OBJECTIVES/GOALS: Identify the interactions between nasopharyngeal bacterial pathogens, commensals, and patient clinical characteristics in relation to the development of viral upper respiratory tract infections (URI) and acute otitis media (AOM) in infants. METHODS/STUDY POPULATION: The subjects were part of a prospective, longitudinal study (2008–2014) of infants to evaluate the prevalence and risks for the development of URI and AOM. Healthy infants (n = 362) were enrolled from near birth and followed to the first episode of AOM up to 12 months of age. Nasopharyngeal specimens and clinical traits were collected at monthly intervals between 1-6 months, month 9, and during viral URI episodes. Subjects were closely followed for AOM development. 16S rRNA sequencing was performed on the nasopharyngeal swabs to identify their bacterial composition. Multidimensional (2, 3, and 4 dimensional) co-presence, co-exclusion, and one-way relation patterns were identified between the microbiome compositions, health status, and other collected clinical traits. RESULTS/ANTICIPATED RESULTS: We analyzed 971 specimens collected monthly and during URI and AOM episodes from 139 infants. Of the 139 enrolled subjects, 96% had 2 or more healthy samples, 77% contracted URI/AOM during the study period, and 60% had at least 1 healthy sample before URI/AOM onset. Otopathogens (Moraxella, Haemophilus, and Streptococcus), Staphylococcus, and Pseudomonas were the most common pathogenic genera. Corynebacterium, Dolosigranulum, and Acinetobacter were 3 most abundant commensal bacterial genera. Samples from infants with AOM in the first year had a significantly higher relative abundance of Haemophilus, Enterobacter, and Yersinia, and lower relative abundance of Corynebacterium, and Pseudomonas compared to samples from infants who did not develop AOM. DISCUSSION/SIGNIFICANCE OF IMPACT: Identification of complex multidimensional interaction patterns within microbial communities and environmental factors is vital to understanding disease onset risk and prevention. Prophylactic microbiome and environmental factor modulation between enterotypes could be used to reduce URI/AOM onset in infants.

OBJECTIVES/SPECIFIC AIMS: To demonstrate the prevalence of ocular complications in patients suffering with Mycoplasma Induced Rash and Mucositis (MIRM). METHODS/STUDY POPULATION: In this retrospective observational study, we identified all patients in our hospital database who were diagnosed with MIRM. Diagnosis was confirmed by clinical information and positive Mycoplasma pneumoniae serology. Only patients with available records with formal ophthalmology consults were included. Clinical and laboratory data were collected from our electronic medical record system to capture key components of their clinical course. RESULTS/ANTICIPATED RESULTS: A total of 12 patients satisfied all inclusion and exclusion criteria and were included in our study. The average age of our included patients was 21.2 ± 14.7 years, and the majority were male vs. female (66.7% vs. 33.3%). In all 24 eyes, the only acute ocular findings included conjunctival hyperemia (n=20, 83.3%), meibomitis (n=4, 16.7%), and conjunctival epithelial defects (n=1, 4.2%). None of the patients required or were recommended to receive an amniotic membrane transplantation in the acute phase. Only 2 patients were followed in the chronic phase, one of whom showed evidence of meibomitis in both eyes. Otherwise, no other chronic complications were seen in either patient with chronic follow-up. DISCUSSION/SIGNIFICANCE OF IMPACT: Ocular complications from MIRM may be much milder in comparison to ocular complications found in other bullous and inflammatory conditions such as Stevens-Johnson Syndrome or Toxic Epidermal Necrolysis. Understanding MIRM’s specific sequelae is important in understanding disease manifestation and prognosis in order to better inform acute and chronic management.

