Protocol for an interview-based method for mapping mental models using causal-loop diagramming and realist interviewing

Causal-loop diagramming, a method from system dynamics, is increasingly used in evaluation to describe individuals' understanding of how policies or programs do or could work ("mental models"). The use of qualitative interviews to inform model development is common, but guidance for how to design and conduct these interviews to elicit causal information in participant mental models is scant. A key strength of semi-structured qualitative interviews is that they let participants speak freely; they are not, however, designed to elicit causal information. Moreover, much of human communication about mental models-particularly larger causal structures such as feedback loops-is implicit. In qualitative research, part of the skill and art of effective interviewing and analysis involves listening for information that is expressed implicitly. Similarly, a skilled facilitator can recognize and inquire about implied causal structures, as is commonly done in group model building. To standardize and make accessible these approaches, we have formalized a protocol for designing and conducting semi-structured interviews tailored to eliciting mental models using causal-loop diagramming. We build on qualitative research methods, system dynamics, and realist interviewing. This novel, integrative method is designed to increase transparency and rigor in the use of interviews for system dynamics and has a variety of potential applications.

System dynamics modeling for cancer prevention and control: A systematic review

Cancer prevention and control requires consideration of complex interactions between multilevel factors. System dynamics modeling, which consists of diagramming and simulation approaches for understanding and managing such complexity, is being increasingly applied to cancer prevention and control, but the breadth, characteristics, and quality of these studies is not known. We searched PubMed, Scopus, APA PsycInfo, and eight peer-reviewed journals to identify cancer-related studies that used system dynamics modeling. A dual review process was used to determine eligibility. Included studies were assessed using quality criteria adapted from prior literature and mapped onto the cancer control continuum. Characteristics of studies and models were abstracted and qualitatively synthesized. 32 studies met our inclusion criteria. A mix of simulation and diagramming approaches were used to address diverse topics, including chemotherapy treatments (16%), interventions to reduce tobacco or e-cigarettes use (16%), and cancer risk from environmental contamination (13%). Models spanned all focus areas of the cancer control continuum, with treatment (44%), prevention (34%), and detection (31%) being the most common. The quality assessment of studies was low, particularly for simulation approaches. Diagramming-only studies more often used participatory approaches. Involvement of participants, description of model development processes, and proper calibration and validation of models showed the greatest room for improvement. System dynamics modeling can illustrate complex interactions and help identify potential interventions across the cancer control continuum. Prior efforts have been hampered by a lack of rigor and transparency regarding model development and testing. Supportive infrastructure for increasing awareness, accessibility, and further development of best practices of system dynamics for multidisciplinary cancer research is needed.

System dynamics modeling for traumatic brain injury: Mini-review of applications

Traumatic brain injury (TBI) is a highly complex phenomenon involving a cascade of disruptions across biomechanical, neurochemical, neurological, cognitive, emotional, and social systems. Researchers and clinicians urgently need a rigorous conceptualization of brain injury that encompasses nonlinear and mutually causal relations among the factors involved, as well as sources of individual variation in recovery trajectories. System dynamics, an approach from systems science, has been used for decades in fields such as management and ecology to model nonlinear feedback dynamics in complex systems. In this mini-review, we summarize some recent uses of this approach to better understand acute injury mechanisms, recovery dynamics, and care delivery for TBI. We conclude that diagram-based approaches like causal-loop diagramming have the potential to support the development of a shared paradigm of TBI that incorporates social support aspects of recovery. When developed using adequate data from large-scale studies, simulation modeling presents opportunities for improving individualized treatment and care delivery.

Reducing opioid use disorder and overdose deaths in the United States: A dynamic modeling analysis

Opioid overdose deaths remain a major public health crisis. We used a system dynamics simulation model of the U.S. opioid-using population age 12 and older to explore the impacts of 11 strategies on the prevalence of opioid use disorder (OUD) and fatal opioid overdoses from 2022 to 2032. These strategies spanned opioid misuse and OUD prevention, buprenorphine capacity, recovery support, and overdose harm reduction. By 2032, three strategies saved the most lives: (i) reducing the risk of opioid overdose involving fentanyl use, which may be achieved through fentanyl-focused harm reduction services; (ii) increasing naloxone distribution to people who use opioids; and (iii) recovery support for people in remission, which reduced deaths by reducing OUD. Increasing buprenorphine providers' capacity to treat more people decreased fatal overdose, but only in the short term. Our analysis provides insight into the kinds of multifaceted approaches needed to save lives.

