COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina.

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e.

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.

Quantifying association and disparities between diabetes complications and COVID-19 outcomes: A retrospective study using electronic health records.

Background: Despite established relationships between diabetic status and an increased risk for COVID-19 severe outcomes, there is a limited number of studies examining the relationships between diabetes complications and COVID-19-related risks. We use the Adapted Diabetes Complications Severity Index to define seven diabetes complications. We aim to understand the risk for COVID-19 infection, hospitalization, mortality, and longer length of stay of diabetes patients with complications.

Informing pandemic response in the face of uncertainty. .

Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.

Can vaccine prioritization reduce disparities in COVID-19 burden for historically marginalized populations?

SARS-CoV-2 vaccination strategies were designed to reduce COVID-19 mortality, morbidity, and health inequities. To assess the impact of vaccination strategies on disparities in COVID-19 burden among historically marginalized populations (HMPs), e.g.

Modeling the Impact of Nonpharmaceutical Interventions on COVID-19 Transmission in K-12 Schools.

Unlabelled: The novel coronavirus SARS-CoV-2 spread across the world causing many waves of COVID-19. Children were at high risk of being exposed to the disease because they were not eligible for vaccination during the first 20 mo of the pandemic in the United States. While children 5 y and older are now eligible to receive a COVID-19 vaccine in the United States, vaccination rates remain low despite most schools returning to in-person instruction.

Use of Modeling to Inform Decision Making in North Carolina during the COVID-19 Pandemic: A Qualitative Study.

Unlabelled: The COVID-19 pandemic has popularized computer-based decision-support models, which are commonly used to inform decision making amidst complexity. Understanding what organizational decision makers prefer from these models is needed to inform model development during this and future crises. We recruited and interviewed decision makers from North Carolina across 9 sectors to understand organizational decision-making processes during the first year of the COVID-19 pandemic ( = 44).

Vaccinating children against COVID-19 is crucial to protect schools and communities.

To evaluate the joint impact of childhood vaccination rates and school masking policies on community transmission and severe outcomes due to COVID-19, we utilized a stochastic, agent-based simulation of North Carolina to test 24 health policy scenarios. In these scenarios, we varied the childhood (ages 5 to 19) vaccination rate relative to the adult's (ages 20 to 64) vaccination rate and the masking relaxation policies in schools. We measured the overall incidence of disease, COVID-19-related hospitalization, and mortality from 2021 July 1 to 2023 July 1.

Checklists in Healthcare: Operational Improvement of Standards using Safety Engineering - Project CHOISSE - A framework for evaluating the effects of checklists on surgical team culture.

The CHOISSE multi-stage framework for evaluating the effects of electronic checklist applications (e-checklists) on surgical team members' perception of their roles, performance, communication, and understanding of checklists is introduced via a pilot study. A prospective interventional cohort study design was piloted to assess the effectiveness of the framework and the sociotechnical effects of the e-checklist. A Delphi process was used to design the stages of the framework based on literature and expert consensus.

Vaccinating Children Against COVID-19 is Essential Prior to the Removal of Non-Pharmaceutical Interventions.

Objectivess: To evaluate the joint impact of childhood vaccination rates and masking policies, in schools and workplaces, on community transmission and severe outcomes due to COVID-19.

Study Design: We utilized a stochastic, agent-based simulation of North Carolina, to evaluate the impact of 24 health policy decisions on overall incidence of disease, COVID-19 related hospitalization, and mortality from July 1, 2021-July 1, 2023.

Results: Universal mask removal in schools in January 2022 could lead to a 38.

Mask Interventions in K12 Schools Can Also Reduce Community Transmission in Fall 2021.

The dominance of the COVID-19 Delta variant has renewed questions about the impact of K12 school policies, including the role of masks, on disease burden. A recent study showed masks and testing could reduce infections in students, but failed to address the impact on the community, while another showed masking is critical to slow disease spread in communities, but did not consider school openings under Delta. We project the impact of school-masking on the community, which can inform policy decisions, and support healthcare system planning.

