Background: Community-level socioeconomic disparities have a significant impact on an individual's health and overall well-being. However, current estimates for poverty threshold, which are often used to assess community-level socioeconomic status, do not account for cost-of-living differences or geography variability. The goals of this study were to compare geographic county-level overlap and gaps in access to care for households within poverty and working poor designations.Methods: Data were obtained for 21 continental United States (US) states from the United Way's Asset Limited, Income Constrained, Employed (ALICE) households for 2021. Raw data contained the percentage of households at the federal poverty level, the percentage of households at the ALICE designations (working poor), and the total households at the county level. Local Moran's I tests for spatial autocorrelation were performed to identify the clustering of poverty and ALICE households. These clusters were overlaid with a 30-min drive time from critical access hospitals' physical addresses. Findings: County-level clusters of ALICE (working poor) households occurred in different areas than the clustering of poverty households. Of particular interest, the extent to which the 30-min drive time to critical care over-lapped with clusters of ALICE or poverty changed depending on the state. Overall, clustering in ALICE and poverty overlapped with 30-min drive times to critical care between 46 and 90% of the time. However, the specific states where disparities in access to care were prominent differed between analyses focused on house-holds in poverty versus the working poor.Interpretations: Findings highlight a disparity in equitable inclusion of individuals across the spectrum of socio-economic status. Furthermore, they suggest that current public health programming and benefits which support low socioeconomic populations may be missing a vulnerable sub-population of working families. Future studies are needed to better understand how to address the health disparities facing individuals who are above the poverty threshold but still struggle economically to meet based needs.

Purpose Rural communities are among the most underserved and resource-scarce populations in the United States. However, there are limited data on COVID-19 outcomes in rural America. This study aims to compare hospitalization rates and inpatient mortality among SARS-CoV-2-infected persons stratified by residential rurality. Methods This retrospective cohort study from the National COVID Cohort Collaborative (N3C) assesses 1,033,229 patients from 44 US hospital systems diagnosed with SARS-CoV-2 infection between January 2020 and June 2021. Primary outcomes were hospitalization and all-cause inpatient mortality. Secondary outcomes were utilization of supplemental oxygen, invasive mechanical ventilation, vasopressor support, extracorporeal membrane oxygenation, and incidence of major adverse cardiovascular events or hospital readmission. The analytic approach estimates 90-day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient adverse events while controlling for major risk factors using Kaplan-Meier survival estimates and mixed-effects logistic regression. Findings Of 1,033,229 diagnosed COVID-19 patients included, 186,882 required hospitalization. After adjusting for demographic differences and comorbidities, urban-adjacent and nonurban-adjacent rural dwellers with COVID-19 were more likely to be hospitalized (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI], 1.16-1.21 and aOR 1.29, CI 1.24-1.1.34) and to die or be transferred to hospice (aOR 1.36, CI 1.29-1.43 and 1.37, CI 1.26-1.50), respectively. All secondary outcomes were more likely among rural patients. Conclusions Hospitalization, inpatient mortality, and other adverse outcomes are higher among rural persons with COVID-19, even after adjusting for demographic differences and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.

Background The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19. Methods Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients’ outcome of death or discharge. Models leveraged the patients’ characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model’s final outcome prediction. Results Gradient Boosted Decision Tree classifiers present the highest prediction accuracy in identifying patient outcomes with area under the receiver operator characteristic curve of 0.90 and accuracy of 0.81 for the outcomes of death or sufficient improvement to be discharged. The resulting model predicts the treatment combinations of anticoagulants and steroids are associated with the highest probability of improvement, followed by combined anticoagulants and targeted antivirals. In contrast, monotherapies of single drugs, including use of anticoagulants without steroid or antivirals are associated with poorer outcomes. Conclusions This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. Analysis of the model’s components suggests benefit to treatment with combination of steroids, antivirals, and anticoagulant medication. The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies.