Validation of a spatial agent-based model for Taenia solium transmission ("CystiAgent") against a large prospective trial of control strategies in northern Peru

BACKGROUND

The pork tapeworm (Taenia solium) is a parasitic helminth that imposes a major health and economic burden on poor rural populations around the world. As recognized by the World Health Organization, a key barrier for achieving control of T. solium is the lack of an accurate and validated simulation model with which to study transmission and evaluate available control and elimination strategies. CystiAgent is a spatially-explicit agent based model for T. solium that is unique among T. solium models in its ability to represent key spatial and environmental features of transmission and simulate spatially targeted interventions, such as ring strategy.

METHODS/PRINCIPAL FINDINGS

We validated CystiAgent against results from the Ring Strategy Trial (RST)-a large cluster-randomized trial conducted in northern Peru that evaluated six unique interventions for T. solium control in 23 villages. For the validation, each intervention strategy was replicated in CystiAgent, and the simulated prevalences of human taeniasis, porcine cysticercosis, and porcine seroincidence were compared against prevalence estimates from the trial. Results showed that CystiAgent produced declines in transmission in response to each of the six intervention strategies, but overestimated the effect of interventions in the majority of villages; simulated prevalences for human taenasis and porcine cysticercosis at the end of the trial were a median of 0.53 and 5.0 percentages points less than prevalence observed at the end of the trial, respectively.

CONCLUSIONS/SIGNIFICANCE

The validation of CystiAgent represented an important step towards developing an accurate and reliable T. solium transmission model that can be deployed to fill critical gaps in our understanding of T. solium transmission and control. To improve model accuracy, future versions would benefit from improved data on pig immunity and resistance, field effectiveness of anti-helminthic treatment, and factors driving spatial clustering of T. solium infections including dispersion and contact with T. solium eggs in the environment.

Understanding transmission and control of the pork tapeworm with CystiAgent: a spatially explicit agent-based model

BACKGROUND

The pork tapeworm, Taenia solium, is a serious public health problem in rural low-resource areas of Latin America, Africa and Asia, where the associated conditions of nuerocysticercosis (NCC) and porcine cysticercosis cause substantial health and economic harms. An accurate and validated transmission model for T. solium would serve as an important new tool for control and elimination, as it would allow for comparison of available intervention strategies, and prioritization of the most effective strategies for control and elimination efforts.

METHODS

We developed a spatially-explicit agent-based model (ABM) for T. solium ("CystiAgent") that differs from prior T. solium models by including a spatial framework and behavioral parameters such as pig roaming, open human defecation, and human travel. In this article, we introduce the structure and function of the model, describe the data sources used to parameterize the model, and apply sensitivity analyses (Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC)) to evaluate model parameters.

RESULTS

LHS-PRCC analysis of CystiAgent found that the parameters with the greatest impact on model uncertainty were the roaming range of pigs, the infectious duration of human taeniasis, use of latrines, and the set of "tuning" parameters defining the probabilities of infection in humans and pigs given exposure to T. solium.

CONCLUSIONS

CystiAgent is a novel ABM that has the ability to model spatial and behavioral features of T. solium transmission not available in other models. There is a small set of impactful model parameters that contribute uncertainty to the model and may impact the accuracy of model projections. Field and laboratory studies to better understand these key components of transmission may help reduce uncertainty, while current applications of CystiAgent may consider calibration of these parameters to improve model performance. These results will ultimately allow for improved interpretation of model validation results, and usage of the model to compare available control and elimination strategies for T. solium.

