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Study Objectives: Screening for early detection of sleep-disordered breathing (SDB) in hospitalized patients has been shown to reduce readmission rates. However, post-discharge polysomnography for confirmation of diagnosis is required. We analyzed factors for "no-shows" using geospatial techniques., Methods: Data were obtained between September 2019 and September 2023. The outcome for the study was patient's no-show rate (non-compliant) for polysomnography post hospital discharge. Predictors included patient age, gender, BMI, health literacy, distressed community index (DCI) score and distance to sleep center for the patient's zip code of residence. Logistic regression was applied to estimate odds of patient compliance at the patient-level utilizing geospatial mapping technique. Geographically weighted logistic regression was applied to estimate the odds of a zip-code having compliant patients., Results: Of the 1318 hospitalized patients established as high risk for SDB and referred for an overnight sleep study who were able to be geocoded, 228 were compliant and 1130 were non-compliant. In non-spatial regression analyses, health literacy (aOR = 1.06, 95%CI = 1.03, 1.09), age (aOR=0.99, 95% CI=0.98, 0.99), and drive time (aOR=0.95, 95% CI = 0.92, 0.97) were identified as statistically significant predictors of patient compliance. Spatial regression analyses identified areas that had high and low predictive probability of patient compliance, as well as which community level factors were co-occurring in those areas., Conclusions: The findings suggest that both patient-level factors and the community where patients live may impact no-show rates. Health literacy was identified as a key modifiable predictor at the patient level. At the community level, we found that predicted probability of patient compliance varied throughout the state. Efforts should focus on enhancing patient education at the individual level and understanding geographical factors to improve compliance., (© 2024 American Academy of Sleep Medicine.)

Objective: Clinical research networks facilitate collaborative research, but data sharing remains a common barrier., Materials and Methods: The TriNetX platform provides real-time access to electronic health record (EHR)-derived, anonymized data from 173 healthcare organizations (HCOs) and tools for queries and analysis. In 2022, 4 pediatric HCOs worked with TriNetX leadership to found the Pediatric Collaboratory Network (PCN), facilitated via a multi-institutional data-use agreement (DUA). The DUA enables collaborative study design and execution, with institutional review board-approved transfer of complete datasets for further analyses on a per-protocol basis., Results and Discussion: Of the 41.2 million children with TriNetX records, the PCN represents nearly 10%. The PCN assisted several early-career investigators to bring study concepts from conception to an international scientific meeting presentation and journal submission., Conclusion: The PCN facilitates EHR vendor-agnostic multicenter pediatric research on the global TriNetX platform. Continued growth of the PCN will advance knowledge in pediatric health., Competing Interests: A.Z. has received honoraria from Sanofi and Takeda for her role as an advisory board member for scientific advisory boards in the past 3 years. N.A.G. has received or has recently received consultancy fees from Anthos Therapeutics, Bayer, Boehringer-Ingelheim, Daiichi Sankyo, and the University of Colorado-affiliated Academic Research Organization CPC Clinical Research for roles in clinical trial planning or oversight committees (eg, advisory committee; steering committee; data and safety monitoring committee) in pharmaceutical industry-sponsored pediatric clinical trials of antithrombotics. All other authors have indicated they have no financial relationships or potential conflicts of interest relevant to this article to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objectives: The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data., Materials and Methods: Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets., Results: Our approach provides better prediction of localized case counts and indicates the impact of the dynamic elements of the pandemic on predictions, such as the influence of the mixture of viral variants in the population and variable testing and vaccination rates during various eras of the pandemic., Discussion: Utilizing real-time public health metrics, including estimated R t from multiple SARS-CoV-2 variants, vaccination rates, and testing information, provided a significant increase in the accuracy of the model during the Omicron and Delta period, thus providing more precise forecasting of daily case counts at the county level. This work provides insights on the influence of various features on predictive performance in rural and non-rural areas., Conclusion: Our proposed framework incorporates available public health metrics with operational data on the impact of testing, vaccination, and current viral variant mixtures in the population to provide a foundation for combining dynamic public health metrics and ML models to deliver forecasting and insights in healthcare domains. It also shows the importance of developing and deploying ML frameworks in rural settings., Competing Interests: The authors have no competing interests to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

