Wakeland et al. (2016) carried out extensive research on the system dynamics simulation model of the opium system to evaluate critical points and relevant medical policies for intervention without restricting the use of opioids for medical purposes. The number of deaths caused by the abuse of pharmaceutical opioids has tremendously increased in less than ten years. Diversion of these drugs from pharmaceutical facilities to other parties such as friends and relatives is responsible for the increased misuse and abuse of medical opioids. Policymakers have put the effort into creating and adjusting medical policies to regulate the abuse, though unsuccessfully as the policy-making systems are complex. Many interactions require consideration during the interventions. This model focused on the changed medical use of pharmaceutical opioids, their diversion to non-medical usage, and the extent of their misuse. Moreover, within the framework of this issue, policymakers may also be one of the beneficiaries. So, for them, to reduce the level of adverse effects due to opioids, the structure of the described model seems to be the most effective.
The model’s loop has five main feedback loops whereby three are linked to the dynamics of two pain relievers used the medical and non-medical users, while the other three analyze the supply and demand of these opioids. The loop differentiates medical and non-medical users and describes the chain through which the drugs move from doctors to non-medical users (Wakeland et al., 2016). Additionally, the loop helps understand the interaction of the demand and supply of opioids to benefit both non-medical users and opioid traffickers from the trade. Fractions and dwell times regulating the stock-flow are then plausibly adjusted to create a balance in the feedback loops that are neither principally demand-controlled nor supply-controlled. After making the necessary adjustments, the model can determine fruitful interventions for the prevailing situation.
The importance of this study lies in several factors that are in one way or another related to the use of opioid drugs. Thus, it becomes possible to identify some dependence, in which among patients diagnosed with a chronic pain condition, those who have been prescribed treatment with opioid painkillers increase. However, opioids have a certain property that leads to the fact that patients become dependent on this drug. Moreover, those patients who take the drugs as prescribed and those who abuse them have a risk of dying from an overdose. The so-called “opioid popularity loop” suggests that more people are becoming non-medical users of the drug (Wakeland et al., 2016). As a result, the popularity of such a substance as an opioid among the population has increased.
The unavailability of independent variables limited their model testing stage; hence sensitive parameters were conducted on all forty-nine parameters to establish their effect on the primary outcomes. According to Wakeland et al. (2016), every parameter varied by thirty percent as per the model design limits and was carefully tuned to achieve correct reference behavior. Previous policies for curbing opioid misuse were then simulated and adjusted using the model, thus striking a balance between the supply and demand. Some of the research policies included popular intervention, tamper resistance, prescription monitoring program intervention, and a multifaceted approach. The tamper resistance and prescription monitoring program interventions were affected by limits of growth, such as insufficient resources. Additionally, the tamper resistance and multiphase ted approach required careful selection and analysis of metrics that may affect policy changes. The popularity intervention occurred as part of a decline in the popularity of opioids for those who use the drug for non-medical purposes (Wakeland et al., 2016). In this context, a sharp reduction in both opioid users and the number of overdose deaths has been achieved.
Discussion and Limitations
The competent researchers on this SD model compared their predictions with the new historical reality, and the relations between them were promising. The system dynamics simulations eased the versions of the fundamental policies, while the predicted and actual trends were almost similar. During the research, the primary limitation was the parameter validity, whereby there were various data gaps. Wakeland et al. (2016) noted that the model focused on trafficking and opioids for chronic pain treatment. It was a limitation as it did not consider the opioids trafficked as treatment of acute pain. To address these limitations, the implementation of the policy must be significantly strengthened (Wakeland et al., 2016). At the same time, experiments conducted within the framework of this model should better demonstrate the consequences of the policy. The limitations, as mentioned earlier, required in-depth examination in future works for the model to be regarded as viable.
In conclusion, the system dynamics simulation model is promising as a policy simulator. Feedback structures that cause interest conflicts are usually prone to craft interventions. Unlike other large data models that make the small systems within them complex to comprehend, this model simplifies the large volumes of data into five feedback loops. Therefore, the SD model can be credited as it facilitates communication between the modelers and the policyholders.
Wakeland, W., Nielsen, A., & Schmidt, T. D. (2016). Gaining policy insight with a system dynamics model of pain medicine prescribing, diversion and abuse. Systems Research and Behavioral Science, 33(3), 400-412. Web.