ESTIMATING PREVALENCE AND TIME COURSE OF SARS-CoV-2 BASED ON NEW HOSPITAL ADMISSIONS AND PCR TESTS: Data posted in the COVID 19 tracking website for RT-PCR (PCR) results and hospital admissions are used to estimate the prevalence of the SARS-CoV-2 pandemic in the United States (1). Hospital admissions mitigate positive sampling bias in PCR tests due to their initially limited test numbers and application as a diagnostic, instead of a surveying tool. As of July 31, the United States' cumulative recovered population is estimated at 47% or 155 million. The remaining susceptible population is 53%, or 47% excepting the 6% infectious population. The estimated mortality rate of the cumulative recovered population is 0.09% death per case. New (Preprint)

2020 ◽  
Author(s):  
Jose Gonzalez
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Hospital Admission ◽  
Time Course ◽  
Hospital Admissions ◽  
Sampling Bias ◽  
The United States ◽  
Rt Pcr ◽  
Admission Data ◽  
Eastern Seaboard

BACKGROUND Serological testing for SARS-CoV-2 antibodies showed a lack of response in close to 50% of formerly afflicted patients. In addition, antibodies were found to be transient, and concentration index to disease severity. These findings made this classical method for the estimation of the recovered population from COVID-19 of limited value. The method presented on this paper relying on % RT-PCR testing and controlling for sampling bias with new hospital admission data provides an effective alternative for estimation of the extent and time course of the SARS-CoV-2 epidemic. OBJECTIVE The method presented on this paper relying on % RT-PCR testing and controlling for sampling bias with new hospital admission data provides an effective alternative for estimation of the extent and time course of the SARS-CoV-2 epidemic. METHODS Daily results for %RT-PCR, Total Test Results, Hospitalized Currently, Hospitalized Cumulative available at COVID-19 Tracking Project are used to estimate mitigation of sampling bias of RT-PCR results and daily Hospital Admissions. Since at high daily testing levels and low % positives RT-PCR evidence of sampling bias disappears, it is correlated to daily Hospital Admissions and this correlate value used to mitigate the % RT-PCR findings where sampling bias is present. This information is used to estimate time course of the infection. Knowing that the disease lasts for an average of 20 days allows the integration of the time course values to obtain cumulative recovered population. RESULTS Prevalence and time course of the SARS-CoV-2 pandemic in the United States are estimated. The recovered population amounts to 47%. The states of the eastern seaboard, as exemplified by New York and Massachusetts, display a sudden early onslaught of the pandemic. While California, Texas, and Florida lagged. Mortality rate is twice higher in the eastern seaboard states compared to the entire nation and the other presented states. Given the large number of the convalescent population mortality is about 0.09% nationwide. CONCLUSIONS Novel approach to estimating time course and prevalence shows that the recovered population is much larger, and consequently, mortality rate (0.09%) about a factor of 10 lower than currently recognized.

scholarly journals ESTIMATING PREVALENCE AND TIME COURSE OF SARS-CoV-2 BASED ON NEW HOSPITAL ADMISSIONS AND PCR TESTS: RELEVANCE TO VACCINATION PROGRAM TACTICAL PLANNING

2020 ◽  
Author(s):  
Jose E Gonzalez
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Time Course ◽  
Hospital Admissions ◽  
The United States ◽  
Vaccination Program ◽  
Reliable Estimate ◽  
Full Time ◽  
Serological Testing ◽  
Susceptible Population

