scholarly journals A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes

JAMIA Open ◽  
2020 ◽  
Author(s):  
Xiang Gao ◽  
Qunfeng Dong
Keyword(s):  
Patient Outcomes ◽  
Posterior Probability ◽  
The United States ◽  
Data Sets ◽  
Medical Outcomes ◽  
Posterior Probability Distribution ◽  
Prevalence Estimation ◽  
Point Estimates ◽  
Asymptomatic Children ◽  
Research Task

Abstract A common research task in COVID-19 studies often involves the prevalence estimation of certain medical outcomes. Although point estimates with confidence intervals are typically obtained, a better approach is to estimate the entire posterior probability distribution of the prevalence, which can be easily accomplished with a standard Bayesian approach using binomial likelihood and its conjugate beta prior distribution. Using two recently published COVID-19 data sets, we performed Bayesian analysis to estimate the prevalence of infection fatality in Iceland and asymptomatic children in the United States.

Asian Americans and Pacific Islanders: Employment Issues in the United States

2011 ◽  
Vol 9 (1-2) ◽  
pp. 58-69
Author(s):  
Marlene Kim
Keyword(s):  
United States ◽  
Asian Americans ◽  
The United States ◽  
Current Data ◽  
Pacific Islanders ◽  
Native Hawaiians ◽  
Data Sets ◽  
High Poverty ◽  
Unemployment Rates

Asian Americans and Pacific Islanders (AAPIs) in the United States face problems of discrimination, the glass ceiling, and very high long-term unemployment rates. As a diverse population, although some Asian Americans are more successful than average, others, like those from Southeast Asia and Native Hawaiians and Pacific Islanders (NHPIs), work in low-paying jobs and suffer from high poverty rates, high unemployment rates, and low earnings. Collecting more detailed and additional data from employers, oversampling AAPIs in current data sets, making administrative data available to researchers, providing more resources for research on AAPIs, and enforcing nondiscrimination laws and affirmative action mandates would assist this population.

Diabetes Discharge Planning and Transitions of Care: A Focused Review

2019 ◽  
Vol 15 (2) ◽  
pp. 111-117 ◽  
Author(s):  
Robin L. Black ◽  
Courtney Duval
Keyword(s):  
Patient Outcomes ◽  
Hospital Admissions ◽  
Discharge Planning ◽  
Medication Management ◽  
Hospital Readmissions ◽  
The United States ◽  
Transitions Of Care ◽  
Discharge Process ◽  
The Impact ◽  
Diabetes Patients

Background: Diabetes is a growing problem in the United States. Increasing hospital admissions for diabetes patients demonstrate the need for evidence-based care of diabetes patients by inpatient providers, as well as the importance of continuity of care when transitioning patients from inpatient to outpatient providers. Methods: A focused literature review of discharge planning and transitions of care in diabetes, conducted in PubMed is presented. Studies were selected for inclusion based on content focusing on transitions of care in diabetes, risk factors for readmission, the impact of inpatient diabetes education on patient outcomes, and optimal medication management of diabetes during care transitions. American Diabetes Association (ADA) guidelines for care of patients during the discharge process are presented, as well as considerations for designing treatment regimens for a hospitalized patient transitioning to various care settings. Results: Multiple factors may make transitions of care difficult, including poor communication, poor patient education, inappropriate follow-up, and clinically complex patients. ADA recommendations provide guidance, but an individualized approach for medication management is needed. Use of scoring systems may help identify patients at higher risk for readmission. Good communication with patients and outpatient providers is needed to prevent patient harm. A team-based approach is needed, utilizing the skills of inpatient and outpatient providers, diabetes educators, nurses, and pharmacists. Conclusion: Structured discharge planning per guideline recommendations can help improve transitions in care for patients with diabetes. A team based, patient-centered approach can help improve patient outcomes by reducing medication errors, delay of care, and hospital readmissions.

Rehabilitation Intensity and Patient Outcomes in Skilled Nursing Facilities in the United States: A Systematic Review

2021 ◽  
Author(s):  
Rachel A Prusynski ◽  
Allison M Gustavson ◽  
Siddhi R Shrivastav ◽  
Tracy M Mroz
Keyword(s):  
United States ◽  
Systematic Review ◽  
Patient Outcomes ◽  
Hospital Readmissions ◽  
The United States ◽  
Skilled Nursing Facilities ◽  
Functional Improvement ◽  
Nursing Facilities ◽  
Skilled Nursing ◽  
The Relationship