OBJECTIVES/SPECIFIC AIMS: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects one in 68 children. Children with ASD have 2 core areas of difficulty: social communication skills and restricted and repetitive interests and patterns of behavior. Children with social skills deficits are at higher risk of developing mental health problems, and underdeveloped social skills predict poorer quality of life in adulthood. Therapies have been developed to help people with ASD improve social abilities in childhood, often involving a clinician directly teaching social skills lessons, either one-on-one or in a group setting. However, children with ASD can become anxious when interacting with other people and have an intrinsic motivation to interact with technology. To capitalize on this interest, this research team developed a robot, the socially animated machine (SAM) to teach social skills to children with ASD. Previous research found that this intervention was feasible and enjoyable for children with ASD and average cognitive ability, and participants improved in complex emotion recognition following intervention. The purpose of this study was to determine whether participants of all IQ levels were motivated by the SAM intervention, and whether they improved on emotion identification, facial recognition, social skills, and adaptive behavior. METHODS/STUDY POPULATION: This study recruited 20 children with ASD ages 5–14. Children completed tasks measuring ASD symptoms, IQ, receptive language, facial recognition, and emotion identification and were assigned to the control group (nonemotion dance games with SAM robot) or the intervention group (emotion games with SAM robot). Parents and teachers completed questionnaires about the child’s social skills. Following the robot intervention, facial recognition, emotion identification, and social skills were measured again, and parents and children rated participant enjoyment during the robot interaction. RESULTS/ANTICIPATED RESULTS: Overall, parents and children in both groups rated the robot interaction as highly enjoyable and motivating (parent ratings: M=26.4 out of 30, child ratings: M=17.5 out of 20). There were no differences between groups on post-test measures when controlling for pre-test scores (all p>0.05). Both groups improved over time on emotion identification accuracy (intervention: M=13.0% improvement, t=2.57, p<0.05; control: M=10.2% improvement, t=2.38, p<0.05) and parent-rated social skills (intervention: pre-test M=113.8, post-test M=100.6, t=−3.37, p=0.01; Control: pre-test M=107.9, post-test M=89.0, t=−2.83, p<0.05; decrease in scores indicates improvement). Teachers saw a decrease in problem behaviors for the intervention group (pre-test M=127.4, post-test M=119.6, t=−3.79, p<0.01, decrease in scores indicates improvement). DISCUSSION/SIGNIFICANCE OF IMPACT: This study shows that children with ASD and all levels of cognitive ability enjoyed and were motivated by the SAM robot intervention. This is particularly important for children with ASD who often have difficulty with attention and motivation. Children who are intrinsically motivated by the learning process will be more likely to benefit from it; therefore, continuing to pursue the methodology of robot-based interventions with this population is a worthwhile endeavor.

OBJECTIVES/GOALS: Tobacco use remains a significant public health problem in rural America. Community health centers (CHCs) can help reduce the burden of tobacco use in rural areas, but we know little about their knowledge and implementation of best practices for tobacco control. This study assessed the knowledge and existence of tobacco control practices in rural CHCs. METHODS/STUDY POPULATION: Using a cross-sectional study design, we electronically surveyed health administrators and providers (n=64) in six rural CHCs in Louisiana between March 2021 and June 2023. The assessment measured 1) knowledge of the U.S. Public Health Service Clinical Practice Guideline for Treating Tobacco Use, 2) the priority given to smoking cessation programming, 3) the presence of best practices for tobacco control programming, such as having a tobacco control champion and team, 4) establishment of treatment and smoke-free campus policies, and 5) existence of referral procedure to external cessation services. We used descriptive statistics to characterize survey participants and responses. RESULTS/ANTICIPATED RESULTS: Most of the respondents were female (80%), White (61.8%), between 35 and 49 years of age (48.1%), and non-smokers (72.7%). Only half (51.6%) reported knowledge of the guideline for treating tobacco use among all respondents. Only a third (32.8%) said their health center gave smoking cessation high priority relative to other health priorities. Only a third (35.9%) reported having a tobacco champion; less than a fifth (18.8%) said they had a tobacco control team at their health center. Although all health centers had a smoke-free campus policy, about a quarter (23.4%) were unaware of the policy. Less than a quarter (23.4%) reported having a written policy for smoking cessation treatment at their health center, and less than half (46.7%) knew about cessation services to which they could refer patients. DISCUSSION/SIGNIFICANCE: Rural CHCs had limited knowledge of the guideline for tobacco use treatment. Smoking cessation lacked priority, and the prevalence of tobacco control best practices implementation was low. Rural CHCs must improve their implementation of guideline-recommend policies and clinical strategies to promote treatment and reduce the burden of tobacco use.