Data posted in the COVID 19 tracking website for RT-PCR (PCR) results and hospital admissions are used to estimate the time course of the SARS-CoV-2 pandemic in the United States (1) and individual states. Hospital admissions mitigate positive sampling bias in PCR tests since these were limited in numbers initially. Additionally, their intent was as a diagnostic rather than a surveying tool. By September 17, the United States' cumulative recovered population is estimated at 45% or 149 million. The remaining susceptible population is 55%, or 50%, excepting the currently infected 5% population. The estimated mortality rate of the cumulative total affected population is 0.13% death. States have followed diverse epidemic time courses. New Jersey and New York show SARS-CoV-2 prevalence of 95% and 82%, respectively. Likewise, each state exhibits relatively low current positive PCR results at 1.2 % and 0.8%. Also, these states show about twice the mortality rate of the nation. By comparison, Florida, California, and Texas showed recovered populations percent around 50%, and higher current PCR positive test results ranging from 5% to 9%. This novel approach provides an improved source of information on the pandemic's full-time course in terms of precision and accuracy in contrast to serological testing, which only views a narrow time slice of its history due to the transient nature of the antibody response and its graduated expression dependency on the severity of the disease. The deficiency of serological testing to estimate the recovered population is made even more acute due to the large proportion of asymptomatic and sub-clinical cases in the COVID-19 pandemic (2,3). T-cell testing, reputedly capable of long-term detection of previously infected individuals, will provide a complete view of the recovered population when it becomes available for large scale use. This New Hospital Admission based method informs a more effective and efficient deployment of a vaccination program since it provides not only a reliable estimate of the susceptible population by state, but it can also provide visibility down to the county level based on COVID-19 hospitalization record independent of PCR testing.

Abstract 3898: Predictors Of Admissions In Patients Presenting To Emergency Department With Transient Ischemic Attacks In United States- A National Survey

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Nauman Tariq ◽  
Saqib A Chaudhry ◽  
Ashter Rizvi ◽  
M Fareed K Suri ◽  
Gustavo J Rodriguez ◽  
...  
Keyword(s):  
United States ◽  
Emergency Department ◽  
Hospital Admission ◽  
Hospital Admissions ◽  
Multivariate Logistic Regression Analysis ◽  
The United States ◽  
Primary Diagnosis ◽  
Transient Ischemic Attacks ◽  
Hospital Type ◽  
Nationally Representative

Background: The estimates of patients who present with transient ischemic attacks (TIA) in the emergency departments (ED) of United states and their disposition including factors that determine hospital admission are not well understood. Objective: We used a nationally representative database to determine the rate and predictors of admission in TIA patients presenting to the ED. Methods: We analyzed the data from National Emergency Department Sample (NEDS 2006-2007) for all patients presenting with primary diagnosis of TIAs in the United States. Samples were weighted to provide national estimates of TIA hospitalizations and identify factors that increase the odds of hospital admission including age, sex, type of insurance, hospital type (urban teaching, urban nonteaching and non urban). Multivariate logistic regression analysis was used to identify predictors of hospital admission. Results: Of the total of 631750 patients presenting with TIA to the EDs in a period of two years in US, 41, 9447 (66.4%) were admitted to the hospital. In the multivariate analysis, independent factors associated with hospital admissions were women (odds ratio[OR] 1.042, 95% confidence interval [CI] 1.014-1.071, p =0.003) , Medicare insurance type (OR 0.82, 95% CI 0.88-0.93, p<0.0001), and urban non-teaching hospital ED (OR 0.825, 95% CI 0.778-0.875, p<0.0001). Conclusion: Approximately 70% of all patients presenting with TIAs to the EDs within United States are admitted. Factors unrelated to patients condition such as insurance status and ED affiliated hospital type play an important role in the decision to admit TIA patients to the hospitals.

