scholarly journals 515. Evolution of Treatment Patterns for Patients Hospitalized with COVID-19 in the United States

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S359-S360
Author(s):  
Kelly Zalocusky ◽  
Shemra Rizzo ◽  
Devika Chawla ◽  
Yifeng Chia ◽  
Tripthi Kamath ◽  
...  
Keyword(s):  
Treatment Guidelines ◽  
The United States ◽  
Hospitalized Patients ◽  
Invasive Ventilation ◽  
Data Set ◽  
Diagnostic Codes ◽  
Dramatic Decline ◽  
The Us ◽  
Optimal Patient Care ◽  
Procedure Codes

Abstract Background COVID-19 remains a threat to public health, with over 30 million cases in the US alone. As understanding of optimal patient care has improved, treatment guidelines have continued to evolve. This study characterized real-world trends in treatment for US patients hospitalized with COVID-19, stratified by whether patients required invasive ventilation. Methods US patients diagnosed and hospitalized with COVID-19 between March 23 and December 31, 2020, in the Optum de-identified COVID-19 electronic health record (EHR) data set were identified. Both drug and procedure codes were used to ascertain medications, and both procedure and diagnostic codes were used to detect invasive ventilation during hospitalization. Medication trends were estimated by computing proportions of hospitalized patients receiving each drug weekly during the study period. Results In this cohort of 71,366 hospitalized patients, the largest observed change in care was related to chloroquine/hydroxychloroquine (HCQ) (Figure). HCQ usage peaked at 87% of patients receiving invasive ventilation (54% without ventilation) in the first week of this study (March 23-29), but declined to < 5% of patients, regardless of ventilation status, by the end of May. In contrast, dexamethasone usage was 10% at baseline in patients receiving ventilation (1% without ventilation) and increased to a steady state of >85% of patients receiving ventilation ( >50% without ventilation) by the end of June. Similarly, remdesivir usage increased sharply from a baseline of 2% of patients and continued to rise to a peak of 79% of patients receiving invasive ventilation (44% without ventilation) in November before declining. Conclusion Meaningful shifts in treatments for US patients hospitalized with COVID-19 were observed from March through December 2020. A dramatic decline was observed for HCQ use, likely owing to safety concerns, while usage of dexamethasone and remdesivir increased as evidence of their efficacy mounted. Across medications, usage was substantially more prevalent among patients requiring invasive ventilation compared with patients with less severe cases. Disclosures Kelly Zalocusky, PhD, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Shemra Rizzo, PhD, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Devika Chawla, PhD MSPH, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Yifeng Chia, PhD, F. Hoffmann-La Roche Ltd (Shareholder)Genentech, Inc. (Employee) Tripthi Kamath, PhD, F. Hoffmann-La Roche Ltd (Shareholder)Genentech, Inc. (Employee) Larry Tsai, MD, F. Hoffmann-La Roche Ltd (Shareholder)Genentech, Inc. (Employee)

scholarly journals 398. Multicenter Evaluation of Outcomes of SARS-CoV-2 Positive Patients Treated at Rural vs Urban Hospitals in the United States

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S301-S301
Author(s):  
Karri A Bauer ◽  
Kalvin Yu ◽  
Vikas Gupta ◽  
Laura A Puzniak
Keyword(s):  
Cohort Analysis ◽  
The United States ◽  
Hospitalized Patients ◽  
Rural Hospital ◽  
Research Database ◽  
Urban Hospital ◽  
Becton Dickinson ◽  
Urban Hospitals ◽  
Rural And Urban ◽  
The Us