OBJECTIVES/GOALS: Well-appearing febrile infants are a model for exploring communication, bias, and health disparities in the pediatric emergency department (ED). Using mixed methods, we will perform an in-depth analysis of disparities and shared decision making, a potentially modifiable driver of inequities. METHODS/STUDY POPULATION: We will conduct a multicenter cross-sectional chart review study of well-appearing febrile infants 29-60 days old treated in the ED and apply multivariable logistic regression to assess the association between 1) race/ethnicity and 2) limited English proficiency with the primary outcome, discharge to home without lumbar puncture and without antibiotics (standard of care). We will concurrently perform an interpretive study using purposive sampling to conduct interviews with: 1) minority parents of febrile infants and 2) ED physicians. By capturing dyadic data, we will triangulate perspectives to elucidate disparities and bias that can emerge in the shared decision making process. RESULTS/ANTICIPATED RESULTS: Forty member institutions of the Pediatric Emergency Medicine Collaborative Research Committee are participating, providing a projected cohort of 3000 infants. In the 6 months since site recruitment, 235 eligible infants have been entered into the dataset (43% minority race/ethnicity, 6% language other than English), 61% of whom received the primary outcome. Chart review has the benefits of 1) ensuring exclusion of ill infants, 2) providing data on interpreter use that is unavailable in administrative datasets, and 3) allowing an analysis of shared decision making. These findings will inform an interpretive study of parent and provider experiences of bias in shared decision making. DISCUSSION/SIGNIFICANCE: We demonstrate the feasibility of a large-scale manual chart review to analyze disparities within a shared decision making context. Partnered with qualitative scholarship, this research will support the development of communication interventions to mitigate implicit bias in the clinical encounter.

OBJECTIVES/GOALS: At least 15 stroke thrombectomies per proceduralist per year are required for Thrombectomy-Capable and Comprehensive Stroke Centers. We sought to estimate the potential impact of these minimum volume thresholds on geographic access to stroke thrombectomy (ST). METHODS/STUDY POPULATION: Using the Florida State Inpatient Database, we will identify patients discharged with acute ischemic stroke from 2015-2019 and all non-federal facilities that performed ?1 ST procedure per year. We will then calculate the proportion of stroke patients who live within 20, 65, 115, 165 and 200 miles (correlating with estimated ground transport times of 1, 2, 3, 4 and 5 hours, respectively) of centers that perform ST using ArcGIS software and evaluate the impact of varying the threshold ST volume required by each facility on this proportion. We will then perform multiple two proportion z-tests to compare proportions of patients within driving distance over time. RESULTS/ANTICIPATED RESULTS: We hypothesize that over time, and particularly after the pivotal trials of ST were published in 2015, that the number of facilities that perform ST have increased over time, which would increase the geographic access to ST. We also hypothesize that since the Joint Commission set the minimum procedural volume for proceduralists at Thrombectomy-Capable and Comprehensive Stroke Center to 15 per year, this would work to increase regionalization and could work to decrease geographic access to ST. However, we hope to elucidate the net impact of the interplay between these two opposing factors on regionalization of care over time which is currently unclear. DISCUSSION/SIGNIFICANCE: Current ST volume thresholds have focused on technical proficiency but may impact regionalization of care and geographic access to ST. Since access to ST is time-sensitive, a data-driven approach and better coordination on a regional level may be necessary to ensure timely access to ST.

