Wayne W. Wakeland

An Analysis of the Root Causes for Opioid-Related Overdose Deaths in the United States

OBJECTIVE A panel of experts in pain medicine and public policy convened to examine root causes and risk factors for opioid-related poisoning deaths and to propose recommendations to reduce death rates. METHODS Panelists reviewed results from a search of PubMed and state and federal government sources to assess frequency, demographics, and risk factors for opioid-related overdose deaths over the past decade. They also reviewed results from a Utah Department of Health study and a summary of malpractice lawsuits involving opioid-related deaths. RESULTS National data demonstrate a pattern of increasing opioid-related overdose deaths beginning in the early 2000s. A high proportion of methadone-related deaths was noted. Although methadone represented less than 5% of opioid prescriptions dispensed, one third of opioid-related deaths nationwide implicated methadone. Root causes identified by the panel were physician error due to knowledge deficits, patient non-adherence to the prescribed medication regimen, unanticipated medical and mental health comorbidities, including substance use disorders, and payer policies that mandate methadone as first-line therapy. Other likely contributors to all opioid-related deaths were the presence of additional central nervous system-depressant drugs (e.g., alcohol, benzodiazepines, and antidepressants) and sleep-disordered breathing. CONCLUSIONS Causes of opioid-related deaths are multifactorial, so solutions must address prescriber behaviors, patient contributory factors, nonmedical use patterns, and systemic failures. Clinical strategies to reduce opioid-related mortality should be empirically tested, should not reduce access to needed therapies, should address risk from methadone as well as other opioids, and should be incorporated into any risk evaluation and mitigation strategies enacted by regulators.

Using Simulation to Evaluate Global Software Development Task Allocation Strategies

We describe a hybrid computer simulation model of the software development process that is specifically architected to study alternative ways to configure global software development (GSD) projects, including phase-based, module-based, and follow-the-sun allocation strategies. The model is a hybrid system dynamics and discrete-event model. In this paper, test cases have been developed for each allocation strategy, and project duration is computed for each configuration under a range of plausible assumptions for key parameters. The primary finding is that although under ideal assumptions follow-the-sun is able to produce impressive reductions in time-to-market, under more realistic assumptions the reverse is true, thus corroborating findings by other researchers. We also conducted a factorial design to examine the impact of GSD factors including distance, culture, language, trust, and time zone on project duration under different task allocation strategies. The analysis reveals that different factors affected the performance of the selected allocation strategies in unique ways. These findings show how the unique ability of our GSD model to represent detailed development processes and work artifact transfer allows researchers to address challenging questions that are critical to GSD project success. Copyright

A systems approach to stress, stressors and resilience in humans

The paper focuses on the biology of stress and resilience and their biomarkers in humans from the system science perspective. A stressor pushes the physiological system away from its baseline state toward a lower utility state. The physiological system may return toward the original state in one attractor basin but may be shifted to a state in another, lower utility attractor basin. While some physiological changes induced by stressors may benefit health, there is often a chronic wear and tear cost due to implementing changes to enable the return of the system to its baseline state and maintain itself in the high utility baseline attractor basin following repeated perturbations. This cost, also called allostatic load, is the utility reduction associated with both a change in state and with alterations in the attractor basin that affect system responses following future perturbations. This added cost can increase the time course of the return to baseline or the likelihood of moving into a different attractor basin following a perturbation. Opposite to this is the systems resilience which influences its ability to return to the high utility attractor basin following a perturbation by increasing the likelihood and/or speed of returning to the baseline state following a stressor. This review paper is a qualitative systematic review; it covers areas most relevant for moving the stress and resilience field forward from a more quantitative and neuroscientific perspective.

A comparison of system dynamics and agent-based simulation applied to the study of cellular receptor dynamics

Cellular receptor dynamics are often analyzed using differential equations, making system dynamics (SD) a candidate methodology. In some cases it may be useful to model the phenomena at the biomolecular level, especially when concentrations and reaction probabilities are low and might lead to unexpected behavior modes. In such cases, agent-based simulation (ABS) may be useful. We show the application of both SD and ABS to simulate non-equilibrium ligand-receptor dynamics over a broad range of concentrations, where the probability of interaction varies from low to very low. Both approaches offer much to the researcher and are complementary. We did not find a clear demarcation indicating when one paradigm or the other would be strongly preferred, although SD is an obvious choice when studying systems at a high level of aggregation and abstraction, and ABS is well suited to studying phenomena at the level of individual receptors and molecules.

The Use of Structural Modeling for Technology Assessment

Abstract Structural modeling (SM) techniques are a set of geometric, semi-quantitative tools that can assist in organizing a technology assessment (TA), developing a rough overview of it, and analyzing various component problems. In this project about 100 SM techniques were identified and seven were tested in detail: ISM, ELECTRE, SPIN, KSIM, QSIM, IMPACT, and XIMP. Guidelines were developed to help the assessor in the choice and proper use of such tools.