Introduction Rural Appalachia is endemic to issues such as substance abuse, poverty, and lack of community support, all of which negatively influence health outcomes. The incidence of pediatric trauma as it relates to substance abuse is of concern in the region, where the rate of positive drug screens in pediatric trauma cases is higher than national average. Methods The West Virginia statewide pediatric trauma database was analyzed in a retrospective cohort study for the years 2009-2019. Variables of interest included injury severity (assessed using Abbreviated Injury Scale (AIS)), drug screening results, and various measures of patient outcome. Results The sample was divided into 2009-2016 presentations (n = 3,356) and 2017-2019 presentations (n = 1,182). Incidence of critical (AIS 5) head injuries (p = 0.007) and serious (AIS 3) neck injuries (p = 0.001) increased as time progressed. Days requiring ventilation increased from 3.1 in 2009–2016 to 6.3 in 2017–2019 (p < 0.001). Drug screens were obtained at a rate of 6.9% in 2009–2016 versus 23.3% in 2017–2019 (p < 0.001). Benzodiazepine use increased from 0.8% to 1.8% (p < 0.001), and opioid use increased from 1% to 4.9% (p < 0.001). Conclusion The increasing severity of pediatric trauma and substance abuse in Appalachia is of significant concern. The use of respiratory drive-depressing drugs has risen, just as the severity of head and neck traumas has increased. These results emphasize the importance of targeted interventions in the rural pediatric population.

Background: Pre-existing cardiovascular diseases (CVD) conditions have prognostic implications in COVID-19. This study assesses the outcomes of COVID-19 infection among CVD hospitalized patients. Methods: We queried a multicentric, National COVID Cohort Collaboration (N3C) registry from March 2020-September 2021 to extract the cases of COVID-19 positive tests. COVID-19 RT-PCR tests done 21 days before or 5 days after the index hospitalization was considered. Patients with chronic heart failure, ischemic or valvular heart disease, peripheral vascular disease, prior cardiac surgery, or the presence of cardiovascular implantable electronic devices before the index admissions were included in CVD group. We reported crude and adjusted odd ratio (aOR), and 95% confidence intervals. Charlson comorbidity index and patient demographics were used for the adjustments. Results: Our study included 601,074 patients; among them 14% had preexisting CVD. The cohort includes 52.5% female, 16% Hispanic/ Latino, 20% Black. Patients with CVD disease had a higher odd of mortality (aOR: 1.16[1.12-1.19];p<0.001) than patients without CVD. Similarly, patients with CVD had a higher odds of MACE (aOR: 2.17[2.12,2.32];p<0.001) than the non-CVD group. The incidence of ECMO and MV utilization

Background: As we have seen SARS-CoV-2 infection after COVID-19 vaccine, this study assesses characteristics of hospitalized patients among vaccinated COVID-19 cases with pre-existing cardiovascular diseases (CVD). Methods: Patients >18 years with a history of CVD who became COVID-19 positive after receiving the COVID-19 vaccine were identified from the N3C database, where patient electronic health records data were contributed from 33 institutions across the U.S. from March 2020-September 2021. The odd ratios of requiring hospitalization adjusted by age, gender, race, ethnicity, and Charlson Comorbitidy Index (CCI) were obtained. Adjusted Odd Ratio (aOR) and confidence intervals were reported in Table 1. Results: We analyzed 4,124 patients (45.6% female, 8.7% Hispanic/Latino, and 10.3% Black). The odds of hospitalization increased with each point of CCI (aOR:1.15 [1.13-1.18];p<0.001). Black patients were more likely to be hospitalized (aOR:1.63[1.31-2.03];p<0.001) compared to White patients. Patients aged 45-60 years had lower (aOR:0.72[0.59-0.88]; p=0.001) decreased odds of hospitalization compared age > 60, though younger age groups did not have significant reduction in the odd of hospitalizations (Table 1).