Warning against long-acting sulphonamides

1966 ◽  
Vol 4 (4) ◽  
pp. 13-13
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Erythema Multiforme ◽  
The United States ◽  
Stevens Johnson Syndrome ◽  
Warning Letter ◽  
Johnson Syndrome ◽  
The Us ◽  
Mucous Membranes ◽  
Long Acting

Last month the US Food and Drug Administration required American manufacturers of long-acting sulphonamides (sulphamethoxypyridazine, Lederkyn - Lederle and Midicel - PD; sulphadimethoxine - Madribon - Roche) to warn prescribers that in rare cases the Stevens-Johnson syndrome may develop as a severe and sometimes fatal side effect. This syndrome is a type of erythema multiforme in which large blisters appear on the skin and especially on the mucous membranes. The manufacturers were also to advise doctors ‘to consider prescribing short-acting sulphonamides first because they are effective for most of the same conditions’. The three drug firms concerned accordingly sent a joint warning letter to all doctors, pointing out that the Stevens-Johnson syndrome is a serious complication with a mortality rate of about 25%. So far 116 cases of this syndrome have been reported in association with the use of long-acting sulphonamides, most of them in the United States. Almost two thirds of the patients were children.

Impact of alcohol and drug use on bicycle and electric scooter injuries and hospital admissions in the United States

Trauma ◽  
2021 ◽  
pp. 146040862110443
Author(s):  
Nikan K Namiri ◽  
Austin W Lee ◽  
Gregory M Amend ◽  
Jason Vargo ◽  
Benjamin N Breyer
Keyword(s):  
United States ◽  
Drug Use ◽  
Head Trauma ◽  
Hospital Admissions ◽  
Body Part ◽  
The United States ◽  
Severe Injuries ◽  
Injury Surveillance System ◽  
Alcohol And Drug Use ◽  
Awareness Campaigns

Introduction Bicycles and electric scooters (e-scooters) are convenient and accessible means of transportation. Participant safety is contingent on available infrastructure and safe riding practices including not riding while intoxicated. Understanding national prevalence and injury characteristics of bicycle and e-scooter riders who ride while intoxicated may promote awareness campaigns for safe riding practices and decrease morbidity. Methods The National Electronic Injury Surveillance System (NEISS) provides national estimates of injuries that present to emergency departments across the United States. We obtained case information on admitting status, body part injured, diagnosis of injury, age, sex, alcohol usage, and drug usage. We then queried NEISS for injuries related to bicycles and e-scooters in 2019. Results A weighted total of 270,571 (95% confidence interval (CI): 204,517–336,625) bicycle injuries occurred in the United States during 2019; alcohol and drug use were associated with 7% (95% CI: 6–9) and 2% (95% CI: 2–3) of all injuries, respectively. Twenty-four percent (CI: 18--31) of alcohol- and 29% (95% CI: 20–41) of drug-related bicycle injuries resulted in hospital admissions, compared to 15% (95% CI: 12–17) of non–alcohol- and 15% (95% CI: 13–18) of non–drug-related injuries ( p < .001 and p = .002, respectively). A total of 28,702 (95% CI: 13,975–43,428) e-scooter injuries occurred in 2019; alcohol and drug use were associated with 8% (95% CI: 5–12) and 1% (95% CI: 1–2) of injuries, respectively. Sixty percent (95% CI: 47–72) of alcohol-related e-scooter injuries resulted in head trauma, compared to 28% (95% CI: 24–32) of non–alcohol-related injuries ( p < .001). Conclusions Intoxication is associated with increasingly severe injuries, hospital admissions, and head trauma in bicycle and e-scooter riders. The findings support awareness campaigns to educate riders about risky practices, improve non-auto infrastructure, and promote helmet usage.

Role of Image-Guided Percutaneous Drainage in Pancreatic Collections

2018 ◽  
Vol 02 (02) ◽  
pp. 125-130
Author(s):  
Katayoun Samadi ◽  
Ronald Arellano
Keyword(s):  
United States ◽  
Acute Pancreatitis ◽  
Percutaneous Drainage ◽  
Hospital Admissions ◽  
Disease Process ◽  
The United States ◽  
Morbidity And Mortality ◽  
Image Guided ◽  
Gastrointestinal Conditions

AbstractAcute pancreatitis is one of the major gastrointestinal conditions that lead to around 300,000 hospital admissions per year in the United States. While mild inflammation of the pancreas is often managed conservatively, progression of the disease process to necrosis significantly increases the overall morbidity and mortality and often requires surgical or other interventional techniques for management. The purpose of this review is to describe the role of percutaneous drainage for the management of complicated pancreatitis.

