Data Needs in Opioid Systems Modeling: Challenges and Future Directions

Autor: Reza Kazemi-Tabriz, Sara Eggers, Tse Yang Lim, Calvin B. Bannister, Lukas Glos, Rosalie Liccardo Pacula, Emily Ewing, Celia A. Stafford, Hawre Jalal, Erin Stringfellow, Mohammad S. Jalali
Rok vydání: 2021
Předmět:
Zdroj: American journal of preventive medicine
ISSN: 0749-3797
DOI: 10.1016/j.amepre.2020.08.017
Popis: Introduction The opioid crisis is a pervasive public health threat in the U.S. Simulation modeling approaches that integrate a systems perspective are used to understand the complexity of this crisis and analyze what policy interventions can best address it. However, limitations in currently available data sources can hamper the quantification of these models. Methods To understand and discuss data needs and challenges for opioid systems modeling, a meeting of federal partners, modeling teams, and data experts was held at the U.S. Food and Drug Administration in April 2019. This paper synthesizes the meeting discussions and interprets them in the context of ongoing simulation modeling work. Results The current landscape of national-level quantitative data sources of potential use in opioid systems modeling is identified, and significant issues within data sources are discussed. Major recommendations on how to improve data sources are to: maintain close collaboration among modeling teams, enhance data collection to better fit modeling needs, focus on bridging the most crucial information gaps, engage in direct and regular interaction between modelers and data experts, and gain a clearer definition of policymakers’ research questions and policy goals. Conclusions This article provides an important step in identifying and discussing data challenges in opioid research generally and opioid systems modeling specifically. It also identifies opportunities for systems modelers and government agencies to improve opioid systems models.
Databáze: OpenAIRE
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