Evidence map›Paper›PMID 38037041›Full record

ArticleBMC health services research2023

Opioid prescriptions among the World Trade Center Health Program population.

Ruiling Liu, Geoffrey M Calvert, Kristi R Anderson, Helen Malcolm, Lauren Cimineri, Hannah Dupont, Marisol Martinez

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 35% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Ruiling LiuWorld Trade Center Health Program, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30329, USA. lmq2@cdc.gov.ORCID https://orcid.org/0009-0006-6357-1603
Geoffrey M CalvertWorld Trade Center Health Program, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30329, USA.ORCID https://orcid.org/0000-0003-3968-8458
Kristi R AndersonWorld Trade Center Health Program, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30329, USA.ORCID https://orcid.org/0000-0001-8364-2778
Helen MalcolmWorld Trade Center Health Program, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30329, USA.ORCID https://orcid.org/0009-0004-4524-7370
Lauren CimineriWorld Trade Center Health Program, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30329, USA.
Hannah DupontWorld Trade Center Health Program, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30329, USA.
Marisol MartinezWorld Trade Center Health Program, National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30329, USA.
Centers for Disease Control and Prevention · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe World Trade Center Health Program (Program) provides limited health care to those directly affected by the 9/11 terrorist attacks. Because of physical/mental trauma arising from the 9/11 attacks, Program members might be at high risk of opioid use. To prevent prescription opioid overuse, in 2018 the Program implemented various measures to improve opioid prescribing and expand access to non-opioid pain management among Program members. However, the characteristics of opioid prescriptions dispensed among this population has never been described.

methodsAdministrative and claims data from 07/01/2011 to 09/30/2022 were used to describe opioid prescriptions dispensed during 2013-2021.

resultsFrom 2013-2021, 108,285 members were Program-enrolled for ≥ 10 months, 4,053 (3.7%) had 22,938 outpatient opioid prescriptions, of which, 62.1% were for cancer-related pain, 11.1% for hospice/end of life care, 4.8% for surgery pain, and 9.8% for acute/chronic pain. Among members with Program-paid diagnostic/treatment claims (n = 70,721), the proportion with opioid prescriptions for cancer/hospice/end of life care increased from 0.5% in 2013 to 1.6% in 2018 (p = 0.010), then decreased to 1.1% in 2021 (p = 0.070), and the proportion for non-cancer surgery/acute/chronic pain decreased from 0.6% in 2013 to 0.23% in 2021 (p = 0.0005). Among members prescribed opioids without cancer/hospice/sickle cell disease, the proportion who started with long-acting opioids or had opioid prescriptions from ≥ 4 prescribers were below 6.5% annually; the proportion receiving a high-dose (≥ 90 morphine milligram equivalents per day [MED]), or with concurrent opioids and benzodiazepines use, or who started opioids with MED ≥ 50 or with long duration (≥ 7 days' supply) were above 10% annually, but decreased since 2017.

conclusionsPrevalence of outpatient opioid prescriptions paid by the Program was very low and prescriptions were primarily dispensed for cancer/hospice/end of life care. Although Program efforts to improve opioid prescribing coincided with improvements in outcomes, ongoing surveillance is needed.

Indexed as

Chronic PainOpioid-Related DisordersAnalgesics, OpioidDrug PrescriptionsHumansPractice Patterns, Physicians'PrescriptionsAnalgesics, OpioidOutpatient opioid prescriptionPrescription indicatorQuality improvementWorld Trade Center Health Program

Identifiers

PMID38037041
PMCPMC10687786
OpenAlexW4389203740

What OpenQuestion holds

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LicenceCC BY
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.