Abstract Objective Exponential increases in rehabilitation intensity in skilled nursing facilities (SNFs) motivated recent changes in Medicare reimbursement policies, which remove financial incentives for providing more minutes of physical therapy, occupational therapy, and speech therapy. Yet there is concern that SNFs will reduce therapy provision and patients will experience worse outcomes. The purpose of this systematic review was to synthesize current evidence on the relationship between therapy intensity and patient outcomes in SNFs. Methods PubMed, Medline, Scopus, Embase, CINAHL, PEDro, and COCHRANE databases were searched. English-language studies published in the United States between 1998 and February 14, 2020, examining the relationship between therapy intensity and community discharge, hospital readmission, length of stay (LOS), and functional improvement for short-stay SNF patients were considered. Data extraction and risk of bias were performed using the American Academy of Neurology (AAN) Classification of Evidence scale for causation questions. AAN criteria were used to assess confidence in the evidence for each outcome. Results Eight observational studies met inclusion criteria. There was moderate evidence that higher intensity therapy was associated with higher rates of community discharge and shorter LOS. One study provided very low-level evidence of associations between higher intensity therapy and lower hospital readmissions after total hip and knee replacement. There was low-level evidence indicating higher intensity therapy is associated with improvements in function. Conclusions This systematic review concludes, with moderate confidence, that higher intensity therapy in SNFs leads to higher community discharge rates and shorter LOS. Future research should improve quality of evidence on functional improvement and hospital readmissions. Impact This systematic review demonstrates that patients in SNFs may benefit from higher intensity therapy. Because new policies no longer incentivize intensive therapy, patient outcomes should be closely monitored to ensure patients in SNFs receive high-quality care.

Origins and Destinations – Social Security Claimant Dynamics

1998 ◽  
Vol 27 (3) ◽  
pp. 351-369 ◽  
Author(s):  
MICHAEL NOBLE ◽  
SIN YI CHEUNG ◽  
GEORGE SMITH
Keyword(s):  
The United States ◽  
Data Sets ◽  
Income Support ◽  
Data Set ◽  
Family Structures ◽  
Highly Educated ◽  
Lone Parents ◽  
Lone Mothers ◽  
Time Period ◽  
Short Time

This article briefly reviews American and British literature on welfare dynamics and examines the concepts of welfare dependency and ‘dependency culture’ with particular reference to lone parents. Using UK benefit data sets, the welfare dynamics of lone mothers are examined to explore the extent to which they inform the debates. Evidence from Housing Benefits data show that even over a relatively short time period, there is significant turnover in the benefits-dependent lone parent population with movement in and out of income support as well as movement into other family structures. Younger lone parents and owner-occupiers tend to leave the data set while older lone parents and council tenants are most likely to stay. Some owner-occupier lone parents may be relatively well off and on income support for a relatively short time between separation and a financial settlement being reached. They may also represent a more highly educated and highly skilled group with easier access to the labour market than renters. Any policy moves paralleling those in the United States to time limit benefit will disproportionately affect older lone parents.

scholarly journals Future of Personalized Medicine in Oncology: A Systems Biology Approach

2010 ◽  
Vol 28 (16) ◽  
pp. 2777-2783 ◽  
Author(s):  
Ana Maria Gonzalez-Angulo ◽  
Bryan T.J. Hennessy ◽  
Gordon B. Mills
Keyword(s):  
Systems Biology ◽  
Patient Outcomes ◽  
Cost Effective ◽  
Molecular Medicine ◽  
Multidimensional Data ◽  
Data Sets ◽  
Therapeutic Modalities ◽  
Molecular Tests ◽  
Therapeutic Decision Making ◽  
The Masses

The development of cost-effective technologies able to comprehensively assess DNA, RNA, protein, and metabolites in patient tumors has fueled efforts to tailor medical care. Indeed validated molecular tests assessing tumor tissue or patient germline DNA already drive therapeutic decision making. However, many theoretical and regulatory challenges must still be overcome before fully realizing the promise of personalized molecular medicine. The masses of data generated by high-throughput technologies are challenging to manage, visualize, and convert to the knowledge required to improve patient outcomes. Systems biology integrates engineering, physics, and mathematical approaches with biologic and medical insights in an iterative process to visualize the interconnected events within a cell that determine how inputs from the environment and the network rewiring that occurs due to the genomic aberrations acquired by patient tumors determines cellular behavior and patient outcomes. A cross-disciplinary systems biology effort will be necessary to convert the information contained in multidimensional data sets into useful biomarkers that can classify patient tumors by prognosis and response to therapeutic modalities and to identify the drivers of tumor behavior that are optimal targets for therapy. An understanding of the effects of targeted therapeutics on signaling networks and homeostatic regulatory loops will be necessary to prevent inadvertent effects as well as to develop rational combinatorial therapies. Systems biology approaches identifying molecular drivers and biomarkers will lead to the implementation of smaller, shorter, cheaper, and individualized clinical trials that will increase the success rate and hasten the implementation of effective therapies into the clinical armamentarium.

scholarly journals Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 859
Author(s):  
Abdulaziz O. AlQabbany ◽  
Aqil M. Azmi
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Real Time ◽  
Data Streams ◽  
Learning Algorithm ◽  
Concept Drift ◽  
The United States ◽  
Careful Consideration ◽  
Data Sets ◽  
Stream Data

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances’ continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms’ efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.

