Table 2.

Key Discussion Points and Potential Paths Forward

Area	Main discussion points	Potential paths forward
Use, misuse, and use disorder
	Inconsistency in definition and measurement is a significant problem for system modelers, who need to identify parameter values that correspond to a single construct. Available data are difficult to interpret, especially contextually. The practice in systems modeling of triangulating data sources surfaces contradictions that threaten the internal and external validity of the model. Details about which individuals were included in data sources and why are often unclear or missing. This is challenging for systems models that rely on defining mutually exclusive groups or “types” of people. In addition to prevalence data, modelers need incidence data. Understanding incidence rates is critical to capturing and validating dynamics over time. Data on the outcomes of marginalized individuals who use opioids, including criminal justice-involved, poor and homeless, and those with co-occurring mental illness, are particularly difficult to find. The lack of data reduces the ability to test targeted policies and address inequities. There is a great need for longitudinal data collected on individuals to give insight into interrelationships of variables over time and their unintended outcomes. In addition, establishing the validity of models depends in part on being able to replicate historical trends.	Standard list of definitions for prioritized terms and variables. Explore innovative efforts to triangulate data sources, harness new and/or novel data sources like social media, and employ synthetic datasets.
Nonfatal overdose
	The incidence of nonfatal overdose is a key data need. Policy interventions tested in a model meant to reduce fatal overdose are compared to a (highly uncertain) baseline level of overdose and survival; reducing uncertainty here is critical. Existing data collection practices limit the accuracy of available proxies for overdose. The size of the underreporting and overreporting margins is unclear.	Text mining through qualitative EMS reports may identify overdoses. Incentives that encourage providers to report the identification of overdose and submit the data frequently. Implementation of Good Samaritan laws to encourage the use of EMS. Improved collaboration between modelers with data systems like PCORnet and Enhanced State Opioid Overdose Surveillance (ESOOS) that are developing overdose metrics.
Illicit opioid supply and demand
	There is limited data available regarding sources of illicit opioids (e.g., laboratories, drug trafficking, or diversion). Such data would allow for more targeted testing of interventions in systems models. There is limited data available regarding the volume and price of illicit opioids, which affects availability of drugs and thus key transition rates (e.g., initiation, escalation) that systems models use to model dynamics. Estimating opioid diversion nationally should be a priority. In systems modeling, these underlying market dynamics are critical to understand shifts in behavior over time. Opioid data alone are insufficient; it is critical to note that heroin-use interventions may have unintended consequences for other substances (e.g., cocaine).	Programs that collect information about purity, street price, and volume of illicit substances. Making computable forms of data publicly accessible by data providers.
Treatment utilization, outcomes, and relapse
	Definitions and measurement of key treatment variables are inconsistent; modelers should agree upon these to facilitate quantification and communication. Efforts to define nebulous variables like recovery and appropriate duration of medications for opioid use disorder (MOUD) should keep in mind the potential effects of terms and definitions on stigma. Systems modelers should seek partnerships with OUD experts who are aware of these issues. There is limited and interspersed data on treatment history, relapse, and long-term remission outcomes. This makes defining transition rates between states nearly impossible, adding uncertainty to systems models. Available data are limited by the caveats of conducting small controlled trials with specific populations. Modelers need data that can be reasonably generalized to larger populations. Propensity scores can be used to generalize data, but this process requires detailed patient data.	Explore treatment outcomes piecemeal, starting with claims data. Use limited treatment utilization data to fill other information gaps. Utilize state treatment administrative databases that track unique individuals to obtain information on relapse and length of recovery. Develop a process to incorporate and triangulate claims data and clinical trial data.

EMS, emergency medical services; OUD, opioid use disorder.