scholarly journals First Report of Potato spindle tuber viroid Naturally Infecting Field Tomatoes in the Dominican Republic

Plant Disease ◽  
2014 ◽  
Vol 98 (5) ◽  
pp. 701-701
Author(s):  
K.-S. Ling ◽  
R. Li ◽  
D. Groth-Helms ◽  
F. M. Assis-Filho
Keyword(s):  
United States ◽  
Dominican Republic ◽  
Mosaic Virus ◽  
Potato Spindle Tuber Viroid ◽  
The United States ◽  
Disease Outbreak ◽  
Ringspot Virus ◽  
Rt Pcr ◽  
Spot Virus ◽  
Three Samples

In recent years, viroid disease outbreaks have resulted in serious economic losses to a number of tomato growers in North America (1,2,3). At least three pospiviroids have been identified as the causal agents of tomato disease, including Potato spindle tuber viroid (PSTVd), Tomato chlorotic dwarf viroid (TCDVd), and Mexican papita viroid (MPVd). In the spring of 2013, a severe disease outbreak with virus-like symptoms (chlorosis and plant stunting) was observed in a tomato field located in the Dominican Republic, whose tomato production is generally exported to the United States in the winter months. The transplants were produced in house. The disease has reached an epidemic level with many diseased plants pulled and disposed of accordingly. Three samples collected in May of 2013 were screened by ELISA against 16 common tomato viruses (Alfalfa mosaic virus, Cucumber mosaic virus, Impatiens necrotic spot virus, Pepino mosaic virus, Potato virus X, Potato virus Y, Tobacco etch virus, Tobacco mosaic virus, Tobacco ringspot virus, Tomato aspermy virus, Tomato bushy stunt virus, Tomato mosaic virus, Tomato ringspot virus, Tomato spotted wilt virus, Groundnut ringspot virus, and Tomato chlorotic spot virus), a virus group (Potyvirus group), three bacteria (Clavibacter michiganensis subsp. michiganensis, Pectobacterium atrosepticum, and Xanthomonas spp.), and Phytophthora spp. No positive result was observed, despite the presence of symptoms typical of a viral-like disease. Further analysis by RT-PCR using Agdia's proprietary pospiviroid group-specific primer resulted in positive reactions in all three samples. To determine which species of pospiviroid was present in these tomato samples, full-genomic products of the expected size (~360 bp) were amplified by RT-PCR using specific primers for PSTVd (4) and cloned using TOPO-TA cloning kit (Invitrogen, CA). A total of 8 to 10 clones from each isolate were selected for sequencing. Sequences from each clone were nearly identical and the predominant sequence DR13-01 was deposited in GenBank (Accession No. KF683200). BLASTn searches into the NCBI database demonstrated that isolate DR13-01 shared 97% sequence identity to PSTVd isolates identified in wild Solanum (U51895), cape gooseberry (EU862231), or pepper (AY532803), and 96% identity to the tomato-infecting PSTVd isolate from the United States (JX280944). The relatively lower genome sequence identity (96%) to the tomato-infecting PSTVd isolate in the United States (JX280944) suggests that PSTVd from the Dominican Republic was likely introduced from a different source, although the exact source that resulted in the current disease outbreak remains unknown. It may be the result of an inadvertent introduction of contaminated tomato seed lots or simply from local wild plants. Further investigation is necessary to determine the likely source and route of introduction of PSTVd identified in the current epidemic. Thus, proper control measures could be recommended for disease management. The detection of this viroid disease outbreak in the Dominican Republic represents further geographic expansion of the viroid disease in tomatoes beyond North America. References: (1). K.-S. Ling and M. Bledsoe. Plant Dis. 93:839, 2009. (2) K.-S. Ling and W. Zhang. Plant Dis. 93:1216, 2009. (3) K.-S. Ling et al. Plant Dis. 93:1075, 2009. (4) A. M. Shamloul et al. Can. J. Plant Pathol. 19:89, 1997.

