Background:Assessing perceptions of the COVID-19 vaccines is essential for understanding vaccine hesitancy and for improving uptake during public health emergencies. In the complicated landscape of COVID-19 vaccine mandates and rampant misinformation, many individuals faced challenges during vaccination decision-making. The purpose of our mixed methods study is to elucidate factors affecting vaccine decision-making and to highlight the discourse surrounding the COVID-19 vaccines in diverse and underserved communities.Methods:This mixed methods study was conducted in Arizona, Florida, Minnesota, and Wisconsin between March and November 2021, combining a cross-sectional survey (n = 3593) and focus groups (n = 47).Results:The groups least likely to report receiving a vaccination were non-Hispanic Whites, Indigenous people, males, and those with moderate socioeconomic status (SES). Those indicating high and low SES reported similar vaccination uptake. Focus group data highlighted resistance to mandates, distrust, misinformation, and concerns about the rapid development surrounding the COVID-19 vaccines. Psychological reactance theory posits that strongly persuasive messaging and social pressure can be perceived as a threat to freedom, encouraging an individual to take action to restore that freedom.Conclusion:Our findings indicate that a subsection of participants felt pressured to get the vaccine, which led to weaker intentions to vaccinate. These results suggest that vaccine rollout strategies should be reevaluated to improve and facilitate informed decision-making.

Objective In-person healthcare delivery is rapidly changing with a shifting employment landscape and technological advances. Opportunities to care for patients in more efficient ways include leveraging technology and focusing on caring for patients in the right place at the right time. We aim to use computer modelling to understand the impact of interventions, such as virtual consultation, on hospital census for referring and referral centres if non-procedural patients are cared for locally rather than transferred.Patients and methods We created computer modelling based on 25 138 hospital transfers between June 2019 and June 2022 with patients originating at one of 17 community-based hospitals and a regional or academic referral centre receiving them. We identified patients that likely could have been cared for at a community facility, with attention to hospital internal medicine and cardiology patients. The model was run for 33 500 days.Results Approximately 121 beds/day were occupied by transferred patients at the academic centre, and on average, approximately 17 beds/day were used for hospital internal medicine and nine beds/day for non-procedural cardiology patients. Typical census for all internal medicine beds is approximately 175 and for cardiology is approximately 70.Conclusion Deferring transfers for patients in favour of local hospitalisation would increase the availability of beds for complex care at the referral centre. Potential downstream effects also include increased patient satisfaction due to proximity to home and viability of the local hospital system/economy, and decreased resource utilisation for transfer systems.

Abstract Background: Individuals from diverse racial/ethnic and disadvantaged backgrounds are underrepresented in cancer clinical trials, compounding the difficulty of adequately understanding, addressing, and reversing health inequities. Social determinants of health (SDOH) factors likely contribute to disparities in cancer trial enrollment. While the presence of SDOH has shown to be associated with clinical trial participation, few studies have explored the role of SDOH in shaping beliefs around the benefits of clinical trials. Thus, this study aims to identify SDOH factors associated with beliefs around the benefits of clinical trial participation. Methods: This is a cross-sectional study of data from the Community Health Assets and Resilience Measures survey. The survey was administered in Fall 2021 to community members (N=3593; 98% > 50 years of age, 68% male, 52% racial/ethnic minority) residing in three states within the Mayo Clinic catchment area. Survey items assessed SDOH factors including financial instability, childcare, transportation, food insecurity, safety, literacy, social isolation, and perceived discrimination. Participant beliefs around the benefits of clinical trial participation was measured by rating level of agreement to 4 statements: ‘People in my community can benefit from participating in clinical trials’, ‘Participation in clinical trials benefits society’, ‘Participation in clinical trials is risky’, ‘I would personally benefit’, and ‘People in my community would benefit’. Response options were dichotomized as ‘agree’ and ‘disagree’ and adjusted stepwise logistic regression analyses were performed to assess factors associated with beliefs about clinical trials. Results: Race/ethnicity was significantly associated with all beliefs with Indigenous populations being significantly more likely to agree that people in their community could benefit from clinical trials, clinical trials benefit society, participation in clinical trials is risky, and that they would personally benefit. In adjusted analyses, financial instability, food insecurity, transportation, childcare, safety, social isolation, literacy, and discrimination were significantly associated with beliefs about the benefits of clinical trial participation. Neighborhood safety was most strongly associated with the belief that ‘People in my community can benefit from participating in clinical trials’ and ‘I would personally benefit from clinical trials’. Health literacy was most strongly associated with the belief that clinical trials benefit society and the community. Finally, the experience of 'people act as if they think you are not smart' (discrimination) was most strongly associated with the belief that clinical trials are risky. Conclusion: Clinical trials are the cornerstone to advancing equitable cancer care. Innovative, multifaceted, community-driven strategies are needed that address negative beliefs around the benefits of clinical trial participation to address existing recruitment and retention shortcomings. Citation Format: Jessica D. Austin, Todd Huschka, Amelia Barwise, Megan Allyse, Sean Phelan. The role of social determinants of health on beliefs around the benefits of clinical trials [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A077.

