Evidence map›Paper›PMID 36115221›Full record

ArticleDrug and alcohol dependence2022

State prescribing cap laws' association with opioid analgesic prescribing and opioid overdose.

Kayla N Tormohlen, Alex D McCourt, Ian Schmid, Elizabeth M Stone, Elizabeth A Stuart, Corey Davis, Mark C Bicket, Emma E McGinty

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Drug and alcohol dependence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

9 citing papers in PubMed, 11 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Kayla N TormohlenDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Room 357, Baltimore, MD 21205, USA. Electronic address: ktormoh1@jhu.edu.
Alex D McCourtDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Baltimore, MD 21205, USA. Electronic address: ammcourt1@jhu.edu.
Ian SchmidDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Baltimore, MD 21205, USA. Electronic address: ian_schmid@jhu.edu.
Elizabeth M StoneDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Baltimore, MD 21205, USA. Electronic address: estone@jhu.edu.
Elizabeth A StuartDepartments of Mental Health, Biostatistics, Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Room 839, Baltimore, MD 21205, USA. Electronic address: estuart@jhu.edu.
Corey DavisHarm Reduction Legal Project, Network for Public Health Law, 7101 York Avenue South, #270, Edina, MN 55435, USA. Electronic address: cdavis@networkforphl.org.
Mark C BicketDepartments of Anesthesiology, Health Management and Policy, University of Michigan, School of Public Health, 1500 E Medical Center Drive, Ann Arbor, MI 48109-5048, USA. Electronic address: mbicket@med.umich.edu.
Emma E McGintyDepartment of Health Policy and Management, Center for Mental Health and Addiction Policy, ALACRITY Center for Health and Longevity in Mental Illness, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Room 359, Baltimore, MD 21205, USA. Electronic address: bmcginty@jhu.edu.
Johns Hopkins University · USNational Health Law Program · USUniversity of Michigan–Ann Arbor · US

Funding

Integrating data for causal inference in behavioral healthT32MH122357 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Rashelle Jean Musci, Elizabeth A. Stuart · 2020 to 2026
$1.8M
Mental Health Services and Systems Training ProgramT32MH109436 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Matthew Eisenberg, Elizabeth A. Stuart · 2017 to 2026
$1.7M
NIMH NIH HHS T32 MH109436NIMH NIH HHS T32 MH122357
6 · The paper itself

Abstract

introductionIn response to the role of opioid prescribing in the U.S. opioid crisis, states have enacted laws intended to curb high risk opioid prescribing practices. This study assessed the effects of state prescribing cap laws that limit the dose and/or duration of dispensed opioid prescriptions on opioid prescribing patterns and opioid overdose.

methodsWe identified 1,414,908 adults from a large U.S. administrative insurance claims database. Treatment states included 32 states that implemented a prescribing cap law between 2017 and 2019. Comparison states included 16 states and DC without a prescribing cap law by 2019. A difference-in-differences approach with staggered policy adoption was used to assess effects of these laws on opioid analgesic prescribing and opioid overdose.

resultsState opioid prescribing cap laws were not associated with changes in the proportion of people receiving opioid analgesic prescriptions, the dose or duration of opioid prescriptions, or opioid overdose. States with laws that imposed days' supply limits only versus days' supply and dosage limits, as well as with specific law provisions also showed no association with opioid prescribing or opioid overdose outcomes.

conclusionsState opioid prescribing cap laws did not appear to impact outcomes related to opioid analgesic prescribing or opioid overdose. These findings are potentially due to the limited scope of these laws, which often apply only to a subset of opioid prescriptions and include professional judgment exemptions.

Indexed as

Drug OverdoseOpiate OverdoseAdultAnalgesics, OpioidHumansOpioid EpidemicPractice Patterns, Physicians'PrescriptionsUnited StatesAnalgesics, OpioidOpioid overdoseOpioid prescribingState policy

Identifiers

PMID36115221
PMCPMC9893520
OpenAlexW4295296492

What OpenQuestion holds

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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.