Poverty and population health - The need for A Paradigm shift to capture the working poor and better inform public health planning.

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.

Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach.

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.

Higher hospitalization and mortality rates among SARS-CoV-2-infected persons in rural America.

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.

Substance Abuse and Rural Appalachian Pediatric Trauma in West Virginia.

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.

Characterizing Long COVID: Deep Phenotype of a Complex Condition.

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FUNDING: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.

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.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.

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.

Clinical Characteristics and Outcomes of COVID-19 in West Virginia.

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.

High prevalence of co-occurring substance use in individuals with opioid use disorder.

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 co-occurring 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 co-occurring 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.