e14016 Background: High-grade gliomas (HGGs), like Glioblastomas (GBMs), are the most common malignant intracranial tumors with dismal prognosis. Rural areas have higher HGG related mortality and lesser research advancements. The objective of this study was to identify zip codes with disproportionate risk of HGGs compared to other brain malignancies. Methods: Patients’ electronic health data for this case-control study was extracted from West Virginia University Hospital Systems, 2010-2021. HGG cases were defined using diagnoses codes ICD10-C71 and ICD9-191 (excluding low grade gliomas). The control group was defined by using diagnoses codes ICD10-D32, C79.3, C71 (excluding HGG cases) and ICD9-225.2, 225.4, 198.3, 191 (excluding HGG cases). Controls were exact matched four controls to every case based upon patient five-year age categories and gender. Clustering was assessed globally through difference in Ripley’s-K between cases and controls; and locally using scan statistics. Results: The locations of HGGs and other brain tumors were clustered in the northern and northeastern regions of WV. Global cluster analyses detected statistically significant dispersion of HGG cases compared to our control group. Local cluster analysis identified multi-zip code cluster of HGG patients in northeastern WV between 2010-2021. The average relative risk of HGG compared to other brain malignancies was 1.25, indicating a 25% increase in risk of HGGs compared to other brain cancers within this region. While the local cluster was not statistically significant, the zip code level estimates of relative risk within the cluster ranged from 0.00 to 5.01. This indicated that certain zip codes within the cluster had five times the risk of HGGs compared to non-HGG brain malignancies relative to zip codes outside of the cluster. Conclusions: Findings indicate community level disparities in risk of HGGs relative to other brain malignancies. Additionally, results suggest that HGG cases are seen across the state at a more dispersed pattern than for non-HGG related brain tumors. These overall spatial trends could potentially be attributable to differences in clinical practice, access to care, or social determinants of health throughout WV. Spatial multivariable modeling techniques are needed to identify the specific neighborhood level factors associated with disproportionate patterns of risk identified for WV HGG patients.

This study examines the clinical characteristics, outcomes and types of management in SARS-CoV-2 infected patients, in the hospitals affiliated with West Virginia University. We included patients from West Virginia with SARS-CoV-2 infection between 15 April to 30 December 2020. Descriptive analysis was performed to summarize the characteristics of patients. Regression analyses were performed to assess the association between baseline characteristics and outcomes. Of 1742 patients, the mean age was 47.5 years (±22.7) and 54% of patients were female. Only 459 patients (26.3%) reported at least one baseline symptom, of which shortness of breath was most common. More than half had at least one comorbidity, with hypertension being the most common. There were 131 severe cases (7.5%), and 84 patients (4.8%) died despite treatment. The mean overall length of hospital stay was 2.6 days (±6.9). Age, male sex, and comorbidities were independent predictors of outcomes. In this study of patients with SARS-CoV-2 infection from West Virginia, older patients with underlying co-morbidities had poor outcomes, and the in-hospital mortality was similar to the national average.

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, R t Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, R t . The second method, ML+R t , is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as R t , county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+R t method and 0.867 for the R t Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+R t method outperforms the R t Only method in identifying larger spikes. Results show that both methods perform adequately in both rural and non-rural predictions. Finally, a detailed discussion on practical issues regarding implementing forecasting models for public health action based on R t is provided, and the potential for further development of machine learning methods that are enhanced by R t .

Abstract Background A major challenge to identifying effective treatments for COVID-19 has been the conflicting results offered by small, often underpowered clinical trials. The World Health Organization (WHO) Ordinal Scale (OS) has been used to measure clinical improvement among clinical trial participants and has the benefit of measuring effect across the spectrum of clinical illness. We modified the WHO OS to enable assessment of COVID-19 patient outcomes using electronic health record (EHR) data. Methods Employing the National COVID Cohort Collaborative (N3C) database of EHR data from 50 sites in the United States, we assessed patient outcomes, April 1,2020 to March 31, 2021, among those with a SARS-CoV-2 diagnosis, using the following modification of the WHO OS: 1=Outpatient, 3=Hospitalized, 5=Required Oxygen (any), 7=Mechanical Ventilation, 9=Organ Support (pressors; ECMO), 11=Death. OS is defined over 4 weeks beginning at first diagnosis and recalculated each week using the patient’s maximum OS value in the corresponding 7-day period. Modified OS distributions were compared across time using a Pearson Chi-Squared test. Results The study sample included 1,446,831 patients, 54.7% women, 14.7% Black, 14.6% Hispanic/Latinx. Pearson Chi-Sq P< 0.0001 was obtained comparing the distribution of 2nd Quarter 2020 OS with the distribution of later time points for Week 4. Table 1. OS at week 1 and 4 by quarter The study sample included 1,446,831 patients, 54.7% women, 14.7% Black, 14.6% Hispanic/Latinx. Pearson Chi-Sq P< 0.0001 was obtained comparing the distribution of 2nd Quarter 2020 OS with the distribution of later time points for Week 4. Conclusion All Week 4 OS distributions significantly improved from the initial period (April-June 2020) compared with subsequent months, suggesting improved management. Further work is needed to determine which elements of care are driving the improved outcomes. Time series analyses must be included when assessing impact of therapeutic modalities across the COVID pandemic time frame. Disclosures Sally L. Hodder, M.D., Gilead (Advisor or Review Panel member)Merck (Grant/Research Support, Advisor or Review Panel member)Viiv Healthcare (Grant/Research Support, Advisor or Review Panel member)