scholarly journals Epidemiology and Factors Affecting Mortality of Hospitalized Patients with Disseminated Intravascular Coagulation in the United States

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2404-2404
Author(s):  
Arya Mariam Roy ◽  
Manojna Konda ◽  
Akshay Goel ◽  
Appalanaidu Sasapu
Keyword(s):  
United States ◽  
African Americans ◽  
Native Americans ◽  
Mortality Rate ◽  
Length Of Stay ◽  
Disseminated Intravascular Coagulation ◽  
The United States ◽  
Hospitalized Patients ◽  
Northeast Region ◽  
Intravascular Coagulation

Introduction Disseminated Intravascular Coagulation (DIC) is a systemic coagulopathy which leads to widespread thrombosis and hemorrhage and ultimately results in multiorgan dysfunction. DIC usually occurs as a complication of illnesses like severe sepsis, malignancies, trauma, acute pancreatitis, burns, and obstetrical complications. The prognosis and mortality of DIC depend on the etiology, however, the mortality of DIC is known to be on the higher side. The aim of the study is to analyze if gender, race, regional differences have any association with the mortality of hospitalized patients with DIC. Method The National Inpatient Sample database from the Healthcare Cost and Utilization Project (HCUP) for the year 2016 was queried for data. We identified hospital admissions for DIC with the International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code D65. The data was analyzed with STATA 16.0 version and univariate and multivariate analysis were performed. We studied the characteristics of all such hospitalizations for the year 2016 and the factors associated with the in-hospital mortality rate (MR) of DIC. We used length of stay, cost of stay as an outcome to determine if gender, race, and location play a role in the mortality. Results A total of 8704 admissions were identified with a diagnosis of DIC during the year 2016. The mean age for admission was found to be 56.48± 0.22. The percentage of admissions in females and males did not have a notable difference (50.57% vs 49.43%). The disease specific MR for DIC was 47.7%. Admission during weekend vs weekdays did not carry a statistically significant difference in terms of MR. Females with DIC were less likely to die in the hospital when compared to males with DIC (OR= 0.906, CI 0.82 - 0.99, p= 0.031). Interestingly, African Americans (AA) with DIC admissions were found to have 24% more risk of dying when compared to Caucasians admitted with DIC (OR= 1.24, CI 1.10 - 1.39, P= 0.00), Native Americans (NA) has 67% more risk of dying when compared to Caucasians (OR= 1.67, CI 1.03 - 2.69, p= 0.035). The mortality rate of NA, AA, Caucasians with DIC was found to be 57%, 52%, 47% respectively. The MR was found to be highest in hospitals of the northeast region (52%), then hospitals in the south (47%), followed by west and mid-west (46%), p= 0.000. Patients admitted to west and mid-west were 24% less likely to die when compared to patients admitted to northeast region hospitals (OR= 0.76, p= 0.001). The average length of stay and cost of stay were also less in west and mid-west regions when compared to north east. The difference in outcomes persisted after adjusting for age, gender, race, hospital division, co-morbid conditions. Conclusion Our study demonstrated that African Americans and Native Americans with DIC have high risk of dying in the hospital. Also, there exists a difference between the mortality rate, length and cost of stay among different regions in the United States. More research is needed to elucidate the factors that might be impacting the location-based variation in mortality. Disclosures No relevant conflicts of interest to declare.

scholarly journals Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Joshua J. Levy ◽  
Rebecca M. Lebeaux ◽  
Anne G. Hoen ◽  
Brock C. Christensen ◽  
Louis J. Vaickus ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Mortality Rate ◽  
Satellite Images ◽  
The United States ◽  
Mortality Rates ◽  
Image Features ◽  
County Level ◽  
Crude Mortality Rate ◽  
Crude Mortality

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

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