endometrial cancer studies. We will compare them to simpler fixed-effects models, aiming to develop a new fixed-effects differential abundance alternative. Methods/Study Population: Starting with simulated data for linear models with a single treatment effect, we will incrementally increase complexity to reflect real-world microbiome data. To observe conditions of misspecification, simulated data of repeated measures will be generated using different distributions, while varying parameters and inter-class correlation. In each condition, linear fixed- and mixed-effects models will be fitted 1000 times to empirically determine p-value, type 1 error rate, and power. We will increase the verisimilitude of our approach by using endometrial cancer data from our lab as a real background, while adding simulated signals. This data will be used to assess extant differential abundance models against our novel fixed-effects model. Results/Anticipated Results: Our initial findings indicate that fixed-effects models maintain control over type 1 error rates in small samples, while mixed-effects models do not perform as reliably. We are now exploring the effects of confounders on type 1 error rates and power in linear models, while our next step will evaluate generalized linear models in a differential abundance context. We expect that the fixed-effects models will continue to be as reliable as mixed-effects models, while being less computationally complex. After establishing our theoretical basis, our novel model should perform comparably to mixed-effects models on pseudo-simulated endometrial cancer data and better at small sample sizes. We will finally apply our model to real endometrial cancer data to identify microbial markers of disease predisposition. Discussion/Significance of Impact: By characterizing and improving these statistical tools, we seek to increase the robustness and power of microbiome study results (especially at small sample sizes), ultimately supporting better-informed translational research decisions. This work underscores the importance of statistical methods in advancing microbiome research.

Objectives/Goals: We aim to explore the associations of race/ethnicity and socioeconomic status (SES): 1) with grip strength, walking speed, and comorbidity index cross-sectionally and 2) with the change in comorbidity index and mortality risk over four years of follow-up in cancer survivors. Both aims will examine the potential mediating role of cytomegalovirus (CMV) infection. Methods/Study Population: This study includes 1,602 cancer survivors (mean age = 72 years, 10% Black, 54% female) from the Health and Retirement Study (HRS), a nationally representative U.S. sample followed for health outcomes until 2020. HRS measured CMV immunoglobulin G (IgG) antibody levels (from blood samples), grip strength, and walking speed in 2016. We will apply linear regression to examine the associations of race/ethnicity and SES with grip strength, walking speed, and comorbidity index cross-sectionally and with the change in comorbidity index over four years of follow-up. We will apply Cox proportional hazard regression to examine the associations of race/ethnicity and SES with mortality over four years of follow-up. In all models, we will investigate the potential mediating role of CMV infection in these associations. Results/Anticipated Results: We expect that CMV infection mediates the associations of race/ethnicity and SES with age-related health outcomes, including muscle weakness (measured by grip strength), decreased functional performance (measured by walking speed), comorbidity index, and mortality in elderly cancer survivors. Discussion/Significance of Impact: If our hypothesis is confirmed, the findings may inform physicians to closely monitor CMV infection among cancer survivors from socially disadvantaged groups and apply treatment if needed. Several oral medications for CMV exist, and CMV vaccines are currently undergoing testing in clinical trials. This will make the treatment for CMV more accessible.

Objectives/Goals: Engage the research professional (RP) workforce in assessing job satisfaction, motivators, barriers, and levels of support throughout the research enterprise. The goal of this collaboration is to foster a positive culture, inform manager training, ensure RP retention, and enhance career mobility pathways. Methods/Study Population: Methods include a HR data compilation/analysis and focus groups for new RP’s (Results/Anticipated Results: This initiative will articulate the current culture and climate of the research enterprise and identify key strategic areas for growth. The UMN Clinical and Translational Science Institute (CTSI) will build off the design of an internal survey at Vanderbilt (2018) to encompass organization-specific challenges, the post-pandemic research landscape, and the UMass Diversity Engagement Survey. This process will also generate specific insights including RP sentiment statements, trends in how RP’s describe their day-to-day work, assessment of barriers, analysis of retention benchmarks, and defining employee hopes/motivators. The CTSI will also identify salient RP growth opportunities, leadership competencies, and areas of non-monetary compensation to improve satisfaction and career mobility. Discussion/Significance of Impact: Targeted interventions will be developed to address RP satisfaction barriers and leverage opportunities for KPI improvement. Results will be disseminated to managers, administrators, and the CTSI network. Resource development will include RP personas, job description/hiring templates, and strategic guides for key operational challenges.

