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OBJECTIVES/GOALS: Many providers share patients resulting in networks where clinical information is exchanged, and which can impact the quality and efficiency of care. Here we analyzed the network properties of a primary care service area (PCSA) in Harris County TX, motivating our ongoing analysis of how they are associated with disparities in cancer screening. METHODS/STUDY POPULATION: Data.All providers (n=731, Medicare 2018) from the PCSA with the most providers in Harris County TX, with gender, specialty, and the number of shared patients. Method. Modeled the data as a network consisting of provider nodes, connected in pairs by edges if they shared >11 patients (an empirically-determined threshold). Analyzed the network structure using (1) modularity maximization and its significance to identify densely-connected communities; (2) degree centralization to measure whether a few providers shared many patients, and betweenness centralization to measure whether a few providers connected densely-connected communities; and (3) chi-squared to measure if pairs of connected providers tended to be of the same gender compared to disconnected provider pairs. RESULTS/ANTICIPATED RESULTS: The results (Fig. 1, http://www.skbhavnani.com/DIVA/Images/Fig-1-SNA-Network.jpg [http://www.skbhavnani.com/DIVA/Images/Fig-1-SNA-Network.jpg]) revealed a fragmented network with 120 small components (connected subnetworks, not part of any larger connected subnetwork), and 1 large component. The large component (n=244) had strong and significant modularity (Q=0.73, z=53.13, P

BackgroundThe literature supports quantifying the maximum force/tension generated by one’s forearm muscles such as the hand grip strength (HGS) to screen for physical and cognitive frailty in older adults. Thus, we postulate that individuals with cerebral palsy (CP), who are at higher risk for premature aging, could benefit from tools that objectively measure muscle strength as a functional biomarker to detect frailty and cognitive decline. This study assesses the clinical relevancy of the former and quantifies isometric muscle strength to determine its association with cognitive function in adults with CP.MethodsAmbulatory adults with CP were identified from a patient registry and were enrolled into this study. Peak rate of force development (RFD) and maximum voluntary isometric contraction of the quadriceps were measured using a commercial isokinetic machine, while HGS was collected with a clinical dynamometer. Dominant and non-dominant side were identified. Standardized cognitive assessments, including the Wechsler Memory and Adult Intelligence Scales IV, Short Test of Mental Status, and the Patient-Reported Outcomes Measurement Information System (PROMIS®) were used to evaluate cognitive function.ResultsA total of 57 participants (32 females; mean age 24.3 [SD 5.3]; GMFCS levels I–IV) were included in the analysis. Although dominant and non-dominant RFD and HGS measures were associated with cognitive function, non-dominant peak RFD showed the strongest associations with cognitive function.ConclusionRFD capacity may reflect age-related neural and physical health and could be a better health indicator than HGS in the CP population.

OBJECTIVES/GOALS: While disease subtypes are critical for precision medicine, most projects use unipartite clustering methods such as k-means which are not fully automated, do not provide statistical significance, and are difficult to interpret. These gaps were addressed through bipartite networks and tested for generalizability on three national databases. METHODS/STUDY POPULATION: Data. All participants with self-reported stroke from the 2010 Health and Retirement Study (HRS), with cases (n=798) having one or more 8 depressive symptoms measured by the Centers for the Epidemiological Study–Depression 8 scale, and controls (n=389) with none of those symptoms. The replication data set consisted of independent identically-defined participants (cases=725, controls=190) from 1998 HRS. Method. (1) Bipartite network analysis and modularity maximization to automatically identify patient-symptom biclusters with significance. (2) Rand Index to measure the replicability of symptom co-occurrences in the replication data. (3) ExplodeLayout to visualize and interpret the subtypes. (4) R libraries to generalize the methods, upload them to CRAN, and then tested on the N3C and All of Us platforms. RESULTS/ANTICIPATED RESULTS: The analysis identified 4 depressive symptom subtypes (https://postimg.cc/Ny8YwXJW) which had significant modularity (Q=0.26, z=3.03, P DISCUSSION/SIGNIFICANCE: We developed generalizable methods to automatically identify biclusters, measure the clustering significance, and visualize the results for interpretation. These methods were successfully tested on three national level data bases. Such generalizable methods should accelerate the analysis of subtypes, and the design of targeted interventions.

OBJECTIVES/GOALS: A growing number of older adults in the United States have multiple social determinants of health (SDoH) that are barriers to effective medical care. We used generalizable machine learning methods to identify and visualize subtypes based on participant-reported SDoH profiles, and their association with delayed medical care (self-reported yes/no). METHODS/STUDY POPULATION: Data. All participants aged >=65 in All of Us with complete data on 18 SDoH self-reported variables, selected through consensus by 2 experienced health services researchers, and guided by Andersen’s behavioral model. Covariates included demographics, and the outcome was delayed medical care . Cases (n=4090) consisted of participants with at least one of the 18 SDoH variables, and controls (n=7414) consisted of participants with none of them. Method. (1) Used bipartite network analysis and modularity maximization to identify participant-SDoH biclusters, and visualize them through ExplodeLayout. (2) Used multivariable logistic regression (adjusted for demographics and corrected through Bonferroni) to measure the odds ratio (OR) of each participant bicluster to the outcome, compared with the controls. RESULTS/ANTICIPATED RESULTS: The analysis identified 7 SDoH subtypes (https://postimg.cc/Vd7Pg4xZ) with statistically significant modularity compared with 100 random permutations of the data (All of Us=.51, Random Mean=.38, z=20, P DISCUSSION/SIGNIFICANCE: The results identified 7 distinct subtypes based on SDoH profiles and their risk for delayed medical care, highlighting the importance of addressing specific combinations of barriers, with affordability having the highest risk. Furthermore, the analytical methods used are generalizable and have been made publicly available on CRAN and All of Us.