A dynamic model of the opioid drug epidemic with implications for policy

Background: The U.S. opioid epidemic has caused substantial harm for over 20 years. Policy interventions have had limited impact and sometimes backfired. Experts recommend a systems modeling approach to address the complexities of opioid policymaking.Objectives: Develop a system dynamics simulation model that reflects the complexities and can anticipate intended and unintended intervention effects.Methods: The model was developed from literature review and data gathering. Its outputs, starting in 1990, were compared against 12 historical time series. Four illustrative interventions were simulated for 2020-2030: reducing prescription dosage by 20%, cutting diversion by 30%, increasing addiction treatment from 45% to 65%, and increasing lay naloxone use from 4% to 20%. Sensitivity testing was performed to determine effects of uncertainties. No human subjects were studied.Results: The model fits historical data well with error percentage averaging 9% across 201 data points. Interventions to reduce dosage and diversion reduce the number of persons with opioid use disorder (PWOUD) by 11% and 16%, respectively, but each of these interventions reduces overdoses by only 1%. Boosting treatment reduces overdoses by 3% but increases PWOUD by 1%. Expanding naloxone reduces overdose deaths by 12% but increases PWOUD by 2% and overdoses by 3%. Combining all four interventions reduces PWOUD by 24%, overdoses by 4%, and deaths by 18%. Uncertainties may affect these numerical results, but policy findings are unchanged.Conclusion: No single intervention significantly reduces both PWOUD and overdose deaths, but a combination strategy can do so. Entering the 2020s, only protective measures like naloxone expansion could significantly reduce overdose deaths.

Diverse Autonomic Nervous System Stress Response Patterns in Childhood Sensory Modulation

The specific role of the autonomic nervous system (ANS) in emotional and behavioral regulation—particularly in relation to automatic processes—has gained increased attention in the sensory modulation literature. This mini-review article summarizes current knowledge about the role of the ANS in sensory modulation, with a focus on the integrated functions of the ANS and the hypothalamic-pituitary-adrenal (HPA) axis and their measurement. Research from the past decade illustrates that sympathetic and parasympathetic interactions are more complex than previously assumed. Patterns of ANS activation vary across individuals, with distinct physiological response profiles influencing the reactivity underlying automatic behavioral responses. This review article advances a deeper understanding of stress and the complex stress patterns within the ANS and HPA axis that contribute to allostatic load (AL). We argue that using multiple physiological measurements to capture individual ANS response variation is critical for effectively treating children with sensory modulation disorder (SMD) and sensory differences. We consider the relative contributions of automatic vs. deliberately controlled processes across large-scale neural networks in the development of sensorimotor function and their associated links with arousal patterns and sensory over- and under-responsivity.

Seasonal patterns in risk factors for Taenia solium transmission: a GPS tracking study of pigs and open human defecation in northern Peru

Background

Taenia solium (cysticercosis) is a parasitic cestode that is endemic in rural populations where open defecation is common and free-roaming pigs have access to human feces. The purpose of this study was to examine the roaming patterns of free-range pigs, and identify areas where T. solium transmission could occur via contact with human feces. We did this by using GPS trackers to log the movement of 108 pigs in three villages of northern Peru. Pigs were tracked for approximately six days each and tracking was repeated in the rainy and dry seasons. Maps of pig ranges were analyzed for size, distance from home, land type and contact with human defecation sites, which were assessed in a community-wide defecation survey.

Results

Consistent with prior GPS studies and spatial analyses, we found that the majority of pigs remained close to home during the tracking period and had contact with human feces in their home areas: pigs spent a median of 79% (IQR: 61–90%) of their active roaming time within 50 m of their homes and a median of 60% of their contact with open defecation within 100 m of home. Extended away-from-home roaming was predominately observed during the rainy season; overall, home range areas were 61% larger during the rainy season compared to the dry season (95% CI: 41–73%). Both home range size and contact with open defecation sites showed substantial variation between villages, and contact with open defecation sites was more frequent among pigs with larger home ranges and pigs living in higher density areas of their village.