OBJECTIVES/GOALS: Lack of a vehicle and the distant location of supermarkets influence the ease with which people can shop for healthy foods. The aim of this qualitative study is to understand how transportation impacts food access and food purchases of Baltimore residents who do not own an automobile. METHODS/STUDY POPULATION: The Lyft Grocery Access Program was piloted in Baltimore, MD from November 2019 through September 2020. Eligible households resided in Healthy Food Priority Areas, formerly known as food deserts, in south and west Baltimore and also did not own a vehicle. Enrolled households were offered discounted Lyft rides to select supermarkets. Participants for the present study will be purposively recruited via email and phone using contact information that was provided by enrolled households during the pilot program. Each in-depth interview will be conducted via Zoom and recorded, transcribed and analyzed for themes by two trained coders. Data collection and analysis will occur simultaneously. Data collection will cease once data saturation is reached and themes will be derived from the data. RESULTS/ANTICIPATED RESULTS: This study is in progress. Anticipated themes may relate to the food environment, transportation and food access. DISCUSSION/SIGNIFICANCE: Access to healthy foods is an important determinant of health, and how food access is impacted by broader aspects of daily living such as transportation will add to the food access literature. Findings may provide new insights that can help inform food policy and transportation planning in urban communities.

OBJECTIVES/GOALS: We developed institution-wide RCR training to include all required elements; support trainees to identify key ethical questions that arise in research; and to identify methods to identify a solution; and disseminate results. METHODS/STUDY POPULATION: In 2011, we participated in developing an experimental model of RCR training led by the University of Michigan. We continue to offer this training model to career development awardees in clinical and translational research at OHSU across the institution. Interactive discussion in faculty and trainee groups includes responsibilities of a researcher and a systematic process to address real world research ethics issues. Each participant identifies a key research issue they have encountered and presents a poster at the final session. We have tracked post-training assessment of participant confidence in ethical decision making and in the range of topics identified by participants. RESULTS/ANTICIPATED RESULTS: Since 2012, 227 scholars and trainees have participated in the program with 44 faculty mentors facilitating. We will describe the current curriculum as it has evolved over the past 12 years, presenting trainees with an approach to identify ethical challenges that arise in their research and identify approaches to find a practical solution. We will report on the specific challenges in research ethics identified by participants over this period and how they have evolved. We will also present pre- and post-training data about confidence in ethical decision making. DISCUSSION/SIGNIFICANCE: This approach to RCR training is well-received, has evolved over time, and has led to dissemination. Success is attributed to allowing choice in topics relevant to trainees and practicality of the stepwise approach that is transferable to any situation.

OBJECTIVES/SPECIFIC AIMS: Emergency department (ED) length of stay (LOS) is one of the important indicators of quality and efficiency of ED service delivery and is reported to be both cause and result of ED crowding. Increased ED LOS is associated with ED crowding, increases service cost and sometimes poor patient outcome. Substance abuse is one of the major determinants of morbidity, mortality and healthcare needs. Substance abuse may confound the healthcare and service needs of patients in the ED irrespective of primary purpose of their ED visit and may lengthen the ED LOS. The aim of this study was to evaluate the effect of patients’ demographic and clinical characteristics and of different patient-related activities such as screening brief intervention and referral to treatment (SBIRT) on the ED LOS of patients discharged from the ED with a diagnosis of substance abuse. METHODS/STUDY POPULATION: We conducted a retrospective data analysis of electronic health records. The study population included 26971 patients who visited our hospital ED between 2013 and 2017, had a history of substance abuse and were discharged from the ED. An accelerated failure time (AFT) model was used to analyze the influence of covariates on patient ED LOS. The predictor factors in the model included age, gender, ED arrival shift and weekday, diagnosis history of mental health and drug use, acuity triage level from 1 to 5, with 1 being worse severity, and whether any lab tests were ordered, SBIRT intervention and whether patient was homeless. The AFT model is an alternative to the Cox Proportional Hazard Ratio model, which directly models the log of ED LOS as a function of a vector of covariates. The model defines the increase or decrease in LOS with the changes in the covariate levels as an acceleration factor or time ratio (TR). RESULTS/ANTICIPATED RESULTS: The overall median ED LOS was 4 hours with IQR of 4.2 hours. The average age of the study population was 39.3 years, 58.6% of the patients were male and 57% where White; 63.4% had a history of drug use; 43% had a history of mental health issue, and 0.4% were homeless. In the analysis using the AFT model, increased age (a year increase, TR =1.01, p =0.008), female sex (TR=1.044, P<0.001), SBIRT (TR=1.525, P <0.001), history of mental health issue (TR=1.117, P<0.00), evening arrival (evening vs night, TR=1.04, p=0.006), history of drug use (drug vs alcohol only, TR=1.04, p=0.001), higher acuity (triage level 1 vs 5, TR=2.795, p <0.001) and homelessness (TR=1.073, P = 0.021) lengthened the ED LOS. In contrast, weekend arrival (TR=0.956, p=0.004) and day shift arrival (day vs night, TR=0.958, p=0.004) shortened the ED LOS. DISCUSSION/SIGNIFICANCE OF IMPACT: We identified gender, age, SBIRT, arrival shift, weekend arrival, mental health status, substance abuse, acuity level and homelessness to be significant predictor of ED LOS. The fact that SBIRT increased the LOS should be balanced with the advantages of engaging patients into substance use disorder treatment. Understanding the determinants of ED LOS in this population may provide useful information for physicians or patients to better anticipate an individual’s LOS and to help administrators plan the ED staffing and other resources mobilization.