Objectives/Goals: The goal of the RC2 Systems Marketing Analysis for Research Translation (SMART) special innovation program is to develop and test a structured approach for working with research teams and communities to accelerate the translation of clinical and community innovations to address health inequities by integrating social marketing with community-based system dynamics. Methods/Study Population: The SMART program is a consultancy service for CTS teams focused on selecting and tailoring implementation strategies for advancing equity. We use social marketing for understanding the alignment of practice innovation feature sets with community priorities for advancing health equity; and community-based system dynamics to understand and refine the dynamics of scaling up and sustaining the implementation of innovations with sufficient reach to address regional health inequities. The program is implemented as community-engaged group model-building workshops with research teams, with follow-up marketing analyses and computer simulation of implementation strategies of innovations and development of implementation roadmaps. We use developmental program evaluation to revise the SMART program. Results/Anticipated Results: Anticipated results from piloting the SMART innovation program with four research teams include (1) design matrices pre and post-workshop for each innovation; (2) system dynamics simulation models and analyses of implementation and scale-up of innovations; (3) analysis of the SMART program for highest impact, with priors for estimating subsequent SMART program performance; and (4) a revised SMART program based on results from the developmental program evaluation. Discussion/Significance of Impact: This work highlights the feasibility and benefits of combining methods that have been cited in implementation science for understanding the complexity of implementation to accelerate the translation of innovations into clinical and community settings for advancing health equity.

Objectives/Goals: Translational researchers spend significant amounts of time finding available datasets and other research data resources for their purposes. Objectives of this program are develop and evaluate a multipronged approach to supporting researchers with existing data resources. Methods/Study Population: We established a dedicated service with expertise in data resources to increase awareness, understanding, and utilization of existing data resources. This program assists investigators and trainees discover appropriate data resources, formulate scientific problems in computable formats, advise on state-of-the-art data analytics, data management, build collaborations, mentor data users, and develop a service pipeline for streamlined data resource project management. This is accomplished through these essential functions: (1) Discover, catalog, document, and manage metadata resources, (2) train and present data resources to the research community, (3) provide individual consultations, and (4) explore and assess novel data resources. Results/Anticipated Results: In a phased approach, the data navigation program is performing outreach to the research community and integrating with existing data efforts on campus, presenting and demonstrating existing data resources, established a consultation service, and building core competencies into long-term usage and navigation of resources across campus. Evaluating the program monthly has shown an increase in various metrics for evaluating commitment and engagement including number of requests for access to data resource, consultations, publications and presentations, co-authorship, and proposals. Unawareness and inappropriate use of data resources leads to delays in performing research and potentially unnecessary duplications of efforts. Discussion/Significance of Impact: Our data navigation program has increased use of data resources in research. Next steps are to continue evaluation and further streamline informatics approaches to data discovery, abstraction, formulation, and analysis. Harmonized data resource programs are important translational science approach to foster the next generation of research.