Conclusions

Our study builds upon prior work showing that pigs predominately roam and have contact with human feces within 50–100 m of the home, and that T. solium transmission is most likely to occur in these concentrated areas of contact. This finding, therefore, supports control strategies that target treatment resources to these areas of increased transmission. Our finding of a seasonal trend in roaming ranges may be useful for control programs relying on pig interventions, and in the field of transmission modeling, which require precise estimates of pig behavior and risk.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3614-5) contains supplementary material, which is available to authorized users.

System Dynamics to Investigate Opioid Use and Chiropractic Care for Chronic Musculoskeletal Pain

OBJECTIVE

The purpose of this investigation was to create a system dynamics (SD) model, including published data and required assumptions, as a tool for future research identifying the role of chiropractic care in the management of chronic, nonmalignant pain in a Canadian population.

METHODS

We present an illustrative case description of how we evaluated the feasibility of conducting a large-scale clinical trial to assess the impact of chiropractic care in mitigating excessive opioid use in Canada. We applied SD modeling using current evidence and key assumptions where such evidence was lacking. Modeling outcomes were highlighted to determine which potential factors were necessary to account for compelling study designs.

RESULTS

Results suggest that a future clinical study diverting patients with nonmalignant musculoskeletal pain early to the chiropractic stream of care could be most effective. System dynamics modeling also highlighted design challenges resulting from unresearched assumptions that needed to be proxied for model completion. Assumptions included changing rates in opioid-associated deaths and rates of success in treatment management of addicted patients.

CONCLUSION

In this case, SD modeling identified current research gaps and strong contenders for appropriate follow-up questions in a clinical research domain, namely the role of chiropractic care in the management of chronic, nonmalignant pain in a Canadian population.

Teen driver system modeling: a tool for policy analysis

BACKGROUND

Motor vehicle crashes remain the leading cause of teen deaths in spite of preventive efforts. Prevention strategies could be advanced through new analytic approaches that allow us to better conceptualize the complex processes underlying teen crash risk. This may help policymakers design appropriate interventions and evaluate their impacts.

METHODS

System Dynamics methodology was used as a new way of representing factors involved in the underlying process of teen crash risk. Systems dynamics modeling is relatively new to public health analytics and is a promising tool to examine relative influence of multiple interacting factors in predicting a health outcome. Dynamics models use explicit statements about the process being studied and depict how the elements within the system interact; this usually leads to discussion and improved insight. A Teen Driver System Model was developed by following an iterative process where causal hypotheses were translated into systems of differential equations. These equations were then simulated to test whether they can reproduce historical teen driving data. The Teen Driver System Model that we developed was calibrated on 47 newly-licensed teen drivers. These teens were recruited and followed over a period of 5-months. A video recording system was used to gather data on their driving events (elevated g-force, near-crash, and crash events) and miles traveled.

RESULTS

The analysis suggests that natural risky driving improvement curve follows a course of a slow improvement, then a faster improvement, and finally a plateau: that is, an S-shaped decline in driving events. Individual risky driving behavior depends on initial risk and driving exposure. Our analysis also suggests that teen risky driving improvement curve is created endogenously by several feedback mechanisms. A feedback mechanism is a chain of variables interacting with each other in such a way they form a closed path of cause and effect relationships.

CONCLUSIONS

Teen risky driving improvement process is created endogenously by several feedback mechanisms. The model proposed in the present article to reflect this improvement process can spark discussion, which may pinpoint to additional processes that can benefit from further empirical research and result in improved insight.

Opioid-Prescribing Continuity and Risky Opioid Prescriptions

We aimed to better understand the association between opioid-prescribing continuity, risky prescribing patterns, and overdose risk. For this retrospective cohort study, we included patients with long-term opioid use, pulling data from Oregon's Prescription Drug Monitoring Program (PDMP), vital records, and hospital discharge registry. A continuity of care index (COCI) score was calculated for each patient, and we defined metrics to describe risky prescribing and overdose. As prescribing continuity increased, likelihood of filling risky opioid prescriptions and overdose hospitalization decreased. Prescribing continuity is an important factor associated with opioid harms and can be calculated using administrative pharmacy data.