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 overlapped 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 households in poverty versus the working poor., Interpretations: Findings highlight a disparity in equitable inclusion of individuals across the spectrum of socioeconomic 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., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Moradi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: The purpose of this study was to examine the relationship between test site availability and testing rate within the context of social determinants of health., Study Design: A retrospective ecological investigation was conducted using statewide COVID-19 testing data between March 2020 and December 2021., Methods: Ordinary least squares and geographically weighted regression were used to estimate state and ZIP code level associations between testing rate and testing sites per capita, adjusting for neighbourhood-level confounders., Results: The findings indicate that site availability is positively associated with the ZIP code level testing rate and that this association is amplified in communities of greater economic deprivation. In addition, economic deprivation is a key factor for consideration when examining ethnic differences in testing in medically underserved states., Conclusion: The study findings could be used to guide the delivery of testing facilities in resource-constrained states., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., (© 2022 The Authors. The Journal of Rural Health published by Wiley Periodicals LLC on behalf of National Rural Health Association.)

Importance: West Virginia prioritized SARS-CoV-2 vaccine delivery to nursing home facilities because of increased risk of severe illness in elderly populations. However, the persistence and protective role of antibody levels remain unclear., Objective: To examine the persistence of humoral immunity after COVID-19 vaccination and the association of SARS-CoV-2 antibody levels and subsequent infection among nursing home residents and staff., Design, Setting, and Participants: In this cross-sectional study, blood samples were procured between September 13 and November 30, 2021, from vaccinated residents and staff at participating nursing home facilities in the state of West Virginia for measurement of SARS-CoV-2 antibody (anti-receptor binding domain [RBD] IgG). SARS-CoV-2 infection and vaccination history were documented during specimen collection and through query of the state SARS-CoV-2 surveillance system through January 16, 2022., Exposure: SARS-CoV-2 vaccination (with BNT162b2, messenger RNA-1273, or Ad26.COV2.S)., Main Outcomes and Measures: Anti-RBD IgG levels were assessed using multivariate analysis to examine associations between time since vaccination or infection, age, sex, booster doses, and vaccine type. Antibody levels from participants who became infected after specimen collection were compared with those without infection to correlate antibody levels with subsequent infection., Results: Among 2139 SARS-CoV-2 vaccinated residents and staff from participating West Virginia nursing facilities (median [range] age, 67 [18-103] years; 1660 [78%] female; 2045 [96%] White), anti-RBD IgG antibody levels decreased with time after vaccination or infection (mean [SE] estimated coefficient, -0.025 [0.0015]; P < .001). Multivariate regression modeling of participants with (n = 608) and without (n = 1223) a known history of SARS-CoV-2 infection demonstrated significantly higher mean (SE) antibody indexes with a third (booster) vaccination (with infection: 11.250 [1.2260]; P < .001; without infection: 8.056 [0.5333]; P < .001). Antibody levels (calculated by dividing the sample signal by the mean calibrator signal) were significantly lower among participants who later experienced breakthrough infection during the Delta surge (median, 2.3; 95% CI, 1.8-2.9) compared with those without breakthrough infection (median, 5.8; 95% CI, 5.5-6.1) (P = .002); however, no difference in absorbance indexes was observed in participants with breakthrough infections occurring after specimen collection (median, 5.9; 95% CI, 3.7-11.1) compared with those without breakthrough infection during the Omicron surge (median, 5.8; 95% CI, 5.6-6.2) (P = .70)., Conclusions and Relevance: In this cross-sectional study, anti-RBD IgG levels decreased after vaccination or infection. Higher antibody responses were found in individuals who received a third (booster) vaccination. Although lower antibody levels were associated with breakthrough infection during the Delta surge, no significant association was found between antibody level and infection observed during the Omicron surge. The findings of this cross-sectional study suggest that among nursing home residents, COVID-19 vaccine boosters are important and updated vaccines effective against emerging SARS-CoV-2 variants are needed.

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., Competing Interests: The authors have no conflict of interest or disclosures., (Copyright © 2022 Joshua Rawson et al.)