Abstract Background The SARS-CoV-2 pandemic has revealed socioeconomic and healthcare inequities in the US. With approximately 20% of the population living in rural areas, there are limitations to healthcare access due to economic constraints, geographical distances, and provider shortages. There is limited data evaluating outcomes associated with SARS-CoV-2 positive patients treated at rural vs. urban hospitals. The aim of the study was to evaluate characteristics and outcomes of SARS-CoV-2 positive patients treated at rural vs. urban hospitals in the US. Methods This was a multicenter, retrospective cohort analysis of adult (≥ 18 years) hospitalized patients from 241 US acute care facilities with >1 day inpatient admission with a discharge or death between 3/6/20-5/15/21 (BD Insights Research Database [Becton, Dickinson & Company, Franklin Lakes, NJ]), which includes both small and large hospitals in rural and urban areas. SARS-CoV-2 infection was identified by a positive PCR or antigen during or < 7 days prior to hospital admission. Descriptive statistics were completed. P value of ≤0.05 was considered statistically significant. Results Overall, 42 (17.4%) and 199 (82.6%) of hospitals were classified as rural and urban, respectively. A total of 304,073 patients were admitted to a rural hospital with 12,644 (4.2%) SARS-CoV-2 positive. In comparison, a total of 2,844,100 patients were treated at an urban hospital with 132,678 (4.7%) SARS-CoV-2 positive. Patients admitted to rural hospitals were older compared to those treated at an urban hospital (65.2 ± 17.3 vs. 61.5 ± 18.7, P=0.001) (Table 1). Patients treated at an urban facility had significantly higher rates of ICU admission, severe sepsis, and mechanical ventilation. ICU length of stay was significantly longer for patients admitted to an urban hospital compared to a rural hospital (8.1 ± 9.9 vs. 6.1 ±7.2 days, P=0.001) (Table 2). No difference in mortality was observed. Table 1. Characteristics of SARS-CoV-2 positive patients treated at rural vs. urban hospitals. Table 2. Outcomes of SARS-CoV-2 patients treated at rural vs. urban hospitals. *Patients with available data. Conclusion In this large multicenter evaluation of hospitalized patients positive for SARS-CoV-2, there were significant differences in patient characteristics. There was no observed difference in mortality. These findings are important in evaluating the pandemic’s impact on patients in rural and urban healthcare settings. Disclosures Karri A. Bauer, PharmD, Merck & Co., Inc. (Employee, Shareholder) Kalvin Yu, MD, BD (Employee) Vikas Gupta, PharmD, BCPS, Becton, Dickinson and Company (Employee, Shareholder) Laura A. Puzniak, PhD, Merck & Co., Inc. (Employee)

scholarly journals Measuring Job Creation, Growth, and Survival among the Universe of Start-ups in the United States Using a Combined Start-up Panel Data Set

ILR Review ◽  
2019 ◽  
Vol 72 (5) ◽  
pp. 1262-1277 ◽  
Author(s):  
Robert W. Fairlie ◽  
Javier Miranda ◽  
Nikolas Zolas
Keyword(s):  
United States ◽  
Panel Data ◽  
The United States ◽  
Future Research ◽  
Data Set ◽  
Growth And Survival ◽  
Start Up ◽  
The Us ◽  
Start Ups ◽  
The Universe

The field of entrepreneurship is growing rapidly and expanding into new areas. This article presents a new compilation of administrative panel data on the universe of business start-ups in the United States, which will be useful for future research in entrepreneurship. To create the US start-up panel data set, the authors link the universe of non-employer firms to the universe of employer firms in the Longitudinal Business Database (LBD). Start-up cohorts of more than five million new businesses per year, which create roughly three million jobs, can be tracked over time. To illustrate the potential of the new start-up panel data set for future research, the authors provide descriptive statistics for a few examples of research topics using a representative start-up cohort.

Explainable machine learning models to understand determinants of COVID-19 mortality in the United States (Preprint)

2020 ◽  
Author(s):  
Piyush Mathur ◽  
Tavpritesh Sethi ◽  
Anya Mathur ◽  
Kamal Maheshwari ◽  
Jacek Cywinski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
The United States ◽  
Training Data ◽  
Mitigation Measures ◽  
Data Set ◽  
Administrative Action ◽  
The Us ◽  
Feature Importance ◽  
Machine Learning Models