Complex analysis of intracranial hypertension using approximate entropy.

Objective:To determine whether decomplexification of intracranial pressure dynamics occurs during periods of severe intracranial hypertension (intracranial pressure >25 mm Hg for >5 mins in the absence of external noxious stimuli) in pediatric patients with intracranial hypertension. Design:Retrospective analysis of clinical case series over a 30-month period from April 2000 through January 2003. Setting:Multidisciplinary 16-bed pediatric intensive care unit. Patients:Eleven episodes of intracranial hypertension from seven patients requiring ventriculostomy catheter for intracranial pressure monitoring and/or cerebral spinal fluid drainage. Interventions:None. Measurements and Main Results:We measured changes in the intracranial pressure complexity, estimated by the approximate entropy (ApEn), as patients progressed from a state of normal intracranial pressure (<25 mm Hg) to intracranial hypertension. We found the ApEn mean to be lower during the intracranial hypertension period than during the stable and recovering periods in all the 11 episodes (0.5158 ± 0.0089, 0.3887 ± 0.077, and 0.5096 ± 0.0158, respectively, p < .01). Both the mean reduction in ApEn from the state of normal intracranial pressure (stable region) to intracranial hypertension (−0.1271) and the increase in ApEn from the ICH region to the recovering region (0.1209) were determined to be statistically significant (p < .01). Conclusions:Our results indicate that decreased complexity of intracranial pressure coincides with periods of intracranial hypertension in brain injury. This suggests that the complex regulatory mechanisms that govern intracranial pressure may be disrupted during acute periods of intracranial hypertension. This phenomenon of decomplexification of physiologic dynamics may have important clinical implications for intracranial pressure management.

Using design of experiments, sensitivity analysis, and hybrid simulation to evaluate changes to a software development process: a case study

Hybrid simulation models combine the high-level project issues of System Dynamics models with the process detail of discrete event simulation models. Hybrid models not only capture the best of both of these simulation paradigms, but they also are able to address new issues that are important in managing complex real-world development projects that neither the System Dynamics nor Discrete Event simulation paradigms are able to address alone. In order to reap the full benefits from a simulation model, a structured approach for analyzing model results is necessary. This article applies Design of Experiments (DOE) and broad range sensitivity analysis (BRSA) to a hybrid system dynamics and discrete event simulation model of a software development process. DOE is used to analyse the interaction effects, such as the degree to which the impact of the process change depends on worker motivation, schedule pressure and other project environmental variables. The sensitivity of the model to parameter changes over a broad range of plausible values is used to analyse the non-linear aspects of the model. The end result is a deeper insight into the conditions under which the process change will succeed, and improved recommendations for process change design and implementation. In this particular study, significant interactions and non-linearities were revealed, supporting the hypothesis that consideration of these complex effects is essential for insightful interpretation of model results and effective decision-making. Copyright

Planning and improving global software development process using simulation

Global software development poses a number of challenges and difficulties as well as significant potential benefits. In order to be successful, companies need to adapt and improve their processes to support this kind of development. Strong project planning and management is also required. Software process simulation modeling has been used to address a variety of issues in software development projects ranging from strategic management, project planning and control, process improvement, to training and understanding. We believe that a hybrid simulation model combining system dynamics and discrete-event models is needed to effectively model global software development projects. In this paper, we describe such a model, and focus on the use of the model to support project planning and process improvement in global software development. Example questions/issues that can be addressed by our GSD model are provided, along with an illustrative application.

Food transportation issues and reducing carbon footprint

Transportation is the largest overall contributor to greenhouse gas emissions in many developed countries. Because food products often travel long distances to reach consumers, one might expect transportation to be the major food-related contributor to greenhouse gas emissions. As we show in this chapter, this is rarely the case. Supply chains tend to be long and complex, especially in the food industry. We provide an overview of supply chain basics and the special issues related to transport and storage of food from the point of production to the retail shelf. We include a variety of carbon audits and discuss the lessons learned from several industry case studies. We evaluate several options for packaging and distributing food and beverage products. We also consider interactions and trade-offs associated with these options. In carrying out these analyses, we are often able to identify opportunities for significant energy savings and overall greenhouse gas emissions reductions.

A review of physiological simulation models of intracranial pressure dynamics

This paper reviews the literature regarding the development, testing, and application of physiology-based computer simulation models of intracranial pressure dynamics. Detailed comparative information is provided in tabular format about the model variables and logic, any data collected, model testing and validation methods, and model results. Several syntheses are given that summarize the research carried out by influential research teams and researchers, review important findings, and discuss the methods employed, limitations, and opportunities for further research.