Evidence map›Paper›PMID 32890222›Full record

ArticleJournal of occupational and environmental medicine2020

Do Injured Workers Receive Opioid Prescriptions Outside the Workers' Compensation System?: The Case of Private Group Health Insurances.

Abay Asfaw, Brian Quay, Chia-Chia Chang

Open access · greenAbstract read
In one paragraph

Article in Journal of occupational and environmental medicine, 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
0.7field-weighted citation impact, top 25% 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

2 citing papers in PubMed, 8 citations in OpenAlex.

  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 at 2 institutions in 2 countries.

Abay AsfawCenters for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health (NIOSH), Economic Research and Support Office (ERSO), Washington, District of Columbia (Dr Asfaw); Centers for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health (NIOSH), Economic Research and Support Office (ERSO), Cincinnati, Ohio (Mr Quay); Centers for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health (NIOSH), Office for Total Worker Health, Washington, District of Columbia (Ms Chang).
Brian Quay
Chia-Chia Chang
National Institute for Occupational Safety and Health · USBurness Corlett Three Quays (United Kingdom) · GB

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

objectivesWe explored the impact of workplace injury on receiving opioid prescriptions from employer-sponsored private group health insurances (GHI) and how long injured workers receive opioid prescriptions after injury.

methodsWe used a difference-in-differences method and MarketScan databases for the years 2013 to 2015.

resultsEstimated odds for injured workers relative to noninjured workers to receive opioid prescriptions from the GHI within 60 and 180 days from the index date of injury were 4.9 and 1.5, respectively. In addition, the number of opioid prescriptions received within 60 days of injury was 2.5 times higher.

conclusionWorkplace injury could be a risk factor for both short and long-term prescription opioid use. Studies that use only workers' compensation medical claim data likely underestimate the magnitude of the impact of workplace injuries on opioid prescriptions.

Indexed as

Occupational InjuriesOpioid-Related DisordersWorkers' CompensationAdolescentAdultAgedAnalgesics, OpioidFemaleHumansInsurance, HealthMaleMiddle AgedYoung AdultAnalgesics, Opioid

Identifiers

PMID32890222
PMCPMC8274348
OpenAlexW3048966737

What OpenQuestion holds

Textmetadata
LicenceTDM
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.