UNSTRUCTURED Introduction The COVID-19 pandemic exhibits an uneven geographic spread which leads to a locational mismatch of testing, mitigation measures and allocation of healthcare resources (human, equipment, and infrastructure).(1) In the absence of effective treatment, understanding and predicting the spread of COVID-19 is unquestionably valuable for public health and hospital authorities to plan for and manage the pandemic. While there have been many models developed to predict mortality, the authors sought to develop a machine learning prediction model that provides an estimate of the relative association of socioeconomic, demographic, travel, and health care characteristics of COVID-19 disease mortality among states in the United States(US). Methods State-wise data was collected for all the features predicting COVID-19 mortality and for deriving feature importance (eTable 1 in the Supplement).(2) Key feature categories include demographic characteristics of the population, pre-existing healthcare utilization, travel, weather, socioeconomic variables, racial distribution and timing of disease mitigation measures (Figure 1 & 2). Two machine learning models, Catboost regression and random forest were trained independently to predict mortality in states on data partitioned into a training (80%) and test (20%) set.(3) Accuracy of models was assessed by R2 score. Importance of the features for prediction of mortality was calculated via two machine learning algorithms - SHAP (SHapley Additive exPlanations) calculated upon CatBoost model and Boruta, a random forest based method trained with 10,000 trees for calculating statistical significance (3-5). Results Results are based on 60,604 total deaths in the US, as of April 30, 2020. Actual number of deaths ranged widely from 7 (Wyoming) to 18,909 (New York).CatBoost regression model obtained an R2 score of 0.99 on the training data set and 0.50 on the test set. Random Forest model obtained an R2 score of 0.88 on the training data set and 0.39 on the test set. Nine out of twenty variables were significantly higher than the maximum variable importance achieved by the shadow dataset in Boruta regression (Figure 2).Both models showed the high feature importance for pre-existing high healthcare utilization reflective in nursing home beds per capita and doctors per 100,000 population. Overall population characteristics such as total population and population density also correlated positively with the number of deaths.Notably, both models revealed a high positive correlation of deaths with percentage of African Americans. Direct flights from China, especially Wuhan were also significant in both models as predictors of death, therefore reflecting early spread of the disease. Associations between deaths and weather patterns, hospital bed capacity, median age, timing of administrative action to mitigate disease spread such as the closure of educational institutions or stay at home order were not significant. The lack of some associations, e.g., administrative action may reflect delayed outcomes of interventions which were not yet reflected in data. Discussion COVID-19 disease has varied spread and mortality across communities amongst different states in the US. While our models show that high population density, pre-existing need for medical care and foreign travel may increase transmission and thus COVID-19 mortality, the effect of geographic, climate and racial disparities on COVID-19 related mortality is not clear. The purpose of our study was not state-wise accurate prediction of deaths in the US, which has already been challenging.(6) Location based understanding of key determinants of COVID-19 mortality, is critically needed for focused targeting of mitigation and control measures. Risk assessment-based understanding of determinants affecting COVID-19 outcomes, using a dynamic and scalable machine learning model such as the two proposed, can help guide resource management and policy framework.

Social Implications of COVID-19 Deaths: Analyzing Race, Ethnicity, Socio-Economic Conditions, Gender, and Age for the US

2020 ◽  
Vol 7 (1) ◽  
pp. 163-180
Author(s):  
Saagar S Kulkarni ◽  
Kathryn E Lorenz
Keyword(s):  
United States ◽  
Significant Role ◽  
The United States ◽  
Social Implications ◽  
Data Sets ◽  
Comprehensive Overview ◽  
Data Set ◽  
The Us ◽  
Multivariable Regression ◽  
Race Ethnicity

This paper examines two CDC data sets in order to provide a comprehensive overview and social implications of COVID-19 related deaths within the United States over the first eight months of 2020. By analyzing the first data set during this eight-month period with the variables of age, race, and individual states in the United States, we found correlations between COVID-19 deaths and these three variables. Overall, our multivariable regression model was found to be statistically significant.  When analyzing the second CDC data set, we used the same variables with one exception; gender was used in place of race. From this analysis, it was found that trends in age and individual states were significant. However, since gender was not found to be significant in predicting deaths, we concluded that, gender does not play a significant role in the prognosis of COVID-19 induced deaths. However, the age of an individual and his/her state of residence potentially play a significant role in determining life or death. Socio-economic analysis of the US population confirms Qualitative socio-economic Logic based Cascade Hypotheses (QLCH) of education, occupation, and income affecting race/ethnicity differently. For a given race/ethnicity, education drives occupation then income, where a person lives, and in turn his/her access to healthcare coverage. Considering socio-economic data based QLCH framework, we conclude that different races are poised for differing effects of COVID-19 and that Asians and Whites are in a stronger position to combat COVID-19 than Hispanics and Blacks.

Stability and Change in Childcare and Employment: Evidence from the United States

2006 ◽  
Vol 195 ◽  
pp. 118-132 ◽  
Author(s):  
Cynthia Miller
Keyword(s):  
Work Experience ◽  
The United States ◽  
Welfare Recipients ◽  
Data Set ◽  
Employment Stability ◽  
Stability And Change ◽  
Employment Retention ◽  
Unique Data ◽  
The Us ◽  
Former Welfare Recipients

Using a unique data set from the US to examine the association between employment stability and childcare stability, we find that childcare use is fairly stable for current and former welfare recipients. In addition, although childcare instability contributes to employment instability, it does not appear to be the major reason women leave their jobs. In this case, employment retention programmes in the US, while not losing focus on childcare issues, should also address other barriers to keeping jobs, such as limited education and lack of work experience.

scholarly journals Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260592
Author(s):  
Peter Sheridan Dodds ◽  
Joshua R. Minot ◽  
Michael V. Arnold ◽  
Thayer Alshaabi ◽  
Jane Lydia Adams ◽  
...  
Keyword(s):  
The United States ◽  
Journalism History ◽  
Specific Kind ◽  
Data Set ◽  
Opinion Aggregation ◽  
Story Generation ◽  
The Us ◽  
Computational Aspects ◽  
Us Presidential Election ◽  
Narrative Control

Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject’s historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day’s 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016–2021. We measure Trump’s narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy—the rate at which a population’s stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd’s murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography.

scholarly journals COVID-19 TREATMENT GUIDELINES FOR HOSPITALIZED PATIENTS AT ST. VINCENT’S MEDICAL CENTER, A LICENSED 473-BED COMMUNITY TEACHING HOSPITAL WITH A LEVEL II TRAUMA CENTER IN THE UNITED STATES

2021 ◽  
Vol 2021 (3) ◽  
pp. 50-55
Author(s):  
Valeriia Klymenko ◽  
Haritha Mopuru ◽  
Arsalan Alvi ◽  
Maria Morel ◽  
Nataliia Dyatlova ◽  
...  
Keyword(s):  
United States ◽  
Teaching Hospital ◽  
Trauma Center ◽  
Treatment Guidelines ◽  
Medical Center ◽  
The United States ◽  
Hospitalized Patients ◽  
Medical Resident ◽  
Level Ii ◽  
Community Teaching

КЕРІВНИЦТВО З ЛІКУВАННЯ COVID-19 ДЛЯ ГОСПІТАЛІЗОВАНИХ ПАЦІЄНТІВ В ЛІЦЕНЗОВАНОМУ 473-ЛІЖКОВОМУ НАВЧАЛЬНОМУ МЕДИЧНОМУ ЦЕНТРІ СВ. ВІНСЕНТА З ТРАВМАТОЛОГІЧНИМ ЦЕНТРОМ ІІ РІВНЯ В СПОЛУЧЕНИХ ШТАТАХ АМЕРИКИ В. Клименко1 , Х. Мупуру1 , А. Алві1 , М. Морель1 , Н. Дятлова2 , М. Еліас1 , Д. Регелманн1 1 Медичний центр Святого Вінсента, Медична допомога Гартворда, Бріджпорт, СТ (США); 2 Програма Резидентури Внутрішньої Медицини Чиказької Медичної Школи в Північно-Західній Лікарні Мак-Генрі, Мак-Генрі, Іллінойс, США Резюме. Мета публікації — обговорення власного досвіду для підвищення ефективності медичної допомоги пацієнтам з коронaвірусною інфекцією. У статті представлено покази для госпіталізації, основні принципи спостереження і терапії хворих з інфекцією COVID-19 у великій університетській клініці США (St. Vincent's Medical Center, штат Коннектикут). В основу гайдлайну покладені дані досліджень RECOVERY, ACTT-1, SOLIDARITY, EMPACTA, REMAP-CAP, BLAZE-1, BLAZE-4. Розглянуто питання противірусної, імуносупресивної, протизапальної, антикоагулятної терапії, застосування моноклональних антитіл. Ключові слова: COVID-19, керівництво, лікування, США. Valeriia Klymenko, MD — medical resident, PGY-2, Tel: +15512578615, email: [email protected], Астма та Алергія, 2021, № 3, С. 50–55.

scholarly journals Predictive Modeling of 30-Day Emergency Hospital Transport of German Patients Using a Personal Emergency Response: Retrospective Study and Comparison with the United States (Preprint)

2020 ◽  
Author(s):  
Jorn op den Buijs ◽  
Marten Pijl ◽  
Andreas Landgraf
Keyword(s):  
United States ◽  
Health Care ◽  
High Risk ◽  
Predictive Model ◽  
The United States ◽  
Data Set ◽  
Targeted Interventions ◽  
Emergency Hospital ◽  
The Us ◽  
Hospital Transport

