Evidence map›Paper›PMID 35845968›Full record

ArticleSubstance abuse : research and treatment2022

Predictors of Pain Reliever Misuse Among Respondents of the United States 2017 National Survey on Drug Use and Health.

Marissa S Matta, Timothy P Janikowski

Open access · goldAbstract read
In one paragraph

Article in Substance abuse : research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 45% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 1 country.

Marissa S MattaQueens College - CUNY, Queens, NY, USA.ORCID https://orcid.org/0000-0002-8007-1372
Timothy P JanikowskiUniversity at Buffalo - SUNY, Buffalo, NY, USA.
Queens College, CUNY · USUniversity at Buffalo, State University of New York · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The risk factors for potential opioid misuse and abuse in patients receiving long-term opioid pain treatment have been a topic of interest in research for many years. There are differences among patients who receive long-term opioid pain treatment. These differences may or may not lead to opioid misuse. This study analyzes the different characteristics and predictors of prescription pain reliever misuse among respondents of the United States 2017 National Survey on Drug Use and Health. It examines the relationships of age, gender, income, perception of risk and availability of heroin, past substance use and alcohol use, the source of the pain relievers, and motivation to misuse pain relievers to pain reliever misuse and if these variables significantly predict pain reliever misuse. Data used in this study were analyzed through sequential multiple linear regression analyses. The significant positive predictors of prescription pain reliever misuse were being 26 or older, perceiving heroin as easily obtainable, and past methamphetamine use. The significant negative predictors of prescription pain reliever misuse were being 12 to 25 years old, perceiving heroin as a great risk, past alcohol use, and obtaining pain relievers from a friend or relative. The goal of this study was to increase the amount of knowledge regarding predictors of prescription opioid misuse to identify those who are at risk and decrease prescription opioid misuse and overdose rates in the United States.

Indexed as

addictionmisuseopioidspain relieversPredictors

Identifiers

PMID35845968
PMCPMC9280814
OpenAlexW4285049587

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.