BACKGROUND:Telehealth has been increasingly adopted by health care systems since the start of the COVID-19 pandemic. Although telehealth may provide convenience for patients and clinicians, there are several barriers to accessing it and using it effectively to provide high-quality patient care.OBJECTIVE:This study was part of a larger multisite community-engaged study conducted to understand the impact of COVID-19 on diverse communities. The work described here explored the perceptions of and experience with telehealth use among diverse and underserved community members during COVID-19.METHODS:We used mixed methods across three regions in the United States (Midwest, Arizona, and Florida) from January to November 2021. We promoted our study through social media and community partnerships, disseminating flyers in English and Spanish. We developed a moderator guide and conducted focus groups in English and Spanish, mostly using a videoconferencing platform. Participants were placed in focus groups with others who shared similar demographic attributes and geographic location. Focus groups were audio-recorded and transcribed. We analyzed our qualitative data using the framework analytic approach. We developed our broader survey using validated scales and with input from community and scientific leaders, which was then distributed through social media in both English and Spanish. We included a previously published questionnaire that had been used to assess perceptions about telehealth among patients with HIV. We analyzed our quantitative data using SAS software and standard statistical approaches. We examined the effect of region, age, ethnicity/race, and education on the use and perceptions of telehealth.RESULTS:We included data from 47 focus groups. Owing to our mode of dissemination, we were not able to calculate a response rate for the survey. However, we received 3447 English-language and 146 Spanish-language responses. Over 90% of participants had internet access and 94% had used telehealth. Approximately half of all participants agreed or strongly agreed that telehealth would be beneficial in the future because it better fit their schedules and they would not need to travel. However, approximately half of the participants also agreed or strongly agreed they would not be able to express themselves well and could not be examined when using telehealth. Indigenous participants were especially concerned about these issues when compared to other racial groups.CONCLUSIONS:This work describes findings from a mixed methods community-engaged research study about telehealth, including perceived benefits and concerns. Although participants enjoyed the benefits of telehealth (eg, not having to travel and easier scheduling), they also had concerns (eg, not being able to express themselves well and not having a physical exam) about telehealth. These sentiments were especially notable among the Indigenous population. Our work highlights the importance of fully understanding the impact of these novel health delivery modalities on the patient experience and actual or perceived quality of care received.

BACKGROUND:Hospitals face the challenge of managing demand for limited computed tomography (CT) resources from multiple patient types while ensuring timely access.METHODS:A discrete event simulation model was created to evaluate CT access time for emergency department (ED) patients at a large academic medical center with six unique CT machines that serve unscheduled emergency, semi-scheduled inpatient, and scheduled outpatient demand. Three operational interventions were tested: adding additional patient transporters, using an alternative creatinine lab, and adding a registered nurse dedicated to monitoring CT patients in the ED.RESULTS:All interventions improved access times. Adding one or two transporters improved ED access times by up to 9.8 minutes (Mann-Whitney (MW) CI: [-11.0,-8.7]) and 10.3 minutes (MW CI [-11.5, -9.2]). The alternative creatinine and RN interventions provided 3-minute (MW CI: [-4.0, -2.0]) and 8.5-minute (MW CI: [-9.7, -8.3]) improvements.CONCLUSIONS:Adding one transporter provided the greatest combination of reduced delay and ability to implement. The projected simulation improvements have been realized in practice.

Objectives This study was conducted to describe patients at risk for prolonged time alone in the emergency department (ED) and to determine the relationship between clinical outcomes, specifically 30-day hospitalization, and patient alone time (PAT) in the ED. Methods An observational cohort design was used to evaluate PAT and patient characteristics in the ED. The study was conducted in a tertiary academic ED that has both adult and pediatric ED facilities and of patients placed in an acute care room for treatment between May 1 and July 31, 2016, excluding behavioral health patients. Simple linear regression and t tests were used to evaluate the relationship between patient characteristics and PAT. Logistic regression was used to evaluate the relationship between 30-day hospitalization and PAT. Results Pediatric patients had the shortest total PAT compared with all older age groups (86.4 minutes versus 131 minutes, P < 0.001). Relationships were seen between PAT and patient characteristics, including age, geographic region, and the severity and complexity of the health condition. Controlling for Charlson comorbidity index and other potentially confounding variables, a logistic regression model showed that patients are more likely to be hospitalized within 30 days after their ED visit, with an odds ratio (95% confidence interval) of 1.056 (1.017-1.097) for each additional hour of PAT. Conclusions Patient alone time is not equal among all patient groups. Study results indicate that PAT is significantly associated with 30-day hospitalization. This conclusion indicates that PAT may affect patient outcomes and warrants further investigation.