Abstract WP178: Acute Stroke Ischemic Symptom Onset-to-Arrival Time in Michigan Hospitals

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Muhammad U Farooq ◽  
Kathie Thomas
Keyword(s):  
African American ◽  
Ischemic Stroke ◽  
Acute Stroke ◽  
Patient Outcomes ◽  
Symptom Onset ◽  
Arrival Time ◽  
Heart Association ◽  
The United States ◽  
Stroke Patients ◽  
White Patients

Objectives: Stroke is the fifth-leading cause of death and the leading cause of disability in the United States. One of the primary goals of the American Heart Association/American Stroke Association is to increase the number of acute stroke patients arriving at emergency departments (EDs) within 1-hour of symptom onset. Earlier treatment with thrombolysis in patients with acute ischemic stroke translates into improved patient outcomes. The objective of this abstract is to examine the association between the use of emergency medical services (EMS) and symptom onset-to-arrival time in patients with ischemic stroke. Methods: A retrospective review of ischemic stroke patients (n = 8873) from 25 Michigan hospitals from January 2012-December 2014 using Get With the Guidelines databases was conducted. Symptom onset-to-ED arrival time and arrival mode were examined. Results: It was found that 17.4% of ischemic stroke patients arrived at the hospitals within 1-hour of symptom onset. EMS transported 69.1% of patients who arrived within 1-hour of symptom onset. During this 1-hour period African American patients (22%) were less likely to use EMS transportation as compared to White patients (72%). The majority of patients, 41.8%, arrived after 6-hours of symptom onset. EMS transported only 40% of patients who arrived after 6-hours of symptom onset. As before, during this 6-hour period African American patients (20%) were also less likely to use EMS transportation as compared to White patients (75%). Symptom onset-to-ED arrival time was shorter for those patients who used EMS. The median pre-hospital delay time was 2.6 hours for those who used EMS versus 6.2 hours for those who did not use EMS. Conclusions: The use of EMS is associated with a decreased pre-hospital delay, early treatment with thrombolysis and improved patient outcomes in ischemic stroke patients. Community interventions should focus on creating awareness especially in minority populations about stroke as a neurological emergency and encourage EMS use amongst stroke patients.

Dynamic Measurement Modeling: Using Nonlinear Growth Models to Estimate Student Learning Capacity

2017 ◽  
Vol 46 (6) ◽  
pp. 284-292 ◽  
Author(s):  
Denis G. Dumas ◽  
Daniel M. McNeish
Keyword(s):  
Multiple Testing ◽  
Large Scale ◽  
Growth Models ◽  
Dynamic Assessment ◽  
The United States ◽  
Dynamic Measurement ◽  
Data Sets ◽  
Data Systems ◽  
Longitudinal Assessment ◽  
Assessment Practice

Single-timepoint educational measurement practices are capable of assessing student ability at the time of testing but are not designed to be informative of student capacity for developing in any particular academic domain, despite commonly being used in such a manner. For this reason, such measurement practice systematically underestimates the potential of students from nondominant socioeconomic or ethnic groups, who may not have had adequate opportunity to develop various academic skills but can nonetheless do so in the future. One long-standing approach to the partial rectification of this issue is dynamic assessment (DA), a technique that features multiple testing occasions integrated with learning opportunities. However, DA is extremely resource intensive to incorporate into educational assessment practice and cannot be applied to extant large-scale data sets. In this article, the authors describe a recently developed statistical technique, dynamic measurement modeling (DMM), which is capable of estimating quantities associated with DA—including student capacity for learning a particular skill—from existing large-scale longitudinal assessment data, allowing the core concepts of DA to be scaled up for use with secondary data sets such as those collected by Statewide Longitudinal Data Systems in the United States. The authors show that by considering several assessments over time, student capacity can be reliably estimated, and these capacity estimates are much less affected by student race/ethnicity, gender, and socioeconomic status than are single-timepoint assessment scores, thereby improving the consequential validity of measurement.

Incorporating Additional Tree and Environmental Variables in a Lodgepole Pine Stem Profile Model

1993 ◽  
Vol 8 (3) ◽  
pp. 86-90
Author(s):  
John C. Byrne
Keyword(s):  
Environmental Variables ◽  
Pinus Contorta ◽  
Lodgepole Pine ◽  
Linear Equations ◽  
The United States ◽  
National Forest ◽  
Data Sets ◽  
Tree Form ◽  
Northern Rocky Mountains ◽  
Profile Model

Abstract A new variable-form segmented stem profile model is developed for lodgepole pine (Pinus contorta) trees from the northern Rocky Mountains of the United States. I improved estimates of stem diameter by predicting two of the model coefficients with linear equations using a measure of tree form, defined as a ratio of dbh and total height. Additional improvements were obtained by fitting this model to individual national forest data sets. Other tree and environmental variables tested but found of little use in improving stem profile estimates were crown ratio, habitat series, elevation, slope percent, and aspect. West. J. Appl. For. 8(3):86-90.

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