Cracking the Code: Using Data to Combat the Opioid Crisis

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

PURPOSE

Standard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo.

METHODS

We describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach.

RESULTS

Using two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption.

CONCLUSIONS

MDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.

Effectiveness of peer recovery support services on stages of the opioid use disorder treatment cascade: A systematic review

BACKGROUND

Peer recovery support services (PRSS) have been increasingly incorporated during the recovery process for opioid use disorder (OUD), yet little is known about the effects of PRSS on clinical outcomes of individuals who misuse opioids. This study systematically synthesized existing literature reporting the effectiveness of PRSS interventions on stages of the OUD treatment cascade.

METHODS

A search conducted on five databases identified studies from database inception to January 26th 2021 that evaluated the effects of PRSS on PRSS engagement, medication for OUD (MOUD) initiation, MOUD retention, opioid and non-opioid misuse, and remission. Characteristics of PRSS interventions, study design, and clinical outcomes were extracted. Methodological quality was assessed with the quality assessment tool for quantitative studies by the Effective Public Health Practice Project.

RESULTS

Of 123 titles, 22 were subjected to full-text review and 12 ultimately met inclusion criteria. Only two studies were randomized control trials, half compared the outcomes of PRSS participants to those of a counterfactual group. Most PRSS were unstandardized and broadly described, involving linkage to treatment (91.7%) or follow-up support (91.7%). MOUD initiation was reported the most often (66.7%), followed by PRSS engagement (33.3%) and opioid use (25.0%). No studies reported findings for MOUD retention or remission. Findings for available outcomes were inconsistent and difficult to compare due to the heterogeneity of PRSS interventions and methodological limitations.

CONCLUSION

Effectiveness of PRSS interventions on stages of the OUD treatment cascade remain inconclusive. Additional research is necessary before supporting the implementation of PRSS on a broad scale.