Objective There is no coordinated cascade testing program for familial hypercholesterolemia (FH) in the U.S. We evaluated the contemporary cost-effectiveness of cascade genetic testing relatives of FH probands with a pathogenic variant. Methods: A simulation model was created to simulate multiple family trees starting with progenitor individuals carrying a pathogenic variant for FH who were followed through several generations. This approach allowed us to examine a family tree that had grown sufficiently to have large numbers of relatives across multiple degrees of relatedness. The model estimated costs and life years gained (LYG) when cascade genetic testing was implemented for relatives of FH probands identified through standard care who were at or older than designated age thresholds (5, 10, 15, 20, 25, 30, 35, 40). Costs were valued in 2018 U.S. dollars. Future costs and LYG projected by the model were discounted at an annual rate of 3%. Results: For 1st degree relatives, cascade testing at every age threshold resulted in a positive number of average LYG per person, though this number decreased as testing was started at higher age thresholds. Testing was not cost-effective if initiated at an age threshold of 40 and older but was cost-effective at younger age thresholds, with a discounted cost per LYG per person of less than $50,000. For 2nd degree relatives, testing was cost-effective with a screening age threshold of 10 but no longer cost-effective at a threshold of 15 or higher. In more distant relatives, cascade genetic testing was not beneficial or cost-effective. Conclusions: Based on our simulation model, cascade genetic testing for FH in the U.S. is cost-effective if started before age 40 in 1st degree relatives and before age 15 in 2nd degree relatives.

Coordinated care is a burgeoning paradigm where patients receive diagnosis and treatment planning involving collaboration between two or more medical specialties to facilitate rapid and effective solutions to complex conditions.A key service metric for coordinated care organizations is how quickly they can move patients through their sequence of appointments at multiple clinical services in the network. Each patient's care path is uncertain when the appointment capacities are being planned, because information about the patient's condition evolves over the course of the patient's care process. Hence, the planning of root (first) appointments is the primary operational lever, as the other appointments evolve stochastically. In this work, we develop a discrete-time queueing network to optimize this root appointment allocation over a cyclic time horizon to maximize the proportion of patients that can complete their care by a class-dependent deadline. The model accounts for several salient features of coordinated care networks, including parallel appointment requests, stochastic paths, and time-varying features. We provide an {exact} characterization of the sojourn time in the network with a doubly-stochastic phase-type distribution and leverage a mean-field model with convergence guarantees to address intractability. We then develop a policy improvement framework that approximates the original stochastic optimization by a sequence of linear programs (LP), where the sojourn time model is parameterized in the policy evaluation step. The LPs are computationally efficient, and our algorithm can solve large-scale stochastic optimization for networks of realistic sizes (e.g., 26 service stations). In a case study of the Mayo Clinic, our solution improves on-time completion to more than 93\%, from 60\% under the current plan. We demonstrate that this is a multifaceted problem, and that ignoring any those facets can lead to poor performance. Simultaneously accounting for all these complexities makes manual template design challenging and highlights the practical significance of our optimization algorithm.

How are the populations of the world likely to shift? Which countries will be impacted by sea-level rise? This paper uses a country-level agent-based dynamic network model to examine shifts in population given network relations among countries, which influences overall population change. Some of the networks considered include: alliance networks, shared language networks, economic influence networks, and proximity networks. Validation of model is done for migration probabilities between countries, as well as for country populations and distributions. The proposed framework provides a way to explore the interaction between climate change and policy factors at a global scale. Bureau 2016). The age distribution is then shifted throughout the simulation through an aging process, as well as actual births and deaths in population. We then validate our model against data for migration probabilities, and country-level observations (population and age distributions). The results are promising, as we illustrate through performance measures such as average of prediction error.

Background: Familial hypercholesterolemia (FH) is an autosomal dominant disorder that significantly increases the risk of premature coronary heart disease (CHD). Unfortunately, no coordinated scree...

Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in the vineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, and increasing in grape losses rates. The objective of this research is to investigate how the variation in workers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost.