OBJECTIVES/SPECIFIC AIMS: Our objectives are to provide opportunities for graduate students, clinical interns, and postdoctoral fellows in traditional training programs to have immersive experiences in clinical research conduct from a CRP perspective. In addition, we aimed to address common causes of job dissatisfaction by providing professional development and networking opportunities for the existing CRP workforce. METHODS/STUDY POPULATION: In collaboration with the CTSA workforce development group, the Duke Office of Clinical Research hosted a site visit for 19 PhD scientists interested in nontraditional career pathways and a short lecture series on project management careers in clinical research. Additionally, we crafted specific clinical research training electives for 20 masters students and 10 dietetic interns. Finally, in collaboration with UNC-CH, we combined Research Professional Networks to provide a pilot joint professional development event for 109 CRPs from both schools. RESULTS/ANTICIPATED RESULTS: The number of Masters students enrolling in the CRP elective grew from 7 students in year 1 to 13 students currently enrolled. A retro-pre/postprogram adapted CRAI survey was issued following program completion. Students self-reported increases in competence across all 24 skills measured. Largest increases were seen in areas specific to CRP roles such as consenting patients, understanding the IRB, and reviewing key study documents. A baseline culture survey issued at the joint Duke/UNC CRP event garnered a 65% response rate and indicated that the principal gaps in professional training are in communications, teamwork, leadership, and professionalism. Moreover, respondents indicated that creating a sense of community and providing networking opportunities were the most important outcomes for future CRP collaborations. Future evaluations of both of these programs will allow us to tailor training to be most effective in strengthening our CRP workforce. DISCUSSION/SIGNIFICANCE OF IMPACT: These initiatives lay the groundwork for the development of a robust training pipeline into CRP careers. Future initiative will apply lessons learned toward creating internship programs aimed at improving diversity and inclusion within these careers. In addition, by addressing the professional development needs of the existing workforce, we create a sustainable environment for well-trained professionals. By evaluating these primary initiatives, we can better define the critical elements that must be included in CRP educational, development, and support programs and subsequently apply these to ultimately improve the clinical and translational research being conducted in academic research settings.

