Evidence map›Paper›PMID 33146732›Full record

ArticleJAMA network open2020

Modeling Mitigation Strategies to Reduce Opioid-Related Morbidity and Mortality in the US.

Jeromie Ballreich, Omar Mansour, Ellen Hu, Francine Chingcuanco, Harold A Pollack, David W Dowdy, G Caleb Alexander

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
5.0field-weighted citation impact, top 4% 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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.

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  17. 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
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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

7 authors at 3 institutions in 1 country.

Jeromie BallreichDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Omar MansourMonument Analytics, Baltimore, Maryland.
Ellen HuMonument Analytics, Baltimore, Maryland.
Francine ChingcuancoMonument Analytics, Baltimore, Maryland.
Harold A PollackMonument Analytics, Baltimore, Maryland.
David W DowdyMonument Analytics, Baltimore, Maryland.
G Caleb AlexanderCenter for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Postnova Analytics (United States) · USJohns Hopkins University · USUniversity of Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: The US opioid epidemic is complex and dynamic, yet relatively little is known regarding its likely future impact and the potential mitigating impact of interventions to address it. Objective: To estimate the future burden of the opioid epidemic and the potential of interventions to address the burden. Design, Setting, and Participants: A decision analytic dynamic Markov model was calibrated using 2010-2018 data from the National Survey on Drug Use and Health, Centers for Disease Control and Prevention, National Health and Nutrition Examination Survey, the US Census, and National Epidemiologic Survey on Alcohol and Related Conditions-III. Data on individuals 12 years or older from the US general population or with prescription opioid medical use; prescription opioid nonmedical use; heroin use; prescription, heroin, or combined prescription and heroin opioid use disorder (OUD); 1 of 7 treatment categories; or nonfatal or fatal overdose were examined. The model was designed to project fatal opioid overdoses between 2020 and 2029. Exposures: The model projected prescribing reductions (5% annually), naloxone distribution (assumed 5% reduction in case-fatality), and treatment expansion (assumed 35% increase in uptake annually for 4 years and 50% relapse reduction), with each compared vs status quo. Main Outcomes and Measures: Projected 10-year overdose deaths and prevalence of OUD. Results: Under status quo, 484 429 (95% confidence band, 390 543-576 631) individuals were projected to experience fatal opioid overdose between 2020 and 2029. Projected decreases in deaths were 0.3% with prescribing reductions, 15.4% with naloxone distribution, and 25.3% with treatment expansion; when combined, these interventions were associated with 179 151 fewer overdose deaths (37.0%) over 10 years. Interventions had a smaller association with the prevalence of OUD; for example, the combined intervention was estimated to reduce OUD prevalence by 27.5%, from 2.47 million in 2019 to 1.79 million in 2029. Model projections were most sensitive to assumptions regarding future rates of fatal and nonfatal overdose. Conclusions and Relevance: The findings of this study suggest that the opioid epidemic is likely to continue to cause tens of thousands of deaths annually over the next decade. Aggressive deployment of evidence-based interventions may reduce deaths by at least a third but will likely have less impact for the number of people with OUD.

Indexed as

Analgesics, OpioidCost of IllnessDrug OverdoseFemaleHumansMaleMarkov ChainsNaloxoneNarcotic AntagonistsOpioid EpidemicOpioid-Related DisordersPractice Patterns, Physicians'United StatesAnalgesics, OpioidNaloxoneNarcotic Antagonists

Identifiers

PMID33146732
PMCPMC7643029
OpenAlexW3094663502

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.