Evidence map›Paper›PMID 32637565›Full record

ArticleAddictive behaviors reports2020

Using prescribing and toxicology data to determine non-medical prescription drug overdose.

Philip Huynh, Grant Victor, Brad Ray

Abstract read
In one paragraph

Article in Addictive behaviors reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Philip HuynhCenter for Behavioral Health and Justice at Wayne State University, Detroit, MI, United States.
Grant VictorCenter for Behavioral Health and Justice at Wayne State University, Detroit, MI, United States.
Brad RayCenter for Behavioral Health and Justice at Wayne State University, Detroit, MI, United States.

Funding

NCIPC CDC HHS U17 CE002721
6 · The paper itself

Abstract

backgroundOverdose deaths have increased dramatically in the United States and are often attributed to prescription opioids. This study presents a framework for "overdose typologies", including non-medical prescription drug use, to more accurately describe drug use patterns.

methodsThis study examined linked prescription drug monitoring program (PDMP) and toxicology data (2016-2018) from accidental overdose deaths from a large metropolitan coroner's office in the Midwest (Indianapolis, Indiana).

resultsIn total, 1,112 accidental overdose deaths occurred and over two-thirds (68.0%; n = 756) were coded as an illicit drug user with no prescription opioid present in the toxicology. The most infrequent categories were prescription opioid users 5.5% (n = 61).

conclusionLinked PDMP and toxicology reports are useful in identifying drug use patterns that contribute to mortality.

Indexed as

Non-medical prescription drug useOpioidsOverdosePDMPToxicology

Identifiers

PMID32637565
PMCPMC7330872

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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.