Evidence map›Paper›PMID 34875874›Full record

ArticleThe American journal of psychiatry2022

Effects of County-Level Opioid Dispensing Rates on Individual-Level Patterns of Prescription Opioid and Heroin Consumption: Evidence From National U.S. Data.

Mike Vuolo, Brian C Kelly

Abstract read
In one paragraph

Article in The American journal of psychiatry, 2022. 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
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1 · What the graph read from it

What it found

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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

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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
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Mike VuoloDepartment of Sociology, Ohio State University, Columbus (Vuolo); Department of Sociology, Purdue University, West Lafayette, Ind. (Kelly).
Brian C KellyDepartment of Sociology, Ohio State University, Columbus (Vuolo); Department of Sociology, Purdue University, West Lafayette, Ind. (Kelly).ORCID 0000-0003-2039-1347

Funding

Effects of State-Level Policy Implementation on Opiate Abuse and MortalityR21DA046447 · NIDA · OHIO STATE UNIVERSITY · PI KELLY, BRIAN C, VUOLO, MICHAEL C · 2018 to 2019
$434k
NIDA NIH HHS R21 DA046447
6 · The paper itself

Abstract

objectiveThe authors examined directly whether county-level changes in opioid dispensing rates affect individual-level prescription opioid misuse, frequency of use, and dependence, as well as the same outcomes for heroin.

methodsUsing data from the restricted-access National Survey on Drug Use and Health, the Centers for Disease Control and Prevention's retail opioid prescription database, the Prescription Drug Abuse Policy System, and the U.S. Census, the authors applied fixed-effects models to determine whether county-level dispensing rates affected prescription opioid outcomes as intended and whether changes in rates adversely affected heroin use outcomes. Bayes factors were used to confirm evidence for null findings.

resultsThe sample included 748,800 respondents age 12 and older from 2006 to 2016. The odds of prescription opioid misuse, increased frequency of misuse, and dependence were 7.2%, 3.5%, and 10.4% higher, respectively, per standard deviation increase in the county-level opioid dispensing rate per 100 persons. There was no evidence for any association between opioid dispensing rates and the three heroin outcomes. The odds ratio was nonsignificant according to frequentist techniques in fixed-effects models, and Bayesian techniques confirmed very strong support for the null hypothesis.

conclusionsCounty-level opioid dispensing rates are directly associated with individual-level prescription opioid misuse, frequency of misuse, and dependence. Changes in dispensing were not associated with population shifts in heroin use. Reductions in opioid dispensing rates have contributed to stemming prior increases in prescription opioid misuse while not adversely affecting heroin use. Physicians and other health care providers can take action to minimize opioid dispensing for tangible benefits regarding prescription opioid misuse without adverse effects on heroin use.

Indexed as

Opioid-Related DisordersPrescription Drug MisuseAnalgesics, OpioidBayes TheoremChildHeroinHumansPrescriptionsUnited StatesAnalgesics, OpioidHeroinHeroinOpioidsPrescription OpioidsSubstance-Related and Addictive Disorders

Identifiers

PMID34875874
PMCPMC8976704

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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.