Evidence map›Paper›PMID 37088864›Full record

ArticleBulletin of mathematical biology2023

Modeling Opioid Abuse: A Case Study of the Opioid Crisis in New England.

Cole Butler, Peter Stechlinski

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Cole ButlerDepartment of Mathematics and Statistics, University of Maine, 5752 Neville Hall, Orono, ME, 04469, USA.
Peter StechlinskiDepartment of Mathematics and Statistics, University of Maine, 5752 Neville Hall, Orono, ME, 04469, USA. peter.stechlinski@maine.edu.ORCID http://orcid.org/0000-0002-5162-4951

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For the past two decades, the USA has been embroiled in a growing prescription drug epidemic. The ripples of this epidemic have been especially apparent in the state of Maine, which has fought hard to mitigate the damage caused by addiction to pharmaceutical and illicit opioids. In this study, we construct a mathematical model of the opioid epidemic incorporating novel features important to better understanding opioid abuse dynamics. These features include demographic differences in population susceptibility, general transmission expressions, and combined consideration of pharmaceutical opioid and heroin abuse. We demonstrate the usefulness of this model by calibrating it with data for the state of Maine. Model calibration is accompanied by sensitivity and uncertainty analysis to quantify potential error in parameter estimates and forecasts. The model is analyzed to determine the mechanisms most influential to the number of opioid abusers and to find effective ways of controlling opioid abuse prevalence. We found that the mechanisms most influential to the overall number of abusers in Maine are those involved in illicit pharmaceutical opioid abuse transmission. Consequently, preventative strategies that controlled for illicit transmission were more effective over alternative approaches, such as treatment. These results are presented with the hope of helping to inform public policy as to the most effective means of intervention.

Indexed as

Drug TraffickingOpioid EpidemicOpioid-Related DisordersAnalgesics, OpioidHeroin DependenceHumansIllicit DrugsMaineModels, BiologicalModels, TheoreticalNew EnglandPharmaceutical PreparationsSubstance-Related DisordersAnalgesics, OpioidIllicit DrugsPharmaceutical PreparationsOpioid abuseOpioid epidemicSensitivity analysisUncertainty analysis

Identifiers

PMID37088864
PMCPMC10122875

What OpenQuestion holds

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Registered trials

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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.