Objectives/Goals: The NIH Common Fund launched Community Partnerships to Advance Science for Society (ComPASS) to study ways to reduce health disparities by addressing underlying structural factors within communities. ComPASS was designed for community organizations to lead research that addresses community needs. Methods/Study Population: ComPASS awarded five health equity research hubs (Hubs) to provide specialized technical support to ComPASS research projects led by community organizations. Expertise provided by the Hubs to the community-led projects will focus on specific facets of community health, including nutrition access, health care access, and built environment. The Hubs support community-led piloting and testing of structural interventions within community settings by providing subject matter expertise in areas including structural and multilevel intervention study design and methods, implementation science, and community engagement. Results/Anticipated Results: The Hubs will provide expertise and support to the community-led research projects around one or more social determinants of health domains: health care access and quality, education access and quality, economic stability, social and community context, and neighborhood and built environment. The Hubs will help identify strategies for measuring health outcomes and assessing the effects of structural and contextual factors on intervention outcomes. We anticipate the ComPASS program will lead to a better understanding of how structural interventions that leverage multi-sectoral partnerships can advance health equity. Discussion/Significance of Impact: Through community-led research, ComPASS projects are implementing structural interventions to address social determinants and advance health equity. The technical scientific support rooted in health equity provided by the Hubs is essential to the success of these research projects.

Objectives/Goals: Audience will understand the engage for equity CBPR conceptual framework used to develop our partnership survey. Audience will better understand our survey design and administration process. Audience will understand our initial experiences, community advisory board member responses, and how the process influenced our engagement efforts. Methods/Study Population: The objective of this assessment is to understand how our community partners perceive our engagement efforts. The questionnaire is based on the 12 constructs of the CBPR engage for equity model. We plan on annually assessing impact and refining engagement approaches in response to community partners’ perspectives. Community Advisory Board members and other partners will complete an online survey and participate in a subsequent discussion and planning session about the responses. We anticipate evaluating approximately 400 community partners who collaborate with the Wright Regional Clinical and Translational Science Awards between 2023 and 2030 with a RedCAP survey and discussion. The instrument uses validated questions from a range of engagement instruments to assess context, partnership structure, processes, outputs, and outcomes. Results/Anticipated Results: We hypothesize that both the survey and subsequent discussion about how respondents reacted to answering the questions will improve our ability to track and adapt our community engagement efforts. Discussion/Significance of Impact: Our annual survey is not project specific and using it longitudinally will inform our engagement process to research that aligns with community needs. We anticipate this will help us to guide our research agenda to align with community needs and define impact of our university–community partnership.

Objectives/Goals: Our study team won a Public Health Answers to Speed Tractable Results (PHASTR) contract to conduct a target trial emulation to answer “Does metformin show a reduction of severe outcomes of COVID-19 or of Long COVID in the N3C Data Enclave?” We quickly delivered an answer due to productive technical and collaboration support in the N3C. Methods/Study Population: Our analytic plan was updated based on helpful feedback from the PHASTR program. We performed a trial emulation analysis using the N3C data, comparing adult new users of metformin to controls prescribed fluvoxamine, fluticasone, ivermectin, or montelukast. The composite outcome was Long COVID or Death (LC/D) within 180 days of COVID infection. We used entropy balancing to estimate the average treatment effect with a weighted log-linear model. Productivity was enhanced by reusing code workbooks and validated codesets from related N3C projects. The team of 4 (physician, informaticist, data programmer, and statistician) and key unpaid advisors spent 10 weeks developing and analyzing the data. Results/Anticipated Results: Totally, 9,660 patients were identified for analysis. After weighting, there were 248 in the metformin and control groups. In the metformin group, 4.0% developed LC/D vs. 8.5% in the control group, with an adjusted risk ratio (aRR) of 0.47 (95% CI 0.25 to 0.89). Results were consistent across subgroups and sensitivity analyses. The PHASTR contract structure helped produce high-quality results quickly by not only providing funding but also requiring a compressed timeline for a small team to focus on the study. The most time was spent on contract execution, enclave provisioning, and too many last-minute download requests. A project final report was submitted in March and a full manuscript was submitted in September. Discussion/Significance of Impact: The analysis was productive because the environment made reuse easy and supported rich collaborations among clinicians, informaticists, epidemiologists, statisticians, and data developers. Advice from PHASTR advisors (Axel) and N3C diabetes domain team members was also key to a faster completion.