Evidence map›Paper›PMID 33272714›Full record

ArticleAmerican journal of preventive medicine2021

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

Mohammad S Jalali, Emily Ewing, Calvin B Bannister, Lukas Glos, Sara Eggers, Tse Yang Lim, Erin Stringfellow, Celia A Stafford, Rosalie Liccardo Pacula, Hawre Jalal and 1 more

Open access · hybridAbstract read
In one paragraph

Article in American journal of preventive medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

  1. Pooled it
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  3. Article
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  5. Assessment of Epidemiological Data and Surveillance in Korea Substance Use Research: Insights and Future Directions.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2024
    Review
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  9. Article
  10. An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2023
    Article
  11. Article
  12. Article
  13. Article
  14. Modeling the evolution of the US opioid crisis for national policy development.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  15. Article
  16. Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 6 institutions in 1 country.

Mohammad S JalaliMGH Institute for Technology Assessment, Harvard Medical School, Boston, Massachusetts; MIT Sloan School of Management, Cambridge, Massachusetts. Electronic address: msjalali@mgh.harvard.edu.
Emily EwingCenter for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.
Calvin B BannisterCenter for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.
Lukas GlosCenter for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.
Sara EggersCenter for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.
Tse Yang LimMIT Sloan School of Management, Cambridge, Massachusetts; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.
Erin StringfellowMGH Institute for Technology Assessment, Harvard Medical School, Boston, Massachusetts.
Celia A StaffordMGH 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.
Rosalie Liccardo PaculaSol 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.
Hawre JalalGraduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania.
Reza Kazemi-TabrizCenter for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.
Center for Drug Evaluation and Research · USHarvard University · USUnited States Food and Drug Administration · USNational Bureau of Economic Research · USUniversity of North Carolina at Chapel Hill · USUniversity of Pittsburgh · US

Funding

Population Modeling of Bladder Cancer Detection and ControlU01CA265750 · NCI · BROWN UNIVERSITY · PI JALAL, HAWRE, TRIKALINOS, THOMAS · 2021 to 2025
$3.4M
Using System Dynamics to Enhance the FDA's Opioids Systems Model and Address the Ongoing CrisisU01FD006868 · FDA · MASSACHUSETTS GENERAL HOSPITAL · PI JALALI, MOHAMMAD S. · 2019 to 2019
$1000k
Understanding the Impact of Opioid Policies on the Opioid Epidemic Using Graphical Causal Models and Causal DiscoveryK01DA048985 · NIDA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI JALAL, HAWRE · 2020 to 2021
$360k
FDA HHS U01 FD006868NCI NIH HHS U01 CA265750NIDA NIH HHS K01 DA048985
6 · The paper itself

Abstract

introductionThe 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.

methodsTo 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Analgesics, OpioidOpioid EpidemicForecastingHumansAnalgesics, Opioid

Identifiers

PMID33272714
PMCPMC8061725
OpenAlexW3109880418

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.