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Recognizing the role of human factors engineering (HFE) in the development of medical devices and combination products that involve devices, the Food and Drug Administration (FDA) now requires human factors (HF) validations before market approval. Manufacturers are responsible for ensuring their products are safe and effective through the application of HFE. However, key stakeholders are still learning and developing capabilities to adapt to the regulatory component. Nonetheless, the lack of the corresponding HF capabilities hinders compliance with the FDA’s expectations, and though ultimate success. No known previous work has looked into FDA HF validation projects to assess the underlying factors and implications of failed submissions. Applying system dynamics (SD), a causal loop diagram (CLD) was developed. CLDs are useful for the exploration of the causal interactions among factors or variables, as well as the underlying feedback structure of a complex system. This work can serve to help manufacturers better understand the FDA’s HF requirement to enable overall product success. Further, with patient safety as a common goal, HF service providers (HFSPs) and regulators should be aware of the need to ensure the consistent quality of the HF element in premarket submissions.

Background: Opioid addiction and overdose rates are reaching unprecedented levels in the U.S., with around 47,736 overdose deaths in 2017. Many stakeholders affect the opioid epidemic, including government entities, healthcare providers and policymakers, and opioid users. Simulation and conceptual modeling can help us understand the dynamics of the opioid epidemic by simplifying the real world and informing policymakers about different health interventions that could reduce the deaths caused by opioid overdose in the United States every year. Objectives: To conduct a scoping review of simulation and conceptual models that propose policies capable of controlling the opioid epidemic. We demonstrate the strengths and limitations of these models and provide a framework for further improvement of future decision support tools. Methods: Using the methodology of a scoping review, we identified articles published after 2000 from eight electronic databases to map the literature that uses simulation and conceptual modeling in developing public health policies to address the opioid epidemic. Results: We reviewed 472 papers of which 14 were appropriate for inclusion. Each used either system dynamics simulation modeling, mathematical modeling, conceptual modeling, or agent-based modeling. All included studies tested and proposed strategies to improve health outcomes related to the opioid epidemic. Factors considered in the models included physicians prescribing opioids, trafficking, users recruiting new users, and doctor shopping; no model investigated the impact of age and spatial factors on the dynamics of the epidemic. Key findings from these studies were (1) prevention of opioid initiation is better than treatment of opioid addiction, (2) the analysis of an intervention’s impact should include both benefits and harms, and (3) interventions with short-term benefits might have a counterproductive impact on the epidemic in long run. Conclusions: While most studies examined the role of prescription opioids and trafficking on this epidemic, the transition of patients from prescription opioid use to nonprescription use including heroin and synthetic opioids such as fentanyl impacts the system significantly and results in an epidemic with quite different characteristics than what it had a decade ago. We recommend including the impact of age and geographic location on the opioid epidemic using modeling methods.

The number of Lyme disease (LD) cases in the northeastern United States has been dramatically increasing with over 300 000 new cases each year. This is due to numerous factors interacting over time including low public awareness of LD, risk behaviours and clothing choices, ecological and climatic factors, an increase in rodents within ecologically fragmented peri-urban built environments and an increase in tick density and infectivity in such environments. We have used a system dynamics (SD) approach to develop a simulation tool to evaluate the significance of risk factors in replicating historical trends of LD cases, and to investigate the influence of different interventions, such as increasing awareness, controlling clothing risk and reducing mouse populations, in reducing LD risk. The model accurately replicates historical trends of LD cases. Among several interventions tested using the simulation model, increasing public awareness most significantly reduces the number of LD cases. This model provides recommendations for LD prevention, including further educational programmes to raise awareness and control behavioural risk. This model has the potential to be used by the public health community to assess the risk of exposure to LD.

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