Reducing the stigma surrounding opioid use disorder: evaluating an opioid overdose prevention training program applied to a diverse population

Background The opioid epidemic is a rapidly growing public health concern in the USA, as the number of overdose deaths continues to increase each year. One strategy for combating the rising number of overdoses is through opioid overdose prevention programs (OOPPs). Objective To evaluate the effectiveness of an innovative OOPP, with changes in knowledge and attitudes serving as the primary outcome measures. Methods The OOPP was developed by a group of medical students under guidance from faculty advisors. Training sessions focused on understanding stigmatizing factors of opioid use disorder (OUD), as well as protocols for opioid overdose reversal through naloxone administration. Pre- and post-surveys were partially adapted from the opioid overdose attitudes and knowledge scales and administered to all participants. Paired t-tests were conducted to assess differences between pre- and post-surveys. Results A total of 440 individuals participated in the training; 381 completed all or the majority of the survey. Participants came from a diverse set of backgrounds, ages, and experiences. All three knowledge questions showed significant improvements. For attitude questions, significant improvements were found in all three questions evaluating confidence, two of three questions assessing attitudes towards overdose reversal, and four of five questions evaluating stigma and attitudes towards individuals with OUD. Conclusions Our innovative OOPP was effective not only in increasing knowledge but also in improving attitudes towards overdose reversal and reducing stigma towards individuals with OUD. Given the strong improvements in attitudes towards those with OUD, efforts should be made to incorporate the unique focus on biopsychosocial and sociohistorical components into future OOPPs.

Evaluation of a Medicaid performance improvement project to reduce high-dose opioid prescriptions

Background In 2015, Oregon’s Medicaid program implemented a performance improvement project to reduce high-dose opioid prescribing across its 16 coordinated care organizations (CCOs). The objective of this study was to evaluate the effect of that program on prescription opioid use and outcomes. Methods Using Medicaid claims data from 2014 to 2017, we conducted interrupted time-series analyses to examine changes in the prescription opioid use and overdose rates before (July 2014 to June 2015) and after (January 2016 to December 2017) implementation of Oregon’s high-dose policy initiative (July 2015 to December 2015). Prescribing outcomes were: 1) total opioid prescriptions 2) high-dose [> 90 morphine milligram equivalents per day] opioid prescriptions, and 3) proportion of opioid prescriptions that were high-dose. Opioid overdose outcomes included emergency department visits or hospitalizations that involved an opioid-related poisoning (total, heroin-involved, non-heroin involved). Analyses were performed at the state and CCO level. Results There was an immediate reduction in high dose opioid prescriptions after the program was implemented (− 1.55 prescription per 1000 enrollee; 95% CI − 2.26 to − 0.84; p < 0.01). Program implementation was also associated with an immediate drop (− 1.29 percentage points; 95% CI − 1.94 to − 0.64 percentage points; p < 0.01) and trend reduction (− 0.23 percentage point per month; 95% CI − 0.33 to − 0.14 percentage points; p < 0.01) in the monthly proportion of high-dose opioid prescriptions. The trend in total, heroin-involved, and non-heroin overdose rates increased significantly following implementation of the program. Conclusions Although Oregon’s high-dose opioid performance improvement project was associated with declines in high-dose opioid prescriptions, rates of opioid overdose did not decrease. Policy efforts to reduce opioid prescribing risks may not be sufficient to address the growing opioid crisis.

Surveillance, Self-Governance, and Mortality: The Impact of Prescription Drug Monitoring Programs on U.S. Overdose Mortality, 2000–2016

Policy mechanisms shaping population health take numerous forms, from behavioral prohibitions to mandates for action to surveillance. Rising drug overdoses undermined the state’s ability to promote population-level health. Using the case of prescription drug monitoring programs (PDMPs), we contend that PDMP implementation highlights state biopower operating via mechanisms of surveillance, whereby prescribers, pharmacists, and patients perceive agency despite choices being constrained. We consider whether such surveillance mechanisms are sufficient or if prescriber/dispenser access or requirements for use are necessary for population health impact. We test whether PDMPs reduced overdose mortality while considering that surveillance may require time to reach effectiveness. PDMPs reduced opioid overdose mortality 2 years postimplementation and sustained effects, with similar effects for prescription opioids, benzodiazepines, and psychostimulants. Access or mandates for action do not reduce mortality beyond surveillance. Overall, PDMP effects on overdose mortality are likely due to self-regulation under surveillance rather than mandated action.

USING SYSTEM DYNAMICS MODELING TO ASSESS THE IMPACT OF CONNECTICUT GOOD SAMARITAN LAWS: A PROTOCOL PAPER

Background We applied a participatory system dynamics (SD) modeling approach to evaluate the effectiveness and impact of Connecticut 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 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 - 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. - The model will be used to understand the impact of Connecticut 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. - The approach can be applied to other jurisdictions, taking into account local conditions and existing Good Samaritan legislation. KEYWORDS: System dynamics modeling, group model building, opioid overdose deaths, opioid use disorder, Good Samaritan laws