The prevailing first-come-first-served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non-urgent patients. This paper develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (1) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non-urgent patients, and (2) increase throughput by 31% with the same service levels and overtime.

Scientific research should be reproducible, and as such also simulation research. However, the question is – is this really the case? In some application areas of simulation, e.g., cell biology, simulation studies cannot be published without data, models, methods, including computer code being made available for evaluation. With the applications and methodological areas of modeling and simulation, how the problem of reproducibility is assessed and addressed differs. The diversity of answers to this question will be illuminated by looking into the area of network simulations, simulation in logistics, in military, and health. Making different scientific cultures, different challenges, and different solutions in discrete event simulation explicit is central to improving the reproducibility and thus quality of discrete event simulation research.

Nowadays, there is an increasing integration of methods from economics and psychology in simulation that allow more rigorous approaches to addressing behavioral issues. One of these approaches is the use of laboratory and field experiments of individual and group decision making concerning human judgment and decision-making under uncertainty. System Dynamics, as a simulation methodology, has been employed successfully as a behavioral experimental tool. Some researchers suggest that System Dynamics models are behavioral models of business systems which uncover intended rationality (theories in use) in business decision making. This tutorial offers an opportunity to explore the antecedents of System Dynamics as a behavioral simulation modeling method and offers examples of uses of System Dynamics in laboratory experiments, field experiments and evaluation of theories-in-use by decision makers.

Deploying wireless sensor networks (WSN) along a barrier line to provide surveillance against illegal intruders is a fundamental sensor-allocation problem. To maximize the detection probability of intruders with a limited number of sensors, we propose an integer non-linear program optimization model which considers multiple types of sensors and targets, probabilistic detection functions and sensor-reliability issues. An agent-based simulation (ABS) model is used to validate the analytic results and evaluate the performance of the WSN under more realistic conditions, such as intruders moving along random paths. Our experiment shows that the results from the optimization model are consistent with the results from the ABS model. This increases our confidence in the ABS model and allows us to conduct a further experiment using moving intruders, which is more realistic, but it is challenging to find an analytic solution. This experiment shows the complementary benefits of using optimization and ABS models.

Simulations are becoming ever more common as a tool for designing complex products. Sensitivity analysis techniques can be applied to these simulations to gain insight, or to reduce the complexity of the problem at hand. However, these simulators are often expensive to evaluate and sensitivity analysis typically requires a large amount of evaluations. Metamodeling has been successfully applied in the past to reduce the amount of required evaluations for design tasks such as optimization and design space exploration. In this paper, we propose a novel sensitivity analysis algorithm for variance and derivative based indices using sequential sampling and metamodeling. Several stopping criteria are proposed and investigated to keep the total number of evaluations minimal. The results show that both variance and derivative based techniques can be accurately computed with a minimal amount of evaluations using fast metamodels and FLOLA-Voronoi or density sequential sampling algorithms.

A well-functioning Emergency Medical Service (EMS) system is a fundamental requirement for saving lives in a Mass Casualty Incident (MCI). While the benefit of strengthening an EMS system is obvious, it is not so evident which components in an EMS system will most contribute to its performance. Using the Emergency Medical Service Simulation model (EMSSim), we test a hypothesis that the social infrastructure and geographic characteristics are key factors in determining the best strategy for the improvement of the EMS system of a particular region. Specifically, we investigate an MCI scenario in three regions – metropolitan, urban, and rural environments, and analyze the factors that will effectively enhance the EMS system in each of these regions.

Classical planning approaches of storage allocation decisions are often conducted iteratively with significant manual effort. Warehouse layouts are generated on the basis of planners’ experiences with the target to reduce the operators’ travel distances and thereby to increase productivity. By combining optimization and simulation in a software-based planning tool, a multitude of mathematically optimized storage allocation scenarios can be generated and analyzed to improve traditional planning approaches. This paper describes a practical case of a German automotive manufacturer’s warehouse allocation problem that is approached using an evolutionary meta-heuristic. The best solutions of the optimization are loaded into a large scale, automatically generated simulation model and evaluated using the company’s real-life data.

The vast majority of stochastic simulation models are imperfect in that they fail to fully emulate the entirety of real dynamics. Despite this, these imperfect models are still useful in practice, so long as one knows how the model is inexact. This inexactness is measured by a discrepancy between the proposed stochastic model and a true stochastic distribution across multiple values of some decision variables. In this paper, we propose a method to learn the discrepancy of a stochastic simulation using data collected from the system of interest. Our approach is a novel Bayesian framework that addresses the requirements for estimation of probability measures.