The Dynamics of Concussion: Mapping Pathophysiology, Persistence, and Recovery With Causal-Loop Diagramming

Despite increasing public awareness and a growing body of literature on the subject of concussion, or mild traumatic brain injury, an urgent need still exists for reliable diagnostic measures, clinical care guidelines, and effective treatments for the condition. Complexity and heterogeneity complicate research efforts and indicate the need for innovative approaches to synthesize current knowledge in order to improve clinical outcomes. Methods from the interdisciplinary field of systems science, including models of complex systems, have been increasingly applied to biomedical applications and show promise for generating insight for traumatic brain injury. The current study uses causal-loop diagramming to visualize relationships between factors influencing the pathophysiology and recovery trajectories of concussive injury, including persistence of symptoms and deficits. The primary output is a series of preliminary systems maps detailing feedback loops, intrinsic dynamics, exogenous drivers, and hubs across several scales, from micro-level cellular processes to social influences. Key system features, such as the role of specific restorative feedback processes and cross-scale connections, are examined and discussed in the context of recovery trajectories. This systems approach integrates research findings across disciplines and allows components to be considered in relation to larger system influences, which enables the identification of research gaps, supports classification efforts, and provides a framework for interdisciplinary collaboration and communication-all strides that would benefit diagnosis, prognosis, and treatment in the clinic.

Concussion As a Multi-Scale Complex System: An Interdisciplinary Synthesis of Current Knowledge

Traumatic brain injury (TBI) has been called "the most complicated disease of the most complex organ of the body" and is an increasingly high-profile public health issue. Many patients report long-term impairments following even "mild" injuries, but reliable criteria for diagnosis and prognosis are lacking. Every clinical trial for TBI treatment to date has failed to demonstrate reliable and safe improvement in outcomes, and the existing body of literature is insufficient to support the creation of a new classification system. Concussion, or mild TBI, is a highly heterogeneous phenomenon, and numerous factors interact dynamically to influence an individual's recovery trajectory. Many of the obstacles faced in research and clinical practice related to TBI and concussion, including observed heterogeneity, arguably stem from the complexity of the condition itself. To improve understanding of this complexity, we review the current state of research through the lens provided by the interdisciplinary field of systems science, which has been increasingly applied to biomedical issues. The review was conducted iteratively, through multiple phases of literature review, expert interviews, and systems diagramming and represents the first phase in an effort to develop systems models of concussion. The primary focus of this work was to examine concepts and ways of thinking about concussion that currently impede research design and block advancements in care of TBI. Results are presented in the form of a multi-scale conceptual framework intended to synthesize knowledge across disciplines, improve research design, and provide a broader, multi-scale model for understanding concussion pathophysiology, classification, and treatment.

Key Data Gaps Regarding the Public Health Issues Associated with Opioid Analgesics

Most pharmaceutical opioids are used to treat pain, and they have been demonstrated to be effective medications for many. Their abuse and misuse pose significant public health concerns in the USA. Research has provided much insight into the prevalence, scope, and drivers of opioid abuse, but a holistic understanding is limited by a lack of available data regarding key aspects of this public health problem. Twelve data gaps were revealed during the creation of a systems-level computer model of medical use, diversion, nonmedical use, and the adverse outcomes associated with opioid analgesics in the USA. Data specific to these gaps would enhance the validity and real-world applications of systems-level models of this public health problem and would increase understanding of the complex system in which use and abuse occur. This paper provides an overview of these gaps, argues for the importance of closing them, and provides specific recommendations for future data collection efforts.

Dynamic model of nonmedical opioid use trajectories and potential policy interventions

BACKGROUND

Nonmedical use of pharmaceutical opioid analgesics (POA) increased dramatically over the past two decades and remains a major health problem in the United States, contributing to over 16 000 accidental poisoning deaths in 2010.

OBJECTIVES

To create a systems-oriented theory/model to explain the historical behaviors of interest, including the various populations of nonmedical opioid users and accidental overdose mortality within those populations. To use the model to explore policy interventions including tamper-resistant drug formulations and strategies for reducing diversion of opioid medicines.