ABSTRACT IMPACT: A machine learning approach using electronic health records can combine descriptive, population-level factors of pressure injury outcomes. OBJECTIVES/GOALS: Pressure injuries cause 60,000 deaths and cost $26 billion annually in the US, but prevention is laborious. We used clinical data to develop a machine learning algorithm for predicting pressure injury risk and prescribe the timing of intervention to help clinicians balance competing priorities. METHODS/STUDY POPULATION: We obtained 94,745 electronic health records with 7,000 predictors to calibrate a predictive algorithm of pressure injury risk. Machine learning was used to mine features predicting changes in pressure injury risk; random forests outperformed neural networks, boosting and bagging in feature selection. These features were fit to multilevel ordered logistic regression to create an algorithm that generated empirical Bayes estimates informing a decision-rule for follow-up based on individual risk trajectories over time. We used cross-validation to verify predictive validity, and constrained optimization to select a best-fit algorithm that reduced the time required to trigger patient follow-up. RESULTS/ANTICIPATED RESULTS: The algorithm significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.60 compared to the Braden Scale, a traditional clinician instrument of pressure injury risk. At a specificity of 0.50, the model achieved a sensitivity of 0.63 within 2.5 patient-days. Machine learning identified categorical increases in risk when patients were prescribed vasopressors (OR=16.4, p<0.001), beta-blockers (OR=4.8, p<0.001), erythropoietin stimulating agents (OR=3.0, p<0.001), or were ordered a urinalysis screen (OR=9.1, p<0.001), lipid panel (OR=5.7, p<0.001) or pre-albumin panel (OR=2.0, p<0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: This algorithm could help hospitals conserve resources within a critical period of patient vulnerability for pressure injury not reimbursed by Medicare. Savings generated by this approach could justify investment in machine learning to develop electronic warning systems for many iatrogenic injuries.

OBJECTIVES/SPECIFIC AIMS: Our study seeks to answer the following questions: (1) To determine whether higher numbers of gravidity and parity are associated with a decreased risk of mild cognitive impairment or dementia; (2) To determine whether higher numbers of gravidity and parity are associated with a decreased risk of amyloid deposition by PET MRI. METHODS/STUDY POPULATION: Our study population includes all female study participants in the Atherosclerosis Risk in Communities (ARIC) study who did not have a diagnosis of dementia before enrollment. Participants were also required to have been evaluated for cognitive impairment in the ARIC-NCS ancillary study, or to have received an MRI PET scan of their brain as part of the ARIC-PET ancillary study. Baseline information on the gravidity and parity of all the women was recorded at initial enrollment. We use statistical analyses and epidemiological measures to explore our study questions. For our first question, we use logistic regression to evaluate the association of gravidity and parity as two separate ordinal variables using adjudicated mild cognitive impairment (MCI) and dementia. For our second question, we use logistic regression to evaluate the association of gravidity and parity (again as ordinal variables) with amyloid positivity. We use STATA for our statistical analyses. RESULTS/ANTICIPATED RESULTS: We hypothesize that increased gravidity and parity will have either no effect or a protective effect against MCI, dementia, and amyloid deposition. Our preliminary analyses show that older age of a woman at first pregnancy and at first live birth are both positively correlated with increased incidence of cognitive impairment. No relationship was found between these surrogates of lifetime estrogen exposure and cerebral amyloid deposition. DISCUSSION/SIGNIFICANCE OF IMPACT: Multiple basic science and clinical research studies have shown that estrogen exposure has an effect on cognitive function, likely through a complex interplay of multiple physiologic systems. Our study expands the research in this area by using a large, established epidemiologic cohort to examine gravidity and parity as important factors in lifetime estrogen exposure as they relate to cognitive impairment and amyloid plaque deposition.

OBJECTIVES/GOALS: During earlier periods of the pandemic, Black and Latinx populations in Michigan have suffered higher rates of infection, hospitalization, and deaths when compared to Whites. We conducted this study to understand how Black and Latinx residents perceived this disproportionate burden. METHODS/STUDY POPULATION: In 2021, 40 semi-structured interviews were conducted virtually in English or Spanish with Black (n=24) and Latinx (n=16) residents in Michigan areas highly impacted by COVID-19: Genesee, Kent, Washtenaw, and Wayne counties. Using a Community-Based Participatory Research (CBPR) approach, we partnered with leaders from 15 community-based organizations and health and human service agencies to develop research questions, an interview protocol, and to interpret the data. We used the data analysis software Dedoose (ver 4.12) for inductive coding (IRR=0.81). This study is a part of the NIH Community Engagement Alliance (CEAL) Against COVID-19 initiative. RESULTS/ANTICIPATED RESULTS: Participants described the significant impact of the pandemic in terms of physical and mental health, job security, and the sheer number of deaths among loved ones. They attributed the impact to comorbidities and social determinants of health disparities exacerbated by the pandemic, including income, housing, access to healthcare, as well as systemic racism. They noted being overrepresented among frontline workers with higher exposure to COVID-19, limited or misinformation about the virus, language barriers, and difficulty with social distancing. Cultural norms that promote being in close proximity, such as intergenerational households, and loss of trusted community leaders were also noted. DISCUSSION/SIGNIFICANCE: Findings reflect the needs of Black and Latinx community members in Michigan and the discussions they feel are important to highlight. We must work strategically with partners and the community to provide transparency and effective leadership, and prioritize addressing systemic disparities in SDoH.

