USING SYSTEM DYNAMICS MODELING TO ASSESS THE IMPACT OF CONNECTICUT’S GOOD SAMARITAN LAWS: A PROTOCOL PAPER
Authors: Ali Syed Shayan , Sabounchi Nasim S. , Heimer Robert , D’Onofrio Gail , Violette Colleen , LaWall Katherine , Heckmann Rebekah 
Published: January 2022

Background

We applied a participatory system dynamics (SD) modeling approach to evaluate the effectiveness and impact of Connecticut’s Good Samaritan Laws (GSLs) that are designed to promote bystander intervention during an opioid overdose event and reduce opioid overdose-related adverse outcomes. Our SD model can be used to predict whether additional revisions of the statutes might make GSLs more effective. SD modeling is a novel approach for assessing the impact of GSLs; and, in this protocol paper, we describe its applicability to our policy question, as well as expected outcomes of this approach.

Methods

This project began in February 2021 and is expected to conclude by March 2022. During this time, a total of six group model-building (GMB) sessions will have been held with key stakeholders to elicit feedback that will, in turn, contribute to the development of a more robust SD model. Session participants include bystanders who witness an overdose, law enforcement personnel, first responders, pharmacists, physicians, and other health care professionals who work in at least two major metropolitan areas of Connecticut (New Haven and Hartford). Due to the restrictions imposed by the COVID-19 pandemic, the sessions are being held virtually via Zoom. The information obtained during these sessions will be integrated with a draft SD model that has already been developed by the modeling team as part of a previous CDC-funded project. Model calibration and policy simulations will then be performed to assess the impact of the current GSLs and to make recommendations for future public policy changes.

Discussion

An SD modeling approach enables capture of complex interrelationships among multiple health outcomes to better assess the drivers of the opioid epidemic in Connecticut. The model’s simulation results are expected not only to align with current real-world data but also to recreate historical trends and infer future trends in a situationally relevant fashion. This will facilitate the work of policy makers who are devising and implementing time-sensitive changes to address opioid overdose-related deaths at the state level. Replicating our approach as described can be applied to make similar improvements in other jurisdictions.

CONTRIBUTIONS TO THE LITERATURE

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System dynamics (SD) modeling and group model-building (GMB) approaches enable the group to start with a simple concept model and apply the collective knowledge of the group to finish the session with a much more developed model that can produce impressively accurate simulation results.

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The model will be used to understand the impact of Connecticut’s Good Samaritan Laws (GSLs), as well as their limitations, and to deduce factors to further improve public health laws to counter opioid overdose-related deaths.

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The approach can be applied to other jurisdictions, taking into account local conditions and existing Good Samaritan legislation.