Abstract Background Despite the success of stroke rehabilitation services, differences in service utilization exist. Some patients with stroke may travel across regions to receive necessary care prescribed by their physician. It is unknown how availability and combinations of post-acute care facilities in local healthcare markets influence use patterns. We present the distribution of skilled nursing, inpatient rehabilitation, and long-term care hospital services across Hospital Service Areas among a national stroke cohort, and we describe drivers of post-acute care service use. Methods We extracted data from 2013 to 2014 of a national stroke cohort using Medicare beneficiaries (174,498 total records across 3232 Hospital Service Areas). Patients’ ZIP code of residence was linked to the facility ZIP code where care was received. If the patient did not live in the Hospital Service Area where they received care, they were considered a “traveler”. We performed multivariable logistic regression to regress traveling status on the care combinations available where the patient lived. Results Although 73.4% of all Hospital Service Areas were skilled nursing-only, only 23.5% of all patients received care in skilled nursing-only Hospital Service Areas; 40.8% of all patients received care in Hospital Service Areas with only inpatient rehabilitation and skilled nursing, which represented only 18.2% of all Hospital Service Areas. Thirty-five percent of patients traveled to a different Hospital Service Area from where they lived. Regarding “travelers,” for those living in a skilled nursing-only Hospital Service Area, 49.9% traveled for care to Hospital Service Areas with only inpatient rehabilitation and skilled nursing. Patients living in skilled nursing-only Hospital Service Areas had more than five times higher odds of traveling compared to those living in Hospital Service Areas with all three facilities. Conclusions Geographically, the vast majority of Hospital Service Areas in the United States that provided rehabilitation services for stroke survivors were skilled nursing-only. However, only about one-third lived in skilled nursing-only Hospital Service Areas; over 35% traveled to receive care. Geographic variation exists in post-acute care; this study provides a foundation to better quantify its drivers. This study presents previously undescribed drivers of variation in post-acute care service utilization among Medicare beneficiaries—the “traveler effect”.

This case report is of a two-time stroke survivor with significant health comorbidities. This report highlights A.R.’s pre-existing, non-neurological vision impairments, stroke-related vision impairments, in addition to cognitive impairment and possible dementia. Information including her past medical history, current functional status, and battery of assessments that were used in the acute care hospital are detailed. Conclusions include the need for comprehensive, valid, and adapted assessments especially when comorbidities are present. We suggest that cognitive assessments that do not rely on vision may have improved the test accuracy in this case.

ABSTRACT IMPACT: Working alongside news staff as community partners is feasible for community engagement to co-create a post-hurricane health assessment and connect it to our academic health center’s disaster response capacity. OBJECTIVES/GOALS: Successful academic-community partnership in post-disaster response depends on shared understanding of impact. Community newspapers could provide valuable insight into health needs and inform strategic recovery plans. Our objective was to determine methodological feasibility of using newspaper stories to identify post-disaster needs. METHODS/STUDY POPULATION: Community-Based Participatory Research principles were applied to engage newspaper staff and conduct qualitative analysis of stories published in the weekly Port Aransas South Jetty newspaper, serving this small rural coastal community. Using directed content analysis, the team derived and validated constructs from Maslow’s Hierarchy of Needs and Phases of Disaster models to create a codebook. Scientists and newspaper staff examined the codebook for congruency regarding interpretation and themes. With copyright permission to access online newspaper files, NVivo software was used to search for Hurricane Harvey-related terms (e.g., ‘Harvey, tropical storm, flood, damage, volunteer’). Stories from 3 days post-Harvey to 6 months post-Harvey were examined and again at anniversary date. RESULTS/ANTICIPATED RESULTS: The weekly South Jetty newspaper was published continuously from August 31, 2017, through the date our study ended, February 22, 2018. Analysis showed themes of the storm and community response to disaster at multiple levels. Harvey caused catastrophic flooding, destruction, on par with 2005 Hurricane Katrina as the costliest storm on record. In Port Aransas, 130 mph winds and a 12-foot storm surge damaged 90% of the buildings. Stories reflected Phases of Response: Pre-disaster, Impact, Heroic, Honeymoon, Disillusionment, and initial phases of Reconstruction and Maslow’s Hierarchy of Needs. Story: ‘It’s not just the physical part of Port Aransas that was hurt by the hurricane. Harvey also wounded the town’s collective psyche. We’ve wept for our losses, then counted our blessings, then wept for our losses again.’ DISCUSSION/SIGNIFICANCE OF FINDINGS: Newspapers were a rich source of post-disaster data. Text and pictures were poignant. Thematic analysis identified stages of recovery. Working alongside news staff as community partners is feasible for community engagement to co-create a post-hurricane health assessment and connect it to our academic health center’s disaster response capacity.