Proximity of Cellular and Physiological Response Failures in Sepsis.

Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages.

The SMART Framework: Selection of Machine Learning Algorithms With ReplicaTions-A Case Study on the Microvascular Complications of Diabetes.

Over 34 million people in the US have diabetes, a major cause of blindness, renal failure, and amputations. Machine learning (ML) models can predict high-risk patients to help prevent adverse outcomes. Selecting the 'best' prediction model for a given disease, population, and clinical application is challenging due to the hundreds of health-related ML models in the literature and the increasing availability of ML methodologies.

Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality.

Importance: Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and COVID-19 morbidity and mortality. The relative importance of vaccination strategies and nonpharmaceutical interventions (NPIs) is not well understood.

Objective: To assess the association of simulated COVID-19 vaccine efficacy and coverage scenarios with and without NPIs with infections, hospitalizations, and deaths.

High-Quality Masks Reduce COVID-19 Infections and Deaths in the US.

Objectives: To evaluate the effectiveness of widespread adoption of masks or face coverings to reduce community transmission of the SARS-CoV-2 virus that causes COVID-19.

Methods: We created an agent-based stochastic network simulation using a variant of the standard SEIR dynamic infectious disease model. We considered a mask order that was initiated 3.

The Joint Impact of COVID-19 Vaccination and Non-Pharmaceutical Interventions on Infections, Hospitalizations, and Mortality: An Agent-Based Simulation.

Background: Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and morbidity and mortality due to COVID-19. This modeling study simulated the comparative and joint impact of COVID-19 vaccine efficacy and coverage with and without non-pharmaceutical interventions (NPIs) on total infections, hospitalizations, and deaths.

Methods: An agent-based simulation model was employed to estimate incident SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths over 18 months for the State of North Carolina, a population of roughly 10.

The value of missing information in severity of illness score development.

Objective: We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes.

Study Design And Setting: We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015.

Not all organ dysfunctions are created equal - Prevalence and mortality in sepsis.

Purpose: While organ dysfunctions within sepsis have been widely studied, interaction between measures of organ dysfunction remains an understudied area. The objective of this study is to quantify the impact of organ dysfunction on in-hospital mortality in infected population.

Materials And Methods: Descriptive and multivariate analyses of retrospective data including patients (age ≥ 18 years) hospitalized at the study hospital from July 2013 to April 2016 who met the criteria for an infection visit (62,057 unique visits).

Simulation model of the relationship between cesarean section rates and labor duration.

Cesarean delivery is the most common major abdominal surgery in many parts of the world, and it accounts for nearly one-third of births in the United States. For a patient who requires a C-section, allowing prolonged labor is not recommended because of the increased risk of infection. However, for a patient who is capable of a successful vaginal delivery, performing an unnecessary C-section can have a substantial adverse impact on the patient's future health.

Diabetes and the hospitalized patient : A cluster analytic framework for characterizing the role of sex, race and comorbidity from 2006 to 2011.

In the US, one in four adults has two or more chronic conditions; this population accounts for two thirds of healthcare spending. Comorbidity, the presence of multiple simultaneous health conditions in an individual, is increasing in prevalence and has been shown to impact patient outcomes negatively. Comorbidities associated with diabetes are correlated with increased incidence of preventable hospitalizations, longer lengths of stay (LOS), and higher costs.

A stochastic model of acute-care decisions based on patient and provider heterogeneity.

The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying APD is difficult, and response timing is critical - delays in response represent a significant and modifiable patient safety issue.

The association between event learning and continuous quality improvement programs and culture of patient safety.

Purpose: To present our approach and results from our quality and safety program and to report their possible impact on our culture of patient safety.

Methods And Materials: We created an event learning system (termed a "good catch" program) and encouraged staff to report any quality or safety concerns in real time. Events were analyzed to assess the utility of safety barriers.

Individualizing and optimizing the use of early warning scores in acute medical care for deteriorating hospitalized patients.

Aim: While early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models.

Methods: The setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011.