Data Needs in Opioid Systems Modeling: Challenges and Future Directions.

Autor: Jalali MS; MGH Institute for Technology Assessment, Harvard Medical School, Boston, Massachusetts; MIT Sloan School of Management, Cambridge, Massachusetts. Electronic address: msjalali@mgh.harvard.edu., Ewing E; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland., Bannister CB; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland., Glos L; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland., Eggers S; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland., Lim TY; MIT Sloan School of Management, Cambridge, Massachusetts; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland., Stringfellow E; MGH Institute for Technology Assessment, Harvard Medical School, Boston, Massachusetts., Stafford CA; MGH Institute for Technology Assessment, Harvard Medical School, Boston, Massachusetts; Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina., Pacula RL; Sol Price School of Public Policy, University of Southern California, Los Angeles, California; Schaeffer Center for Health Policy & Economics, University of Southern California, Los Angeles, California; National Bureau of Economic Research, Cambridge, Massachusetts., Jalal H; Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania., Kazemi-Tabriz R; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.
Jazyk: angličtina
Zdroj: American journal of preventive medicine [Am J Prev Med] 2021 Feb; Vol. 60 (2), pp. e95-e105. Date of Electronic Publication: 2020 Dec 01.
DOI: 10.1016/j.amepre.2020.08.017
Abstrakt: 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.
(Copyright © 2020 American Journal of Preventive Medicine. All rights reserved.)
Databáze: MEDLINE
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