Evidence map›Paper›PMID 36561925›Full record

ArticleFrontiers in digital health2022

Changes in postoperative opioid prescribing across three diverse healthcare systems, 2010-2020.

Jean Coquet, Alban Zammit, Oualid El Hajouji, Keith Humphreys, Steven M Asch, Thomas F Osborne, Catherine M Curtin, Tina Hernandez-Boussard

Open access · goldAbstract read
In one paragraph

Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Observational
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 2 institutions in 1 country.

Jean CoquetDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, United States.
Alban ZammitComputational & Mathematical Engineering, Stanford University, Stanford, CA, United States.
Oualid El HajoujiComputational & Mathematical Engineering, Stanford University, Stanford, CA, United States.
Keith HumphreysUnited States Department of Veterans Affairs, Palo Alto Healthcare System, Palo Alto, CA, United States.
Steven M AschDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, United States.
Thomas F OsborneUnited States Department of Veterans Affairs, Palo Alto Healthcare System, Palo Alto, CA, United States.
Catherine M CurtinDepartment of Surgery, VA Palo Alto Health Care System, Menlo Park, CA, United States.
Tina Hernandez-BoussardDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, United States.
Stanford University · USUnited States Department of Veterans Affairs · US

Funding

Advancing Knowledge Discovery for Postoperative Pain ManagementR01LM013362 · NLM · STANFORD UNIVERSITY · PI HERNANDEZ-BOUSSARD, TINA · 2019 to 2023
$3.2M
NLM NIH HHS R01 LM013362
6 · The paper itself

Abstract

Objective: The opioid crisis brought scrutiny to opioid prescribing. Understanding how opioid prescribing patterns and corresponding patient outcomes changed during the epidemic is essential for future targeted policies. Many studies attempt to model trends in opioid prescriptions therefore understanding the temporal shift in opioid prescribing patterns across populations is necessary. This study characterized postoperative opioid prescribing patterns across different populations, 2010-2020. Data Source: Administrative data from Veteran Health Administration (VHA), six Medicaid state programs and an Academic Medical Center (AMC). Data extraction: Surgeries were identified using the Clinical Classifications Software. Study Design: Trends in average daily discharge Morphine Milligram Equivalent (MME), postoperative pain and subsequent opioid prescription were compared using regression and likelihood ratio test statistics. Principal Findings: The cohorts included 595,106 patients, with populations that varied considerably in demographics. Over the study period, MME decreased significantly at VHA (37.5-30.1; Conclusions: VHA and Medicaid programs decreased opioid prescribing over the past decade, with differing response times and rates. In 2020, these systems achieved comparable opioid prescribing patterns and outcomes despite having very different populations. Acknowledging and incorporating these temporal distribution shifts into data learning models is essential for robust and generalizable models.

Indexed as

opioidpostoperative painprescribing patternssurgerytemporal pattern mining

Identifiers

PMID36561925
PMCPMC9763443
OpenAlexW4311630969

What OpenQuestion holds

Textmetadata
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.