OBJECTIVES/GOALS: Chronic stress has vast implications for health. Pathophysiological dysregulation, as evidenced by allostatic load, is associated with increased morbidity and mortality. Health disparities exist in both the incidence and outcomes of chronic stress. This study investigates the intersection of identity, allostatic load, and depression. METHODS/STUDY POPULATION: A nationally representative sample of pre-pandemic health data from the 2017-2020 cycle of the National Health and Nutrition Examination Survey (NHANES) was used to assess variation in depression and allostatic load by identity status. Datasets containing biomarker, mental health, and identity data were merged using Stata. An index of allostatic load was created by generating quartiles for nine biomarkers of cardiovascular (cholesterol, triglycerides, systolic and diastolic blood pressure), metabolic (glucose, body mass index, albumin, creatinine), and immune (c-reactive protein) functioning, and summing the total high risk biomarkers per person. Depression scores were averaged across nine items from the patient health questionnaire, and dichotomous identity variables (e.g., race) were generated. RESULTS/ANTICIPATED RESULTS: People identifying as female (t = 8.25, p < .001) or Black (t = 7.18, p < .001) have higher allostatic load scores, whereas people identifying as White (t = -2.64, p < .01) or Asian (t = -3.80, p < .001) have lower allostatic load scores. People identifying as female (t = 10.76, p < .001), White (t = 2.66, p < .01), or Another/Mixed race (t = 6.23, p < .001) have higher levels of depression, whereas people identifying as Asian (t = 9.17, p < .001) have lower levels of depression. Multiple regression analyses indicate a significant effect of depression on allostatic load when controlling for sociodemographic variables (B = 0.33, SE = 0.05, t = 7.02, p < .001). The identity*depression interaction increases allostatic load for females (B = 0.43, SE = 0.10, t = 4.21, p < .001) and racial/ethnic minority males (B = 0.25, SE = 0.10, t = 2.62, p < .01). DISCUSSION/SIGNIFICANCE: This study highlights differences in the experience of allostatic load and depression based on identity. Depression exerts an independent and moderating effect on allostatic load. Findings have implications for health disparity research, and highlight the dynamic intersection of identity, mental, and physical health in the face of chronic stress.

OBJECTIVES/SPECIFIC AIMS: Abnormal eating behavior is a core and distinguishing diagnostic feature of behavioral variant frontotemporal dementia (bvFTD) that differentiates it from other neurodegenerative disorders and late-life psychiatric conditions. Though it has been proposed that hyperphagia in bvFTD results from cognitive dysfunction, the observation of altered sweet preferences and food foraging indicate that bvFTD is accompanied by fundamental dietary changes associated with hypothalamic and insular atrophy. In the current study, we examined how cognitive dysfunction contributes to abnormal feeding behavior in bvFTD. METHODS/STUDY POPULATION: We analyzed first-visit eating and neuropsychological data from the National Alzheimer’s Coordinating Center database (7 centers; September 2017 data freeze) in a subset of bvFTD patients with clinician-rated characterization of disturbed feeding severity. Group differences in cognitive domains of attention, processing speed, language, memory, and executive functioning were examined between patients with abnormal eating behavior (n=59) and a demographically-matched sample of patients with normal feeding behavior (n=60). Group differences in informant-reported empathy, behavioral inhibition, and depressive symptoms were also examined. RESULTS/ANTICIPATED RESULTS: Cognitive profiles in bvFTD patients did not vary as a function of disturbed feeding behavior. In a subset of cases pathologically-confirmed at autopsy, processing speed was better in cases with abnormal feeding behavior. No significant group differences were found for behavioral indices. DISCUSSION/SIGNIFICANCE OF IMPACT: These findings suggest that cognitive dysfunction is not the sole driver of abnormal eating behavior in bvFTD. Future studies with comprehensive characterization of feeding behavior, cognition and physiological/neuroimaging indices are warranted.