METHODS

A system dynamics model was constructed to represent the population of people who initiate nonmedical POA usage. The model incorporates use trajectories including development of use disorders, transitions from reliance on informal sharing to paying for drugs, transition from oral administration to tampering to facilitate non-oral routes of administration, and transition to heroin use by some users, as well as movement into and out of the population through quitting and mortality. Empirical support was drawn from national surveys (NSDUH, TEDS, MTF, and ARCOS) and published studies.

RESULTS

The model was able to replicate the patterns seen in the historical data for each user population, and the associated overdose deaths. Policy analysis showed that both tamper-resistant formulations and interventions to reduce informal sharing could significantly reduce nonmedical user populations and overdose deaths in the long term, but the modeled effect sizes require additional empirical support.

CONCLUSION

Creating a theory/model that can explain system behaviors at a systems level scale is feasible and facilitates thorough evaluation of policy interventions.

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 system's 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.

Data Sources Regarding the Nonmedical Use of Pharmaceutical Opioids in the United States

OBJECTIVE

Recent increases in the nonmedical use of pharmaceutical opioids and the adverse outcomes associated with them have stimulated a large amount of research and data collection on this public health problem. Systematic organization of the available data sources is needed to facilitate ongoing research, analysis, and evaluation. This work offers a systematic categorization of data sources regarding the nonmedical use of pharmaceutical opioids in the United States.

METHODS

A list of keywords regarding the nonmedical use of pharmaceutical opioids was used to conduct systematic searches in PubMed®. Filtration of search results generated 92 peer-reviewed academic articles, published between January 1995 and April 2012, as well as a number of primary data sources. Lists of topics were developed independently by two researchers which were later compared and consolidated. All sources were then categorized according to their relevance to each of these topics and according to their capacity for geographical and longitudinal trend analysis.

RESULTS

Tables cataloging data sources can be used to identify data relevant to specific topics in diversion, nonmedical use, and adverse outcomes associated with pharmaceutical opioids, and they illustrate global trends in data coverage, identifying several topics that have minimal data. A network diagram illustrates global trends in data coverage, showing variation among sources in the number of topics they cover, as well as variation among topics in the number of sources that cover them.

CONCLUSIONS

The categorization of data sources is hoped to facilitate ongoing research, analysis, and evaluation of this public health problem by serving as a guide for researchers, policy makers, and others who seek data regarding the nonmedical use of pharmaceutical opioids in the United States.

Modeling the impact of simulated educational interventions on the use and abuse of pharmaceutical opioids in the United States: a report on initial efforts

Three educational interventions were simulated in a system dynamics model of the medical use, trafficking, and nonmedical use of pharmaceutical opioids. The study relied on secondary data obtained in the literature for the period of 1995 to 2008 as well as expert panel recommendations regarding model parameters and structure. The behavior of the resulting systems-level model was tested for fit against reference behavior data. After the base model was tested, logic to represent three educational interventions was added and the impact of each intervention on simulated overdose deaths was evaluated over a 7-year evaluation period, 2008 to 2015. Principal findings were that a prescriber education intervention not only reduced total overdose deaths in the model but also reduced the total number of persons who receive opioid analgesic therapy, medical user education not only reduced overdose deaths among medical users but also resulted in increased deaths from nonmedical use, and a "popularity" intervention sharply reduced overdose deaths among nonmedical users while having no effect on medical use. System dynamics modeling shows promise for evaluating potential interventions to ameliorate the adverse outcomes associated with the complex system surrounding the use of opioid analgesics to treat pain.

From Lundberg to SIM-ICP: Computational Physiology and Modeling Intracranial Pressure

A simplified dynamic model generates patient-specific estimates of intracranial pressure and suggests a path forward for bringing simulated (SIM) physiologic models to the patient’s bedside.