BACKGROUND Predictive analytics based on data from remote monitoring of elderly via a personal emergency response system (PERS) in the United States can identify subscribers at high risk for emergency hospital transport. These risk predictions can subsequently be used to proactively target interventions and prevent avoidable, costly health care use. It is, however, unknown if PERS-based risk prediction with targeted interventions could also be applied in the German health care setting. OBJECTIVE The objectives were to develop and validate a predictive model of 30-day emergency hospital transport based on data from a German PERS provider and compare the model with our previously published predictive model developed on data from a US PERS provider. METHODS Retrospective data of 5805 subscribers to a German PERS service were used to develop and validate an extreme gradient boosting predictive model of 30-day hospital transport, including predictors derived from subscriber demographics, self-reported medical conditions, and a 2-year history of case data. Models were trained on 80% (4644/5805) of the data, and performance was evaluated on an independent test set of 20% (1161/5805). Results were compared with our previously published prediction model developed on a data set of PERS users in the United States. RESULTS German PERS subscribers were on average aged 83.6 years, with 64.0% (743/1161) females, with 65.4% (759/1161) reported 3 or more chronic conditions. A total of 1.4% (350/24,847) of subscribers had one or more emergency transports in 30 days in the test set, which was significantly lower compared with the US data set (2455/109,966, 2.2%). Performance of the predictive model of emergency hospital transport, as evaluated by area under the receiver operator characteristic curve (AUC), was 0.749 (95% CI 0.721-0.777), which was similar to the US prediction model (AUC=0.778 [95% CI 0.769-0.788]). The top 1% (12/1161) of predicted high-risk patients were 10.7 times more likely to experience an emergency hospital transport in 30 days than the overall German PERS population. This lift was comparable to a model lift of 11.9 obtained by the US predictive model. CONCLUSIONS Despite differences in emergency care use, PERS-based collected subscriber data can be used to predict use outcomes in different international settings. These predictive analytic tools can be used by health care organizations to extend population health management into the home by identifying and delivering timelier targeted interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource use.

Cheerleader or Critic? Television News Coverage in Canada and the United States of the US Invasion of Panama

1994 ◽  
Vol 27 (3) ◽  
pp. 581-604 ◽  
Author(s):  
Walter C. Soderlund ◽  
Ronald H. Wagenberg ◽  
Ian C. Pemberton
Keyword(s):  
United States ◽  
Foreign Policy ◽  
Media Coverage ◽  
Television News ◽  
News Coverage ◽  
The United States ◽  
Military Force ◽  
Military Operations ◽  
Data Set ◽  
The Us

AbstractThe role of mass media in reporting United States military operations is a subject on which there is considerable interest as well as diversity of opinion. The significance of media coverage has been recognized by both supporters and opponents of American use of military force to achieve foreign policy objectives. However, analysts disagree on whether the media tend to be supportive or critical of such ventures.This study examines the above question with respect to the US invasion of Panama which began on December 20, 1989. Coverage of the invasion by three American networks (ABC, CBS and NBC) and two Canadian networks (CBC and CTV) in their major nightly television newscasts was compared for a 23-day period from December 15, 1989 to January 6, 1990. The data set picks up material on Panama beginning five days prior to the invasion and continues for three days following the surrender of General Noriega. In total 197 news stories are analyzed.Examined in the study are factors such as volume of coverage (number of stories and running time); placement of items in the newscast; substantive issues given prominence; news sources utilized, and whether these sources were favourable or unfavourable toward US foreign policy positions; positive and negative “images” presented of the key actors involved in the invasion (Manuel Noriega, Guillermo Endara and George Bush); and whether overall, in both text and visual impact, the story was likely to be interpreted as either pro- or anti-invasion by viewers.

scholarly journals Identifying Priority Areas for Increasing the Supply of Medication-Assisted Treatments for Opioid Use Disorder: A Geospatial Approach

10.36469/9787 ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 75-83
Author(s):  
Michael Topmiller ◽  
Peter J. Mallow ◽  
Aaron T. Vissman ◽  
Jene Grandmont
Keyword(s):  
Unmet Need ◽  
Analytical Framework ◽  
The United States ◽  
Opioid Use ◽  
Data Set ◽  
Treatment Need ◽  
Priority Areas ◽  
Number Of Patients ◽  
The Us ◽  
Unmet Treatment Need

Background: The opioid epidemic has disproportionately affected several areas across the United States (US), with research indicating that these areas may be underserved and lack access to sufficient medication-assisted treatment (MAT) options. The objective of this study was to introduce a geospatial analytical framework for identifying sub-state priority areas to target federal allocation of MAT training and resources. Methods: We used a geospatial analytical framework, which integrated multiple substance use measures and layers of geographic information. Measures included estimates of illicit drug dependence and unmet treatment need from the National Survey on Drug Use and Health (NSDUH), opioid-related admissions from the Treatment Episode Data Set: Admissions (TEDs-A), and Drug Enforcement Agency (DEA) waiver practitioner data from the Substance Abuse and Mental Health Services Administration (SAMHSA). Analyses included standard deviation outlier mapping, local indicators of spatial autocorrelation (LISA), and map overlays. Results: We identified twenty-nine opioid dependence priority areas, eleven unmet treatment need priority areas, and seven low MAT capacity priority areas, located across the US, including southeastern Ohio, western Indiana, the District of Columbia, New England, and northern and southern California. Conclusions: This study identified several areas across the US that have unmet need for MAT. Targeting these areas will allow for the most effective deployment of cost-effective MAT resources to aid the greatest number of patients with opioid use disorders.

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