OBJECTIVES/GOALS: HelpBeatCOVID19.org, a novel self-reporting symptom tracking surveillance system, is based at the University of Alabama, Birmingham. Helpbeatcovid19.org captures social determinants of health (SDOH) data. This presentation will report research in progress to understand the utility of self-reported data with communicable disease outbreaks. METHODS/STUDY POPULATION: Individuals voluntarily completed an online questionnaire at HelpBeatCOVID19.org which captured SDOH data and other disease surveillance variables including zip code, gender, age group, race, ethnicity, symptoms, underlying conditions, type of home (e.g., single-family, mobile home, etc.), and household COVID-19 diagnosis status. The data are stored on HIPAA-compliant servers. De-identified self-reported data were culled from the HelpBeatCOVID19 database, cleaned, sorted, and analyzed by zip code. Using STATA/SE 16.1, we employed regression analysis to determine if there might be any statistically significant associations that could be made based on zip codes, especially where there are health disparities in historically African American neighborhoods in Jefferson County. RESULTS/ANTICIPATED RESULTS: To date, 102,308 people have reported their symptoms in HelpBeatCOVID19. Of those, 77,903 are from Alabama. More than half of the people who completed HelpBeatCOVID19.org reported zero symptoms. However, 19.3% of Alabamians reported having underlying health conditions. Midfield, AL, a predominantly African-American neighborhood (81.1%), has 74.1% of people reporting underlying conditions where the median household income is $38,750. By comparison, Vestavia Hills, AL, a more affluent neighborhood with an 88.8% White population and median household income being $109,485, had more people participating in HelpBeatCOVID19 (3,920), yet a smaller percentage (15.2%) with underlying health conditions. Final results will be reported during the ACTS Conference. DISCUSSION/SIGNIFICANCE: Our analysis of the data reveals that in Jefferson County, AL, a greater number of people in affluent communities participated in the study. Whereas state-wide, a greater percentage of individuals indicated that they had zero symptoms. Identifying self-reported underlying conditions that impact persons with COVID-19 symptoms will be significant.

OBJECTIVES/GOALS: Penn State CTSI supports KL2 career development awards for faculty seeking to become leaders in clinical and translational research. CTSAs can benefit from a better understanding of KL2 applicant profiles and trainee outcomes. Predictive modeling of KL2 records provides insights into institutional processes and continuous improvement goals. METHODS/STUDY POPULATION: Collecting KL2 application records at Penn State CTSI from 2017 to 2023, comprising both accepted and not accepted candidate profiles, this study used a generalized logistic mixed model with binomial distribution to understand the factors predictive of KL2 trainee acceptance, (n=47). The following factors were modeled as potentially predictive of scholars’ acceptance: Institution-specific Processes—Campus; Terminal Degree Type; College of Residency, Applicant Demographics and Portfolio—Minoritized or Protected Groups; Mean Application Score; Rurality Focus; Gender, and Outcomes—Post-Program h-index. RESULTS/ANTICIPATED RESULTS: Only Campus and Degree were significant factors predictive of trainee acceptance (r<.0001), with a particular campus and the MD degree-designation both exerting selectional pressures on acceptance rates. Applicant demographics were not significant historical factors in selection despite the most recent trainee cohort comprised of all women. Similarly, while our CTSA focuses on rural inequality and accessibility, a research proposal focused on rurality was not a significant factor for acceptance. Notably, NIH-scaled application scores and post-program h-indices were not significant for accepted and non-accepted applicants. DISCUSSION/SIGNIFICANCE: The absence of applicant-focused selectional pressure is striking—Penn State CTSI does not significantly select for gender, URM, or URP status. Administration is now empowered to intentionally engage, recruit, and retain from our other affiliated campuses and colleges.