Background: Heart failure is becoming increasingly common among patients under 50 years of age, particularly in African Americans and patients with stimulant use disorder. Yet the sources of these disparities remain poorly understood. This study identified key demographic and clinical factors associated with stimulant use disorder in a largely rural heart failure patient registry., Methods: Patient records reporting a diagnosis of heart failure between January 2008 and March 2020 were requested from West Virginia University Hospital Systems (n=37,872). Odds of stimulant use disorder were estimated by demographic group (age, race, sex), insurance carrier, and clinical comorbidities using logistic regression., Results: Multivariable regression analysis identified higher odds of stimulant use disorder among Black/African Americans (1.95 [1.32, 2.77]) and patients who report drinking one or more alcoholic drinks per week (2.23 [1.72, 2.88]). Lower odds of stimulant use disorder were identified among patients with hypertension (0.59 [0.47, 0.73]), or diabetes (0.65 [0.52, 0.81]).. Likewise, lower odds of stimulant use disorder were noted among females, patients older than 30 years of age and those not enrolled in Medicaid., Conclusion: These results highlight the alarming extent to which Medicaid enrollees, Black/African Americans, people aged 18-24 and 25-44, or persons with a past alcohol use disorder diagnosis are associated with stimulant use disorder among heart failure populations living in largely rural areas. Additionally, they emphasize the need to develop policies and refine clinical care that affects this vulnerable population's prognoses., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+Rt, 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 Rt, 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+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt 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 Rt is provided, and the potential for further development of machine learning methods that are enhanced by Rt., Competing Interests: No authors have competing interests.

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-CXoV-2 infections. In this study, we describe and compare 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 Rt, 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. We also find that both methods perform adequately in both rural and non-rural predictions. Finally, we provide a detailed discussion on practical issues regarding implementing forecasting models for public health action based on R t , and the potential for further development of machine learning methods that are enhanced by R t.

Purpose: Social determinants of health and racial inequalities impact healthcare access and subsequent coronavirus testing. Limited studies have described the impact of these inequities on rural minorities living in Appalachia. This study investigates factors affecting testing in rural communities., Methods: PCR testing data were obtained for March through September 2020. Spatial regression analyses were fit at the census tract level. Model outcomes included testing and positivity rate. Covariates included rurality, percent Black population, food insecurity, and area deprivation index (a comprehensive indicator of socioeconomic status)., Results: Small clusters in coronavirus testing were detected sporadically, while test positivity clustered in mideastern and southwestern WV. In regression analyses, percent food insecurity (IRR = 3.69×10 9 , [796, 1.92×10 16 ]), rurality (IRR=1.28, [1.12, 1.48]), and percent population Black (IRR = 0.88, [0.84, 0.94]) had substantial effects on coronavirus testing. However, only percent food insecurity (IRR = 5.98 × 10 4 , [3.59, 1.07×10 9 ]) and percent Black population (IRR = 0.94, [0.90, 0.97]) displayed substantial effects on the test positivity rate., Conclusions: Findings highlight disparities in coronavirus testing among communities with rural minorities. Limited testing in these communities may misrepresent coronavirus incidence., (Copyright © 2021. Published by Elsevier Inc.)

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.

Retrieval of a memory appears to render it unstable until the memory is once again re-stabilized or reconsolidated. Although the occurrence and consequences of reconsolidation have received much attention in recent years, the specific mechanisms that underlie the process of reconsolidation have not been fully described. Here, we present the first electrophysiological model of the synaptic plasticity changes underlying the different stages of reconsolidation of a conditioned fear memory. In this model, retrieval of a fear memory results in immediate but transient alterations in synaptic plasticity, mediated by modified expression of the glutamate receptor subunits GluA1 and GluA2 in the hippocampus of rodents. Retrieval of a memory results in an immediate impairment in LTP, which is enhanced 6h following memory retrieval. Conversely, memory retrieval results in an immediate enhancement of LTD, which decreases with time. These changes in plasticity are accompanied by decreased expression of GluA2 receptor subunits. Recovery of LTP and LTD correlates with progressive overexpression of GluA2 receptor subunits. The contribution of the GluA2 receptor was confirmed by interfering with receptor expression at the postsynaptic sites. Blocking GluA2 endocytosis restored LTP and attenuated LTD during the initial portion of the reconsolidation period. These findings suggest that altered GluA2 receptor expression is one of the mechanisms that controls different forms of synaptic plasticity during reconsolidation., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Spontaneous recovery in extinction appears to be inversely related to the acquisition-to-extinction interval, but it remains unclear why this is the case. Rat subjects trained with one of three interference paradigms exhibited less spontaneous recovery of the original response after delayed than immediate interference, regardless of whether interference resulted in attenuated fear (extinction, CS-Shock followed by CS-noShock), acquisition of conditioned fear (latent inhibition, CS-noShock followed by CS-Shock), or acquisition of a response (counterconditioning, CS-Shock followed by CS-Sucrose). We suggest that delaying interference treatment increases the relative similarity of the interference and test contexts, facilitating retrieval of the interfering association and attenuating recovery of the original response.