A survival advantage termed the “obesity paradox” for improved survival in overweight and obese heart failure (HF) patients has been previously demonstrated. To our knowledge this relationship has not been demonstrated in a rural population, therefore we examined this relationship in a rural,

Objective: Funding to address the current opioid epidemic has focused on treatment of opioid use disorder (OUD); however, rates of other substance use disorders (SUDs) remain high and non-opioid related overdoses account for nearly 30% of overdoses. This study assesses the prevalence of co-occurring substance use in West Virginia (WV) to inform treatment strategies. The objective of this study was to assess the prevalence of, and demographic and clinical characteristics (including age, gender, hepatitis C virus (HCV) status) associated with, co-occurring substance use among patients with OUD in WV. Methods: This retrospective study utilized the West Virginia Clinical and Translation Science Institute Integrated Data Repository, comprised of Electronic Medical Record (EMR) data from West Virginia University Medicine. Deidentified data were extracted from inpatient psychiatric admissions and emergency department (ED) healthcare encounters between 2009 and 2018. Eligible patients were those with OUD who had a positive urine toxicology screen for opioids at the time of their initial encounter with the healthcare system. Extracted data included results of comprehensive urine toxicology testing during the study timeframe. Results: 3,127 patients met the inclusion criteria of whom 72.8% had co-occurring substance use. Of those who were positive for opioids and at least one additional substance, benzodiazepines were the most common cooccurring substances (57.4% of patients yielded a positive urine toxicology screen for both substances), followed by cannabis (53.1%), cocaine (24.5%) and amphetamine (21.6%). Individuals who used co-occurring substances were younger than those who were positive for opioids alone (P < 0.001). There was a higher prevalence of individuals who used co-occurring substances that were HCV positive in comparison to those who used opioids alone (P < 0.001). There were limited gender differences noted between individuals who used cooccurring substances and those who used opioids alone. Among ED admissions who were positive for opioids, 264 were diagnosed with substance toxicity/overdose, 78.4% of whom had co-occurring substance use (benzodiazepines: 65.2%; cannabis: 44.4%; cocaine: 28.5%; amphetamine: 15.5%). Across the 10-year timespan, the greatest increase for the entire sample was in the rate of co-occurring amphetamine and opioid use (from 12.6% in 2014 to 47.8% in 2018). Conclusions: These data demonstrate that the current substance use epidemic extends well beyond opioids, suggesting that comprehensive SUD prevention and treatment strategies are needed, especially for those substances which do not yet have any evidence-based and/or medication treatments available.

The prevalence of obesity in U.S. adults is 38% and class III obesity (BMI ≥40 kg/m2) approaches 8%, many have comorbid atrial fibrillation (AF). Recent recommendations caution against novel oral anticoagulant (NOAC) use for thromboembolic (TE) prophylaxis in obese patients and advise against use

Increased distance to center has been associated with decreased access to coronary care and cardiac surgical procedures. No study to our knowledge has examined the relationship between distance to center and heart failure (HF) outcomes, therefore our objective was to examine the relationship between