System dynamics modeling as a potentially useful tool in analyzing mitigation strategies to reduce overdose deaths associated with pharmaceutical opioid treatment of chronic pain

OBJECTIVE

To illustrate a system-level, simulation-based approach for evaluating mitigation strategies to address the dramatic rise in abuse, addiction, and overdose deaths associated with the use of pharmaceutical opioid analgesics to treat chronic pain. SIMULATED INTERVENTIONS: Making available drug formulations with increased tamper-resistance, prescriber education programs, and programs that reduce rates of medical user-related abuse and addiction. SIMULATED OUTCOME MEASURE: Number of overdose deaths of medical users of pharmaceutical opioid analgesics, including those who abuse or have become addicted.

METHODS

A demonstration system dynamics model is developed, tested, and used to evaluate the impact of candidate mitigation strategies on the outcome measures.

RESULTS

Tamper-resistant drug products will likely reduce overdose death rates but may not reduce overall deaths if there is increased prescribing. Prescriber education would likely reduce deaths through a reduction in patient access to pharmaceutical opioid analgesics.

CONCLUSIONS

The system dynamics approach may have potential for opioid-related policy evaluation. However, metrics must be carefully selected, and trade-offs may be involved. For example, it may be difficult to limit negative outcomes associated with pharmaceutical opioids without adversely affecting chronic pain patients' access to pharmaceutical treatment. Ultimately, a combination of metrics and value judgments will be needed to properly evaluate mitigation strategies.

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.

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.

Calibrating an intracranial pressure dynamics model with clinical data - a progress report

We describe the calibration of a computer model of intracranial pressure (ICP) dynamics to correspond with annotated clinical data taken from a patient being treated for elevated ICP due to a traumatic brain injury. The research protocol employed during treatment includes adjusting the elevation of the head of the bed, adjusting the ventilator settings to induce mild hyperventilation and hypoventilation, and adjusting the height of the cerebrospinal fluid drainage system. The model behavior corresponds to the experimental data quite well in the case of the changing the head of the bed, but less well in the case of changing the ventilator settings.

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.

Pulse and mean intracranial pressure analysis in pediatric traumatic brain injury

OBJECTIVE

We investigated the relationship between the intracranial pulse pressure (ICPpp) and the mean intracranial pressure (ICP(M)) in pediatric patients with traumatic brain injury (TBI).

METHODS

We screened ICP records of 42 patients admitted to the Pediatric Intensive Care Unit at Doernbecher Children's Hospital (OHSU) for segments in which the ICPM varied at least 5 mmHg. We found 54 ICP segments in 9 pediatric TBI patients (ages 0.2-17.8 years, mean = 9.9). ICP was continuously monitored (fs = 125 Hz). We used an automatic algorithm to detect ICP beat components. We then calculated the ICPpp and ICPM for each beat and created density plots of ICPpp vs. ICPM.

RESULTS

The coefficient of linear correlation was r > 0.70 in 43/54 segments (p < 0.01). We found that an underlying linear relationship exits between ICPpp and ICPM in most 1-hour records of pediatric patients with TBI. This finding is consistent with the data in adult studies, suggesting that children with TBI demonstrate similar changes in brain compliance. However, density plots revealed that there are also nonlinear ICPpp-ICPM patterns present that are not captured by linear metrics.

CONCLUSION

Although there is an underlying linear relationship between ICPpp and ICPM, nonlinear patterns are also present. Further research is required to determine if specific nonlinear ICPpp-ICPM patterns correlate with clinically significant information.

A computer model of intracranial pressure dynamics during traumatic brain injury that explicitly models fluid flows and volumes

A model of intracranial pressure (ICP) dynamics that uses fluid volumes as primary state variables is presented, along with clinical data for two subjects with elevated ICP. The data includes annotations to indicate the precise timing of clinical changes in cerebral spinal fluid drainage, head of bed elevation, and minute ventilation. The response to changes in the clinical parameters was used to calibrate the model to correspond to specific subjects by estimating values for key characteristics such as hematoma volume and CSF uptake resistance. The error in mean ICP predicted by the model was less than 2 mmHg when cerebral spinal fluid is drained and the head of bed elevation was increased. The error in mean ICP predicted by the model exceeded 5 mmHg during an episode when the head of bed was decreased and also during a reduction in minute ventilation. The estimated values for hematoma volume and other subject characteristics were plausible but could not be verified empirically.